diff --git a/.github/workflows/package-install.yml b/.github/workflows/package-install.yml
index 96964c660..35ece80fd 100644
--- a/.github/workflows/package-install.yml
+++ b/.github/workflows/package-install.yml
@@ -2,15 +2,14 @@ name: Package Install
 
 on:
   pull_request:
-  push:
     branches: [main]
-  workflow_dispatch:
 
 permissions:
   contents: read
 
 jobs:
   install-smoke-test:
+    if: startsWith(github.head_ref, 'release/')
     runs-on: ubuntu-latest
 
     steps:
@@ -26,7 +25,7 @@ jobs:
           curl -LsSf https://astral.sh/uv/install.sh | sh
           echo "$HOME/.cargo/bin" >> "$GITHUB_PATH"
 
-      - name: Build wheel
+      - name: Build managed-runtime wheel
         run: python scripts/build_package.py --wheel
 
       - name: Smoke test base wheel import
@@ -42,7 +41,7 @@ jobs:
           cd "$project_dir"
           uv init --name art-base-install-smoke --python 3.12 --bare
           uv add "openpipe-art @ file://${wheel_path}"
-          uv run python -c "import importlib.util; assert importlib.util.find_spec('numpy') is None; assert importlib.util.find_spec('torch') is None; import art; from art.pipeline_trainer import PipelineTrainer; print(art.__name__, PipelineTrainer.__name__)"
+          uv run python -c "import importlib.util; assert importlib.util.find_spec('numpy') is not None; assert importlib.util.find_spec('torch') is None; import art; from art import ServerlessBackend; from art.pipeline_trainer import PipelineTrainer; print(art.__name__, ServerlessBackend.__name__, PipelineTrainer.__name__)"
           uv add "weave==0.52.41"
           uv run python -c "from weave.trace.settings import override_settings; import art"
 
@@ -60,3 +59,140 @@ jobs:
           uv init --name art-install-smoke --python 3.12 --bare
           uv add "openpipe-art[backend] @ file://${wheel_path}"
           uv sync
+
+      - name: Resolve published Megatron profiles
+        run: |
+          wheel_path="$(python - <<'PY'
+          from pathlib import Path
+
+          print(next(Path("dist").glob("openpipe_art-*.whl")).resolve())
+          PY
+          )"
+
+          for profile in megatron megatron-cu130; do
+            case "$profile" in
+              megatron) index=cu128 ;;
+              megatron-cu130) index=cu130 ;;
+            esac
+            project_dir="$(mktemp -d)"
+            cd "$project_dir"
+            uv init --name "art-${profile}-resolve" --python 3.12 --bare
+            uv add --no-sync \
+              --index-strategy unsafe-best-match \
+              --extra-index-url "https://download.pytorch.org/whl/${index}" \
+              "openpipe-art[${profile}] @ file://${wheel_path}"
+          done
+
+      - name: Resolve published Tinker profile
+        run: |
+          wheel_path="$(python - <<'PY'
+          from pathlib import Path
+
+          print(next(Path("dist").glob("openpipe_art-*.whl")).resolve())
+          PY
+          )"
+
+          project_dir="$(mktemp -d)"
+          cd "$project_dir"
+          uv init --name art-tinker-resolve --python 3.12 --bare
+          uv add --no-sync "openpipe-art[tinker] @ file://${wheel_path}"
+
+      - name: Smoke test distributed wheel surface on CPU
+        env:
+          ART_VLLM_RUNTIME_CACHE_DIR: ${{ runner.temp }}/art-vllm-runtime-cache
+        run: |
+          wheel_path="$(python - <<'PY'
+          from pathlib import Path
+
+          print(next(Path("dist").glob("openpipe_art-*.whl")).resolve())
+          PY
+          )"
+
+          project_dir="$(mktemp -d)"
+          cd "$project_dir"
+          uv init --name art-distributed-install-smoke --python 3.12 --bare
+          uv venv --python 3.12
+          uv pip install --python .venv/bin/python \
+            "openpipe-art @ file://${wheel_path}"
+          uv pip install --python .venv/bin/python \
+            --index-url https://download.pytorch.org/whl/cpu \
+            "torch==2.11.0"
+          uv pip install --python .venv/bin/python \
+            "msgspec>=0.21.0" \
+            "torchmonarch==0.6.0" \
+            "transformers==5.12.1"
+
+          .venv/bin/python - <<'PY'
+          import sys
+          from importlib.metadata import metadata
+          from pathlib import Path
+          import subprocess
+          import tempfile
+          import venv
+
+          import art
+          import art.distributed as distributed
+
+          assert Path(art.__file__).resolve().is_relative_to(Path(sys.prefix))
+          assert "art.distributed.art_runtime" not in sys.modules
+          from art.distributed import (  # noqa: E402
+              ArtRuntime,
+              ClusterSpec,
+              HostSpec,
+              InstalledAsyncCallable,
+              NcclTransportSpec,
+              PackingRequest,
+              compile_topology,
+          )
+
+          assert all(
+              value is not None
+              for value in (
+                  ArtRuntime,
+                  ClusterSpec,
+                  HostSpec,
+                  InstalledAsyncCallable,
+                  NcclTransportSpec,
+                  PackingRequest,
+                  compile_topology,
+              )
+          )
+          assert "monarch" not in sys.modules
+          assert "PackingRequest" in distributed.__all__
+          assert "NcclTransportSpec" in distributed.__all__
+          assert {"distributed", "megatron"} <= set(
+              metadata("openpipe-art").get_all("Provides-Extra") or ()
+          )
+          bundle = Path(art.__file__).with_name("_megatron_runtime")
+          assert (bundle / "manifest.json").is_file()
+          assert (bundle / "uv.lock").is_file()
+
+          from art.megatron.runtime.managed import _copy_art
+
+          with tempfile.TemporaryDirectory() as temp_dir:
+              managed = Path(temp_dir) / "managed"
+              venv.EnvBuilder(with_pip=False).create(managed)
+              managed_python = managed / "bin" / "python"
+              _copy_art(managed_python)
+              managed_site = Path(
+                  subprocess.check_output(
+                      [
+                          str(managed_python),
+                          "-c",
+                          "import sysconfig; print(sysconfig.get_paths()['purelib'])",
+                      ],
+                      text=True,
+                  ).strip()
+              )
+              copied_bundle = managed_site / "art" / "_megatron_runtime"
+              assert (copied_bundle / "manifest.json").is_file()
+              assert (copied_bundle / "nixl-de8115ca.tar.gz").is_file()
+              assert not (managed_site / "art" / "_vllm_runtime").exists()
+          PY
+
+          PYTHONPATH="$GITHUB_WORKSPACE/examples/multinode" timeout 150s \
+            .venv/bin/art-monarch local \
+            --program program:main \
+            --port 0 \
+            --startup-timeout 90
+          test ! -e "$ART_VLLM_RUNTIME_CACHE_DIR"
diff --git a/.github/workflows/prek.yml b/.github/workflows/prek.yml
index b5b649b4c..695e158b5 100644
--- a/.github/workflows/prek.yml
+++ b/.github/workflows/prek.yml
@@ -13,8 +13,7 @@ env:
   CI_PYTHON_MM: "3.12"
   CI_UV_CACHE_RELEASE_TAG: "prek-uv-cache"
   CI_UV_CACHE_ASSET_PREFIX: "prek-uv-cache"
-  CI_APEX_PARALLEL_BUILD: "8"
-  CI_APEX_NVCC_THREADS: "1"
+  CI_BUILD_JOBS: "8"
   CI_UV_BUILD_SLOTS: "2"
   UV_CACHE_DIR: "/root/.cache/uv"
   UV_LINK_MODE: "copy"
@@ -36,11 +35,11 @@ jobs:
           fp="$(python3 scripts/ci/compute_uv_fingerprint.py \
             --pyproject pyproject.toml \
             --uv-lock uv.lock \
+            --megatron-pyproject megatron_runtime/pyproject.toml \
+            --megatron-uv-lock megatron_runtime/uv.lock \
             --base-image "${CI_BASE_IMAGE}" \
             --python-mm "${CI_PYTHON_MM}" \
-            --torch-cuda-arch-list "${TORCH_CUDA_ARCH_LIST}" \
-            --ci-apex-parallel-build "${CI_APEX_PARALLEL_BUILD}" \
-            --ci-apex-nvcc-threads "${CI_APEX_NVCC_THREADS}")"
+            --torch-cuda-arch-list "${TORCH_CUDA_ARCH_LIST}")"
           echo "fingerprint=${fp}" >> "${GITHUB_OUTPUT}"
           echo "Expected uv cache fingerprint: ${fp}"
 
@@ -187,14 +186,7 @@ jobs:
 
       - name: Install Megatron dependencies
         run: |
-          original_pyproject="$(mktemp)"
-          cp pyproject.toml "${original_pyproject}"
-          cleanup() {
-            mv "${original_pyproject}" pyproject.toml
-          }
-          trap cleanup EXIT
-
-          cudnn_path="${GITHUB_WORKSPACE}/.venv/lib/python${CI_PYTHON_MM}/site-packages/nvidia/cudnn"
+          cudnn_path="${GITHUB_WORKSPACE}/megatron_runtime/.venv/lib/python${CI_PYTHON_MM}/site-packages/nvidia/cudnn"
           export CUDNN_PATH="${cudnn_path}"
           export CUDNN_HOME="${cudnn_path}"
           export CUDNN_INCLUDE_PATH="${cudnn_path}/include"
@@ -203,16 +195,13 @@ jobs:
           export LIBRARY_PATH="${CUDNN_LIBRARY_PATH}${LIBRARY_PATH:+:${LIBRARY_PATH}}"
           export LD_LIBRARY_PATH="${CUDNN_LIBRARY_PATH}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
           export UV_CONCURRENT_BUILDS="${CI_UV_BUILD_SLOTS}"
-          export CMAKE_BUILD_PARALLEL_LEVEL="${CI_APEX_PARALLEL_BUILD}"
-          export MAX_JOBS="${CI_APEX_PARALLEL_BUILD}"
-          export NINJAFLAGS="-j${CI_APEX_PARALLEL_BUILD}"
-          python3 scripts/ci/apply_ci_uv_build_overrides.py \
-            --pyproject pyproject.toml \
-            --apex-parallel-build "${CI_APEX_PARALLEL_BUILD}" \
-            --apex-nvcc-threads "${CI_APEX_NVCC_THREADS}"
-          echo "CI uv build overrides: APEX_PARALLEL_BUILD=${CI_APEX_PARALLEL_BUILD}, NVCC_APPEND_FLAGS=--threads ${CI_APEX_NVCC_THREADS}, UV_CONCURRENT_BUILDS=${CI_UV_BUILD_SLOTS}"
+          export CMAKE_BUILD_PARALLEL_LEVEL="${CI_BUILD_JOBS}"
+          export MAX_JOBS="${CI_BUILD_JOBS}"
+          export NINJAFLAGS="-j${CI_BUILD_JOBS}"
           uv --version
-          uv sync --extra megatron --extra langgraph --extra plotting --group dev --frozen --python "${CI_PYTHON_MM}"
+          uv sync --extra langgraph --extra plotting --group dev --frozen --python "${CI_PYTHON_MM}"
+          uv sync --project megatron_runtime --extra cuda12 --group test --frozen --python "${CI_PYTHON_MM}"
+          uv pip install --python megatron_runtime/.venv/bin/python --no-deps --editable .
 
       - name: Run prek hooks (lint, format, typecheck, uv.lock)
         run: |
@@ -223,9 +212,10 @@ jobs:
 
       - name: Run Megatron lightweight tests
         run: |
-          uv run --no-sync python -c "import megatron.core.packed_seq_params"
-          uv run --no-sync pytest --nbval --current-env --tb=short \
+          megatron_runtime/.venv/bin/python -c "import megatron.core.packed_seq_params"
+          megatron_runtime/.venv/bin/python -m pytest --nbval --current-env --tb=short \
             tests/unit/test_megatron_reference_logprobs.py \
+            tests/unit/test_preprocessing_tokenize.py::test_gemma4_normalizes_json_tool_arguments_for_mapping_template \
             tests/unit/test_moe_routing_replay.py \
             tests/unit/test_moe_routing_real_path.py \
             tests/unit/test_pipeline_trainer_local_backend.py \
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 75dca450f..5698fe454 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -106,8 +106,10 @@ jobs:
                   str(runtime_python),
                   "-c",
                   "import torch, vllm; "
+                  "from art_vllm_runtime.fast_metrics import FastMetricsSharedWriter; "
                   "from art_vllm_runtime.policy_spans import "
                   "_patch_lora_update_coordinator; "
+                  "writer = FastMetricsSharedWriter(); writer.close(); "
                   "_patch_lora_update_coordinator(); "
                   "print('runtime compatibility ok')",
               ],
@@ -137,22 +139,22 @@ jobs:
           git tag v${{ needs.build-package.outputs.version }}
           git push origin v${{ needs.build-package.outputs.version }}
 
-      - name: Publish draft release
+      - name: Upload assets to draft release
         env:
           GH_TOKEN: ${{ github.token }}
         run: |
           if gh release view v${{ needs.build-package.outputs.version }} --json isDraft | jq -r '.isDraft' | grep -q true; then
-            gh release edit v${{ needs.build-package.outputs.version }} --draft=false
+            gh release upload v${{ needs.build-package.outputs.version }} dist/*
           else
             echo "::error::No draft release found for v${{ needs.build-package.outputs.version }}"
             exit 1
           fi
 
-      - name: Upload assets to release
+      - name: Publish release
         env:
           GH_TOKEN: ${{ github.token }}
         run: |
-          gh release upload v${{ needs.build-package.outputs.version }} dist/*
+          gh release edit v${{ needs.build-package.outputs.version }} --draft=false
 
       - name: Publish to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
diff --git a/.gitignore b/.gitignore
index ed198af87..1fa995717 100644
--- a/.gitignore
+++ b/.gitignore
@@ -24,3 +24,4 @@ replays/
 !/src/art/wandb/**
 /src/art/wandb/__pycache__/
 scratch/
+/progress_log.md
diff --git a/.skyignore b/.skyignore
index 527b38f02..4c70b5c53 100644
--- a/.skyignore
+++ b/.skyignore
@@ -2,6 +2,9 @@ __pycache__/
 .art/
 # .env
 .venv/
+.ruff_cache/
+.pytest_cache/
+scratch/
 grpo_trainer_lora_model/
 logs/
 shared_cache.db
@@ -13,5 +16,6 @@ dist/
 dev/art-e/data/
 replays/
 /trajectories/
+/progress_log.md
 .DS_Store
 # .local/
diff --git a/THIRD-PARTY-NOTICES b/THIRD-PARTY-NOTICES
index 0fbb5463c..2a743b41e 100644
--- a/THIRD-PARTY-NOTICES
+++ b/THIRD-PARTY-NOTICES
@@ -51,3 +51,24 @@ This project vendors a modified HybridEP runtime derived from DeepEP:
 The applicable license text is retained at:
 
 - src/art/megatron/_hybrid_ep/LICENSE
+
+Megatron release wheels also embed pinned source archives used only as headers
+when building ART's HybridEP extension:
+
+- NVIDIA NIXL 1.3.2 (commit de8115ca97d3f8fb63a4988e9b4d4a038b2e0f72),
+  Apache License 2.0, https://github.com/ai-dynamo/nixl
+- OpenUCX 1.21.0, BSD 3-Clause License,
+  https://github.com/openucx/ucx
+
+The complete license and notice files remain in their respective bundled source
+archives. ART links HybridEP against the matching NIXL and UCX libraries shipped
+by the official `nixl-cu12` or `nixl-cu13` wheel; it does not redistribute those
+libraries itself.
+
+On first cross-host HybridEP use, ART may download the checksum-pinned etcd
+3.5.33 executable from its official GitHub release. etcd is licensed under the
+Apache License 2.0: https://github.com/etcd-io/etcd
+
+The CUDA 12 managed Megatron environment installs NVIDIA Apex from its pinned
+25.09 source tag under the BSD 3-Clause License:
+https://github.com/NVIDIA/apex
diff --git a/dev/sft/sft-from-file.py b/dev/sft/sft-from-file.py
index deed4595c..d549588f3 100644
--- a/dev/sft/sft-from-file.py
+++ b/dev/sft/sft-from-file.py
@@ -9,8 +9,10 @@
 
 
 async def main():
-    backend = MegatronBackend()
-
+    art.init_megatron_runtime_config(
+        topology=art.MegatronTopologyConfig(),
+        packed_sequence_length=4096,
+    )
     model_name = "run-" + "".join(
         random.choices("abcdefghijklmnopqrstuvwxyz0123456789", k=8)
     )
@@ -20,14 +22,14 @@ async def main():
         project="sft-from-file",
         base_model="Qwen/Qwen3.6-35B-A3B",
     )
-    await model.register(backend)
-
-    await train_sft_from_file(
-        model=model,
-        file_path="dev/sft/dataset.jsonl",
-        epochs=1,
-        peak_lr=2e-4,
-    )
+    async with MegatronBackend() as backend:
+        await model.register(backend)
+        await train_sft_from_file(
+            model=model,
+            file_path="dev/sft/dataset.jsonl",
+            epochs=1,
+            peak_lr=2e-4,
+        )
 
     print("Training complete!")
 
diff --git a/dev/sft/sft-warmup.py b/dev/sft/sft-warmup.py
index b14a3d056..a8719f2b4 100644
--- a/dev/sft/sft-warmup.py
+++ b/dev/sft/sft-warmup.py
@@ -43,7 +43,10 @@ async def rl_rollout(model: art.TrainableModel, prompt: str) -> art.Trajectory:
 async def main():
     load_dotenv()
 
-    backend = MegatronBackend()
+    art.init_megatron_runtime_config(
+        topology=art.MegatronTopologyConfig(),
+        packed_sequence_length=int(os.environ.get("PACKED_SEQUENCE_LENGTH", "4096")),
+    )
     model_name = "sft-warmup-" + "".join(
         random.choices("abcdefghijklmnopqrstuvwxyz0123456789", k=8)
     )
@@ -53,75 +56,76 @@ async def main():
         project="sft-warmup",
         base_model="Qwen/Qwen2.5-7B-Instruct",
     )
-    await model.register(backend)
-
-    # ========================================================================
-    # Phase 1: SFT
-    # ========================================================================
-    print("\n[Phase 1] SFT training...")
-    for chunk in create_sft_dataset_iterator(
-        SFT_TRAJECTORIES, batch_size=1, peak_lr=1e-5
-    ):
-        await model.train_sft(chunk.trajectories, chunk.config)
-    print("SFT phase 1 complete.")
-
-    # ========================================================================
-    # Phase 2: RL (GRPO)
-    # ========================================================================
-    print("\n[Phase 2] RL training...")
-    prompt = "respond with yes, no, or maybe"
-
-    for i in range(10):
-        print(f"  RL step {i + 1}")
-        train_groups = await art.gather_trajectory_groups(
-            [
-                art.TrajectoryGroup(rl_rollout(model, prompt) for _ in range(6))
-                for _ in range(12)
-            ]
-        )
-        await model.train(train_groups)
-    print("RL phase 2 complete.")
-
-    # ========================================================================
-    # Phase 3: SFT again
-    # ========================================================================
-    print("\n[Phase 3] SFT training again...")
-    for chunk in create_sft_dataset_iterator(
-        SFT_TRAJECTORIES, batch_size=1, peak_lr=1e-5
-    ):
-        await model.train_sft(chunk.trajectories, chunk.config)
-    print("SFT phase 3 complete.")
-
-    # ========================================================================
-    # Phase 4: RL (GRPO) again
-    # ========================================================================
-    print("\n[Phase 4] RL training...")
-    prompt = "respond with yes, no, or maybe"
-
-    for i in range(10):
-        print(f"  RL step {i + 1}")
-        train_groups = await art.gather_trajectory_groups(
-            [
-                art.TrajectoryGroup(rl_rollout(model, prompt) for _ in range(6))
-                for _ in range(12)
-            ]
+    async with MegatronBackend() as backend:
+        await model.register(backend)
+
+        # ========================================================================
+        # Phase 1: SFT
+        # ========================================================================
+        print("\n[Phase 1] SFT training...")
+        for chunk in create_sft_dataset_iterator(
+            SFT_TRAJECTORIES, batch_size=1, peak_lr=1e-5
+        ):
+            await model.train_sft(chunk.trajectories, chunk.config)
+        print("SFT phase 1 complete.")
+
+        # ========================================================================
+        # Phase 2: RL (GRPO)
+        # ========================================================================
+        print("\n[Phase 2] RL training...")
+        prompt = "respond with yes, no, or maybe"
+
+        for i in range(10):
+            print(f"  RL step {i + 1}")
+            train_groups = await art.gather_trajectory_groups(
+                [
+                    art.TrajectoryGroup(rl_rollout(model, prompt) for _ in range(6))
+                    for _ in range(12)
+                ]
+            )
+            await model.train(train_groups)
+        print("RL phase 2 complete.")
+
+        # ========================================================================
+        # Phase 3: SFT again
+        # ========================================================================
+        print("\n[Phase 3] SFT training again...")
+        for chunk in create_sft_dataset_iterator(
+            SFT_TRAJECTORIES, batch_size=1, peak_lr=1e-5
+        ):
+            await model.train_sft(chunk.trajectories, chunk.config)
+        print("SFT phase 3 complete.")
+
+        # ========================================================================
+        # Phase 4: RL (GRPO) again
+        # ========================================================================
+        print("\n[Phase 4] RL training...")
+        prompt = "respond with yes, no, or maybe"
+
+        for i in range(10):
+            print(f"  RL step {i + 1}")
+            train_groups = await art.gather_trajectory_groups(
+                [
+                    art.TrajectoryGroup(rl_rollout(model, prompt) for _ in range(6))
+                    for _ in range(12)
+                ]
+            )
+            await model.train(train_groups)
+        print("RL phase 4 complete.")
+
+        # ========================================================================
+        # Test: Check model output
+        # ========================================================================
+        print("\n[Test] Model output after training:")
+        client = model.openai_client()
+        completion = await client.chat.completions.create(
+            messages=[{"role": "user", "content": "respond with yes, no, or maybe"}],
+            model=model.get_inference_name(),
+            max_tokens=10,
         )
-        await model.train(train_groups)
-    print("RL phase 4 complete.")
-
-    # ========================================================================
-    # Test: Check model output
-    # ========================================================================
-    print("\n[Test] Model output after training:")
-    client = model.openai_client()
-    completion = await client.chat.completions.create(
-        messages=[{"role": "user", "content": "respond with yes, no, or maybe"}],
-        model=model.get_inference_name(),
-        max_tokens=10,
-    )
-    print(f"Response: {completion.choices[0].message.content}")
+        print(f"Response: {completion.choices[0].message.content}")
 
-    print("\nAll phases complete!")
+        print("\nAll phases complete!")
 
 
 if __name__ == "__main__":
diff --git a/dev/yes-no-maybe-megatron.py b/dev/yes-no-maybe-megatron.py
index 9a85ff518..5bce36b96 100644
--- a/dev/yes-no-maybe-megatron.py
+++ b/dev/yes-no-maybe-megatron.py
@@ -198,7 +198,10 @@ async def main() -> None:
         )
     )
 
-    backend = MegatronBackend()
+    art.init_megatron_runtime_config(
+        topology=art.MegatronTopologyConfig(),
+        packed_sequence_length=packed_sequence_length,
+    )
     model = art.TrainableModel(
         run_name=model_name,
         name=model_name,
@@ -214,7 +217,7 @@ async def main() -> None:
     prompts = prompts[: int(os.environ.get("PROMPTS_LIMIT", str(len(prompts))))]
     eval_prompts = prompts[: int(os.environ.get("EVAL_PROMPTS", "24"))]
 
-    try:
+    async with MegatronBackend() as backend:
         print(json.dumps({"event": "register_start"}), flush=True)
         await model.register(backend)
         print(
@@ -294,7 +297,6 @@ async def main() -> None:
                 model,
                 train_groups,
                 learning_rate=learning_rate,
-                packed_sequence_length=packed_sequence_length,
             )
             print(
                 json.dumps(
@@ -327,8 +329,6 @@ async def main() -> None:
                 ),
                 flush=True,
             )
-    finally:
-        await backend.close()
 
 
 if __name__ == "__main__":
diff --git a/dev/yes_no_maybe_trainability.py b/dev/yes_no_maybe_trainability.py
index 019e34603..d86f73b8c 100644
--- a/dev/yes_no_maybe_trainability.py
+++ b/dev/yes_no_maybe_trainability.py
@@ -247,7 +247,7 @@ def make_backend(
     if backend_name == "local":
         return LocalBackend(path=art_path, in_process=in_process)
     if backend_name == "megatron":
-        return MegatronBackend(path=art_path, in_process=in_process)
+        return MegatronBackend(path=art_path)
     raise ValueError(f"Unsupported BACKEND={backend_name!r}")
 
 
diff --git a/docker/art-gpu.Dockerfile b/docker/art-gpu.Dockerfile
index 74631c3dd..576ff902b 100644
--- a/docker/art-gpu.Dockerfile
+++ b/docker/art-gpu.Dockerfile
@@ -3,8 +3,6 @@ ARG ART_SHA=unknown
 ARG UV_VERSION=0.11.7
 ARG BUILD_JOBS=2
 ARG UV_CONCURRENT_BUILDS=1
-ARG APEX_PARALLEL_BUILD=2
-ARG APEX_NVCC_THREADS=1
 ARG TORCH_CUDA_ARCH_LIST=9.0
 ARG CUDNN_PACKAGE_VERSION=9.10.2.21
 ARG SKYPILOT_VERSION=0.12.0
@@ -15,8 +13,6 @@ FROM ${BASE_IMAGE} AS builder
 ARG UV_VERSION
 ARG BUILD_JOBS
 ARG UV_CONCURRENT_BUILDS
-ARG APEX_PARALLEL_BUILD
-ARG APEX_NVCC_THREADS
 ARG TORCH_CUDA_ARCH_LIST
 ARG CUDNN_PACKAGE_VERSION
 
@@ -26,8 +22,6 @@ ENV CUDA_HOME=/usr/local/cuda-12.8 \
     UV_PYTHON_INSTALL_DIR=/opt/uv-python \
     UV_LINK_MODE=copy \
     UV_CONCURRENT_BUILDS=${UV_CONCURRENT_BUILDS} \
-    APEX_PARALLEL_BUILD=${APEX_PARALLEL_BUILD} \
-    NVCC_APPEND_FLAGS=--threads\ ${APEX_NVCC_THREADS} \
     TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST} \
     CMAKE_BUILD_PARALLEL_LEVEL=${BUILD_JOBS} \
     MAX_JOBS=${BUILD_JOBS} \
@@ -49,6 +43,7 @@ RUN if ! getent group messagebus >/dev/null; then groupadd -r messagebus; fi \
 WORKDIR /opt/src/art
 COPY pyproject.toml uv.lock ./
 COPY vllm_runtime/pyproject.toml vllm_runtime/uv.lock ./vllm_runtime/
+COPY megatron_runtime/pyproject.toml megatron_runtime/uv.lock ./megatron_runtime/
 
 RUN /opt/conda/bin/python -m pip install --no-cache-dir "nvidia-cudnn-cu12==${CUDNN_PACKAGE_VERSION}" \
  && mkdir -p /usr/local/cuda-12.8/include /usr/local/cuda-12.8/lib64 \
@@ -61,8 +56,8 @@ RUN /opt/conda/bin/python -m pip install --no-cache-dir "nvidia-cudnn-cu12==${CU
       dst="/usr/local/cuda-12.8/lib64/$(basename "$src")"; \
       if [ ! -e "$dst" ]; then ln -s "$src" "$dst" && printf '%s\n' "$dst" >> /tmp/art-cudnn-symlinks.txt; fi; \
     done \
- && UV_LINK_MODE=hardlink uv sync --frozen --extra megatron --no-install-project --python 3.12 \
- && rm -rf .venv \
+ && UV_LINK_MODE=hardlink uv sync --project megatron_runtime --frozen --extra cuda12 --no-install-project --no-dev --python 3.12 \
+ && rm -rf megatron_runtime/.venv \
  && UV_LINK_MODE=hardlink uv sync --frozen --extra backend --extra tinker --no-install-project --python 3.12 \
  && rm -rf .venv \
  && cd vllm_runtime \
@@ -77,8 +72,6 @@ ARG ART_SHA
 ARG UV_VERSION
 ARG BUILD_JOBS
 ARG UV_CONCURRENT_BUILDS
-ARG APEX_PARALLEL_BUILD
-ARG APEX_NVCC_THREADS
 ARG TORCH_CUDA_ARCH_LIST
 ARG SKYPILOT_VERSION
 ARG SKY_REMOTE_RAY_VERSION
@@ -89,8 +82,6 @@ ENV CUDA_HOME=/usr/local/cuda-12.8 \
     UV_PYTHON_INSTALL_DIR=/opt/uv-python \
     UV_LINK_MODE=copy \
     UV_CONCURRENT_BUILDS=${UV_CONCURRENT_BUILDS} \
-    APEX_PARALLEL_BUILD=${APEX_PARALLEL_BUILD} \
-    NVCC_APPEND_FLAGS=--threads\ ${APEX_NVCC_THREADS} \
     TORCH_CUDA_ARCH_LIST=${TORCH_CUDA_ARCH_LIST} \
     CMAKE_BUILD_PARALLEL_LEVEL=${BUILD_JOBS} \
     MAX_JOBS=${BUILD_JOBS} \
diff --git a/docs/docs.json b/docs/docs.json
index 2b99e176e..741dadbb1 100644
--- a/docs/docs.json
+++ b/docs/docs.json
@@ -47,6 +47,7 @@
           "getting-started/about",
           "getting-started/quick-start",
           "getting-started/installation-setup",
+          "getting-started/multi-node",
           "getting-started/notebooks",
           "getting-started/faq"
         ]
diff --git a/docs/getting-started/installation-setup.mdx b/docs/getting-started/installation-setup.mdx
index 25a1a0ada..a2c48a7a4 100644
--- a/docs/getting-started/installation-setup.mdx
+++ b/docs/getting-started/installation-setup.mdx
@@ -17,11 +17,10 @@ pip install openpipe-art
 The ART server can be run locally on any machine with a GPU. To install the backend dependencies required for training and inference, you can install the `backend` extra:
 
 ```bash
-pip install openpipe-art[backend]
+pip install --extra-index-url https://download.pytorch.org/whl/cu128 \
+  "openpipe-art[backend]"
 ```
 
-
-
 ```python
 from art import TrainableModel, gather_trajectory_groups
 from art.local.backend import LocalBackend
@@ -39,6 +38,32 @@ await model.register(backend)
 ... the rest of your code ...
 ```
 
+CUDA 13 hosts use `openpipe-art[backend-cu130]` and the PyTorch `cu130` index.
+
+### Running Megatron
+
+On a supported CUDA 12 trainer image, one install command provides ART's
+controller, Monarch runtime, and the locked Megatron runtime contract:
+
+```bash
+pip install --extra-index-url https://download.pytorch.org/whl/cu128 \
+  "openpipe-art[megatron]"
+```
+
+CUDA 13 hosts use `openpipe-art[megatron-cu130]` with
+`https://download.pytorch.org/whl/cu130`. Megatron currently requires Python
+3.12. The first trainer launch materializes the exact trainer environment in a
+content-addressed node-local cache. No ART checkout or `setup.sh` invocation is
+required.
+
+The image remains responsible for the NVIDIA driver and CUDA toolkit. For
+cross-host training it must also provide the NCCL network transport, MOFED/RDMA
+devices, and the kernel capabilities described in the multi-node deployment
+guide. ART validates these before allocating the model.
+
+Tinker users install `openpipe-art[tinker]`; serverless users need only
+`openpipe-art`. These profiles do not install Megatron or vLLM dependencies.
+
 ### Using a managed autoscaling backend
 
 Instead of managing the GPUs and training processes yourself, you can optionally send inference and training requests to the W&B Training cluster, which autoscales to match your job's demand. To do so, install `openpipe-art` without any extras and use `ServerlessBackend`:
diff --git a/docs/getting-started/multi-node.mdx b/docs/getting-started/multi-node.mdx
new file mode 100644
index 000000000..48cd1699c
--- /dev/null
+++ b/docs/getting-started/multi-node.mdx
@@ -0,0 +1,306 @@
+---
+title: "Multi-node deployment"
+sidebarTitle: "Multi-node deployment"
+icon: "network-wired"
+---
+
+ART's distributed runtime consumes a Monarch host mesh. SkyPilot can provision
+that mesh, but it is a deployment tool rather than an ART dependency. The ART
+process does not launch another SkyPilot cluster from inside its allocation.
+
+## Controller program
+
+The bootstrap accepts a source script or import path to a top-level async
+function. A script reference such as `train.py:main` adds the script's directory
+to every ART-owned worker's Python path, so source synchronized by SkyPilot does
+not need to be packaged first. SkyPilot runs the bootstrap on every node, but
+only rank 0 imports the module and invokes the controller.
+`examples/multinode/program.py` is a complete CPU-runnable Yes/No/Maybe smoke:
+
+```python
+import asyncio
+import os
+import socket
+
+import art
+from art.distributed import (
+    ArtLaunchContext,
+    ArtRuntime,
+    InstalledAsyncCallable,
+    compile_topology,
+)
+
+REWARDS = {"yes": 0.5, "no": 0.75, "maybe": 1.0}
+
+
+async def rollout(
+    _model: art.TrainableModel, answer: str, _config: None
+) -> art.Trajectory:
+    messages: art.MessagesAndChoices = [
+        {"role": "user", "content": f"Respond with {answer}."},
+        {"role": "assistant", "content": answer},
+    ]
+    return art.Trajectory(
+        messages_and_choices=messages,
+        reward=REWARDS[answer],
+        metadata={
+            "answer": answer,
+            "hostname": socket.gethostname(),
+            "process_id": os.getpid(),
+        },
+    )
+
+
+async def main(launch: ArtLaunchContext) -> None:
+    host_count = launch.host_count
+    runtime = await ArtRuntime.start(
+        launch.host_mesh,
+        compile_topology(
+            cluster=launch.homogeneous_cluster(
+                cpu_slots=1,
+                startup_timeout_s=90,
+                rpc_timeout_s=30,
+            )
+        ),
+    )
+    try:
+        workers = tuple(range(host_count))
+        executor = runtime.rollout_executor(
+            InstalledAsyncCallable.from_callable(rollout),
+            target_workers=host_count,
+        )
+        executor.set_workers(workers)
+        model = art.TrainableModel(
+            name="multinode-smoke",
+            project="art",
+            base_model="not-loaded",
+            run_name="multinode-smoke",
+        )
+        trajectories = []
+        for answer in REWARDS:
+            trajectories.extend(
+                await asyncio.gather(
+                    *(
+                        executor.run(worker, rollout, model, answer, None)
+                        for worker in workers
+                    )
+                )
+            )
+        answers = [
+            str(trajectory.metadata["answer"]) for trajectory in trajectories
+        ]
+        expected = [answer for answer in REWARDS for _ in workers]
+        placements = {
+            (trajectory.metadata["hostname"], trajectory.metadata["process_id"])
+            for trajectory in trajectories
+        }
+        if answers != expected or len(placements) != host_count:
+            raise RuntimeError(
+                f"distributed rollout mismatch: {answers=}, {placements=}"
+            )
+        print(f"ART_MULTINODE_SMOKE_PASS hosts={host_count} answers={answers}")
+    finally:
+        await runtime.close()
+```
+
+The controller receives an `ArtLaunchContext` once, while the top-level
+`rollout` runs in one process on each host. The context owns the attached host
+mesh and builds a homogeneous typed cluster without exposing provider
+environment variables. Both functions must be installed or synchronized at the
+same import paths on every node; ART sends verified import references and never
+ships opaque closures. The dummy `TrainableModel` is serialized for the rollout
+contract but never loaded, so this validates the package, host admission,
+process placement, public trajectory types, and cleanup without a GPU or
+inference server.
+
+The distributed service APIs are opt-in. Existing single-node programs continue
+to construct and use `LocalBackend` exactly as before:
+
+```python
+from art.local import LocalBackend
+
+backend = LocalBackend()
+```
+
+## SkyPilot
+
+Start with `examples/multinode/skypilot.yaml`. It is an intentionally CPU-only
+two-node smoke that runs all three bounded rollouts on each host without
+reserving training GPUs or provisioning the managed vLLM runtime.
+
+For source-based GPU training, replace its resources and setup with the desired
+topology and run the CUDA-detecting cluster setup:
+
+```yaml
+resources:
+  accelerators: H200:8
+
+setup: |
+  set -euo pipefail
+  INSTALL_MULTINODE=true bash scripts/setup.sh
+
+run: |
+  set -euo pipefail
+  export NCCL_NET=IB
+  exec .venv/bin/art-monarch skypilot \
+    --program train.py:main
+```
+
+`set -euo pipefail` is required because SkyPilot runs multiline setup under
+Bash without enabling fail-fast behavior. For source development,
+`scripts/setup.sh` selects the CUDA-matched root and private trainer locks and
+builds HybridEP. It does not install system packages. The image or cluster
+bootstrap must provide the NVIDIA driver and toolkit, native build tools,
+NCCL network transport, MOFED/RDMA devices, and the required kernel modules.
+The CPU example only syncs the root `distributed` extra.
+
+Setup is cluster provisioning, not service startup. `art-monarch`, trainer
+actors, and managed vLLM processes never invoke these shell scripts. Source
+checkouts launch the already-built `vllm_runtime/.venv`; release wheels may
+materialize their bundled, locked vLLM environment into a content-addressed
+cache on first use.
+
+Any GPU workload spanning hosts must set one explicit NCCL network contract in
+its `ClusterSpec`, for example
+`nccl_transport=NcclTransportSpec(net_name="IB")`, and set `NCCL_NET` to that
+exact registered name on every node. `IB` covers built-in InfiniBand/RoCE;
+external network plugins use their registered NCCL name. Before model
+allocation, ART runs a small collective in both the trainer and managed-vLLM
+environments and requires each rank to report that exact selected module. It
+never retries with Socket. Deployment qualification remains responsible for
+all-GPU bandwidth, GPU Direct RDMA, HCA, and GID validation.
+
+Cross-host HybridEP uses `NixlTransportSpec()`. If `metadata_store` is omitted,
+the controller starts a checksum-pinned etcd process, publishes its routable
+endpoint, health-checks it from every host, and owns its cleanup. An explicitly
+managed endpoint remains supported.
+
+If `ART_VLLM_RUNTIME_BIN` is set, it must point directly to a standard
+`.venv/bin/art-vllm-runtime-server` executable. ART derives the matching Python,
+runtime root, environment, and working directory from that path so the preflight
+cannot certify a different runtime. Arbitrary command wrappers fail closed.
+
+For a published release wheel, install the profile matching the host CUDA
+toolkit. For CUDA 12:
+
+```yaml
+setup: |
+  set -euo pipefail
+  uv venv --python 3.12 --seed .venv
+  .venv/bin/pip install \
+    --extra-index-url https://download.pytorch.org/whl/cu128 \
+    "openpipe-art[megatron]==VERSION"
+```
+
+For CUDA 13, use `openpipe-art[megatron-cu130]==VERSION` and the PyTorch
+`cu130` index. These commands install ART, Monarch, and the CUDA-specific NIXL
+wheel. The example uses the supported image's `uv` installation to provision
+Python 3.12. The first trainer launch materializes the pinned Megatron environment,
+builds source-only CUDA components such as CUDA 12 Apex and ART's HybridEP when
+needed, and reuses the immutable result on later launches. NIXL and its UCX GDA
+plugin come from the official relocatable wheel; ART bundles only the matching
+pinned headers needed to build HybridEP.
+
+Release wheels use content-addressed managed Megatron and vLLM runtime bundles.
+Only wheels built with `scripts/build_package.py` contain those bundles. The
+install profile provides `uv`; first use needs package-index access unless the
+node-local cache was prewarmed.
+
+`examples/multinode/skypilot_training.yaml` runs a real two-host DP2 SFT step
+from the published wheel. It synchronizes only the user program, not an ART
+checkout. Point `ART_SHARED_ROOT` at an existing path mounted on every host,
+then launch:
+
+```fish
+sky launch -c art-multinode-training \
+  --env ART_SHARED_ROOT=/mnt/shared/art-multinode-release \
+  examples/multinode/skypilot_training.yaml
+```
+
+Launch it from the project root:
+
+```fish
+sky launch -c art-multinode examples/multinode/skypilot.yaml
+```
+
+One task rank runs on each allocated node. Every rank owns one Monarch worker
+subprocess; rank 0 also attaches the host mesh and runs the controller program.
+Rank-0 program completion or failure closes the lifecycle sockets and releases
+the peer task ranks. No manual SSH or per-node command is required.
+
+Each task invocation owns fresh worker loops and terminates them after the host
+mesh shuts down. A later `sky exec` starts new loops; ART does not reattach a
+second controller to completed workers.
+
+Ctrl-C disconnects SkyPilot log streaming; it does not stop the remote job.
+Check the queue and cancel explicitly when needed:
+
+```fish
+sky queue art-multinode
+sky cancel art-multinode JOB_ID
+```
+
+Only after the previous job is terminal, reuse an existing cluster without
+rerunning setup:
+
+```fish
+sky exec art-multinode examples/multinode/skypilot.yaml
+```
+
+`sky exec` synchronizes the workdir before scheduling, so running it while the
+previous job is live can change files under that job. Use `sky launch` instead
+when setup, mounts, the image, SkyPilot config, a wheel, `pyproject.toml`, or a
+lockfile changed. Setting `num_nodes: 1` uses the same controller on one node.
+
+For a local process that explicitly wants the same Monarch service APIs, ART
+can own one loopback worker directly:
+
+```fish
+.venv/bin/art-monarch local \
+  --program examples/multinode/program.py:main \
+  --port 0 \
+  --startup-timeout 90
+```
+
+Port `0` selects a fresh loopback port. `ArtRuntime.start_local(...)` is the
+equivalent library API. It accepts the same one-host compiled topology used by
+multi-node code and owns the worker for the runtime lifetime.
+
+SkyPilot provides `SKYPILOT_NODE_RANK`, `SKYPILOT_NODE_IPS`, and
+`SKYPILOT_NUM_NODES`; ART validates and translates them internally. Port
+`22222` is the Monarch worker port and `22223` is its job-lifecycle port. Pass
+`--port N` to reserve `N` and `N + 1` instead. These ports must be reachable
+between allocated nodes but must not be publicly exposed: ART's Monarch runtime
+uses unauthenticated `trust_all_connections` transport.
+
+## Existing SSH hosts
+
+For preallocated machines, start and own all workers from one controller
+command:
+
+```fish
+.venv/bin/art-monarch ssh \
+  --host gpu-a=10.0.0.10 \
+  --host gpu-b=10.0.0.11 \
+  --python /shared/project/.venv/bin/python \
+  --program /shared/project/train.py:main
+```
+
+Each value is `SSH_TARGET=WORKER_HOST`. Omit `=WORKER_HOST` when the SSH target
+is also the private address to which Monarch should bind. The controller must
+have both passwordless SSH access to every `SSH_TARGET` and a direct trusted
+private or VPN route to every `WORKER_HOST:N`. SSH options such as `ProxyJump`
+or `--ssh-arg=-F` affect only launch and stop commands; they do not tunnel
+Monarch traffic. SSH mode uses only worker port `N`, not SkyPilot's lifecycle
+port `N + 1`.
+
+The selected Python executable and source script must exist at the same paths on
+every host. ART uses non-interactive SSH, verifies that each launch-specific
+worker PID owns its listener, and monitors each foreground SSH process for the
+controller lifetime. A pre-existing listener is a hard error rather than a
+worker to reattach. SIGTERM and SIGHUP trigger bounded remote cleanup before the
+controller exits.
+
+The lower-level `worker` and `controller` subcommands remain available for
+schedulers or process supervisors that own worker lifecycle themselves. Those
+supervisors must replace worker loops before a subsequent controller attach.
diff --git a/examples/multinode/README.md b/examples/multinode/README.md
new file mode 100644
index 000000000..bee0583df
--- /dev/null
+++ b/examples/multinode/README.md
@@ -0,0 +1,68 @@
+# ART multi-node smoke
+
+`program.py` is a bounded CPU example using only public ART APIs. Its top-level
+controller receives a typed launch context and admits the attached hosts, then
+its top-level rollout returns one synthetic Yes/No/Maybe `Trajectory` per host
+for each answer. It never loads a model or starts Megatron or vLLM.
+
+Run the same controller on one local Monarch worker from the project root:
+
+```fish
+.venv/bin/art-monarch local \
+  --program examples/multinode/program.py:main \
+  --port 0 \
+  --startup-timeout 90
+```
+
+Or let SkyPilot run it on every node in one allocation:
+
+```fish
+sky launch -c art-multinode examples/multinode/skypilot.yaml
+```
+
+SkyPilot synchronizes `workdir` and runs `setup` on every node before starting
+the same `run` command on every node. ART starts one Monarch worker per node and
+calls `program:main` only on rank 0. User controllers and rollouts must remain
+importable at the same paths on every node; ART sends import references rather
+than pickled closures.
+
+The CPU smoke installs ART's `distributed` extra from the synchronized source
+checkout. `skypilot_training.yaml` is the corresponding real two-host Megatron
+qualification. It synchronizes only this example directory and installs a
+published ART wheel, so no ART checkout or setup script exists on the cluster.
+Set `ART_SHARED_ROOT` to storage mounted at the same path on every host before
+launching it:
+
+```fish
+sky launch -c art-multinode-training \
+  --env ART_SHARED_ROOT=/mnt/shared/art-multinode-release \
+  examples/multinode/skypilot_training.yaml
+```
+
+The default training run uses one GPU per host and DP2. Set
+`ART_TRAINER_RANKS_PER_HOST`, `ART_EXAMPLE_MODEL`, and the
+`ART_EXAMPLE_{TP,CP,EP,PP}` variables to qualify larger topologies. Set
+`ART_EXAMPLE_USE_NIXL=1` when an EP group crosses hosts; ART then provisions its
+metadata store and builds the multi-node HybridEP runtime automatically.
+
+Use `sky launch` after changing setup. Reuse unchanged CPU-smoke and training
+clusters without rerunning setup with, respectively:
+
+```fish
+sky exec art-multinode examples/multinode/skypilot.yaml
+sky exec art-multinode-training examples/multinode/skypilot_training.yaml
+```
+
+GPU workloads spanning hosts must also set `NCCL_NET` on every node and provide
+the same exact registered name through `ClusterSpec.nccl_transport`. ART proves
+that selected module before trainer or vLLM model allocation and never falls
+back to Socket. `ART_VLLM_RUNTIME_BIN`, when set, must point directly to a
+standard `.venv/bin/art-vllm-runtime-server`; arbitrary wrappers fail closed.
+
+Each invocation terminates every worker loop before the task exits. Reusing the
+cluster starts fresh loops; Monarch 0.6 worker addresses are generation-owned and
+completed loops are not reattached.
+
+Setting `num_nodes: 1` exercises the same API on one node. Do not expose the
+default private ports `22222` and `22223`; pinned Monarch 0.6 does not
+authenticate its transport.
diff --git a/examples/multinode/program.py b/examples/multinode/program.py
new file mode 100644
index 000000000..d8b0f28fe
--- /dev/null
+++ b/examples/multinode/program.py
@@ -0,0 +1,86 @@
+from __future__ import annotations
+
+import asyncio
+import os
+import socket
+
+import art
+from art.distributed import (
+    ArtLaunchContext,
+    ArtRuntime,
+    InstalledAsyncCallable,
+    compile_topology,
+)
+
+REWARDS = {"yes": 0.5, "no": 0.75, "maybe": 1.0}
+
+
+async def rollout(
+    _model: art.TrainableModel, answer: str, _config: None
+) -> art.Trajectory:
+    messages: art.MessagesAndChoices = [
+        {"role": "user", "content": f"Respond with {answer}."},
+        {"role": "assistant", "content": answer},
+    ]
+    return art.Trajectory(
+        messages_and_choices=messages,
+        reward=REWARDS[answer],
+        metadata={
+            "answer": answer,
+            "hostname": socket.gethostname(),
+            "process_id": os.getpid(),
+        },
+    )
+
+
+async def main(launch: ArtLaunchContext) -> None:
+    host_count = launch.host_count
+    runtime = await ArtRuntime.start(
+        launch.host_mesh,
+        compile_topology(
+            cluster=launch.homogeneous_cluster(
+                cpu_slots=1,
+                startup_timeout_s=90,
+                rpc_timeout_s=30,
+            )
+        ),
+    )
+    try:
+        workers = tuple(range(host_count))
+        executor = runtime.rollout_executor(
+            InstalledAsyncCallable.from_callable(rollout),
+            target_workers=host_count,
+        )
+        executor.set_workers(workers)
+        model = art.TrainableModel(
+            name="multinode-smoke",
+            project="art",
+            base_model="not-loaded",
+            run_name="multinode-smoke",
+        )
+        trajectories: list[art.Trajectory] = []
+        for answer in REWARDS:
+            trajectories.extend(
+                await asyncio.gather(
+                    *(
+                        executor.run(worker, rollout, model, answer, None)
+                        for worker in workers
+                    )
+                )
+            )
+        answers = [str(trajectory.metadata["answer"]) for trajectory in trajectories]
+        expected = [answer for answer in REWARDS for _ in workers]
+        placements = {
+            (trajectory.metadata["hostname"], trajectory.metadata["process_id"])
+            for trajectory in trajectories
+        }
+        if answers != expected or len(placements) != host_count:
+            raise RuntimeError(
+                f"distributed rollout mismatch: answers={answers}, placements={placements}"
+            )
+        print(
+            f"ART_MULTINODE_SMOKE_PASS hosts={host_count} answers={answers}",
+            flush=True,
+        )
+    finally:
+        await runtime.close()
diff --git a/examples/multinode/skypilot.yaml b/examples/multinode/skypilot.yaml
new file mode 100644
index 000000000..a7baae344
--- /dev/null
+++ b/examples/multinode/skypilot.yaml
@@ -0,0 +1,22 @@
+name: art-multinode
+
+num_nodes: 2
+
+workdir: .
+
+resources:
+  cpus: 4+
+  memory: 8+
+
+envs:
+  PYTHONUTF8: "1"
+
+setup: |
+  set -euo pipefail
+  uvx uv@0.11.7 sync --frozen --no-dev --extra distributed
+
+run: |
+  set -euo pipefail
+  exec .venv/bin/art-monarch skypilot \
+    --program examples/multinode/program.py:main \
+    --startup-timeout 90
diff --git a/examples/multinode/skypilot_training.yaml b/examples/multinode/skypilot_training.yaml
new file mode 100644
index 000000000..2ae2c1250
--- /dev/null
+++ b/examples/multinode/skypilot_training.yaml
@@ -0,0 +1,27 @@
+name: art-multinode-training
+
+num_nodes: 2
+
+workdir: examples/multinode
+
+resources:
+  accelerators: H200:1
+
+envs:
+  ART_SHARED_ROOT:
+  ART_VERSION: "0.5.19"
+  NCCL_NET: IB
+
+setup: |
+  set -euo pipefail
+  test -d "${ART_SHARED_ROOT}"
+  uv venv --python 3.12 --seed "$HOME/art-release-example"
+  "$HOME/art-release-example/bin/pip" install \
+    --extra-index-url https://download.pytorch.org/whl/cu128 \
+    "openpipe-art[megatron]==${ART_VERSION}"
+
+run: |
+  set -euo pipefail
+  exec "$HOME/art-release-example/bin/art-monarch" skypilot \
+    --program train.py:main \
+    --startup-timeout 900
diff --git a/examples/multinode/train.py b/examples/multinode/train.py
new file mode 100644
index 000000000..80d30e99c
--- /dev/null
+++ b/examples/multinode/train.py
@@ -0,0 +1,122 @@
+from __future__ import annotations
+
+import hashlib
+import os
+from pathlib import Path
+from urllib.parse import urlparse
+
+import art
+from art.distributed import (
+    ArtLaunchContext,
+    ArtRuntime,
+    EndpointSpec,
+    ModelServiceMemberSpec,
+    ModelServiceSpec,
+    NcclTransportSpec,
+    NixlTransportSpec,
+    TrainerMeshSpec,
+    VllmParallelSpec,
+    compile_topology,
+)
+from art.megatron.backend import MegatronBackend
+
+
+async def main(launch: ArtLaunchContext) -> None:
+    model_name = "multinode-release-smoke"
+    base_model = os.environ.get("ART_EXAMPLE_MODEL", "Qwen/Qwen3-0.6B-Base")
+    artifact_root = Path(os.environ["ART_SHARED_ROOT"]).expanduser().resolve()
+    ranks_per_host = int(os.environ.get("ART_TRAINER_RANKS_PER_HOST", "1"))
+    nccl_net = os.environ.setdefault("NCCL_NET", "IB")
+    cluster = launch.homogeneous_cluster(
+        cpu_slots=2,
+        gpu_ids=tuple(range(ranks_per_host)),
+        artifact_root=str(artifact_root),
+        cache_root="/tmp/art-cache",
+        nccl_transport=NcclTransportSpec(net_name=nccl_net),
+        nixl_transport=(
+            NixlTransportSpec()
+            if os.environ.get("ART_EXAMPLE_USE_NIXL") == "1"
+            else None
+        ),
+        startup_timeout_s=900,
+    )
+    topology = art.MegatronTopologyConfig(
+        tp=int(os.environ.get("ART_EXAMPLE_TP", "1")),
+        cp=int(os.environ.get("ART_EXAMPLE_CP", "1")),
+        ep=int(os.environ.get("ART_EXAMPLE_EP", "1")),
+        pp=int(os.environ.get("ART_EXAMPLE_PP", "1")),
+    )
+    art.init_megatron_runtime_config(
+        topology=topology,
+        packed_sequence_length=512,
+    )
+    leader_host = urlparse(cluster.hosts[0].worker_address).hostname
+    if leader_host is None:
+        raise RuntimeError("controller worker address has no host")
+    model_service = ModelServiceSpec(
+        name=model_name,
+        members=(
+            ModelServiceMemberSpec(
+                member_id="inference",
+                host_id=cluster.hosts[0].host_id,
+                node_rank=0,
+                gpu_ids=(cluster.hosts[0].gpu_ids[0],),
+            ),
+        ),
+        leader_endpoint=EndpointSpec(host=leader_host, port=8000),
+        rendezvous=EndpointSpec(host=leader_host, port=29500),
+        base_model=base_model,
+        runtime_fingerprint=hashlib.sha256(base_model.encode()).hexdigest(),
+        parallel=VllmParallelSpec(),
+        temporal_gpu_sharing=True,
+    )
+    runtime = await ArtRuntime.start(
+        launch.host_mesh,
+        compile_topology(
+            cluster=cluster,
+            rollout_host_ids=(),
+            trainer=TrainerMeshSpec(
+                ranks=cluster.gpu_placements(),
+                topology=topology,
+            ),
+            model_services=(model_service,),
+        ),
+    )
+    try:
+        async with MegatronBackend(runtime=runtime) as backend:
+            model = art.TrainableModel(
+                name=model_name,
+                project="art-release",
+                run_name="multinode-release-smoke",
+                base_model=base_model,
+                _internal_config={"init_args": {"max_seq_length": 512}},
+                report_metrics=[],
+            )
+            await model.register(backend)
+            await model.train_sft(
+                [
+                    art.Trajectory(
+                        messages_and_choices=[
+                            {"role": "user", "content": "Answer yes."},
+                            {"role": "assistant", "content": "Yes."},
+                        ],
+                        reward=1.0,
+                    ),
+                    art.Trajectory(
+                        messages_and_choices=[
+                            {"role": "user", "content": "Answer no."},
+                            {"role": "assistant", "content": "No."},
+                        ],
+                        reward=1.0,
+                    ),
+                ],
+                art.TrainSFTConfig(learning_rate=1e-6, batch_size=2),
+                log_metrics=False,
+            )
+            print(
+                f"ART_MULTINODE_TRAIN_PASS hosts={launch.host_count} "
+                f"ranks={len(cluster.gpu_placements())} step={await model.get_step()}",
+                flush=True,
+            )
+    finally:
+        await runtime.close()
diff --git a/megatron_runtime/README.md b/megatron_runtime/README.md
new file mode 100644
index 000000000..a36cffe24
--- /dev/null
+++ b/megatron_runtime/README.md
@@ -0,0 +1,29 @@
+# ART Megatron runtime
+
+This private lock project defines the trainer environment materialized by ART on
+each trainer host. It is bundled into release wheels and is not a separately
+published package.
+
+The root package owns orchestration and lightweight CPU services. Monarch starts
+trainer ranks with this runtime's Python executable, keeping Megatron's compiled
+dependency stack exact without exposing source-only dependencies through ART's
+published wheel metadata.
+
+The CUDA-specific profiles own different layers of the runtime:
+
+- PyTorch, Megatron Core/Bridge, Transformer Engine, FlashAttention, and the
+  model kernels are locked here.
+- CUDA 12 builds pinned NVIDIA Apex with its CUDA extensions because Megatron's
+  gradient-accumulation fusion imports `fused_weight_gradient_mlp_cuda`. CUDA 13
+  uses ART's explicitly unfused provider path and does not install Apex.
+- The official `nixl-cu12` and `nixl-cu13` wheels provide Python bindings,
+  `libnixl`, and relocatable UCX libraries/plugins. ART's release wheel embeds
+  the matching NIXL and UCX source headers required to compile HybridEP.
+- ART builds its own HybridEP source only for trainer topologies that use EP.
+  Cross-host EP additionally validates the image's GDA/RDMA capabilities before
+  compilation.
+
+The supported cluster image still owns the NVIDIA driver and CUDA toolkit,
+native build tools, NCCL network transport, MOFED/RDMA devices, and required
+kernel modules. A Python package cannot install or safely replace those host
+capabilities.
diff --git a/megatron_runtime/pyproject.toml b/megatron_runtime/pyproject.toml
new file mode 100644
index 000000000..2525f9e73
--- /dev/null
+++ b/megatron_runtime/pyproject.toml
@@ -0,0 +1,185 @@
+[project]
+name = "art-megatron-runtime"
+version = "0.1.0"
+requires-python = ">=3.12,<3.13"
+dependencies = [
+    "aiohttp>=3.13.0",
+    "apache-tvm-ffi==0.1.11",
+    "anthropic>=0.77.0",
+    "flash-attn-4==4.0.0b5",
+    "flash-linear-attention==0.5.0",
+    "flashinfer-cubin==0.6.8.post1",
+    "flashinfer-python==0.6.8.post1",
+    "litellm>=1.71.1,<=1.82.0",
+    "megatron-bridge==0.4.0rc0",
+    "megatron-core==0.17.0",
+    "ml-dtypes>=0.5.0",
+    "msgspec>=0.21.0",
+    "nest-asyncio>=1.6.0",
+    "ninja>=1.11.1",
+    "numpy<2",
+    "nvidia-cutlass-dsl==4.5.2",
+    "nvidia-ml-py==13.580.82",
+    "nvidia-modelopt>=0.42.0a0",
+    "nvidia-resiliency-ext<0.5",
+    "openai>=2.14.0",
+    "peft>=0.14.0",
+    "polars>=1.26.0",
+    "pydantic>=2.12",
+    "pybind11>=2.13.6",
+    "python-dotenv>=1.0.0",
+    "quack-kernels==0.3.9",
+    "requests>=2.32.0",
+    "scipy>=1.17.0,<1.18",
+    "setproctitle>=1.3.6",
+    "setuptools>=78.1.0",
+    "tblib>=3.0.0",
+    "tilelang==0.1.10",
+    "torchmonarch==0.6.0",
+    "transformers==5.12.1",
+    "typer>=0.15.2",
+    "typing-extensions>=4.13",
+    "uv>=0.11.7",
+    "weave>=0.52.41",
+]
+
+[project.optional-dependencies]
+cuda12 = [
+    "apex",
+    "nixl-cu12==1.3.2",
+    "nvidia-cuda-cccl-cu12==12.9.27",
+    "torch==2.11.0+cu128",
+    "torchvision==0.26.0+cu128",
+    "transformer-engine==2.11.0",
+    "transformer-engine-cu12==2.11.0",
+    "transformer-engine-torch==2.11.0",
+]
+cuda13 = [
+    "nixl-cu13==1.3.2",
+    "nvidia-nccl-cu13==2.28.9",
+    "torch==2.11.0+cu130",
+    "torchvision==0.26.0+cu130",
+    "transformer-engine==2.14.1",
+    "transformer-engine-cu13==2.14.1",
+    "transformer-engine-torch==2.14.1",
+]
+
+[dependency-groups]
+test = [
+    "nbval>=0.11.0",
+    "pytest>=8.4.1",
+    "pytest-asyncio>=1.1.0",
+]
+
+[tool.uv]
+required-version = ">=0.11.7"
+conflicts = [[{ extra = "cuda12" }, { extra = "cuda13" }]]
+override-dependencies = [
+    "click==8.2.0",
+    "flashinfer-python==0.6.8.post1",
+    "megatron-core==0.17.0",
+    "numpy<2",
+    "nvidia-cublas==13.2.2.2 ; sys_platform == 'linux'",
+    "nvidia-resiliency-ext<0.5",
+    "quack-kernels==0.3.9",
+]
+exclude-dependencies = ["pynvml", "emerging-optimizers", "causal-conv1d", "mamba-ssm"]
+no-build-isolation-package = [
+    "apex",
+    "megatron-bridge",
+    "nv-grouped-gemm",
+    "transformer-engine",
+    "transformer-engine-cu12",
+    "transformer-engine-cu13",
+    "transformer-engine-torch",
+]
+
+[tool.uv.extra-build-dependencies]
+apex = ["torch>=2.11.0"]
+megatron-core = ["pybind11", "setuptools"]
+nv-grouped-gemm = ["torch>=2.11.0"]
+transformer-engine-torch = ["torch>=2.11.0"]
+
+[tool.uv.extra-build-variables]
+apex = { APEX_CPP_EXT = "1", APEX_CUDA_EXT = "1", APEX_FAST_LAYER_NORM = "1", APEX_PARALLEL_BUILD = "8", NVCC_APPEND_FLAGS = "--threads 1" }
+transformer-engine-torch = { NVTE_NO_LOCAL_VERSION = "1" }
+
+[[tool.uv.dependency-metadata]]
+name = "apex"
+version = "0.1"
+requires-dist = ["packaging"]
+
+[[tool.uv.dependency-metadata]]
+name = "megatron-bridge"
+version = "0.5.0+e1a207ac"
+requires-dist = [
+    "accelerate",
+    "comet-ml",
+    "datasets",
+    "diffusers",
+    "einops",
+    "flash-linear-attention",
+    "flashinfer-cubin",
+    "flashinfer-python",
+    "hydra-core",
+    "imageio",
+    "imageio-ffmpeg",
+    "megatron-core",
+    "mistral-common",
+    "mlflow",
+    "nvidia-resiliency-ext",
+    "omegaconf",
+    "open-clip-torch",
+    "peft",
+    "pyyaml",
+    "qwen-vl-utils",
+    "regex",
+    "rich",
+    "six",
+    "tensorboard",
+    "timm",
+    "torch",
+    "tqdm",
+    "transformers",
+    "typing-extensions",
+    "wandb",
+]
+
+[[tool.uv.dependency-metadata]]
+name = "transformer-engine-torch"
+version = "2.11.0"
+requires-dist = [
+    "einops",
+    "onnx",
+    "onnxscript",
+    "packaging",
+    "pydantic",
+    "torch",
+    "transformer-engine-cu12",
+]
+
+[tool.uv.sources]
+apex = { git = "https://github.com/NVIDIA/apex.git", rev = "25.09" }
+flash-attn-4 = { url = "https://files.pythonhosted.org/packages/24/f7/01ee2576ce41f9884d291ee21861ef194afc0b2b1ce3bd175fc7a6e1b133/flash_attn_4-4.0.0b5-py3-none-any.whl" }
+megatron-bridge = { git = "https://github.com/NVIDIA-NeMo/Megatron-Bridge.git", rev = "e1a207ac757e5d0ed94d8ffbe1cbd28e81d8c084" }
+torch = [
+    { index = "pytorch-cu128", extra = "cuda12" },
+    { index = "pytorch-cu130", extra = "cuda13" },
+]
+torchvision = [
+    { index = "pytorch-cu128", extra = "cuda12" },
+    { index = "pytorch-cu130", extra = "cuda13" },
+]
+transformer-engine-torch = [
+    { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "v2.11", subdirectory = "transformer_engine/pytorch", extra = "cuda12" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu130"
+url = "https://download.pytorch.org/whl/cu130"
+explicit = true
diff --git a/megatron_runtime/uv.lock b/megatron_runtime/uv.lock
new file mode 100644
index 000000000..2cec383fa
--- /dev/null
+++ b/megatron_runtime/uv.lock
@@ -0,0 +1,5005 @@
+version = 1
+revision = 3
+requires-python = "==3.12.*"
+resolution-markers = [
+    "sys_platform == 'darwin'",
+    "sys_platform != 'darwin'",
+]
+conflicts = [[
+    { package = "art-megatron-runtime", extra = "cuda12" },
+    { package = "art-megatron-runtime", extra = "cuda13" },
+]]
+
+[manifest]
+overrides = [
+    { name = "click", specifier = "==8.2.0" },
+    { name = "flashinfer-python", specifier = "==0.6.8.post1" },
+    { name = "megatron-core", specifier = "==0.17.0" },
+    { name = "numpy", specifier = "<2" },
+    { name = "nvidia-cublas", marker = "sys_platform == 'linux'", specifier = "==13.2.2.2" },
+    { name = "nvidia-resiliency-ext", specifier = "<0.5" },
+    { name = "quack-kernels", specifier = "==0.3.9" },
+]
+excludes = [
+    "causal-conv1d",
+    "emerging-optimizers",
+    "mamba-ssm",
+    "pynvml",
+]
+
+[[manifest.dependency-metadata]]
+name = "apex"
+version = "0.1"
+requires-dist = ["packaging"]
+
+[[manifest.dependency-metadata]]
+name = "megatron-bridge"
+version = "0.5.0+e1a207ac"
+requires-dist = ["accelerate", "comet-ml", "datasets", "diffusers", "einops", "flash-linear-attention", "flashinfer-cubin", "flashinfer-python", "hydra-core", "imageio", "imageio-ffmpeg", "megatron-core", "mistral-common", "mlflow", "nvidia-resiliency-ext", "omegaconf", "open-clip-torch", "peft", "pyyaml", "qwen-vl-utils", "regex", "rich", "six", "tensorboard", "timm", "torch", "tqdm", "transformers", "typing-extensions", "wandb"]
+
+[[manifest.dependency-metadata]]
+name = "transformer-engine-torch"
+version = "2.11.0"
+requires-dist = ["einops", "onnx", "onnxscript", "packaging", "pydantic", "torch", "transformer-engine-cu12"]
+
+[[package]]
+name = "abnf"
+version = "2.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "typing-extensions" },
+]
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+wheels = [
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+]
+
+[[package]]
+name = "accelerate"
+version = "1.14.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "huggingface-hub" },
+    { name = "numpy" },
+    { name = "packaging" },
+    { name = "psutil" },
+    { name = "pyyaml" },
+    { name = "safetensors" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "extra == 'extra-20-art-megatron-runtime-cuda12'" },
+    { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "extra == 'extra-20-art-megatron-runtime-cuda13'" },
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+]
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+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/26/91/33de49538444d4aafbe47415c450c2f9abab1733e1226f276b496672f46c/z3_solver-4.15.4.0-py3-none-win32.whl", hash = "sha256:be3bc916545c96ffbf89e00d07104ff14f78336e55db069177a1bfbcc01b269d", size = 13191672, upload-time = "2025-10-29T18:11:58.424Z" },
+    { url = "https://files.pythonhosted.org/packages/03/d6/a0b135e4419df475177ae78fc93c422430b0fd8875649486f9a5989772e6/z3_solver-4.15.4.0-py3-none-win_amd64.whl", hash = "sha256:00e35b02632ed085ea8199fb230f6015e6fc40554a6680c097bd5f060e827431", size = 16259597, upload-time = "2025-10-29T18:12:01.14Z" },
+]
+
+[[package]]
+name = "zipp"
+version = "4.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/b9/d8/eab98a517c14134c0b2eb4e2387bc5f457334293ec5d2dd3857ec2966802/zipp-4.1.0.tar.gz", hash = "sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602", size = 26214, upload-time = "2026-05-18T20:08:57.967Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3a/13/547360d81e6d88d58492968ffda9f9542854f11310ee556fef14260cc886/zipp-4.1.0-py3-none-any.whl", hash = "sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f", size = 10238, upload-time = "2026-05-18T20:08:57.045Z" },
+]
diff --git a/pyproject.toml b/pyproject.toml
index e70c0313a..79b84be53 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,11 +17,29 @@ dependencies = [
     "polars>=1.26.0",
     "tblib>=3.0.0",
     "nest-asyncio>=1.6.0",
+    "numpy<2",
     "setproctitle>=1.3.6",
 ]
 
 [project.optional-dependencies]
 plotting = ["matplotlib>=3.10.1", "seaborn>=0.13.2"]
+distributed = [
+    "aiohttp>=3.13.0",
+    "msgspec>=0.21.0",
+    "torch==2.11.0+cu128 ; sys_platform == 'linux' or sys_platform == 'win32'",
+    "torch==2.11.0 ; sys_platform == 'darwin'",
+    "torchmonarch==0.6.0",
+    "transformers>=5.2.0,<=5.12.1",
+    "uv>=0.11.7",
+]
+distributed-cu130 = [
+    "aiohttp>=3.13.0",
+    "msgspec>=0.21.0",
+    "torch==2.11.0+cu130 ; sys_platform == 'linux'",
+    "torchmonarch==0.6.0",
+    "transformers>=5.2.0,<=5.12.1",
+    "uv>=0.11.7",
+]
 tensors = ["torch==2.11.0"]
 
 backend = [
@@ -30,7 +48,8 @@ backend = [
     "bitsandbytes>=0.45.2,!=0.50.0",
     "unsloth==2026.3.3",
     "unsloth-zoo==2026.3.1",
-    "torch==2.11.0",
+    "torch==2.11.0+cu128 ; sys_platform == 'linux' or sys_platform == 'win32'",
+    "torch==2.11.0 ; sys_platform == 'darwin'",
     "torchao==0.16.0",
     "accelerate==1.7.0",
     "awscli>=1.38.1",
@@ -46,34 +65,55 @@ backend = [
     "gql>=4.0.0",
     "nvidia-cudnn-frontend<1.21 ; sys_platform == 'linux'",
     "nvidia-resiliency-ext<0.5 ; sys_platform == 'linux'",
+    "uv>=0.11.7",
 ]
-megatron = [
-    "numpy<2",
-    "torch==2.11.0",
-    "torchvision==0.26.0",
-    "flash-attn-4==4.0.0b5",
-    "flashinfer-cubin==0.6.8.post1",
-    "flashinfer-python==0.6.8.post1",
-    "ninja>=1.11.1",
-    "quack-kernels==0.3.7",
-    "apex",
-    "transformer-engine==2.11.0",
-    "transformer-engine-cu12==2.11.0",
-    "transformer-engine-torch==2.11.0",
-    "megatron-core==0.17.0",
-    "pybind11>=2.13.6",
+backend-cu130 = [
+    "peft>=0.14.0",
+    "hf-xet>=1.1.0",
+    "bitsandbytes>=0.45.2,!=0.50.0",
+    "unsloth==2026.3.3",
+    "unsloth-zoo==2026.3.1",
+    "torch==2.11.0+cu130 ; sys_platform == 'linux'",
+    "torchao==0.16.0",
+    "accelerate==1.7.0",
+    "awscli>=1.38.1",
     "setuptools>=78.1.0",
-    "megatron-bridge==0.4.0rc0",
-    "nvidia-cuda-cccl-cu12==12.9.27 ; sys_platform == 'linux'",
-    "tilelang==0.1.10 ; sys_platform == 'linux' and platform_machine == 'x86_64'",
-    "causal-conv1d==1.6.1 ; sys_platform == 'linux' and platform_machine == 'x86_64' and python_full_version < '3.12'",
-    "mamba-ssm==2.3.1 ; sys_platform == 'linux' and platform_machine == 'x86_64' and python_full_version < '3.12'",
-    "nvidia-ml-py==13.580.82",
-    "nvidia-modelopt>=0.42.0a0 ; sys_platform != 'darwin'",
+    "wandb==0.28.0",
+    "transformers==5.2.0",
+    "duckdb>=1.0.0",
+    "pyarrow>=15.0.0",
+    "trl==0.20.0",
+    "nbclient>=0.10.1",
+    "pytest>=8.4.1",
+    "nbmake>=1.5.5",
+    "gql>=4.0.0",
+    "nvidia-cudnn-frontend<1.21 ; sys_platform == 'linux'",
+    "nvidia-nccl-cu13==2.28.9 ; sys_platform == 'linux'",
     "nvidia-resiliency-ext<0.5 ; sys_platform == 'linux'",
+    "uv>=0.11.7",
+]
+megatron = [
+    "aiohttp>=3.13.0",
+    "msgspec>=0.21.0",
+    "numpy<2",
+    "nixl-cu12==1.3.2 ; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "peft>=0.14.0",
+    "torch==2.11.0+cu128 ; sys_platform == 'linux' or sys_platform == 'win32'",
+    "torch==2.11.0 ; sys_platform == 'darwin'",
+    "torchmonarch==0.6.0",
     "transformers==5.12.1",
-    "scipy>=1.17.0",
-    "ml-dtypes>=0.5.0 ; python_full_version < '3.13'",
+    "uv>=0.11.7",
+]
+megatron-cu130 = [
+    "aiohttp>=3.13.0",
+    "msgspec>=0.21.0",
+    "numpy<2",
+    "nixl-cu13==1.3.2 ; sys_platform == 'linux' and platform_machine == 'x86_64'",
+    "peft>=0.14.0",
+    "torch==2.11.0+cu130 ; sys_platform == 'linux'",
+    "torchmonarch==0.6.0",
+    "transformers==5.12.1",
+    "uv>=0.11.7",
 ]
 
 langgraph = [
@@ -99,6 +139,7 @@ tinker = [
 
 [project.scripts]
 art = "art.cli:app"
+art-monarch = "art.distributed.monarch_bootstrap:main"
 
 [build-system]
 requires = ["hatchling"]
@@ -174,84 +215,64 @@ conflicts = [
         { extra = "tinker" },
         { extra = "megatron" },
     ],
+    [
+        { extra = "distributed" },
+        { extra = "distributed-cu130" },
+    ],
+    [
+        { extra = "distributed" },
+        { extra = "backend-cu130" },
+    ],
+    [
+        { extra = "distributed" },
+        { extra = "megatron-cu130" },
+    ],
+    [
+        { extra = "distributed-cu130" },
+        { extra = "backend" },
+    ],
+    [
+        { extra = "distributed-cu130" },
+        { extra = "megatron" },
+    ],
+    [
+        { extra = "backend" },
+        { extra = "backend-cu130" },
+    ],
+    [
+        { extra = "backend" },
+        { extra = "megatron-cu130" },
+    ],
+    [
+        { extra = "backend-cu130" },
+        { extra = "megatron" },
+    ],
+    [
+        { extra = "backend-cu130" },
+        { extra = "megatron-cu130" },
+    ],
+    [
+        { extra = "megatron" },
+        { extra = "megatron-cu130" },
+    ],
+    [
+        { extra = "tinker" },
+        { extra = "backend-cu130" },
+    ],
+    [
+        { extra = "tinker" },
+        { extra = "distributed-cu130" },
+    ],
+    [
+        { extra = "tinker" },
+        { extra = "megatron-cu130" },
+    ],
 ]
 override-dependencies = [
     "click==8.2.0",
-    "megatron-core==0.17.0",
     "numpy<2",
+    "nvidia-cublas==13.2.2.2 ; sys_platform == 'linux'",
     "nvidia-resiliency-ext<0.5",
-    "quack-kernels==0.3.7",
-    "transformer-engine==2.11.0",
-    "torch==2.11.0",
-    "torchvision==0.26.0",
-]
-exclude-dependencies = ["pynvml", "emerging-optimizers", "causal-conv1d", "mamba-ssm"]
-no-build-isolation-package = ["apex", "transformer-engine", "transformer-engine-cu12", "transformer-engine-torch", "megatron-bridge", "nv-grouped-gemm"]
-
-[tool.uv.extra-build-dependencies]
-apex = ["torch>=2.11.0"]
-megatron-core = ["pybind11", "setuptools"]
-nv-grouped-gemm = ["torch>=2.11.0"]
-transformer-engine-torch = ["torch>=2.11.0"]
-
-[tool.uv.extra-build-variables]
-apex = { APEX_CPP_EXT = "1", APEX_CUDA_EXT = "1", APEX_FAST_LAYER_NORM = "1", APEX_PARALLEL_BUILD = "16", NVCC_APPEND_FLAGS = "--threads 4" }
-transformer-engine-torch = { NVTE_NO_LOCAL_VERSION = "1" }
-
-[[tool.uv.dependency-metadata]]
-name = "apex"
-version = "0.1"
-requires-dist = ["packaging"]
-
-# Keep Bridge's runtime deps explicit here and let ART's megatron extra own the
-# Transformers pin validated by model-support handlers in this branch.
-[[tool.uv.dependency-metadata]]
-name = "megatron-bridge"
-version = "0.5.0+e1a207ac"
-requires-dist = [
-    "accelerate",
-    "comet-ml",
-    "datasets",
-    "diffusers",
-    "einops",
-    "flash-linear-attention",
-    "flashinfer-cubin",
-    "flashinfer-python",
-    "hydra-core",
-    "imageio",
-    "imageio-ffmpeg",
-    "megatron-core",
-    "mistral-common",
-    "mlflow",
-    "nvidia-resiliency-ext",
-    "omegaconf",
-    "open-clip-torch",
-    "peft",
-    "pyyaml",
-    "qwen-vl-utils",
-    "regex",
-    "rich",
-    "six",
-    "tensorboard",
-    "timm",
-    "torch",
-    "tqdm",
-    "transformers",
-    "typing-extensions",
-    "wandb",
-]
-
-[[tool.uv.dependency-metadata]]
-name = "transformer-engine-torch"
-version = "2.11.0"
-requires-dist = [
-    "einops",
-    "onnx",
-    "onnxscript",
-    "packaging",
-    "pydantic",
-    "torch",
-    "transformer-engine-cu12",
 ]
 
 [tool.ty.src]
@@ -310,8 +331,11 @@ allowed-unresolved-imports = [
     "einops.**",
     "fla.**",
     "megatron.**",
+    "monarch.**",
+    "msgspec.**",
     "quack.**",
     "safetensors.**",
+    "scipy.**",
     "transformer_engine.**",
     "triton.**",
 ]
@@ -336,15 +360,22 @@ dev = [
 ]
 
 [tool.uv.sources]
-torch = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }]
-torchvision = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }]
-apex = { git = "https://github.com/NVIDIA/apex.git", rev = "25.09" }
-flash-attn-4 = { url = "https://files.pythonhosted.org/packages/24/f7/01ee2576ce41f9884d291ee21861ef194afc0b2b1ce3bd175fc7a6e1b133/flash_attn_4-4.0.0b5-py3-none-any.whl" }
-megatron-bridge = { git = "https://github.com/NVIDIA-NeMo/Megatron-Bridge.git", rev = "e1a207ac757e5d0ed94d8ffbe1cbd28e81d8c084" }
+torch = [
+    { index = "pytorch-cu128", extra = "distributed", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { index = "pytorch-cu128", extra = "backend", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { index = "pytorch-cu128", extra = "megatron", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { index = "pytorch-cu130", extra = "distributed-cu130", marker = "sys_platform == 'linux'" },
+    { index = "pytorch-cu130", extra = "backend-cu130", marker = "sys_platform == 'linux'" },
+    { index = "pytorch-cu130", extra = "megatron-cu130", marker = "sys_platform == 'linux'" },
+]
 panza = { git = "https://github.com/corbt/panza.git" }
-transformer-engine-torch = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "v2.11", subdirectory = "transformer_engine/pytorch" }
 
 [[tool.uv.index]]
 name = "pytorch-cu128"
 url = "https://download.pytorch.org/whl/cu128"
 explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu130"
+url = "https://download.pytorch.org/whl/cu130"
+explicit = true
diff --git a/scripts/build_package.py b/scripts/build_package.py
index 9168b9089..00a760d5e 100644
--- a/scripts/build_package.py
+++ b/scripts/build_package.py
@@ -11,12 +11,16 @@
 import tarfile
 import tempfile
 import tomllib
+import urllib.request
 import zipfile
 
 ROOT = Path(__file__).resolve().parents[1]
-BUNDLE_DIR = ROOT / "src" / "art" / "_vllm_runtime"
-BUNDLE_MARKER = BUNDLE_DIR / ".art_generated"
+VLLM_BUNDLE_DIR = ROOT / "src" / "art" / "_vllm_runtime"
+MEGATRON_BUNDLE_DIR = ROOT / "src" / "art" / "_megatron_runtime"
 PROTOCOL_VERSION = 1
+NATIVE_ASSETS = json.loads(
+    (ROOT / "src/art/megatron/runtime/native_assets.json").read_text()
+)
 
 
 def run(command: list[str], *, cwd: Path = ROOT) -> None:
@@ -36,14 +40,14 @@ def sha256_file(path: Path) -> str:
     return digest.hexdigest()
 
 
-def clean_bundle_dir() -> None:
-    if not BUNDLE_DIR.exists():
+def clean_bundle_dir(bundle_dir: Path) -> None:
+    if not bundle_dir.exists():
         return
-    if not BUNDLE_MARKER.exists():
+    if not (bundle_dir / ".art_generated").exists():
         raise RuntimeError(
-            f"Refusing to remove non-generated runtime bundle directory: {BUNDLE_DIR}"
+            f"Refusing to remove non-generated runtime bundle directory: {bundle_dir}"
         )
-    shutil.rmtree(BUNDLE_DIR)
+    shutil.rmtree(bundle_dir)
 
 
 def build_runtime_wheel(runtime_dist: Path) -> Path:
@@ -65,7 +69,7 @@ def build_runtime_wheel(runtime_dist: Path) -> Path:
     return wheels[0]
 
 
-def write_bundle(runtime_wheel: Path) -> None:
+def write_vllm_bundle(runtime_wheel: Path) -> None:
     root_project = read_pyproject(ROOT / "pyproject.toml")["project"]
     runtime_project = read_pyproject(ROOT / "vllm_runtime" / "pyproject.toml")[
         "project"
@@ -73,10 +77,10 @@ def write_bundle(runtime_wheel: Path) -> None:
     pyproject = ROOT / "vllm_runtime" / "pyproject.toml"
     lockfile = ROOT / "vllm_runtime" / "uv.lock"
 
-    BUNDLE_DIR.mkdir(parents=True)
-    shutil.copy2(pyproject, BUNDLE_DIR / "pyproject.toml")
-    shutil.copy2(lockfile, BUNDLE_DIR / "uv.lock")
-    shutil.copy2(runtime_wheel, BUNDLE_DIR / runtime_wheel.name)
+    VLLM_BUNDLE_DIR.mkdir(parents=True)
+    shutil.copy2(pyproject, VLLM_BUNDLE_DIR / "pyproject.toml")
+    shutil.copy2(lockfile, VLLM_BUNDLE_DIR / "uv.lock")
+    shutil.copy2(runtime_wheel, VLLM_BUNDLE_DIR / runtime_wheel.name)
 
     manifest = {
         "art_package": root_project["name"],
@@ -92,10 +96,57 @@ def write_bundle(runtime_wheel: Path) -> None:
         "lockfile": "uv.lock",
         "lockfile_sha256": sha256_file(lockfile),
     }
-    (BUNDLE_DIR / "manifest.json").write_text(
+    (VLLM_BUNDLE_DIR / "manifest.json").write_text(
         json.dumps(manifest, indent=2, sort_keys=True) + "\n"
     )
-    BUNDLE_MARKER.write_text("generated by scripts/build_package.py\n")
+    (VLLM_BUNDLE_DIR / ".art_generated").write_text(
+        "generated by scripts/build_package.py\n"
+    )
+
+
+def download(url: str, destination: Path, expected_sha256: str) -> None:
+    request = urllib.request.Request(url, headers={"User-Agent": "openpipe-art-build"})
+    with urllib.request.urlopen(request) as response, destination.open("wb") as output:
+        shutil.copyfileobj(response, output)
+    if sha256_file(destination) != expected_sha256:
+        raise RuntimeError(f"Downloaded runtime asset hash mismatch: {url}")
+
+
+def write_megatron_bundle() -> None:
+    run(["uv", "lock", "--project", "megatron_runtime", "--check"])
+    root_project = read_pyproject(ROOT / "pyproject.toml")["project"]
+    runtime_project = read_pyproject(ROOT / "megatron_runtime" / "pyproject.toml")[
+        "project"
+    ]
+    pyproject = ROOT / "megatron_runtime" / "pyproject.toml"
+    lockfile = ROOT / "megatron_runtime" / "uv.lock"
+    MEGATRON_BUNDLE_DIR.mkdir(parents=True)
+    shutil.copy2(pyproject, MEGATRON_BUNDLE_DIR / "pyproject.toml")
+    shutil.copy2(lockfile, MEGATRON_BUNDLE_DIR / "uv.lock")
+    archives = []
+    for filename, asset in NATIVE_ASSETS.items():
+        download(asset["url"], MEGATRON_BUNDLE_DIR / filename, asset["sha256"])
+        archives.append({"filename": filename, "sha256": asset["sha256"]})
+    manifest = {
+        "art_package": root_project["name"],
+        "art_version": root_project["version"],
+        "runtime_package": runtime_project["name"],
+        "runtime_version": runtime_project["version"],
+        "protocol_version": PROTOCOL_VERSION,
+        "python": runtime_project["requires-python"],
+        "pyproject": {
+            "filename": "pyproject.toml",
+            "sha256": sha256_file(pyproject),
+        },
+        "lockfile": {"filename": "uv.lock", "sha256": sha256_file(lockfile)},
+        "source_archives": archives,
+    }
+    (MEGATRON_BUNDLE_DIR / "manifest.json").write_text(
+        json.dumps(manifest, indent=2, sort_keys=True) + "\n"
+    )
+    (MEGATRON_BUNDLE_DIR / ".art_generated").write_text(
+        "generated by scripts/build_package.py\n"
+    )
 
 
 def build_root_package(*, wheel_only: bool, out_dir: Path) -> None:
@@ -122,10 +173,16 @@ def verify_wheel(wheel: Path) -> None:
         "art/_vllm_runtime/manifest.json",
         "art/_vllm_runtime/pyproject.toml",
         "art/_vllm_runtime/uv.lock",
+        "art/_megatron_runtime/manifest.json",
+        "art/_megatron_runtime/pyproject.toml",
+        "art/_megatron_runtime/uv.lock",
+        "art/_megatron_runtime/nixl-de8115ca.tar.gz",
+        "art/_megatron_runtime/ucx-1.21.0.tar.gz",
         "art/megatron/_hybrid_ep/LICENSE",
         "art/megatron/_hybrid_ep/setup.py",
         "art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh",
         "art/megatron/_hybrid_ep/deep_ep/hybrid_ep_buffer.py",
+        "art/megatron/runtime/native_assets.json",
     }
     with zipfile.ZipFile(wheel) as archive:
         names = set(archive.namelist())
@@ -162,10 +219,16 @@ def verify_sdist(sdist: Path) -> None:
         "src/art/_vllm_runtime/manifest.json",
         "src/art/_vllm_runtime/pyproject.toml",
         "src/art/_vllm_runtime/uv.lock",
+        "src/art/_megatron_runtime/manifest.json",
+        "src/art/_megatron_runtime/pyproject.toml",
+        "src/art/_megatron_runtime/uv.lock",
+        "src/art/_megatron_runtime/nixl-de8115ca.tar.gz",
+        "src/art/_megatron_runtime/ucx-1.21.0.tar.gz",
         "src/art/megatron/_hybrid_ep/LICENSE",
         "src/art/megatron/_hybrid_ep/setup.py",
         "src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh",
         "src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_buffer.py",
+        "src/art/megatron/runtime/native_assets.json",
     }
     with tarfile.open(sdist) as archive:
         names = set(archive.getnames())
@@ -212,15 +275,18 @@ def main() -> int:
     if not out_dir.is_absolute():
         out_dir = ROOT / out_dir
 
-    clean_bundle_dir()
+    clean_bundle_dir(VLLM_BUNDLE_DIR)
+    clean_bundle_dir(MEGATRON_BUNDLE_DIR)
     try:
         with tempfile.TemporaryDirectory() as temp_dir:
             runtime_wheel = build_runtime_wheel(Path(temp_dir))
-            write_bundle(runtime_wheel)
+            write_vllm_bundle(runtime_wheel)
+            write_megatron_bundle()
             build_root_package(wheel_only=args.wheel, out_dir=out_dir)
             verify_dist(out_dir, wheel_only=args.wheel)
     finally:
-        clean_bundle_dir()
+        clean_bundle_dir(VLLM_BUNDLE_DIR)
+        clean_bundle_dir(MEGATRON_BUNDLE_DIR)
     return 0
 
 
diff --git a/scripts/ci/apply_ci_uv_build_overrides.py b/scripts/ci/apply_ci_uv_build_overrides.py
deleted file mode 100644
index 6284e2a87..000000000
--- a/scripts/ci/apply_ci_uv_build_overrides.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env python3
-"""Apply CI-only uv build overrides to a pyproject.toml file."""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-import re
-
-
-def _build_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description="Rewrite CI-sensitive uv extra-build-variables in pyproject.toml."
-    )
-    parser.add_argument(
-        "--pyproject",
-        type=Path,
-        required=True,
-        help="Path to the pyproject.toml file to rewrite in place.",
-    )
-    parser.add_argument(
-        "--apex-parallel-build",
-        type=int,
-        required=True,
-        help="Value to write for APEX_PARALLEL_BUILD.",
-    )
-    parser.add_argument(
-        "--apex-nvcc-threads",
-        type=int,
-        required=True,
-        help="Value to write for NVCC_APPEND_FLAGS=--threads <n>.",
-    )
-    return parser
-
-
-def _replace_once(text: str, pattern: str, replacement: str, label: str) -> str:
-    updated, count = re.subn(pattern, replacement, text, count=1)
-    if count != 1:
-        raise SystemExit(f"Expected exactly one {label} entry in pyproject.toml.")
-    return updated
-
-
-def main() -> int:
-    args = _build_parser().parse_args()
-    if args.apex_parallel_build <= 0:
-        raise SystemExit("--apex-parallel-build must be a positive integer.")
-    if args.apex_nvcc_threads <= 0:
-        raise SystemExit("--apex-nvcc-threads must be a positive integer.")
-    if not args.pyproject.is_file():
-        raise SystemExit(f"pyproject file not found: {args.pyproject}")
-
-    text = args.pyproject.read_text(encoding="utf-8")
-    text = _replace_once(
-        text,
-        r'APEX_PARALLEL_BUILD = "[0-9]+"',
-        f'APEX_PARALLEL_BUILD = "{args.apex_parallel_build}"',
-        "APEX_PARALLEL_BUILD",
-    )
-    text = _replace_once(
-        text,
-        r'NVCC_APPEND_FLAGS = "--threads [0-9]+"',
-        f'NVCC_APPEND_FLAGS = "--threads {args.apex_nvcc_threads}"',
-        "NVCC_APPEND_FLAGS",
-    )
-    args.pyproject.write_text(text, encoding="utf-8")
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())
diff --git a/scripts/ci/build_and_push_uv_cache.sh b/scripts/ci/build_and_push_uv_cache.sh
index 5e7535a66..3232d5a8b 100755
--- a/scripts/ci/build_and_push_uv_cache.sh
+++ b/scripts/ci/build_and_push_uv_cache.sh
@@ -11,8 +11,6 @@ UV_CACHE_ASSET_PREFIX="${UV_CACHE_ASSET_PREFIX:-prek-uv-cache}"
 BUILD_JOBS="${BUILD_JOBS:-auto}"
 AUTO_BUILD_JOBS_MAX="${AUTO_BUILD_JOBS_MAX:-8}"
 UV_BUILD_SLOTS="${UV_BUILD_SLOTS:-2}"
-CI_APEX_PARALLEL_BUILD="${CI_APEX_PARALLEL_BUILD:-8}"
-CI_APEX_NVCC_THREADS="${CI_APEX_NVCC_THREADS:-1}"
 TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST:-9.0}"
 KEEP_COUNT="${KEEP_COUNT:-4}"
 PART_SIZE_MB="${PART_SIZE_MB:-1900}"
@@ -158,6 +156,8 @@ compute_fingerprint() {
   python3 "${REPO_ROOT}/scripts/ci/compute_uv_fingerprint.py" \
     --pyproject "${REPO_ROOT}/pyproject.toml" \
     --uv-lock "${REPO_ROOT}/uv.lock" \
+    --megatron-pyproject "${REPO_ROOT}/megatron_runtime/pyproject.toml" \
+    --megatron-uv-lock "${REPO_ROOT}/megatron_runtime/uv.lock" \
     --base-image "${BASE_IMAGE}" \
     --python-mm "${PYTHON_MM}" \
     --torch-cuda-arch-list "${TORCH_CUDA_ARCH_LIST}"
@@ -221,40 +221,9 @@ ensure_release_exists() {
     --notes "Managed cache assets for prek CI dependency bootstrap."
 }
 
-resolve_apex_parallel_build() {
-  local compile_jobs="$1"
-
-  [[ "${compile_jobs}" =~ ^[1-9][0-9]*$ ]] || fail "compile_jobs must be a positive integer."
-  [[ "${CI_APEX_PARALLEL_BUILD}" =~ ^[1-9][0-9]*$ ]] || fail "CI_APEX_PARALLEL_BUILD must be a positive integer."
-
-  local apex_parallel_build="${CI_APEX_PARALLEL_BUILD}"
-  if ((apex_parallel_build > compile_jobs)); then
-    apex_parallel_build="${compile_jobs}"
-  fi
-  printf '%s\n' "${apex_parallel_build}"
-}
-
-constrain_temp_pyproject_for_ci_build() {
-  local pyproject_path="$1"
-  local apex_parallel_build="$2"
-  local nvcc_threads="$3"
-
-  [[ -f "${pyproject_path}" ]] || fail "pyproject not found: ${pyproject_path}"
-  [[ "${apex_parallel_build}" =~ ^[1-9][0-9]*$ ]] || fail "apex_parallel_build must be a positive integer."
-  [[ "${nvcc_threads}" =~ ^[1-9][0-9]*$ ]] || fail "CI_APEX_NVCC_THREADS must be a positive integer."
-
-  log "Applying cache-build overrides: APEX_PARALLEL_BUILD=${apex_parallel_build}, NVCC_APPEND_FLAGS=--threads ${nvcc_threads}."
-  python3 "${SCRIPT_DIR}/apply_ci_uv_build_overrides.py" \
-    --pyproject "${pyproject_path}" \
-    --apex-parallel-build "${apex_parallel_build}" \
-    --apex-nvcc-threads "${nvcc_threads}"
-}
-
 build_cache_archive() {
   local archive_path="$1"
   local compile_jobs="$2"
-  local apex_parallel_build
-  apex_parallel_build="$(resolve_apex_parallel_build "${compile_jobs}")"
 
   TMP_DIR="$(mktemp -d)"
   UV_CACHE_DIR="${TMP_DIR}/uv-cache"
@@ -262,7 +231,9 @@ build_cache_archive() {
 
   cp "${REPO_ROOT}/pyproject.toml" "${TMP_DIR}/pyproject.toml"
   cp "${REPO_ROOT}/uv.lock" "${TMP_DIR}/uv.lock"
-  constrain_temp_pyproject_for_ci_build "${TMP_DIR}/pyproject.toml" "${apex_parallel_build}" "${CI_APEX_NVCC_THREADS}"
+  mkdir -p "${TMP_DIR}/megatron_runtime"
+  cp "${REPO_ROOT}/megatron_runtime/pyproject.toml" "${TMP_DIR}/megatron_runtime/pyproject.toml"
+  cp "${REPO_ROOT}/megatron_runtime/uv.lock" "${TMP_DIR}/megatron_runtime/uv.lock"
 
   pushd "${TMP_DIR}" >/dev/null
   export UV_CACHE_DIR
@@ -274,7 +245,7 @@ build_cache_archive() {
   export NINJAFLAGS="-j${compile_jobs}"
   export TORCH_CUDA_ARCH_LIST
 
-  local cudnn_path="${TMP_DIR}/.venv/lib/python${PYTHON_MM}/site-packages/nvidia/cudnn"
+  local cudnn_path="${TMP_DIR}/megatron_runtime/.venv/lib/python${PYTHON_MM}/site-packages/nvidia/cudnn"
   export CUDNN_PATH="${cudnn_path}"
   export CUDNN_HOME="${cudnn_path}"
   export CUDNN_INCLUDE_PATH="${cudnn_path}/include"
@@ -283,10 +254,12 @@ build_cache_archive() {
   export LIBRARY_PATH="${CUDNN_LIBRARY_PATH}${LIBRARY_PATH:+:${LIBRARY_PATH}}"
   export LD_LIBRARY_PATH="${CUDNN_LIBRARY_PATH}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"
 
-  log "Building split uv cache with compile_jobs=${compile_jobs}, apex_parallel_build=${apex_parallel_build}, nvcc_threads=${CI_APEX_NVCC_THREADS}, cuda_arch_list=${TORCH_CUDA_ARCH_LIST}, and uv_concurrent_builds=${UV_BUILD_SLOTS}."
-  uv sync --frozen --extra megatron --extra langgraph --extra plotting --group dev --no-install-project --python "${PYTHON_MM}"
+  log "Building split uv cache with compile_jobs=${compile_jobs}, cuda_arch_list=${TORCH_CUDA_ARCH_LIST}, and uv_concurrent_builds=${UV_BUILD_SLOTS}."
+  uv sync --frozen --extra langgraph --extra plotting --group dev --no-install-project --python "${PYTHON_MM}"
+  uv sync --project megatron_runtime --frozen --extra cuda12 --group test --no-install-project --python "${PYTHON_MM}"
   uv sync --frozen --extra backend --extra tinker --extra langgraph --extra plotting --group dev --no-install-project --python "${PYTHON_MM}"
   rm -rf .venv
+  rm -rf megatron_runtime/.venv
 
   log "Packing uv cache archive to ${archive_path}."
   rm -f "${archive_path}"
diff --git a/scripts/ci/compute_uv_fingerprint.py b/scripts/ci/compute_uv_fingerprint.py
index a200251c0..834948339 100755
--- a/scripts/ci/compute_uv_fingerprint.py
+++ b/scripts/ci/compute_uv_fingerprint.py
@@ -32,6 +32,18 @@ def _build_parser() -> argparse.ArgumentParser:
         default=Path("uv.lock"),
         help="Path to uv.lock",
     )
+    parser.add_argument(
+        "--megatron-pyproject",
+        type=Path,
+        default=Path("megatron_runtime/pyproject.toml"),
+        help="Path to the managed Megatron runtime pyproject.toml",
+    )
+    parser.add_argument(
+        "--megatron-uv-lock",
+        type=Path,
+        default=Path("megatron_runtime/uv.lock"),
+        help="Path to the managed Megatron runtime lock file",
+    )
     parser.add_argument(
         "--base-image",
         default="pytorch/pytorch:2.9.0-cuda12.8-cudnn9-devel",
@@ -53,18 +65,6 @@ def _build_parser() -> argparse.ArgumentParser:
         default=16,
         help="Fingerprint length (hex chars)",
     )
-    parser.add_argument(
-        "--ci-apex-parallel-build",
-        type=int,
-        default=8,
-        help="CI override for APEX_PARALLEL_BUILD used by cache build and restore.",
-    )
-    parser.add_argument(
-        "--ci-apex-nvcc-threads",
-        type=int,
-        default=1,
-        help="CI override for NVCC_APPEND_FLAGS=--threads <n> used by cache build and restore.",
-    )
     return parser
 
 
@@ -76,14 +76,22 @@ def main() -> int:
         raise SystemExit(f"pyproject file not found: {args.pyproject}")
     if not args.uv_lock.exists():
         raise SystemExit(f"uv lock file not found: {args.uv_lock}")
+    if not args.megatron_pyproject.exists():
+        raise SystemExit(
+            f"Megatron pyproject file not found: {args.megatron_pyproject}"
+        )
+    if not args.megatron_uv_lock.exists():
+        raise SystemExit(f"Megatron uv lock file not found: {args.megatron_uv_lock}")
 
     payload: dict[str, Any] = {
         "inputs": {
             "pyproject_sha256": _sha256_file(args.pyproject),
             "uv_lock_sha256": _sha256_file(args.uv_lock),
+            "megatron_pyproject_sha256": _sha256_file(args.megatron_pyproject),
+            "megatron_uv_lock_sha256": _sha256_file(args.megatron_uv_lock),
         },
         "ci_context": {
-            "fingerprint_schema_version": 10,
+            "fingerprint_schema_version": 12,
             "cache_kind": "full_uv_cache",
             "cache_scope": "prek_split_extras_group_dev",
             "cache_target": "uv_cache",
@@ -98,8 +106,6 @@ def main() -> int:
             "base_image": args.base_image,
             "python_mm": args.python_mm,
             "torch_cuda_arch_list": args.torch_cuda_arch_list,
-            "ci_apex_parallel_build": args.ci_apex_parallel_build,
-            "ci_apex_nvcc_threads": args.ci_apex_nvcc_threads,
         }
     )
     canonical = json.dumps(payload, separators=(",", ":"), sort_keys=True)
diff --git a/scripts/ci/trainer-rank-checkpoint-acceptance.sky.yaml b/scripts/ci/trainer-rank-checkpoint-acceptance.sky.yaml
index 247cd6b11..525bb2088 100644
--- a/scripts/ci/trainer-rank-checkpoint-acceptance.sky.yaml
+++ b/scripts/ci/trainer-rank-checkpoint-acceptance.sky.yaml
@@ -7,8 +7,7 @@ resources:
   image_id: docker:docker.io/bradhiltonnw/art-gpu:latest
 
 setup: |
-  uv sync --frozen --extra megatron --group dev
-  uv run --no-sync python -m art.megatron.hybrid_ep_setup
+  INSTALL_VLLM_RUNTIME=false bash src/art/megatron/setup.sh
 
 run: |
   set -euo pipefail
@@ -17,13 +16,17 @@ run: |
   test -n "${fixture}"
   rm -rf "${result}"
   mkdir -p "${result}"
+  runtime_python="$(
+    .venv/bin/python -c 'from art.megatron.runtime.managed import ensure_megatron_runtime; print(ensure_megatron_runtime(art_build_sha256="trainer-rank-ci").python)'
+  )"
+  test -x "${runtime_python}"
 
   exercise() {
     ranks="$1"
     operation="$2"
     source="$3"
     output="$4"
-    uv run --no-sync torchrun --standalone --nproc-per-node="${ranks}" \
+    "${runtime_python}" -m torch.distributed.run --standalone --nproc-per-node="${ranks}" \
       dev/trainer_rank_checkpoint_acceptance.py "${operation}" \
       --source "${source}" --output "${output}" \
       --output-json "${output}.json"
diff --git a/scripts/ci/trainer-rank-gpu-tests.sh b/scripts/ci/trainer-rank-gpu-tests.sh
index ffe0b9499..d17df9e5e 100755
--- a/scripts/ci/trainer-rank-gpu-tests.sh
+++ b/scripts/ci/trainer-rank-gpu-tests.sh
@@ -3,8 +3,12 @@ set -euo pipefail
 
 export CUDA_VISIBLE_DEVICES=0,1
 export PYTHONUNBUFFERED=1
+runtime_python="$(
+  .venv/bin/python -c 'from art.megatron.runtime.managed import ensure_megatron_runtime; print(ensure_megatron_runtime(art_build_sha256="trainer-rank-ci").python)'
+)"
+test -x "${runtime_python}"
 
-uv run --no-sync pytest --tb=short \
+"${runtime_python}" -m pytest --tb=short \
   tests/unit/test_trainer_rank_custom_tensors.py \
   tests/integration/megatron/cp_attn/test_attention_packed_vs_flattened.py \
   'tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_packed_correctness.py::test_gdn_cp_packed_sibling_order_matches_cp1_oracle[2]' \
@@ -17,7 +21,7 @@ uv run --no-sync pytest --tb=short \
   'tests/integration/megatron/lora/test_dynamic_lora_slots.py::test_trainer_rank_tp_head_backward_matches_unsharded_oracle[2]'
 
 ART_MEGATRON_CONTEXT_PARALLEL_SIZE=2 \
-  uv run --no-sync torchrun --standalone --nproc-per-node=2 \
+  "${runtime_python}" -m torch.distributed.run --standalone --nproc-per-node=2 \
     dev/trainer_rank_check.py \
     --model Qwen/Qwen3-0.6B \
     --layers 1 \
diff --git a/scripts/ci/trainer-rank-gpu.sky.yaml b/scripts/ci/trainer-rank-gpu.sky.yaml
index cc1eb578c..7f399c332 100644
--- a/scripts/ci/trainer-rank-gpu.sky.yaml
+++ b/scripts/ci/trainer-rank-gpu.sky.yaml
@@ -7,8 +7,9 @@ resources:
   image_id: docker:docker.io/bradhiltonnw/art-gpu:latest
 
 setup: |
-  uv sync --frozen --extra megatron --group dev
-  uv run --no-sync python -m art.megatron.hybrid_ep_setup
+  INSTALL_VLLM_RUNTIME=false bash src/art/megatron/setup.sh
+  uv sync --project megatron_runtime --extra cuda12 --group test \
+    --frozen --no-install-project --inexact
 
 run: |
   timeout --signal=TERM --kill-after=30s 20m \
diff --git a/scripts/setup.sh b/scripts/setup.sh
index cc34695f3..9417e1c04 100755
--- a/scripts/setup.sh
+++ b/scripts/setup.sh
@@ -1,4 +1,9 @@
 #!/bin/bash
+set -euo pipefail
+
+script_dir="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+repo_root="$(cd -- "${script_dir}/.." && pwd)"
+cd "${repo_root}"
 
 # Load environment variables from .env file if it exists
 if [ -f .env ]; then
@@ -7,7 +12,7 @@ if [ -f .env ]; then
         # Skip comments and empty lines
         [[ $line =~ ^#.*$ ]] && continue
         [[ -z $line ]] && continue
-        
+
         key="${line%%=*}"
         current_value="${!key-}"
         if [ -z "${!key+x}" ] ||
@@ -21,36 +26,55 @@ if [ -f .env ]; then
 fi
 
 if ! command -v sudo >/dev/null 2>&1; then
-    sudo_path="/usr/local/bin/sudo"
-    if [ ! -w /usr/local/bin ]; then
-        sudo_path="$HOME/.local/bin/sudo"
-        mkdir -p "$HOME/.local/bin"
-        export PATH="$HOME/.local/bin:$PATH"
+    if [ "$(id -u)" -ne 0 ]; then
+        echo "setup requires root or passwordless sudo" >&2
+        exit 1
     fi
-
+    sudo_path=/usr/local/bin/sudo
     cat <<'EOF' > "$sudo_path"
 #!/bin/sh
+if [ "${1:-}" = "-n" ]; then
+    shift
+fi
 exec "$@"
 EOF
-    chmod +x "$sudo_path"
+    chmod +x /usr/local/bin/sudo
 fi
 
+export PATH="$HOME/.local/bin:$HOME/.cargo/bin:/opt/conda/bin:$PATH"
 need_pkgs=()
 command -v git >/dev/null 2>&1 || need_pkgs+=("git")
 command -v curl >/dev/null 2>&1 || need_pkgs+=("curl")
 command -v tmux >/dev/null 2>&1 || need_pkgs+=("tmux")
 
+install_multinode=${INSTALL_MULTINODE:-false}
+if [ "$install_multinode" != "true" ] && [ "$install_multinode" != "false" ]; then
+    echo "INSTALL_MULTINODE must be true or false" >&2
+    exit 1
+fi
 if [ "${#need_pkgs[@]}" -gt 0 ]; then
-    apt-get update
-    apt-get install -y "${need_pkgs[@]}"
+    if [ "$(id -u)" -eq 0 ]; then
+        apt-get update
+        apt-get install -y "${need_pkgs[@]}"
+    elif sudo -n true >/dev/null 2>&1; then
+        sudo -n apt-get update
+        sudo -n apt-get install -y "${need_pkgs[@]}"
+    else
+        echo "setup requires passwordless sudo to install: ${need_pkgs[*]}" >&2
+        exit 1
+    fi
 fi
 
 # Configure git user name and email
-git config --global user.name "${GIT_USER_NAME}"
-git config --global user.email "${GIT_USER_EMAIL}"
+if [ -n "${GIT_USER_NAME:-}" ]; then
+    git config --global user.name "${GIT_USER_NAME}"
+fi
+if [ -n "${GIT_USER_EMAIL:-}" ]; then
+    git config --global user.email "${GIT_USER_EMAIL}"
+fi
 git config --global --add safe.directory "$(pwd)"
 
-if [ "${GIT_RESET_CLEAN:-true}" = "true" ]; then
+if [ "${GIT_RESET_CLEAN:-false}" = "true" ]; then
     # Reset any uncommitted changes to the last commit
     git reset --hard HEAD
 
@@ -60,19 +84,31 @@ else
     echo "Skipping git reset/clean (GIT_RESET_CLEAN is not true). Preserving synced working tree."
 fi
 
-# Install astral-uv (standalone version)
-# Always prepend standalone install path so it takes precedence over system/conda uv
-export PATH="$HOME/.local/bin:$HOME/.cargo/bin:$PATH"
-if command -v uv >/dev/null 2>&1; then
-    echo "Using $(uv --version)"
-elif ! curl -LsSf https://astral.sh/uv/install.sh | sh; then
+readonly uv_version=0.11.7
+if ! uv --version 2>/dev/null | grep -q "^uv ${uv_version} "; then
+    curl -LsSf "https://astral.sh/uv/${uv_version}/install.sh" | sh
+fi
+if ! uv --version; then
     echo "Failed to install uv." >&2
     exit 1
 fi
 
-# Sync the dependencies
-if [ "${INSTALL_EXTRAS:-false}" = "true" ]; then
-    uv sync --extra backend --extra tinker --extra langgraph --extra plotting --frozen
+backend_extra=backend
+if [ -f /usr/local/cuda/version.json ] &&
+    grep -Eq '"version"[[:space:]]*:[[:space:]]*"13\.' /usr/local/cuda/version.json; then
+    backend_extra=backend-cu130
+fi
+
+if [ "$install_multinode" = "true" ]; then
+    if [ "${INSTALL_EXTRAS:-false}" = "true" ]; then
+        echo "INSTALL_EXTRAS is incompatible with the Megatron environment" >&2
+        exit 1
+    fi
+    /bin/bash "${repo_root}/src/art/megatron/setup.sh"
 else
-    uv sync --extra backend --frozen
+    sync_extras=(--extra "$backend_extra")
+    if [ "${INSTALL_EXTRAS:-false}" = "true" ]; then
+        sync_extras+=(--extra tinker --extra langgraph --extra plotting)
+    fi
+    uv sync "${sync_extras[@]}" --frozen
 fi
diff --git a/src/art/__init__.py b/src/art/__init__.py
index 01cf2436e..dbbddd7d3 100644
--- a/src/art/__init__.py
+++ b/src/art/__init__.py
@@ -20,7 +20,10 @@
 
 from dotenv import load_dotenv
 
+from .utils.cache_dirs import configure_model_cache_env
+
 load_dotenv()
+configure_model_cache_env()
 
 if os.getenv("SUPPRESS_LITELLM_SERIALIZATION_WARNINGS", "1") == "1":
     from art.utils.suppress_litellm_serialization_warnings import (
@@ -29,12 +32,6 @@
 
     suppress_litellm_serialization_warnings()
 
-# torch.cuda.MemPool doesn't currently support expandable_segments which is used in sleep mode
-conf = os.getenv("PYTORCH_CUDA_ALLOC_CONF", "").split(",")
-if "expandable_segments:True" in conf:
-    conf.remove("expandable_segments:True")
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = ",".join(conf)
-
 # Import unsloth before transformers, peft, and trl only in backend processes that
 # explicitly request it. Unsloth is an optional backend dependency, not a base ART
 # import dependency.
diff --git a/src/art/_backend_training.py b/src/art/_backend_training.py
index dcaa7f7fb..45ff2b135 100644
--- a/src/art/_backend_training.py
+++ b/src/art/_backend_training.py
@@ -11,6 +11,28 @@
 from .trajectories import TrajectoryGroup
 from .types import TrainConfig
 
+_GRADIENT_WORKLOAD_METRICS = {
+    "data/gradient_step_nonpadding_logical_tokens": (
+        "data/step_nonpadding_logical_tokens"
+    ),
+    "data/gradient_step_loss_bearing_tokens": "data/step_loss_bearing_tokens",
+    "data/gradient_step_executed_token_equivalents": (
+        "data/step_executed_token_equivalents"
+    ),
+    "data/gradient_step_nominal_schedule_capacity_tokens": (
+        "data/step_nominal_schedule_capacity_tokens"
+    ),
+    "data/gradient_step_dummy_executed_token_equivalents": (
+        "data/step_dummy_executed_token_equivalents"
+    ),
+    "data/gradient_step_dummy_schedule_capacity_tokens": (
+        "data/step_dummy_schedule_capacity_tokens"
+    ),
+    "pipeline/gradient_step_real_microbatches": "pipeline/global_real_microbatches",
+    "pipeline/gradient_step_dummy_microbatches": ("pipeline/global_dummy_microbatches"),
+}
+_GRADIENT_TRAIN_TIME = "time/gradient_step_train_s"
+
 
 def build_rl_train_configs(
     *,
@@ -38,12 +60,16 @@ def build_rl_train_configs(
     num_trajectories_learning_rate_multiplier_power: float | None = None,
     kl_ref_adapter_path: str | None = None,
     optimizer_save_interval: int = 5,
+    final_training_step: int | None = None,
+    grad_accumulation_sequences: int | None = None,
 ) -> tuple[TrainConfig, dev.TrainConfig]:
     config = TrainConfig(
         learning_rate=learning_rate,
         kl_penalty_coef=kl_penalty_coef,
         kl_penalty_source=kl_penalty_source,
+        grad_accumulation_sequences=grad_accumulation_sequences,
         optimizer_save_interval=optimizer_save_interval,
+        final_training_step=final_training_step,
     )
     dev_config: dev.TrainConfig = {
         "advantage_balance": advantage_balance,
@@ -98,12 +124,15 @@ def aggregate_rl_training_metrics(
 ) -> dict[str, float]:
     groups_list = list(trajectory_groups)
     avg_metrics = average_metric_samples(training_metrics)
+    _aggregate_megatron_workload(training_metrics, avg_metrics)
     tokens_per_second = avg_metrics.pop("tokens_per_second", None)
     if (
         tokens_per_second is not None
-        and "throughput/train_packed_tok_per_s" not in avg_metrics
+        and "throughput/train_executed_tok_equiv_per_s" not in avg_metrics
     ):
-        avg_metrics["throughput/train_packed_tok_per_s"] = float(tokens_per_second)
+        avg_metrics["throughput/train_executed_tok_equiv_per_s"] = float(
+            tokens_per_second
+        )
     summary = summarize_trajectory_groups(groups_list)
     avg_metrics.setdefault(
         "time/step_backend_train_s", time.monotonic() - trainer_started
@@ -119,3 +148,58 @@ def aggregate_rl_training_metrics(
         }
     )
     return avg_metrics
+
+
+def _aggregate_megatron_workload(
+    training_metrics: list[dict[str, float]],
+    output: dict[str, float],
+) -> None:
+    raw_keys = (*_GRADIENT_WORKLOAD_METRICS, _GRADIENT_TRAIN_TIME)
+    if not any(any(key in sample for key in raw_keys) for sample in training_metrics):
+        return
+    for index, sample in enumerate(training_metrics):
+        missing = [key for key in raw_keys if key not in sample]
+        if missing:
+            raise ValueError(
+                f"Megatron gradient-step metrics {index} are incomplete: {missing}"
+            )
+
+    totals = {
+        raw_key: sum(float(sample[raw_key]) for sample in training_metrics)
+        for raw_key in raw_keys
+    }
+    for raw_key in raw_keys:
+        output.pop(raw_key, None)
+    for raw_key, step_key in _GRADIENT_WORKLOAD_METRICS.items():
+        output[step_key] = totals[raw_key]
+
+    train_s = totals[_GRADIENT_TRAIN_TIME]
+    output["time/step_train_s"] = train_s
+    for raw_key, rate_key in (
+        (
+            "data/gradient_step_nonpadding_logical_tokens",
+            "throughput/train_nonpadding_logical_tok_per_s",
+        ),
+        (
+            "data/gradient_step_loss_bearing_tokens",
+            "throughput/train_loss_bearing_tok_per_s",
+        ),
+        (
+            "data/gradient_step_executed_token_equivalents",
+            "throughput/train_executed_tok_equiv_per_s",
+        ),
+        (
+            "data/gradient_step_nominal_schedule_capacity_tokens",
+            "throughput/train_nominal_capacity_tok_per_s",
+        ),
+    ):
+        output[rate_key] = totals[raw_key] / train_s if train_s > 0 else 0.0
+    logical = totals["data/gradient_step_nonpadding_logical_tokens"]
+    nominal = totals["data/gradient_step_nominal_schedule_capacity_tokens"]
+    dummy = totals["data/gradient_step_dummy_schedule_capacity_tokens"]
+    output["data/step_unused_packed_capacity_tokens"] = max(
+        0.0, nominal - dummy - logical
+    )
+    output["data/step_unused_and_dummy_ratio"] = (
+        max(0.0, nominal - logical) / nominal if nominal > 0 else 0.0
+    )
diff --git a/src/art/dev/engine.py b/src/art/dev/engine.py
index 8446c3272..af40bfea0 100644
--- a/src/art/dev/engine.py
+++ b/src/art/dev/engine.py
@@ -122,6 +122,7 @@ class EngineArgs(TypedDict, total=False):
     override_generation_config: dict[str, Any] | None
     enable_sleep_mode: bool
     enable_expert_parallel: bool
+    moe_backend: str
     enable_return_routed_experts: bool
     model_impl: str
 
diff --git a/src/art/dev/get_model_config.py b/src/art/dev/get_model_config.py
index 8f3cf0331..cf15d83ee 100644
--- a/src/art/dev/get_model_config.py
+++ b/src/art/dev/get_model_config.py
@@ -31,9 +31,12 @@ def get_model_config(
         config = InternalModelConfig()
     if "peft_args" in config:
         raise ValueError(PEFT_ARGS_MIGRATION_MESSAGE)
+    if "rollout_weights_mode" in config:
+        raise ValueError(
+            "rollout_weights_mode has been removed; ART always serves native LoRA adapters"
+        )
 
     dedicated = is_dedicated_mode(config)
-    rollout_weights_mode = config.get("rollout_weights_mode", "lora")
     rollout_weight_update_mode = config.get("rollout_weight_update_mode", "step_lora")
 
     if dedicated:
@@ -44,10 +47,14 @@ def get_model_config(
     configured_init_args = config.get("init_args", {})
     init_args = InitArgs(
         load_in_4bit=True,
-        max_seq_length=max_seq_length_from_model_config(
-            base_model,
-            revision=configured_init_args.get("revision"),
-            token=configured_init_args.get("token"),
+        max_seq_length=(
+            configured_init_args["max_seq_length"]
+            if "max_seq_length" in configured_init_args
+            else max_seq_length_from_model_config(
+                base_model,
+                revision=configured_init_args.get("revision"),
+                token=configured_init_args.get("token"),
+            )
         ),
         model_name=base_model,
     )
@@ -68,9 +75,7 @@ def get_model_config(
     )
     if lora_config:
         merged_lora_config.update(lora_config)
-    if rollout_weights_mode == "lora" and "lora_target_modules" not in config.get(
-        "engine_args", {}
-    ):
+    if "lora_target_modules" not in config.get("engine_args", {}):
         engine_args["lora_target_modules"] = vllm_lora_config_for_model(
             base_model,
             dict(merged_lora_config),
@@ -99,7 +104,6 @@ def get_model_config(
         init_args=init_args,
         engine_args=engine_args,
         lora_config=merged_lora_config,
-        rollout_weights_mode=rollout_weights_mode,
         rollout_weight_update_mode=rollout_weight_update_mode,
         tinker_args=config.get("tinker_args"),
         trainer_args=trainer_args,
@@ -112,4 +116,8 @@ def get_model_config(
         result["inference_gpu_ids"] = config["inference_gpu_ids"]
     if "vllm_runtime" in config:
         result["vllm_runtime"] = config["vllm_runtime"]
+    if "megatron_model_initialization" in config:
+        result["megatron_model_initialization"] = config[
+            "megatron_model_initialization"
+        ]
     return result
diff --git a/src/art/dev/model.py b/src/art/dev/model.py
index 830a1021b..814d77464 100644
--- a/src/art/dev/model.py
+++ b/src/art/dev/model.py
@@ -5,7 +5,6 @@
 
 from .engine import EngineArgs
 
-RolloutWeightsMode = Literal["lora", "merged"]
 RolloutWeightUpdateMode = Literal["step_lora", "in_flight_lora"]
 VllmRuntimeMode = Literal["managed", "external"]
 
@@ -133,12 +132,7 @@ class InternalModelConfig(TypedDict, total=False):
             inference run on separate GPUs.
         inference_gpu_ids: GPU IDs for vLLM inference (e.g., [1]). When set
             with trainer_gpu_ids, enables dedicated mode.
-        rollout_weights_mode: How inference weights are applied in vLLM.
-            - "lora": load LoRA adapters into vLLM directly
-            - "merged": keep training LoRA adapters, but push merged weights
-              into vLLM for inference
         rollout_weight_update_mode: How LoRA rollout weights are registered
-            when rollout_weights_mode is "lora".
             - "step_lora": load one adapter per policy step
             - "in_flight_lora": update one derived LoRA slot in place while
               recording token-level policy spans
@@ -164,7 +158,6 @@ class InternalModelConfig(TypedDict, total=False):
     trainer_args: "TrainerArgs"
     trainer_gpu_ids: list[int]
     inference_gpu_ids: list[int]
-    rollout_weights_mode: "RolloutWeightsMode"
     rollout_weight_update_mode: "RolloutWeightUpdateMode"
     chat_template_kwargs: dict[str, object]
     chat_template: str
@@ -173,6 +166,7 @@ class InternalModelConfig(TypedDict, total=False):
     chat_template_tool_schema_format: Literal["default", "vllm_openai"]
     vllm_runtime: VllmRuntimeArgs
     allow_unvalidated_arch: bool
+    megatron_model_initialization: Literal["pretrained", "random"]
 
 
 class BackendModelConfig(InternalModelConfig, total=False):
diff --git a/src/art/dev/validate.py b/src/art/dev/validate.py
index 43fc9b97f..fcd0febdb 100644
--- a/src/art/dev/validate.py
+++ b/src/art/dev/validate.py
@@ -5,7 +5,6 @@
 
 from .model import (
     InternalModelConfig,
-    RolloutWeightsMode,
     RolloutWeightUpdateMode,
     VllmRuntimeMode,
 )
@@ -32,13 +31,6 @@ def is_dedicated_mode(config: InternalModelConfig) -> bool:
     )
 
 
-def _rollout_weights_mode(config: InternalModelConfig) -> RolloutWeightsMode:
-    mode = config.get("rollout_weights_mode", "lora")
-    if mode in {"lora", "merged"}:
-        return mode
-    raise ValueError("rollout_weights_mode must be either 'lora' or 'merged'")
-
-
 def _rollout_weight_update_mode(
     config: InternalModelConfig,
 ) -> RolloutWeightUpdateMode:
@@ -56,10 +48,13 @@ def validate_dedicated_config(config: InternalModelConfig) -> None:
     Raises ValueError if the configuration is invalid.
     Does nothing if neither trainer_gpu_ids nor inference_gpu_ids is set (shared mode).
     """
+    if "rollout_weights_mode" in config:
+        raise ValueError(
+            "rollout_weights_mode has been removed; ART always serves native LoRA adapters"
+        )
     has_trainer = "trainer_gpu_ids" in config
     has_inference = "inference_gpu_ids" in config
-    rollout_weights_mode = _rollout_weights_mode(config)
-    rollout_weight_update_mode = _rollout_weight_update_mode(config)
+    _rollout_weight_update_mode(config)
     external = is_external_vllm_mode(config)
 
     if external:
@@ -67,10 +62,6 @@ def validate_dedicated_config(config: InternalModelConfig) -> None:
         assert isinstance(runtime_config, Mapping)
         if not runtime_config.get("server_url"):
             raise ValueError("vllm_runtime.server_url is required for external mode")
-        if rollout_weights_mode != "lora":
-            raise ValueError(
-                "vllm_runtime.mode='external' requires rollout_weights_mode='lora'"
-            )
         if has_trainer and not config["trainer_gpu_ids"]:
             raise ValueError("trainer_gpu_ids must be non-empty")
         if "fast_inference" in config.get("init_args", {}):
@@ -78,14 +69,6 @@ def validate_dedicated_config(config: InternalModelConfig) -> None:
                 "fast_inference is no longer supported; ART always uses an external "
                 "vLLM runtime"
             )
-        if (
-            rollout_weight_update_mode == "in_flight_lora"
-            and rollout_weights_mode != "lora"
-        ):
-            raise ValueError(
-                "rollout_weight_update_mode='in_flight_lora' requires "
-                "rollout_weights_mode='lora'"
-            )
         return
 
     if has_trainer != has_inference:
@@ -93,21 +76,6 @@ def validate_dedicated_config(config: InternalModelConfig) -> None:
             "trainer_gpu_ids and inference_gpu_ids must both be set or both unset"
         )
 
-    if rollout_weights_mode == "merged" and not has_trainer:
-        raise ValueError(
-            "rollout_weights_mode='merged' requires dedicated mode "
-            "(set both trainer_gpu_ids and inference_gpu_ids)"
-        )
-
-    if (
-        rollout_weight_update_mode == "in_flight_lora"
-        and rollout_weights_mode != "lora"
-    ):
-        raise ValueError(
-            "rollout_weight_update_mode='in_flight_lora' requires "
-            "rollout_weights_mode='lora'"
-        )
-
     if "fast_inference" in config.get("init_args", {}):
         raise ValueError(
             "fast_inference is no longer supported; ART always uses an external "
@@ -136,17 +104,6 @@ def validate_dedicated_config(config: InternalModelConfig) -> None:
             "match len(inference_gpu_ids)"
         )
 
-    if trainer_gpu_ids[0] != 0:
-        raise ValueError(
-            "trainer_gpu_ids must start at GPU 0 (training runs in-process)"
-        )
-
-    expected = list(range(len(trainer_gpu_ids)))
-    if trainer_gpu_ids != expected:
-        raise ValueError(
-            "trainer_gpu_ids must be contiguous starting from 0 (e.g., [0], [0,1])"
-        )
-
     if config.get("engine_args", {}).get("enable_sleep_mode"):
         raise ValueError(
             "enable_sleep_mode is incompatible with dedicated mode "
diff --git a/src/art/distributed/__init__.py b/src/art/distributed/__init__.py
new file mode 100644
index 000000000..c7c5e0e32
--- /dev/null
+++ b/src/art/distributed/__init__.py
@@ -0,0 +1,91 @@
+from importlib import import_module
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from .art_runtime import ArtRuntime, DistributedPackedBatch
+    from .data_plane import PackedBatchRef, TensorSpec
+    from .launch import ArtLaunchContext
+    from .packing import PackingRequest
+    from .rollout import (
+        DistributedRolloutExecutor,
+        InProcessRolloutWorker,
+        InstalledAsyncCallable,
+        LocalRolloutExecutor,
+        RolloutExecutor,
+    )
+    from .specs import (
+        ArtRuntimeConfig,
+        ClusterSpec,
+        EndpointSpec,
+        GpuPlacement,
+        HostServiceHealth,
+        HostSpec,
+        ModelServiceMemberSpec,
+        ModelServiceSpec,
+        NcclTransportSpec,
+        NixlTransportSpec,
+        RuntimeTopology,
+        TrainerMeshSpec,
+        VllmParallelSpec,
+    )
+    from .topology import compile_topology
+    from .vllm_replica import (
+        HostMemberLaunchRequest,
+        HostMemberState,
+        ManagedVllmHostLauncher,
+        ReplicaFailure,
+        ReplicaHostLauncher,
+        ReplicaLaunchTemplate,
+        ReplicaManager,
+        ReplicaState,
+        ReplicaUpdateReport,
+    )
+
+_EXPORTS = {
+    "ArtLaunchContext": ".launch",
+    "ArtRuntime": ".art_runtime",
+    "ArtRuntimeConfig": ".specs",
+    "ClusterSpec": ".specs",
+    "DistributedPackedBatch": ".art_runtime",
+    "DistributedRolloutExecutor": ".rollout",
+    "EndpointSpec": ".specs",
+    "GpuPlacement": ".specs",
+    "HostMemberLaunchRequest": ".vllm_replica",
+    "HostMemberState": ".vllm_replica",
+    "HostServiceHealth": ".specs",
+    "HostSpec": ".specs",
+    "InProcessRolloutWorker": ".rollout",
+    "InstalledAsyncCallable": ".rollout",
+    "LocalRolloutExecutor": ".rollout",
+    "ManagedVllmHostLauncher": ".vllm_replica",
+    "ModelServiceMemberSpec": ".specs",
+    "ModelServiceSpec": ".specs",
+    "NcclTransportSpec": ".specs",
+    "NixlTransportSpec": ".specs",
+    "PackingRequest": ".packing",
+    "PackedBatchRef": ".data_plane",
+    "ReplicaHostLauncher": ".vllm_replica",
+    "ReplicaFailure": ".vllm_replica",
+    "ReplicaLaunchTemplate": ".vllm_replica",
+    "ReplicaManager": ".vllm_replica",
+    "ReplicaState": ".vllm_replica",
+    "ReplicaUpdateReport": ".vllm_replica",
+    "RolloutExecutor": ".rollout",
+    "RuntimeTopology": ".specs",
+    "TensorSpec": ".data_plane",
+    "TrainerMeshSpec": ".specs",
+    "VllmParallelSpec": ".specs",
+    "compile_topology": ".topology",
+}
+
+__all__ = list(_EXPORTS)
+
+
+def __getattr__(name: str) -> Any:
+    try:
+        module = _EXPORTS[name]
+    except KeyError:
+        raise AttributeError(name) from None
+    value = getattr(import_module(module, __name__), name)
+    globals()[name] = value
+    return value
diff --git a/src/art/distributed/adapter_transport.py b/src/art/distributed/adapter_transport.py
new file mode 100644
index 000000000..e6d030075
--- /dev/null
+++ b/src/art/distributed/adapter_transport.py
@@ -0,0 +1,678 @@
+from __future__ import annotations
+
+import base64
+import hashlib
+import importlib
+import json
+import os
+from pathlib import Path
+import socket
+from threading import Condition, Lock
+import time
+from typing import Any, Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+import torch
+
+from art.utils.safetensors import PreparedSafetensors, save_prepared_safetensors
+
+
+class _TransportRecord(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class AdapterTransferTarget(_TransportRecord):
+    transport: Literal["local", "nixl"] = "nixl"
+    host_id: str = Field(min_length=1)
+    generation_id: str = Field(min_length=1)
+    path: str = Field(min_length=1)
+    remote_agent: str = Field(min_length=1)
+    remote_metadata_b64: str = Field(min_length=1)
+    remote_address: int = Field(ge=0)
+    remote_device_id: int = Field(ge=0)
+    slot_id: int = Field(ge=0)
+    capacity_bytes: int = Field(gt=0)
+    prepare_s: float = Field(ge=0)
+    pool_wait_s: float = Field(ge=0)
+    registration_s: float = Field(ge=0)
+
+
+class AdapterReceiveResult(_TransportRecord):
+    host_id: str = Field(min_length=1)
+    generation_id: str = Field(min_length=1)
+    path: str = Field(min_length=1)
+    tensor_bytes: int = Field(gt=0)
+    config_bytes: int = Field(gt=0)
+    materialization_s: float = Field(ge=0)
+    slot_id: int = Field(default=0, ge=0)
+    used_bytes: int = Field(default=0, ge=0)
+    capacity_bytes: int = Field(default=0, ge=0)
+    prepare_s: float = Field(default=0, ge=0)
+    pool_wait_s: float = Field(default=0, ge=0)
+    registration_s: float = Field(default=0, ge=0)
+    sender_staging_s: float = Field(default=0, ge=0)
+    sender_registration_s: float = Field(default=0, ge=0)
+
+
+class AdapterTransferNotification(_TransportRecord):
+    generation_id: str = Field(min_length=1)
+    used_bytes: int = Field(gt=0)
+    adapter_config: dict[str, Any]
+    sender_staging_s: float = Field(ge=0)
+    sender_registration_s: float = Field(ge=0)
+
+
+class _PendingReceive:
+    def __init__(
+        self,
+        *,
+        target: AdapterTransferTarget,
+        slot: "_RegisteredSlot",
+    ) -> None:
+        self.target = target
+        self.slot = slot
+
+
+class _PendingLocalReceive:
+    def __init__(
+        self,
+        *,
+        target: AdapterTransferTarget,
+        listener: socket.socket,
+    ) -> None:
+        self.target = target
+        self.listener = listener
+
+
+class _RegisteredSlot:
+    def __init__(
+        self,
+        slot_id: int,
+        block: torch.Tensor,
+        registration: Any,
+    ) -> None:
+        self.slot_id = slot_id
+        self.block = block
+        self.registration = registration
+        self.generation_id: str | None = None
+
+
+def _load_nixl() -> tuple[Any, Any, Any]:
+    from .nixl_runtime import configure_nixl_environment
+
+    configure_nixl_environment()
+    for name in ("nixl_cu13", "nixl_cu12", "nixl"):
+        try:
+            module = importlib.import_module(name)
+        except ModuleNotFoundError:
+            continue
+        return (
+            module.nixl_agent,
+            module.nixl_agent_config,
+            module.nixl_thread_sync_t,
+        )
+    raise RuntimeError(
+        "NIXL Python bindings are unavailable; install ART with the megatron "
+        "or megatron-cu130 extra"
+    )
+
+
+def _new_agent(name: str) -> Any:
+    agent_type, config_type, sync_type = _load_nixl()
+    return agent_type(
+        name,
+        config_type(
+            enable_prog_thread=True,
+            enable_listen_thread=False,
+            backends=["UCX"],
+            sync_mode=sync_type.NIXL_THREAD_SYNC_STRICT,
+        ),
+    )
+
+
+def _adapter_template_bytes(path: str) -> int:
+    root = Path(path)
+    model_path = root / "adapter_model.safetensors"
+    model_bytes = model_path.stat().st_size
+    if model_bytes <= 8:
+        raise RuntimeError(f"Adapter template is empty: {path}")
+    with (root / "adapter_config.json").open("r", encoding="utf-8") as source:
+        config = json.load(source)
+    if not isinstance(config, dict):
+        raise RuntimeError(f"Adapter config must be an object: {path}")
+    if config.get("art_lora_format") != "vllm":
+        raise RuntimeError(f"Adapter template is not in vLLM format: {path}")
+    return model_bytes
+
+
+def _copy_payload(payload: PreparedSafetensors, block: torch.Tensor) -> None:
+    offset = 0
+    for chunk in payload.chunks:
+        block.narrow(0, offset, chunk.numel()).copy_(chunk)
+        offset += chunk.numel()
+    if offset != payload.nbytes:
+        raise RuntimeError("Adapter payload copy was incomplete")
+
+
+class AdapterSnapshotReceiver:
+    """Owns receive buffers for immutable LoRA generations."""
+
+    def __init__(
+        self, host_id: str, output_root: str, *, pool_capacity: int = 2
+    ) -> None:
+        if pool_capacity < 1:
+            raise ValueError("adapter receive pool capacity must be positive")
+        self.host_id = host_id
+        self.output_root = Path(output_root) / "adapter_transfers"
+        self.pool_capacity = pool_capacity
+        self._agent: Any | None = None
+        self._pending: dict[str, _PendingReceive] = {}
+        self._local_pending: dict[str, _PendingLocalReceive] = {}
+        self._slots: list[_RegisteredSlot] = []
+        self._condition = Condition()
+        self._notifications: dict[str, AdapterTransferNotification] = {}
+        self._materialized: set[str] = set()
+        self._agent_lock = Lock()
+        self._closed = False
+
+    def prepare(
+        self,
+        generation_id: str,
+        template_path: str,
+        timeout_s: float = 300.0,
+        transport: Literal["local", "nixl"] = "nixl",
+    ) -> AdapterTransferTarget:
+        if transport == "local":
+            return self._prepare_local(generation_id, template_path, timeout_s)
+        prepare_started = time.monotonic()
+        required_bytes = _adapter_template_bytes(template_path)
+        wait_started = time.monotonic()
+        with self._condition:
+            if self._closed:
+                raise RuntimeError("adapter receive pool is closed")
+            if generation_id in self._pending:
+                raise RuntimeError(f"Adapter receive already exists: {generation_id}")
+            slot, registration_s = self._acquire_slot(
+                required_bytes, deadline=wait_started + timeout_s
+            )
+            slot.generation_id = generation_id
+            pool_wait_s = time.monotonic() - wait_started - registration_s
+        try:
+            with self._agent_lock:
+                agent = self._require_agent()
+                remote_agent = agent.name
+                metadata = base64.b64encode(agent.get_agent_metadata()).decode()
+            path = str((self.output_root / generation_id).absolute())
+            target = AdapterTransferTarget(
+                host_id=self.host_id,
+                generation_id=generation_id,
+                path=path,
+                remote_agent=remote_agent,
+                remote_metadata_b64=metadata,
+                remote_address=slot.block.data_ptr(),
+                remote_device_id=0,
+                slot_id=slot.slot_id,
+                capacity_bytes=slot.block.numel(),
+                prepare_s=time.monotonic() - prepare_started,
+                pool_wait_s=max(0.0, pool_wait_s),
+                registration_s=registration_s,
+            )
+        except BaseException:
+            self._release_slot(slot, generation_id)
+            raise
+        self._pending[generation_id] = _PendingReceive(
+            target=target,
+            slot=slot,
+        )
+        return target
+
+    def _prepare_local(
+        self,
+        generation_id: str,
+        template_path: str,
+        timeout_s: float,
+    ) -> AdapterTransferTarget:
+        prepare_started = time.monotonic()
+        required_bytes = _adapter_template_bytes(template_path)
+        wait_started = time.monotonic()
+        with self._condition:
+            while len(self._local_pending) >= self.pool_capacity:
+                remaining_s = wait_started + timeout_s - time.monotonic()
+                if remaining_s <= 0:
+                    raise TimeoutError("local adapter receive pool remained full")
+                self._condition.wait(remaining_s)
+            if self._closed:
+                raise RuntimeError("adapter receive pool is closed")
+            if generation_id in self._local_pending or generation_id in self._pending:
+                raise RuntimeError(f"Adapter receive already exists: {generation_id}")
+            socket_path = (
+                "/tmp/art-lora-"
+                + hashlib.sha256(
+                    f"{self.host_id}:{generation_id}:{os.getpid()}".encode()
+                ).hexdigest()[:24]
+                + ".sock"
+            )
+            listener = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
+            try:
+                listener.bind(socket_path)
+                listener.listen(1)
+                listener.setblocking(False)
+                local_root = Path(
+                    os.environ.get(
+                        "ART_LOCAL_ADAPTER_TRANSFER_ROOT",
+                        "/dev/shm/art_adapter_transfers",
+                    )
+                )
+                target = AdapterTransferTarget(
+                    transport="local",
+                    host_id=self.host_id,
+                    generation_id=generation_id,
+                    path=str((local_root / self.host_id / generation_id).absolute()),
+                    remote_agent=socket_path,
+                    remote_metadata_b64="-",
+                    remote_address=0,
+                    remote_device_id=0,
+                    slot_id=0,
+                    capacity_bytes=required_bytes,
+                    prepare_s=time.monotonic() - prepare_started,
+                    pool_wait_s=time.monotonic() - wait_started,
+                    registration_s=0.0,
+                )
+            except BaseException:
+                listener.close()
+                Path(socket_path).unlink(missing_ok=True)
+                raise
+            self._local_pending[generation_id] = _PendingLocalReceive(
+                target=target,
+                listener=listener,
+            )
+        return target
+
+    def poll(self, generation_id: str) -> AdapterReceiveResult | None:
+        if generation_id in self._local_pending:
+            return self._poll_local(generation_id)
+        pending = self._pending.get(generation_id)
+        if pending is None:
+            raise RuntimeError(f"Unknown adapter receive: {generation_id}")
+        notification = self._take_notification(generation_id)
+        if notification is None:
+            return None
+        if notification.used_bytes > pending.slot.block.numel():
+            self._finish(generation_id)
+            raise RuntimeError("Adapter payload exceeds its prepared receive capacity")
+        started = time.monotonic()
+        path = Path(pending.target.path)
+        if path.exists():
+            self._finish(generation_id)
+            raise RuntimeError(f"Adapter transfer path already exists: {path}")
+        try:
+            path.mkdir(parents=True)
+            save_prepared_safetensors(
+                PreparedSafetensors(
+                    (pending.slot.block.narrow(0, 0, notification.used_bytes),)
+                ),
+                path / "adapter_model.safetensors",
+            )
+            with (path / "adapter_config.json").open("w", encoding="utf-8") as output:
+                json.dump(notification.adapter_config, output, indent=2, sort_keys=True)
+                output.write("\n")
+            materialization_s = time.monotonic() - started
+            model_bytes = (path / "adapter_model.safetensors").stat().st_size
+            config_bytes = (path / "adapter_config.json").stat().st_size
+        except BaseException:
+            if path.exists():
+                from shutil import rmtree
+
+                rmtree(path)
+            raise
+        finally:
+            self._finish(generation_id)
+        self._materialized.add(generation_id)
+        return AdapterReceiveResult(
+            host_id=self.host_id,
+            generation_id=generation_id,
+            path=str(path),
+            tensor_bytes=model_bytes,
+            config_bytes=config_bytes,
+            materialization_s=materialization_s,
+            slot_id=pending.target.slot_id,
+            used_bytes=notification.used_bytes,
+            capacity_bytes=pending.target.capacity_bytes,
+            prepare_s=pending.target.prepare_s,
+            pool_wait_s=pending.target.pool_wait_s,
+            registration_s=pending.target.registration_s,
+            sender_staging_s=notification.sender_staging_s,
+            sender_registration_s=notification.sender_registration_s,
+        )
+
+    def _poll_local(self, generation_id: str) -> AdapterReceiveResult | None:
+        pending = self._local_pending[generation_id]
+        try:
+            connection, _ = pending.listener.accept()
+        except BlockingIOError:
+            return None
+        try:
+            connection.settimeout(60.0)
+            payload = bytearray()
+            while chunk := connection.recv(64 * 1024):
+                payload.extend(chunk)
+            notification = AdapterTransferNotification.model_validate_json(payload)
+            if notification.generation_id != generation_id:
+                raise RuntimeError("local adapter notification has wrong generation")
+            path = Path(pending.target.path)
+            model_path = path / "adapter_model.safetensors"
+            config_path = path / "adapter_config.json"
+            if not model_path.is_file() or not config_path.is_file():
+                raise RuntimeError("local adapter transfer is incomplete")
+            self._materialized.add(generation_id)
+            return AdapterReceiveResult(
+                host_id=self.host_id,
+                generation_id=generation_id,
+                path=str(path),
+                tensor_bytes=model_path.stat().st_size,
+                config_bytes=config_path.stat().st_size,
+                materialization_s=notification.sender_staging_s,
+                slot_id=pending.target.slot_id,
+                used_bytes=notification.used_bytes,
+                capacity_bytes=pending.target.capacity_bytes,
+                prepare_s=pending.target.prepare_s,
+                pool_wait_s=pending.target.pool_wait_s,
+                registration_s=0.0,
+                sender_staging_s=notification.sender_staging_s,
+                sender_registration_s=0.0,
+            )
+        finally:
+            connection.close()
+            self._finish_local(generation_id)
+
+    def release(self, generation_id: str) -> None:
+        from shutil import rmtree
+
+        if generation_id in self._pending:
+            self._finish(generation_id)
+        if generation_id in self._local_pending:
+            self._finish_local(generation_id)
+        with self._agent_lock:
+            self._notifications.pop(generation_id, None)
+        self._materialized.discard(generation_id)
+        for root in (
+            self.output_root,
+            Path(
+                os.environ.get(
+                    "ART_LOCAL_ADAPTER_TRANSFER_ROOT",
+                    "/dev/shm/art_adapter_transfers",
+                )
+            )
+            / self.host_id,
+        ):
+            path = root / generation_id
+            if path.exists():
+                rmtree(path)
+
+    def _finish_local(self, generation_id: str) -> None:
+        pending = self._local_pending.pop(generation_id)
+        pending.listener.close()
+        Path(pending.target.remote_agent).unlink(missing_ok=True)
+        with self._condition:
+            self._condition.notify()
+
+    def _require_agent(self) -> Any:
+        if self._agent is None:
+            self._agent = _new_agent(f"art-lora-receiver-{self.host_id}-{os.getpid()}")
+        return self._agent
+
+    def _take_notification(
+        self, generation_id: str
+    ) -> AdapterTransferNotification | None:
+        with self._agent_lock:
+            for messages in self._require_agent().get_new_notifs().values():
+                for message in messages:
+                    notification = AdapterTransferNotification.model_validate_json(
+                        message
+                    )
+                    self._notifications[notification.generation_id] = notification
+            return self._notifications.pop(generation_id, None)
+
+    def _finish(self, generation_id: str) -> None:
+        pending = self._pending.pop(generation_id)
+        self._release_slot(pending.slot, generation_id)
+
+    def _release_slot(self, slot: _RegisteredSlot, generation_id: str) -> None:
+        with self._condition:
+            if slot.generation_id != generation_id:
+                raise RuntimeError("adapter receive slot ownership changed")
+            slot.generation_id = None
+            self._condition.notify()
+
+    def _acquire_slot(
+        self, used_bytes: int, *, deadline: float
+    ) -> tuple[_RegisteredSlot, float]:
+        while True:
+            free = [slot for slot in self._slots if slot.generation_id is None]
+            fitting = [slot for slot in free if slot.block.numel() >= used_bytes]
+            if fitting:
+                return min(fitting, key=lambda slot: slot.block.numel()), 0.0
+            if free or len(self._slots) < self.pool_capacity:
+                previous = min(free, key=lambda slot: slot.block.numel(), default=None)
+                slot_id = len(self._slots) if previous is None else previous.slot_id
+                capacity = used_bytes
+                started = time.monotonic()
+                block = torch.empty(capacity, dtype=torch.uint8)
+                with self._agent_lock:
+                    agent = self._require_agent()
+                    registration = agent.register_memory((block,), backends=["UCX"])
+                    if previous is not None:
+                        agent.deregister_memory(previous.registration, backends=["UCX"])
+                if previous is None:
+                    slot = _RegisteredSlot(slot_id, block, registration)
+                    self._slots.append(slot)
+                else:
+                    previous.block = block
+                    previous.registration = registration
+                    slot = previous
+                return slot, time.monotonic() - started
+            remaining_s = deadline - time.monotonic()
+            if remaining_s <= 0:
+                raise TimeoutError("adapter receive pool remained full")
+            self._condition.wait(remaining_s)
+            if self._closed:
+                raise RuntimeError("adapter receive pool closed while waiting")
+
+    def close(self) -> None:
+        with self._condition:
+            self._closed = True
+            self._condition.notify_all()
+        for generation_id in (
+            *self._pending,
+            *self._local_pending,
+            *self._materialized,
+        ):
+            self.release(generation_id)
+        if self._agent is not None:
+            with self._agent_lock:
+                for slot in self._slots:
+                    self._agent.deregister_memory(slot.registration, backends=["UCX"])
+        self._slots.clear()
+
+
+class NixlAdapterSender:
+    """Transfers one immutable CPU snapshot to one or more prepared hosts."""
+
+    def __init__(self) -> None:
+        self._agent: Any | None = None
+        self._block: torch.Tensor | None = None
+        self._registration: Any | None = None
+        self._remote_agents: dict[tuple[str, str], str] = {}
+
+    def send(
+        self,
+        payload: PreparedSafetensors,
+        adapter_config: dict[str, Any],
+        targets: tuple[AdapterTransferTarget, ...],
+    ) -> None:
+        if not targets:
+            return
+        first = targets[0]
+        if any(target.generation_id != first.generation_id for target in targets[1:]):
+            raise RuntimeError("Adapter transfer targets disagree")
+        used_bytes = payload.nbytes
+        if any(used_bytes > target.capacity_bytes for target in targets):
+            raise RuntimeError("Adapter payload exceeds prepared receive capacity")
+        agent = self._require_agent()
+        sender_registration_s = self._ensure_capacity(used_bytes)
+        assert self._block is not None
+        staging_started = time.monotonic()
+        _copy_payload(payload, self._block)
+        notification = (
+            AdapterTransferNotification(
+                generation_id=first.generation_id,
+                used_bytes=used_bytes,
+                adapter_config=adapter_config,
+                sender_staging_s=time.monotonic() - staging_started,
+                sender_registration_s=sender_registration_s,
+            )
+            .model_dump_json()
+            .encode()
+        )
+        for target in targets:
+            local_descriptors = agent.get_xfer_descs(
+                (self._block.narrow(0, 0, used_bytes),)
+            )
+            key = (target.host_id, target.remote_metadata_b64)
+            remote_agent = self._remote_agents.get(key)
+            if remote_agent is None:
+                remote_agent = agent.add_remote_agent(
+                    base64.b64decode(target.remote_metadata_b64)
+                )
+                if isinstance(remote_agent, bytes):
+                    remote_agent = remote_agent.decode()
+                self._remote_agents[key] = remote_agent
+            if remote_agent != target.remote_agent:
+                raise RuntimeError("NIXL target returned the wrong agent identity")
+            handle = agent.initialize_xfer(
+                "WRITE",
+                local_descriptors,
+                agent.get_xfer_descs(
+                    [
+                        (
+                            target.remote_address,
+                            used_bytes,
+                            target.remote_device_id,
+                        )
+                    ],
+                    mem_type="DRAM",
+                ),
+                remote_agent,
+                notification,
+                backends=["UCX"],
+            )
+            try:
+                state = agent.transfer(handle)
+                while state == "PROC":
+                    time.sleep(0.001)
+                    state = agent.check_xfer_state(handle)
+                if state != "DONE":
+                    raise RuntimeError(
+                        f"NIXL adapter transfer failed for {target.host_id}"
+                    )
+            finally:
+                handle.release()
+
+    def _ensure_capacity(self, used_bytes: int) -> float:
+        if self._block is not None and self._block.numel() >= used_bytes:
+            return 0.0
+        capacity = max(
+            used_bytes,
+            2 * (0 if self._block is None else self._block.numel()),
+        )
+        block = torch.empty(capacity, dtype=torch.uint8)
+        agent = self._require_agent()
+        started = time.monotonic()
+        registration = agent.register_memory((block,), backends=["UCX"])
+        if self._registration is not None:
+            agent.deregister_memory(self._registration, backends=["UCX"])
+        self._block = block
+        self._registration = registration
+        return time.monotonic() - started
+
+    def close(self) -> None:
+        if self._agent is not None:
+            for remote_agent in self._remote_agents.values():
+                self._agent.remove_remote_agent(remote_agent)
+        self._remote_agents.clear()
+        if self._agent is not None and self._registration is not None:
+            self._agent.deregister_memory(self._registration, backends=["UCX"])
+        self._block = None
+        self._registration = None
+
+    def _require_agent(self) -> Any:
+        if self._agent is None:
+            self._agent = _new_agent(f"art-lora-sender-{os.getpid()}")
+        return self._agent
+
+
+class AdapterSnapshotSender:
+    """Dispatches immutable snapshots over the transport selected by each target."""
+
+    def __init__(self) -> None:
+        self._nixl: NixlAdapterSender | None = None
+
+    def send(
+        self,
+        snapshot: Any,
+        targets: tuple[AdapterTransferTarget, ...],
+        *,
+        prepared_tensors: PreparedSafetensors,
+    ) -> None:
+        transports = {target.transport for target in targets}
+        if not targets:
+            return
+        if len(transports) != 1:
+            raise RuntimeError("adapter transfer targets mix transports")
+        if transports == {"nixl"}:
+            if self._nixl is None:
+                self._nixl = NixlAdapterSender()
+            self._nixl.send(
+                prepared_tensors,
+                {**snapshot.adapter_config, "art_lora_format": "vllm"},
+                targets,
+            )
+            return
+        self._send_local(snapshot, targets, prepared_tensors=prepared_tensors)
+
+    @staticmethod
+    def _send_local(
+        snapshot: Any,
+        targets: tuple[AdapterTransferTarget, ...],
+        *,
+        prepared_tensors: PreparedSafetensors,
+    ) -> None:
+        from art.megatron.weights.lora_publish import save_vllm_lora_snapshot
+
+        first = targets[0]
+        snapshot_config = {**snapshot.adapter_config, "art_lora_format": "vllm"}
+        if any(target.generation_id != first.generation_id for target in targets):
+            raise RuntimeError("local adapter transfer target changed")
+        for target in targets:
+            started = time.monotonic()
+            save_vllm_lora_snapshot(
+                snapshot,
+                target.path,
+                prepared_tensors=prepared_tensors,
+            )
+            notification = AdapterTransferNotification(
+                generation_id=target.generation_id,
+                used_bytes=prepared_tensors.nbytes,
+                adapter_config=snapshot_config,
+                sender_staging_s=time.monotonic() - started,
+                sender_registration_s=0.0,
+            ).model_dump_json()
+            with socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) as client:
+                client.settimeout(60.0)
+                client.connect(target.remote_agent)
+                client.sendall(notification.encode())
+
+    def close(self) -> None:
+        if self._nixl is not None:
+            self._nixl.close()
+            self._nixl = None
diff --git a/src/art/distributed/art_runtime.py b/src/art/distributed/art_runtime.py
new file mode 100644
index 000000000..8d24f966e
--- /dev/null
+++ b/src/art/distributed/art_runtime.py
@@ -0,0 +1,1185 @@
+from __future__ import annotations
+
+import asyncio
+from collections import Counter
+from collections.abc import Awaitable, Callable
+import logging
+import time
+from typing import Any, Literal
+from urllib.parse import urlparse
+import uuid
+
+from pydantic import BaseModel, ConfigDict
+
+from art.megatron.runtime.managed import MegatronRuntimeInfo
+from art.megatron.runtime.specs import TrainerRuntimeSpec, TrainingRunSpec
+from art.utils.lifecycle import complete_task
+
+from .artifact_preflight import (
+    ArtifactProbeCommand,
+    ArtifactProbeOperation,
+    ArtifactProbeResult,
+    ArtifactProbeSpec,
+    ArtifactRootPreflightError,
+)
+from .data_plane import PackedBatchLeaseSet, fanout_packed_batch
+from .host_admission import (
+    HostAdmissionReport,
+    HostAdmissionRequest,
+    RuntimeFingerprint,
+    build_runtime_fingerprint,
+    runtime_package_names,
+    validate_host_admission,
+)
+from .monarch_bootstrap import (
+    _start_worker,
+    _stop_worker,
+    activate_cpu_child_virtualenv,
+    activate_trainer_child_virtualenv,
+    attach_controller,
+    monarch_identifier,
+    require_local_worker_address,
+)
+from .monarch_runtime import (
+    MonarchPackedBatchInbox,
+    MonarchPackedBatchSource,
+    MonarchPackingEndpoint,
+    MonarchRolloutWorkerEndpoint,
+    MonarchTrajectoryQueueEndpoint,
+    MonarchVllmHostLauncher,
+    call_remote,
+)
+from .nccl_preflight import (
+    NcclPreflightSessionRequest,
+    NcclProbeRequest,
+    NcclProbeResult,
+    NcclRendezvousRequest,
+    NcclRendezvousResult,
+)
+from .packing import PackingRequest, PackingResult
+from .rollout import DistributedRolloutExecutor, InstalledAsyncCallable
+from .specs import (
+    ArtRuntimeConfig,
+    EndpointSpec,
+    GpuId,
+    GpuPlacement,
+    HostServiceHealth,
+    ModelServiceSpec,
+    NixlTransportSpec,
+    RuntimeTopology,
+)
+from .vllm_replica import (
+    ReplicaFailure,
+    ReplicaLaunchTemplate,
+    ReplicaManager,
+    ReplicaState,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class DistributedPackedBatch(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    leases: PackedBatchLeaseSet
+    packed_group_shapes: tuple[Any, ...]
+    trainable_assistant_tokens: int
+    loss_bearing_tokens: int
+    non_padding_tokens: int
+    trajectory_log_path: str | None = None
+    packing_rpc_s: float = 0.0
+    trajectory_fetch_s: float = 0.0
+    packing_core_s: float = 0.0
+    trajectory_log_wait_s: float = 0.0
+    packed_batch_finalize_s: float = 0.0
+    packed_batch_fanout_s: float = 0.0
+    packing_generation_id: str
+
+
+class ArtRuntime:
+    """Run-scoped owner of ART host services, trainer meshes, and vLLM services."""
+
+    def __init__(
+        self,
+        host_mesh: Any,
+        topology: RuntimeTopology,
+        *,
+        config: ArtRuntimeConfig | None = None,
+        owns_host_mesh: bool = False,
+    ) -> None:
+        self.host_mesh = host_mesh
+        self.topology = topology
+        self.config = config or ArtRuntimeConfig()
+        self.owns_host_mesh = owns_host_mesh
+        self.runtime_id = uuid.uuid4().hex
+        self._host_procs: dict[str, Any] = {}
+        self._host_services: dict[str, Any] = {}
+        self._adapter_procs: dict[str, Any] = {}
+        self._adapter_services: dict[str, Any] = {}
+        self._rollout_procs: dict[str, Any] = {}
+        self._rollout_actors: dict[str, Any] = {}
+        self._trainer_runs: set[Any] = set()
+        self._live_batches: dict[str, tuple[str, ...]] = {}
+        self._model_services: dict[str, ReplicaManager] = {}
+        self._closeables: set[Any] = set()
+        self._next_packing_host = 0
+        self._nccl_preflight_lock = asyncio.Lock()
+        self._nccl_preflights: set[
+            tuple[str, tuple[tuple[str, GpuId], ...], str, str | None]
+        ] = set()
+        self._runtime_packages = runtime_package_names(trainer=False)
+        self._trainer_runtime_cache: dict[
+            tuple[tuple[str, ...], bool, bool], MegatronRuntimeInfo
+        ] = {}
+        self._nixl_transport: NixlTransportSpec | None = topology.cluster.nixl_transport
+        self._controller_fingerprint: RuntimeFingerprint
+        self._admitted_hosts: dict[str, HostAdmissionReport] = {}
+        self._artifact_probe = (
+            ArtifactProbeSpec(
+                artifact_root=topology.cluster.artifact_root,
+                runtime_id=self.runtime_id,
+                host_ids=tuple(host.host_id for host in topology.cluster.hosts),
+            )
+            if topology.cluster.artifact_root is not None
+            else None
+        )
+        self._close_task: asyncio.Task[None] | None = None
+        self._local_worker: Any | None = None
+        self._started = False
+        self._closed = False
+
+    @classmethod
+    async def start(
+        cls,
+        host_mesh: Any,
+        topology: RuntimeTopology,
+        *,
+        config: ArtRuntimeConfig | None = None,
+        owns_host_mesh: bool = False,
+    ) -> "ArtRuntime":
+        runtime = cls(
+            host_mesh,
+            topology,
+            config=config,
+            owns_host_mesh=owns_host_mesh,
+        )
+        return await runtime._start()
+
+    @classmethod
+    async def start_local(
+        cls,
+        topology: RuntimeTopology,
+        *,
+        config: ArtRuntimeConfig | None = None,
+    ) -> "ArtRuntime":
+        requested_address = require_local_worker_address(
+            tuple(host.worker_address for host in topology.cluster.hosts)
+        )
+        worker = _start_worker(
+            requested_address,
+            startup_timeout_s=topology.cluster.startup_timeout_s,
+        )
+        address = worker.address
+        if address != requested_address:
+            host = topology.cluster.hosts[0].model_copy(
+                update={"worker_address": address}
+            )
+            cluster = topology.cluster.model_copy(update={"hosts": (host,)})
+            topology = RuntimeTopology(
+                cluster=cluster,
+                rollout_host_ids=topology.rollout_host_ids,
+                trainer=topology.trainer,
+                model_services=topology.model_services,
+            )
+        try:
+            host_mesh = await attach_controller(
+                (address,),
+                name=f"art_local_{uuid.uuid4().hex}",
+                startup_timeout_s=topology.cluster.startup_timeout_s,
+                owned_workers=(worker,),
+            )
+        except BaseException as startup_error:
+            try:
+                await asyncio.to_thread(_stop_worker, worker)
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "local ART runtime startup and cleanup failed",
+                    [startup_error, cleanup_error],
+                ) from None
+            raise
+        try:
+            runtime = cls(host_mesh, topology, config=config, owns_host_mesh=True)
+        except BaseException as startup_error:
+            try:
+                await asyncio.wait_for(
+                    host_mesh.shutdown(), topology.cluster.rpc_timeout_s
+                )
+                await asyncio.to_thread(_stop_worker, worker)
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "local ART runtime construction and cleanup failed",
+                    [startup_error, cleanup_error],
+                ) from None
+            raise
+        runtime._local_worker = worker
+        return await runtime._start()
+
+    async def _start(self) -> "ArtRuntime":
+        try:
+            await self._start_host_services()
+        except BaseException as startup_error:
+            try:
+                await self.close()
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "ART runtime startup and cleanup failed",
+                    [startup_error, cleanup_error],
+                ) from None
+            raise
+        return self
+
+    async def _start_host_services(self) -> None:
+        from .monarch_actor import AdapterTransferHostService, ArtHostService
+
+        async with asyncio.timeout(self.topology.cluster.startup_timeout_s):
+            for index, host in enumerate(self.topology.cluster.hosts):
+                data_plane_host = urlparse(host.worker_address).hostname
+                host_mesh = self.host_mesh.slice(hosts=index)
+                proc = host_mesh.spawn_procs(
+                    per_host={"service": 1},
+                    bootstrap=activate_cpu_child_virtualenv,
+                    name=monarch_identifier(
+                        f"art_host_{self.runtime_id}_{host.host_id}"
+                    ),
+                )
+                self._host_procs[host.host_id] = proc
+                actor = proc.spawn(
+                    monarch_identifier(f"art_service_{self.runtime_id}_{host.host_id}"),
+                    ArtHostService,
+                    HostAdmissionRequest(
+                        host_id=host.host_id,
+                        node_rank=host.node_rank,
+                        expected_gpu_ids=host.gpu_ids,
+                        runtime_packages=self._runtime_packages,
+                    ).model_dump_json(),
+                    self.config.packed_batch_capacity_bytes,
+                    self.config.vllm_output_root,
+                    data_plane_host,
+                )
+                self._host_services[host.host_id] = actor
+                adapter_proc = host_mesh.spawn_procs(
+                    per_host={"adapter": 1},
+                    bootstrap=activate_cpu_child_virtualenv,
+                    name=monarch_identifier(
+                        f"art_adapter_host_{self.runtime_id}_{host.host_id}"
+                    ),
+                )
+                self._adapter_procs[host.host_id] = adapter_proc
+                adapter_actor = adapter_proc.spawn(
+                    monarch_identifier(f"art_adapter_{self.runtime_id}_{host.host_id}"),
+                    AdapterTransferHostService,
+                    host.host_id,
+                    self.config.vllm_output_root,
+                )
+                self._adapter_services[host.host_id] = adapter_actor
+            await asyncio.gather(
+                *(actor.initialized for actor in self._host_services.values()),
+                *(actor.initialized for actor in self._adapter_services.values()),
+            )
+            self._controller_fingerprint, reports = await asyncio.gather(
+                asyncio.to_thread(build_runtime_fingerprint, self._runtime_packages),
+                asyncio.gather(
+                    *(
+                        call_remote(actor.admission)
+                        for actor in self._host_services.values()
+                    )
+                ),
+            )
+            self._admitted_hosts = validate_host_admission(
+                self.topology.cluster.hosts,
+                reports,
+                expected_runtime=self._controller_fingerprint,
+            )
+            self._validate_nccl_transport_environment()
+            await self._resolve_nixl_transport()
+            await self._preflight_artifact_root()
+            await self._preflight_nixl_metadata_store()
+        self._started = True
+        for report in self._admitted_hosts.values():
+            gpus = ",".join(
+                f"{gpu.index}={gpu.uuid}@{gpu.pci_bus_id}"
+                for gpu in report.assigned_gpus
+            )
+            logger.info(
+                "admitted ART host %s hostname=%s boot_id=%s gpus=[%s] runtime=%s",
+                report.host_id,
+                report.hostname,
+                report.boot_id,
+                gpus,
+                report.runtime.sha256,
+            )
+
+    async def health(self) -> dict[str, HostServiceHealth]:
+        async with asyncio.timeout(self.topology.cluster.rpc_timeout_s):
+            values = await asyncio.gather(
+                *(call_remote(actor.health) for actor in self._host_services.values())
+            )
+        health = {value.host_id: value for value in values}
+        if len(health) != len(values) or health.keys() != self._admitted_hosts.keys():
+            raise RuntimeError("host-service liveness membership changed")
+        for host_id, value in health.items():
+            admitted = self._admitted_hosts[host_id]
+            if (value.hostname, value.process_id) != (
+                admitted.hostname,
+                admitted.process_id,
+            ):
+                raise RuntimeError(f"host service {host_id!r} identity changed")
+        return health
+
+    @property
+    def nixl_transport(self) -> NixlTransportSpec | None:
+        return self._nixl_transport
+
+    async def _resolve_nixl_transport(self) -> None:
+        transport = self._nixl_transport
+        if transport is None or transport.metadata_store is not None:
+            return
+        controller = self.topology.cluster.controller_host_id
+        timeout_s = min(60.0, self.topology.cluster.startup_timeout_s)
+        endpoint = await call_remote(
+            self._host_services[controller].start_nixl_metadata_store,
+            self.runtime_id,
+            timeout_s,
+        )
+        if not isinstance(endpoint, EndpointSpec) or not endpoint.is_routable:
+            raise RuntimeError(
+                "managed NIXL metadata store returned an invalid endpoint"
+            )
+        self._nixl_transport = transport.model_copy(update={"metadata_store": endpoint})
+
+    async def _ensure_megatron_runtime(
+        self,
+        host_ids: tuple[str, ...],
+        *,
+        require_hybrid_ep: bool,
+        multinode: bool,
+    ) -> MegatronRuntimeInfo:
+        key = (host_ids, require_hybrid_ep, multinode)
+        if cached := self._trainer_runtime_cache.get(key):
+            return cached
+        infos = await asyncio.gather(
+            *(
+                call_remote(
+                    self._host_services[host_id].ensure_megatron_runtime,
+                    require_hybrid_ep,
+                    multinode,
+                )
+                for host_id in host_ids
+            )
+        )
+        contracts = {info.model_dump_json() for info in infos}
+        if len(contracts) != 1:
+            detail = " ".join(
+                f"{host_id}={info.runtime.sha256}"
+                for host_id, info in zip(host_ids, infos, strict=True)
+            )
+            raise RuntimeError(f"Megatron runtimes differ across hosts: {detail}")
+        info = infos[0]
+        self._trainer_runtime_cache[key] = info
+        logger.info(
+            "admitted Megatron runtime hosts=%s profile=%s variant=%s runtime=%s",
+            ",".join(host_ids),
+            info.profile,
+            info.variant,
+            info.runtime.sha256,
+        )
+        return info
+
+    async def _preflight_launch(
+        self,
+        *,
+        runtime_kind: Literal["trainer", "vllm"],
+        placements: tuple[GpuPlacement, ...],
+        master_addr: str | None = None,
+        runtime_python: str | None = None,
+    ) -> None:
+        selected = tuple(
+            next(value for value in placements if value.host_id == host_id)
+            for host_id in dict.fromkeys(value.host_id for value in placements)
+        )
+        if len(selected) < 2:
+            await self.health()
+            return
+        transport = self.topology.cluster.nccl_transport
+        if transport is None:
+            raise RuntimeError("multi-host GPU launch has no NCCL transport contract")
+        key = (
+            runtime_kind,
+            tuple((value.host_id, value.gpu_id) for value in selected),
+            transport.net_name,
+            runtime_python,
+        )
+        deadline = (
+            asyncio.get_running_loop().time() + self.topology.cluster.startup_timeout_s
+        )
+        cleanup_budget_s = min(10.0, self.topology.cluster.startup_timeout_s * 0.1)
+        operation_deadline = deadline - cleanup_budget_s
+        async with asyncio.timeout_at(deadline):
+            await self._nccl_preflight_lock.acquire()
+        try:
+            async with asyncio.timeout_at(operation_deadline):
+                await self.health()
+            if key in self._nccl_preflights:
+                return
+            probe_id = uuid.uuid4().hex
+            failure: BaseException | None = None
+            try:
+                async with asyncio.timeout_at(operation_deadline):
+                    leader = selected[0]
+                    if master_addr is None:
+                        worker_address = self._host(leader.host_id).worker_address
+                        parsed = urlparse(worker_address)
+                        if parsed.scheme != "tcp" or parsed.hostname is None:
+                            raise ValueError(
+                                f"NCCL preflight requires a TCP worker address, got "
+                                f"{worker_address!r}"
+                            )
+                        master_addr = parsed.hostname
+                    phase_timeout_s = max(
+                        0.001,
+                        (operation_deadline - asyncio.get_running_loop().time()) * 0.45,
+                    )
+                    session = NcclPreflightSessionRequest(
+                        probe_id=probe_id,
+                        lease_s=max(
+                            0.001,
+                            operation_deadline - asyncio.get_running_loop().time(),
+                        ),
+                    )
+                    session_results = await asyncio.gather(
+                        *(
+                            call_remote(
+                                self._host_services[
+                                    placement.host_id
+                                ].start_nccl_preflight_session,
+                                session,
+                            )
+                            for placement in selected
+                        ),
+                        return_exceptions=True,
+                    )
+                    session_failures = [
+                        result
+                        for result in session_results
+                        if isinstance(result, BaseException)
+                    ]
+                    if session_failures:
+                        raise BaseExceptionGroup(
+                            "NCCL preflight session admission failed",
+                            session_failures,
+                        )
+                    rendezvous = await call_remote(
+                        self._host_services[leader.host_id].nccl_preflight_rendezvous,
+                        NcclRendezvousRequest(
+                            probe_id=probe_id,
+                            runtime_kind=runtime_kind,
+                            master_addr=master_addr,
+                            timeout_s=phase_timeout_s,
+                            runtime_python=runtime_python,
+                        ),
+                    )
+                    if not isinstance(rendezvous, NcclRendezvousResult):
+                        raise RuntimeError("NCCL preflight returned an invalid store")
+                    requests = tuple(
+                        NcclProbeRequest(
+                            probe_id=probe_id,
+                            runtime_kind=runtime_kind,
+                            rank=rank,
+                            world_size=len(selected),
+                            master_addr=master_addr,
+                            master_port=rendezvous.port,
+                            gpu_id=placement.gpu_id,
+                            net_name=transport.net_name,
+                            timeout_s=phase_timeout_s,
+                            runtime_python=runtime_python,
+                        )
+                        for rank, placement in enumerate(selected)
+                    )
+                    results = await asyncio.gather(
+                        *(
+                            call_remote(
+                                self._host_services[placement.host_id].nccl_preflight,
+                                request,
+                            )
+                            for placement, request in zip(
+                                selected, requests, strict=True
+                            )
+                        ),
+                        return_exceptions=True,
+                    )
+                    failures = [
+                        result
+                        for result in results
+                        if isinstance(result, BaseException)
+                    ]
+                    if failures:
+                        raise BaseExceptionGroup(
+                            f"{runtime_kind} NCCL transport preflight failed", failures
+                        )
+                    reports = tuple(
+                        result
+                        for result in results
+                        if isinstance(result, NcclProbeResult)
+                    )
+                    expected = tuple(
+                        (placement.host_id, rank, transport.net_name)
+                        for rank, placement in enumerate(selected)
+                    )
+                    if (
+                        tuple(
+                            (report.host_id, report.rank, report.net_name)
+                            for report in reports
+                        )
+                        != expected
+                    ):
+                        raise RuntimeError(
+                            "NCCL preflight returned inconsistent membership"
+                        )
+            except BaseException as error:
+                failure = error
+            cleanup_failures, cleanup_cancelled = await complete_task(
+                asyncio.create_task(
+                    self._cancel_nccl_preflight(
+                        selected,
+                        probe_id,
+                        timeout_s=max(
+                            0.001, deadline - asyncio.get_running_loop().time()
+                        ),
+                    )
+                )
+            )
+            if cleanup_cancelled is not None:
+                if failure is not None:
+                    cleanup_cancelled.add_note(f"NCCL preflight also failed: {failure}")
+                raise cleanup_cancelled
+            if failure is not None:
+                if cleanup_failures:
+                    raise BaseExceptionGroup(
+                        f"{runtime_kind} NCCL preflight and cleanup failed",
+                        [failure, *cleanup_failures],
+                    ) from None
+                raise failure
+            if cleanup_failures:
+                raise BaseExceptionGroup(
+                    f"{runtime_kind} NCCL preflight cleanup failed",
+                    cleanup_failures,
+                )
+            self._nccl_preflights.add(key)
+        finally:
+            self._nccl_preflight_lock.release()
+
+    async def _cancel_nccl_preflight(
+        self,
+        placements: tuple[GpuPlacement, ...],
+        probe_id: str,
+        *,
+        timeout_s: float,
+    ) -> list[BaseException]:
+        try:
+            async with asyncio.timeout(timeout_s):
+                results = await asyncio.gather(
+                    *(
+                        call_remote(
+                            self._host_services[
+                                placement.host_id
+                            ].cancel_nccl_preflight,
+                            probe_id,
+                        )
+                        for placement in placements
+                    ),
+                    return_exceptions=True,
+                )
+        except BaseException as error:
+            return [error]
+        return [result for result in results if isinstance(result, BaseException)]
+
+    def _validate_nccl_transport_environment(self) -> None:
+        transport = self.topology.cluster.nccl_transport
+        if transport is None:
+            return
+        mismatches = {
+            host_id: dict(report.runtime.environment).get("NCCL_NET")
+            for host_id, report in self._admitted_hosts.items()
+            if dict(report.runtime.environment).get("NCCL_NET") != transport.net_name
+        }
+        if mismatches:
+            raise RuntimeError(
+                f"NCCL_NET must equal {transport.net_name!r} on every host: "
+                f"{mismatches}"
+            )
+
+    async def _preflight_nixl_metadata_store(self) -> None:
+        transport = self._nixl_transport
+        if transport is None:
+            return
+        if transport.metadata_store is None:
+            raise RuntimeError("NIXL metadata store was not resolved")
+        host_ids = tuple(self._host_services)
+        probe_timeout_s = min(5.0, self.topology.cluster.rpc_timeout_s)
+        async with asyncio.timeout(self.topology.cluster.rpc_timeout_s):
+            results = await asyncio.gather(
+                *(
+                    call_remote(
+                        self._host_services[host_id].nixl_metadata_store_health,
+                        transport.metadata_store.url,
+                        probe_timeout_s,
+                    )
+                    for host_id in host_ids
+                )
+            )
+        if tuple(results) != host_ids:
+            raise RuntimeError("NIXL metadata-store preflight membership changed")
+
+    async def _preflight_artifact_root(self) -> None:
+        if self._artifact_probe is None:
+            return
+        try:
+            await self._artifact_probe_phase("initialize", owner_only=True)
+            contenders = self._artifact_probe.host_ids[1:]
+            if contenders:
+                await self._artifact_probe_phase("hold_lock", owner_only=True)
+                await self._artifact_probe_phase("check_lock_held", host_ids=contenders)
+                await self._artifact_probe_phase("release_lock", owner_only=True)
+                for host_id in contenders:
+                    await self._artifact_probe_phase(
+                        "check_lock_released", host_ids=(host_id,)
+                    )
+            for operation in (
+                "create",
+                "read_created",
+                "rename",
+                "read_renamed",
+                "delete",
+            ):
+                await self._artifact_probe_phase(operation)
+            await self._artifact_probe_phase("finalize", owner_only=True)
+        except BaseException as preflight_error:
+            cleanup_failures = await self._cleanup_artifact_probe()
+            if cleanup_failures:
+                raise BaseExceptionGroup(
+                    "artifact_root preflight and cleanup failed",
+                    [preflight_error, *cleanup_failures],
+                ) from None
+            raise
+
+    async def _artifact_probe_phase(
+        self,
+        operation: ArtifactProbeOperation,
+        *,
+        owner_only: bool = False,
+        host_ids: tuple[str, ...] | None = None,
+    ) -> None:
+        if self._artifact_probe is None:
+            return
+        if host_ids is None:
+            host_ids = (
+                self._artifact_probe.host_ids[:1]
+                if owner_only
+                else self._artifact_probe.host_ids
+            )
+        command = ArtifactProbeCommand(spec=self._artifact_probe, operation=operation)
+        async with asyncio.timeout(self.topology.cluster.rpc_timeout_s):
+            results: list[ArtifactProbeResult] = await asyncio.gather(
+                *(
+                    call_remote(
+                        self._host_services[host_id].artifact_root_probe, command
+                    )
+                    for host_id in host_ids
+                )
+            )
+        for host_id, result in zip(host_ids, results, strict=True):
+            if result.error_type is not None:
+                raise ArtifactRootPreflightError(result)
+            if result.host_id != host_id or result.operation != operation:
+                raise RuntimeError(
+                    f"invalid artifact_root preflight response from host {host_id!r}"
+                )
+
+    async def _cleanup_artifact_probe(self) -> list[BaseException]:
+        failures: list[BaseException] = []
+        for operation, owner_only in (("cleanup", False), ("finalize", True)):
+            try:
+                await self._artifact_probe_phase(operation, owner_only=owner_only)
+            except BaseException as error:
+                if not (
+                    operation == "finalize"
+                    and isinstance(error, ArtifactRootPreflightError)
+                    and error.result.error_type == "FileNotFoundError"
+                ):
+                    failures.append(error)
+        return failures
+
+    def rollout_executor(
+        self,
+        rollout_callable: InstalledAsyncCallable,
+        *,
+        target_workers: int,
+    ) -> DistributedRolloutExecutor:
+        self._require_open()
+        self._start_rollout_workers()
+        hosts = {
+            host_id: tuple(
+                MonarchRolloutWorkerEndpoint(
+                    actor.slice(rollout=slot),
+                    timeout_s=self.topology.cluster.rpc_timeout_s,
+                )
+                for slot in range(self._host(host_id).cpu_slots)
+            )
+            for host_id, actor in self._rollout_actors.items()
+        }
+        return DistributedRolloutExecutor(
+            callable=rollout_callable,
+            hosts=hosts,
+            target_workers=target_workers,
+            queue_endpoint=MonarchTrajectoryQueueEndpoint(
+                self._host_services[self.topology.cluster.controller_host_id]
+            ),
+            trajectory_capacity_records=self.config.trajectory_capacity_records,
+            trajectory_capacity_bytes=self.config.trajectory_capacity_bytes,
+        )
+
+    def _start_rollout_workers(self) -> None:
+        if self._rollout_actors:
+            return
+        from .monarch_actor import RolloutWorkerService
+
+        for index, host in enumerate(self.topology.cluster.hosts):
+            if host.host_id not in self.topology.rollout_host_ids:
+                continue
+            data_plane_host = urlparse(host.worker_address).hostname
+            if data_plane_host is None:
+                raise ValueError(f"host {host.host_id!r} has no routable address")
+            proc = self.host_mesh.slice(hosts=index).spawn_procs(
+                per_host={"rollout": host.cpu_slots},
+                bootstrap=activate_cpu_child_virtualenv,
+                name=monarch_identifier(
+                    f"art_rollout_{self.runtime_id}_{host.host_id}"
+                ),
+            )
+            actor = proc.spawn(
+                monarch_identifier(
+                    f"art_rollout_worker_{self.runtime_id}_{host.host_id}"
+                ),
+                RolloutWorkerService,
+                self.config.trajectory_capacity_records,
+                self.config.trajectory_capacity_bytes,
+                data_plane_host,
+            )
+            self._rollout_procs[host.host_id] = proc
+            self._rollout_actors[host.host_id] = actor
+
+    def _host(self, host_id: str) -> Any:
+        return next(
+            host for host in self.topology.cluster.hosts if host.host_id == host_id
+        )
+
+    async def pack(self, request: PackingRequest) -> DistributedPackedBatch | None:
+        self._require_open()
+        trainer = self.topology.trainer
+        if trainer is None:
+            raise RuntimeError("runtime topology has no trainer mesh")
+        trainer_hosts = tuple(dict.fromkeys(rank.host_id for rank in trainer.ranks))
+        source_host = trainer_hosts[self._next_packing_host % len(trainer_hosts)]
+        self._next_packing_host += 1
+        source_service = self._host_services[source_host]
+        batch_id = uuid.uuid4().hex
+        self._live_batches[batch_id] = trainer_hosts
+        try:
+            publisher = None
+            wire_request = request
+            if request.trajectory_groups:
+                from .trajectory_store import publish_trajectory_bundles
+
+                controller = self._host(self.topology.cluster.controller_host_id)
+                data_plane_host = urlparse(controller.worker_address).hostname
+                if data_plane_host is None:
+                    raise ValueError("controller has no routable address")
+                transfer, publisher = await publish_trajectory_bundles(
+                    request.trajectory_groups,
+                    stream_id=batch_id,
+                    advertise_host=data_plane_host,
+                )
+                wire_request = request.model_copy(
+                    update={"trajectory_groups": (), "trajectory_transfer": transfer}
+                )
+            try:
+                packing_rpc_started = time.monotonic()
+                result: PackingResult = await MonarchPackingEndpoint(
+                    source_service
+                ).pack(
+                    wire_request,
+                    batch_id,
+                    transfer_timeout_s=self.topology.cluster.rpc_timeout_s,
+                )
+                packing_rpc_s = time.monotonic() - packing_rpc_started
+            finally:
+                if publisher is not None:
+                    await publisher.close()
+            if result.ref is None:
+                self._live_batches.pop(batch_id)
+                return None
+            if result.generation_id != request.generation_id:
+                raise RuntimeError("packing host returned the wrong generation ID")
+            if result.ref.batch_id != batch_id:
+                raise RuntimeError("packing host returned the wrong batch ID")
+            host_refs = {source_host: result.ref}
+            destinations = {
+                host_id: MonarchPackedBatchInbox(self._host_services[host_id])
+                for host_id in trainer_hosts
+                if host_id != source_host
+            }
+            fanout_started = time.monotonic()
+            if destinations:
+                host_refs.update(
+                    await fanout_packed_batch(
+                        ref=result.ref,
+                        source_endpoint=MonarchPackedBatchSource(source_service),
+                        inboxes=destinations,
+                        timeout_s=self.topology.cluster.rpc_timeout_s,
+                    )
+                )
+            packed_batch_fanout_s = time.monotonic() - fanout_started
+            leases = PackedBatchLeaseSet(ref=result.ref, host_refs=host_refs)
+        except BaseException as error:
+            await self._reclaim_after_failure(batch_id, error)
+            raise
+        return DistributedPackedBatch(
+            leases=leases,
+            packed_group_shapes=result.packed_group_shapes,
+            trainable_assistant_tokens=result.trainable_assistant_tokens,
+            loss_bearing_tokens=result.loss_bearing_tokens,
+            non_padding_tokens=result.non_padding_tokens,
+            trajectory_log_path=result.trajectory_log_path,
+            packing_rpc_s=packing_rpc_s,
+            trajectory_fetch_s=result.trajectory_fetch_s,
+            packing_core_s=result.packing_core_s,
+            trajectory_log_wait_s=result.trajectory_log_wait_s,
+            packed_batch_finalize_s=result.packed_batch_finalize_s,
+            packed_batch_fanout_s=packed_batch_fanout_s,
+            packing_generation_id=result.generation_id,
+        )
+
+    async def release_batch(self, batch: DistributedPackedBatch) -> None:
+        await self._reclaim_batch(batch.leases.ref.batch_id, fence=False)
+
+    async def _reclaim_batch(self, batch_id: str, *, fence: bool) -> None:
+        hosts = self._live_batches.get(batch_id)
+        if hosts is None:
+            return
+
+        async def reclaim(host_id: str) -> None:
+            inbox = MonarchPackedBatchInbox(self._host_services[host_id])
+            await inbox.reclaim(batch_id, fence=fence)
+
+        results = await asyncio.gather(
+            *(reclaim(host_id) for host_id in hosts),
+            return_exceptions=True,
+        )
+        failures = [result for result in results if isinstance(result, BaseException)]
+        if failures:
+            raise BaseExceptionGroup("failed to reclaim packed batch", failures)
+        if self._live_batches.get(batch_id) == hosts:
+            self._live_batches.pop(batch_id)
+
+    async def _reclaim_after_failure(
+        self, batch_id: str, primary: BaseException
+    ) -> None:
+        try:
+            _, cancelled = await complete_task(
+                asyncio.create_task(self._reclaim_batch(batch_id, fence=True))
+            )
+            if cancelled is not None:
+                primary.add_note("packed-batch reclamation observed cancellation")
+        except BaseException as cleanup_error:
+            primary.add_note(
+                "packed-batch reclamation also failed: "
+                f"{type(cleanup_error).__name__}: {cleanup_error}"
+            )
+
+    async def start_trainer(
+        self, runtime_spec: TrainerRuntimeSpec, run_spec: TrainingRunSpec
+    ) -> Any:
+        self._require_open()
+        if self.topology.trainer is None:
+            raise RuntimeError("runtime topology has no trainer mesh")
+        if runtime_spec.trainer_mesh != self.topology.trainer:
+            raise ValueError("trainer runtime mesh does not match compiled topology")
+        host_ids = [rank.host_id for rank in runtime_spec.trainer_mesh.ranks]
+        counts = Counter(host_ids)
+        if len(set(counts.values())) != 1:
+            raise ValueError("Monarch trainer hosts require equal ranks per host")
+        ordered_hosts = tuple(dict.fromkeys(host_ids))
+        expected = tuple(
+            host.host_id
+            for host in self.topology.cluster.hosts
+            if host.host_id in counts
+        )
+        if ordered_hosts != expected:
+            raise ValueError("trainer ranks must use cluster host order")
+        indices = [
+            index
+            for index, host in enumerate(self.topology.cluster.hosts)
+            if host.host_id in counts
+        ]
+        if indices != list(range(indices[0], indices[-1] + 1)):
+            raise ValueError("trainer hosts must be contiguous in the cluster mesh")
+        hybrid_ep = runtime_spec.hybrid_ep
+        if hybrid_ep is not None and hybrid_ep.multinode:
+            if hybrid_ep.nixl_transport != self._nixl_transport:
+                raise ValueError(
+                    "trainer NIXL transport does not match the resolved runtime transport"
+                )
+            await self._preflight_nixl_metadata_store()
+        runtime_info = await self._ensure_megatron_runtime(
+            ordered_hosts,
+            require_hybrid_ep=hybrid_ep is not None,
+            multinode=hybrid_ep.multinode if hybrid_ep is not None else False,
+        )
+        await self._preflight_launch(
+            runtime_kind="trainer",
+            placements=runtime_spec.trainer_mesh.ranks,
+            runtime_python=runtime_info.python,
+        )
+        selected = self.host_mesh.slice(
+            hosts=slice(indices[0], indices[-1] + 1)
+        ).with_python_executable(runtime_info.python)
+        from art.megatron.runtime.monarch import (
+            MonarchTrainerRun,
+            MonarchTrainerSupervision,
+            spawn_monarch_trainer_actors,
+        )
+
+        supervision = MonarchTrainerSupervision(run_spec.run_id)
+        proc = None
+        try:
+            proc = selected.spawn_procs(
+                per_host={"trainer": next(iter(counts.values()))},
+                bootstrap=activate_trainer_child_virtualenv,
+                name=monarch_identifier(
+                    f"art_trainer_{supervision.token}_{self.runtime_id}"
+                ),
+            )
+            async with asyncio.timeout(self.topology.cluster.startup_timeout_s):
+                (
+                    actors,
+                    rank_processes,
+                    cp_lookahead_ports,
+                ) = await spawn_monarch_trainer_actors(proc, runtime_spec, supervision)
+        except BaseException as startup_error:
+            try:
+                if proc is not None:
+                    async with asyncio.timeout(self.topology.cluster.rpc_timeout_s):
+                        await proc.stop()
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "trainer startup and cleanup failed",
+                    [startup_error, cleanup_error],
+                ) from None
+            finally:
+                supervision.close()
+            raise
+        run = MonarchTrainerRun(
+            runtime_spec,
+            run_spec,
+            actors,
+            proc,
+            supervision,
+            rank_processes,
+            cp_lookahead_ports,
+        )
+        self._trainer_runs.add(run)
+        return run
+
+    async def stop_trainer(self, run: Any) -> None:
+        await run.close()
+        self._trainer_runs.discard(run)
+
+    def register_closeable(self, closeable: Any) -> None:
+        self._require_open()
+        self._closeables.add(closeable)
+
+    async def start_model_service(
+        self,
+        spec: ModelServiceSpec,
+        template: ReplicaLaunchTemplate,
+        *,
+        on_failure: Callable[[ReplicaFailure], Awaitable[None]] | None = None,
+    ) -> ReplicaState:
+        self._require_open()
+        configured = {service.name: service for service in self.topology.model_services}
+        if configured.get(spec.name) != spec:
+            raise ValueError(
+                "model service does not match the compiled runtime topology"
+            )
+        if spec.name in self._model_services:
+            raise RuntimeError(f"model service {spec.name!r} is already managed")
+        await self._preflight_launch(
+            runtime_kind="vllm",
+            placements=tuple(
+                GpuPlacement(host_id=member.host_id, gpu_id=member.gpu_ids[0])
+                for member in spec.members
+            ),
+            master_addr=spec.rendezvous.host,
+        )
+        launchers = {
+            member.host_id: MonarchVllmHostLauncher(
+                self._host_services[member.host_id],
+                self._adapter_services[member.host_id],
+            )
+            for member in spec.members
+        }
+        manager = ReplicaManager(
+            spec,
+            launchers,
+            template,
+            on_failure=on_failure,
+            startup_timeout_s=self.topology.cluster.startup_timeout_s,
+            rpc_timeout_s=self.topology.cluster.rpc_timeout_s,
+        )
+        self._model_services[spec.name] = manager
+        return await manager.start()
+
+    def model_service(self, name: str) -> ReplicaManager:
+        try:
+            return self._model_services[name]
+        except KeyError:
+            raise RuntimeError(f"model service {name!r} is not managed") from None
+
+    async def stop_model_service(self, name: str) -> ReplicaState:
+        manager = self.model_service(name)
+        state = await manager.stop()
+        self._model_services.pop(name, None)
+        return state
+
+    async def close(self) -> None:
+        if self._close_task is not None and self._close_task.done():
+            try:
+                self._close_task.result()
+            except BaseException:
+                self._close_task = None
+        if self._close_task is None:
+            self._closed = True
+            self._close_task = asyncio.create_task(self._close())
+        await asyncio.shield(self._close_task)
+
+    async def _close(self) -> None:
+        failures: list[BaseException] = []
+
+        async def collect(name: str, *awaitables: Any) -> bool:
+            if not awaitables:
+                return True
+            tasks = {asyncio.ensure_future(awaitable) for awaitable in awaitables}
+            try:
+                done, pending = await asyncio.wait(
+                    tasks, timeout=self.topology.cluster.rpc_timeout_s
+                )
+            except BaseException:
+                for task in tasks:
+                    task.cancel()
+                await asyncio.gather(*tasks, return_exceptions=True)
+                raise
+            for task in pending:
+                task.cancel()
+            await asyncio.gather(*pending, return_exceptions=True)
+            group_failed = bool(pending)
+            if pending:
+                failures.append(
+                    TimeoutError(
+                        f"{name} exceeded {self.topology.cluster.rpc_timeout_s}s"
+                    )
+                )
+            for task in done:
+                try:
+                    task.result()
+                except BaseException as error:
+                    failures.append(error)
+                    group_failed = True
+            return not group_failed
+
+        if await collect(
+            "dependent shutdown", *(value.aclose() for value in self._closeables)
+        ):
+            self._closeables.clear()
+        await collect(
+            "model-service shutdown",
+            *(self.stop_model_service(name) for name in tuple(self._model_services)),
+        )
+        if await collect(
+            "trainer shutdown", *(run.close() for run in self._trainer_runs)
+        ):
+            self._trainer_runs.clear()
+        await collect(
+            "packed batch reclamation",
+            *(
+                self._reclaim_batch(batch_id, fence=True)
+                for batch_id in tuple(self._live_batches)
+            ),
+        )
+        if await collect(
+            "rollout actor shutdown",
+            *(
+                call_remote(actor.slice(rollout=slot).close)
+                for host_id, actor in self._rollout_actors.items()
+                for slot in range(self._host(host_id).cpu_slots)
+            ),
+        ):
+            self._rollout_actors.clear()
+        if await collect(
+            "rollout process shutdown",
+            *(proc.stop() for proc in self._rollout_procs.values()),
+        ):
+            self._rollout_procs.clear()
+        if await collect(
+            "adapter transfer service shutdown",
+            *(call_remote(actor.close) for actor in self._adapter_services.values()),
+        ):
+            self._adapter_services.clear()
+        if await collect(
+            "adapter transfer process shutdown",
+            *(proc.stop() for proc in self._adapter_procs.values()),
+        ):
+            self._adapter_procs.clear()
+        if await collect(
+            "host service shutdown",
+            *(call_remote(actor.close) for actor in self._host_services.values()),
+        ):
+            self._host_services.clear()
+        if await collect(
+            "host process shutdown",
+            *(proc.stop() for proc in self._host_procs.values()),
+        ):
+            self._host_procs.clear()
+        if self.owns_host_mesh and await collect(
+            "host mesh shutdown", self.host_mesh.shutdown()
+        ):
+            self.owns_host_mesh = False
+        if self._local_worker is not None:
+            try:
+                await asyncio.to_thread(_stop_worker, self._local_worker)
+            except BaseException as error:
+                failures.append(error)
+            else:
+                self._local_worker = None
+        if failures:
+            raise BaseExceptionGroup("ART runtime teardown failed", failures)
+
+    async def __aenter__(self) -> "ArtRuntime":
+        self._require_open()
+        return self
+
+    async def __aexit__(self, *_error: object) -> None:
+        await self.close()
+
+    def _require_open(self) -> None:
+        if not self._started or self._closed:
+            raise RuntimeError("ART runtime is not active")
diff --git a/src/art/distributed/artifact_preflight.py b/src/art/distributed/artifact_preflight.py
new file mode 100644
index 000000000..74096f767
--- /dev/null
+++ b/src/art/distributed/artifact_preflight.py
@@ -0,0 +1,245 @@
+from __future__ import annotations
+
+import errno
+import fcntl
+import os
+from pathlib import Path
+import stat
+import threading
+from typing import Annotated, Literal, TypeAlias
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+ArtifactProbeOperation: TypeAlias = Literal[
+    "initialize",
+    "create",
+    "read_created",
+    "rename",
+    "read_renamed",
+    "hold_lock",
+    "check_lock_held",
+    "release_lock",
+    "check_lock_released",
+    "delete",
+    "finalize",
+    "cleanup",
+]
+
+_HELD_FLOCKS: dict[tuple[str, str], int] = {}
+_FLOCK_GUARD = threading.Lock()
+
+
+class _Contract(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class ArtifactProbeSpec(_Contract):
+    artifact_root: str = Field(min_length=1)
+    runtime_id: str = Field(pattern=r"^[0-9a-f]{32}$")
+    host_ids: tuple[Annotated[str, Field(min_length=1)], ...] = Field(min_length=1)
+
+
+class ArtifactProbeCommand(_Contract):
+    spec: ArtifactProbeSpec
+    operation: ArtifactProbeOperation
+
+
+class ArtifactProbeResult(_Contract):
+    host_id: str = Field(min_length=1)
+    operation: ArtifactProbeOperation
+    path: str = Field(min_length=1)
+    error_type: str | None = None
+    message: str | None = None
+
+    @model_validator(mode="after")
+    def _validate_error(self) -> ArtifactProbeResult:
+        if (self.error_type is None) != (self.message is None):
+            raise ValueError("artifact probe error fields must be set together")
+        return self
+
+
+class ArtifactRootPreflightError(RuntimeError):
+    def __init__(self, result: ArtifactProbeResult) -> None:
+        self.result = result
+        super().__init__(
+            f"artifact_root preflight failed on host {result.host_id!r} during "
+            f"{result.operation} at {result.path}: {result.error_type}: {result.message}"
+        )
+
+
+def execute_artifact_probe(
+    host_id: str, command: ArtifactProbeCommand
+) -> ArtifactProbeResult:
+    directory = _probe_directory(command.spec)
+    try:
+        _execute(host_id, command, directory)
+        return ArtifactProbeResult(
+            host_id=host_id, operation=command.operation, path=str(directory)
+        )
+    except Exception as error:
+        return ArtifactProbeResult(
+            host_id=host_id,
+            operation=command.operation,
+            path=str(directory),
+            error_type=type(error).__name__,
+            message=str(error) or type(error).__name__,
+        )
+
+
+def _execute(host_id: str, command: ArtifactProbeCommand, directory: Path) -> None:
+    spec = command.spec
+    try:
+        host_index = spec.host_ids.index(host_id)
+    except ValueError:
+        raise RuntimeError(f"host {host_id!r} is not assigned to this probe") from None
+    root = Path(spec.artifact_root)
+    created = directory / f"{host_index}.created"
+    renamed = directory / f"{host_index}.renamed"
+    lock = directory / "advisory.lock"
+    lock_key = (spec.runtime_id, host_id)
+    operation = command.operation
+    if (
+        operation in {"initialize", "hold_lock", "release_lock", "finalize"}
+        and host_index
+    ):
+        raise RuntimeError(f"only host {spec.host_ids[0]!r} may {operation} the probe")
+    if operation in {"check_lock_held", "check_lock_released"} and not host_index:
+        raise RuntimeError(f"host {spec.host_ids[0]!r} owns the probe lock")
+    if operation == "initialize":
+        if not stat.S_ISDIR(root.stat().st_mode):
+            raise NotADirectoryError(f"not a directory: {root}")
+        directory.mkdir(mode=0o700)
+        _fsync(root)
+    elif operation == "create":
+        with created.open("xb") as handle:
+            handle.write(_payload(spec, host_index))
+            handle.flush()
+            os.fsync(handle.fileno())
+        _fsync(directory)
+        _read(created, spec, host_index)
+    elif operation == "read_created":
+        for index in range(len(spec.host_ids)):
+            _read(directory / f"{index}.created", spec, index)
+    elif operation == "rename":
+        created.rename(renamed)
+        _fsync(directory)
+        _read(renamed, spec, host_index)
+    elif operation == "read_renamed":
+        for index in range(len(spec.host_ids)):
+            _read(directory / f"{index}.renamed", spec, index)
+    elif operation == "hold_lock":
+        _hold_flock(lock, lock_key)
+    elif operation == "check_lock_held":
+        _check_flock(lock, should_block=True)
+    elif operation == "release_lock":
+        _release_flock(lock_key)
+    elif operation == "check_lock_released":
+        _check_flock(lock, should_block=False)
+    elif operation == "delete":
+        renamed.unlink()
+        if not host_index and len(spec.host_ids) > 1:
+            lock.unlink(missing_ok=True)
+        _fsync(directory)
+        _absent(created)
+        _absent(renamed)
+    elif operation == "finalize":
+        directory.rmdir()
+        _fsync(root)
+    elif operation == "cleanup":
+        _release_flock(lock_key, required=False)
+        try:
+            directory.stat()
+        except FileNotFoundError:
+            return
+        paths = (created, renamed, lock) if not host_index else (created, renamed)
+        for path in paths:
+            try:
+                path.unlink()
+            except FileNotFoundError:
+                pass
+        _fsync(directory)
+
+
+def _probe_directory(spec: ArtifactProbeSpec) -> Path:
+    return Path(spec.artifact_root) / f".art-runtime-preflight-{spec.runtime_id}"
+
+
+def _payload(spec: ArtifactProbeSpec, host_index: int) -> bytes:
+    return f"art-runtime-preflight-v1\n{spec.runtime_id}\n{host_index}\n".encode()
+
+
+def _read(path: Path, spec: ArtifactProbeSpec, host_index: int) -> None:
+    if path.read_bytes() != _payload(spec, host_index):
+        raise RuntimeError(f"artifact probe payload mismatch at {path}")
+
+
+def _absent(path: Path) -> None:
+    try:
+        path.lstat()
+    except FileNotFoundError:
+        return
+    raise FileExistsError(f"artifact probe path still exists: {path}")
+
+
+def _hold_flock(path: Path, key: tuple[str, str]) -> None:
+    with _FLOCK_GUARD:
+        if key in _HELD_FLOCKS:
+            raise RuntimeError(f"artifact probe lock is already held: {path}")
+        descriptor = os.open(path, os.O_CREAT | os.O_EXCL | os.O_RDWR, 0o600)
+        try:
+            fcntl.flock(descriptor, fcntl.LOCK_EX | fcntl.LOCK_NB)
+            os.fsync(descriptor)
+            _fsync(path.parent)
+        except BaseException:
+            os.close(descriptor)
+            path.unlink(missing_ok=True)
+            raise
+        _HELD_FLOCKS[key] = descriptor
+
+
+def _release_flock(key: tuple[str, str], *, required: bool = True) -> None:
+    with _FLOCK_GUARD:
+        descriptor = _HELD_FLOCKS.pop(key, None)
+        if descriptor is None:
+            if required:
+                raise RuntimeError("artifact probe lock is not held")
+            return
+        try:
+            fcntl.flock(descriptor, fcntl.LOCK_UN)
+        finally:
+            os.close(descriptor)
+
+
+def _check_flock(path: Path, *, should_block: bool) -> None:
+    descriptor = os.open(path, os.O_RDWR)
+    acquired = False
+    try:
+        try:
+            fcntl.flock(descriptor, fcntl.LOCK_EX | fcntl.LOCK_NB)
+        except OSError as error:
+            if error.errno not in (errno.EACCES, errno.EAGAIN):
+                raise
+            if not should_block:
+                raise RuntimeError(
+                    f"artifact probe lock remained held after release: {path}"
+                ) from error
+        else:
+            acquired = True
+            if should_block:
+                raise RuntimeError(
+                    f"artifact probe lock was acquired while owner held it: {path}"
+                )
+    finally:
+        try:
+            if acquired:
+                fcntl.flock(descriptor, fcntl.LOCK_UN)
+        finally:
+            os.close(descriptor)
+
+
+def _fsync(path: Path) -> None:
+    descriptor = os.open(path, os.O_RDONLY | getattr(os, "O_DIRECTORY", 0))
+    try:
+        os.fsync(descriptor)
+    finally:
+        os.close(descriptor)
diff --git a/src/art/distributed/data_plane.py b/src/art/distributed/data_plane.py
new file mode 100644
index 000000000..dc2fbd00d
--- /dev/null
+++ b/src/art/distributed/data_plane.py
@@ -0,0 +1,1067 @@
+from __future__ import annotations
+
+import asyncio
+from collections.abc import Callable, Mapping
+from multiprocessing import resource_tracker, shared_memory
+import os
+import secrets
+import socket
+from threading import Thread
+import time
+from typing import Any, Coroutine, Protocol, TypeVar, cast
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+PACKED_BATCH_FORMAT = "art_packed_rl_v2"
+_DTYPE_BYTES = {
+    "bool": 1,
+    "uint8": 1,
+    "uint16": 2,
+    "int8": 1,
+    "int16": 2,
+    "float16": 2,
+    "bfloat16": 2,
+    "int32": 4,
+    "float32": 4,
+    "int64": 8,
+    "float64": 8,
+}
+_STREAM_CHUNK_BYTES = 4 << 20
+T = TypeVar("T")
+
+
+class _Contract(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class TensorSpec(_Contract):
+    name: str = Field(min_length=1)
+    dtype: str = Field(min_length=1)
+    shape: tuple[int, ...]
+    offset: int = Field(ge=0)
+    byte_count: int = Field(ge=0)
+
+    @model_validator(mode="after")
+    def _validate_storage(self) -> "TensorSpec":
+        if any(dimension < 0 for dimension in self.shape):
+            raise ValueError("tensor dimensions must be non-negative")
+        item_size = _DTYPE_BYTES.get(self.dtype)
+        if item_size is None:
+            raise ValueError(f"unsupported packed tensor dtype {self.dtype!r}")
+        if _numel(self.shape) * item_size != self.byte_count:
+            raise ValueError("tensor byte_count does not match dtype and shape")
+        return self
+
+
+class MoeRoutingReplaySpec(_Contract):
+    num_layers: int = Field(ge=1)
+    topk: int = Field(ge=1)
+    num_experts: int = Field(ge=1, le=65_536)
+    packed_tokens: int = Field(ge=0)
+
+
+class PrefixTreePackingStatsSpec(_Contract):
+    logical_tokens: int = Field(ge=0)
+    physical_tokens: int = Field(ge=0)
+
+
+class PackedBatchRef(_Contract):
+    batch_id: str = Field(min_length=1)
+    owner_actor_id: str = Field(min_length=1)
+    lease_id: str = Field(min_length=1)
+    format: str = PACKED_BATCH_FORMAT
+    shared_memory_name: str = Field(min_length=1)
+    owner_process_id: int = Field(ge=1)
+    tensors: tuple[TensorSpec, ...]
+    num_sequences: int = Field(ge=1)
+    sequence_length: int = Field(ge=1)
+    byte_count: int = Field(ge=0)
+    storage_byte_count: int = Field(ge=1)
+    pixel_values_present: tuple[bool, ...]
+    image_grid_thw_present: tuple[bool, ...]
+    moe_routing_replay: MoeRoutingReplaySpec | None = None
+    prefix_tree_packing_stats: PrefixTreePackingStatsSpec | None = None
+    group_ids: tuple[str, ...] = ()
+    record_ids: tuple[str, ...] = ()
+    min_source_version: int = Field(default=0, ge=0)
+    max_source_version: int = Field(default=0, ge=0)
+
+    @model_validator(mode="after")
+    def _validate_manifest(self) -> "PackedBatchRef":
+        if self.format != PACKED_BATCH_FORMAT:
+            raise ValueError(f"unsupported packed-batch format {self.format!r}")
+        names = [tensor.name for tensor in self.tensors]
+        if len(set(names)) != len(names):
+            raise ValueError("tensor manifest names must be unique")
+        if sum(tensor.byte_count for tensor in self.tensors) != self.byte_count:
+            raise ValueError("packed-batch byte_count does not match tensor manifest")
+        core_dtypes = {
+            "tokens": "int64",
+            "group_ids": "int64",
+            "parent_ids": "int64",
+            "input_pos": "int64",
+            "assistant_mask": "bool",
+            "logprobs": "float32",
+            "advantages": "float32",
+            "weights": "float32",
+        }
+        specs = {tensor.name: tensor for tensor in self.tensors}
+        if not core_dtypes.keys() <= specs.keys():
+            raise ValueError("packed-batch tensor manifest is missing core tensors")
+        core_shape = (self.num_sequences, self.sequence_length)
+        if any(
+            specs[name].dtype != dtype or specs[name].shape != core_shape
+            for name, dtype in core_dtypes.items()
+        ):
+            raise ValueError("core packed tensor dtype or shape is invalid")
+        if (
+            len(self.pixel_values_present) != self.num_sequences
+            or len(self.image_grid_thw_present) != self.num_sequences
+        ):
+            raise ValueError("multimodal presence manifests must match num_sequences")
+        expected_optional = {
+            f"pixel_values/{index}"
+            for index, present in enumerate(self.pixel_values_present)
+            if present
+        } | {
+            f"image_grid_thw/{index}"
+            for index, present in enumerate(self.image_grid_thw_present)
+            if present
+        }
+        if any(
+            specs[name].dtype
+            != ("float32" if name.startswith("pixel_values/") else "int64")
+            for name in expected_optional
+        ):
+            raise ValueError("multimodal packed tensor dtype is invalid")
+        if "original_logprobs" in specs:
+            expected_optional.add("original_logprobs")
+            if (
+                specs["original_logprobs"].dtype != "float32"
+                or specs["original_logprobs"].shape != core_shape
+            ):
+                raise ValueError("original_logprobs dtype or shape is invalid")
+        replay_names = {"moe_routing_replay/expert_indices"}
+        if self.moe_routing_replay is not None:
+            if not replay_names <= specs.keys():
+                raise ValueError("MoE routing replay manifest is incomplete")
+            expected_optional |= replay_names
+            replay = self.moe_routing_replay
+            replay_dtype = "uint8" if replay.num_experts <= 256 else "uint16"
+            if specs[
+                "moe_routing_replay/expert_indices"
+            ].dtype != replay_dtype or specs[
+                "moe_routing_replay/expert_indices"
+            ].shape != (replay.num_layers, *core_shape, replay.topk):
+                raise ValueError("MoE routing replay tensor dtype or shape is invalid")
+        if set(specs) != set(core_dtypes) | expected_optional:
+            raise ValueError("packed-batch tensor manifest has unexpected tensors")
+        previous_end = 0
+        for tensor in self.tensors:
+            if tensor.offset < previous_end:
+                raise ValueError("packed-batch tensor storage must not overlap")
+            if tensor.offset + tensor.byte_count > self.storage_byte_count:
+                raise ValueError(
+                    f"tensor {tensor.name!r} exceeds shared-memory storage"
+                )
+            previous_end = tensor.offset + tensor.byte_count
+        if self.max_source_version < self.min_source_version:
+            raise ValueError("max_source_version must be >= min_source_version")
+        return self
+
+
+class PackedBatchLeaseSet(_Contract):
+    """One logical batch and its host-local physical leases."""
+
+    ref: PackedBatchRef
+    host_refs: dict[str, PackedBatchRef]
+
+    @model_validator(mode="after")
+    def _validate_hosts(self) -> "PackedBatchLeaseSet":
+        if not self.host_refs:
+            raise ValueError("packed batch requires at least one host lease")
+        logical = _logical_ref(self.ref)
+        if any(_logical_ref(ref) != logical for ref in self.host_refs.values()):
+            raise ValueError("host leases must describe the same logical packed batch")
+        return self
+
+
+class BatchReservation(_Contract):
+    reservation_id: str = Field(min_length=1)
+    batch_id: str = Field(min_length=1)
+    storage_byte_count: int = Field(ge=1)
+
+
+class PackedBatchTransfer(_Contract):
+    batch_id: str = Field(min_length=1)
+    host: str = Field(min_length=1)
+    port: int = Field(ge=1, le=65535)
+    token: str = Field(pattern=r"^[0-9a-f]{64}$")
+    byte_count: int = Field(ge=1)
+
+
+class ByteStreamTransfer(_Contract):
+    stream_id: str = Field(min_length=1)
+    host: str = Field(min_length=1)
+    port: int = Field(ge=1, le=65535)
+    token: str = Field(pattern=r"^[0-9a-f]{64}$")
+    byte_count: int = Field(ge=1)
+
+
+class DataPlaneStats(_Contract):
+    capacity_bytes: int
+    used_bytes: int
+    reserved_bytes: int
+    peak_bytes: int
+    created_bytes: int
+    copied_bytes: int
+    transmitted_bytes: int
+    copy_count: int
+    batches: int
+    leases: int
+
+
+class PackedBatchCapacityError(RuntimeError):
+    pass
+
+
+class PackedBatchLeaseError(RuntimeError):
+    pass
+
+
+class _Entry:
+    def __init__(self, shm: shared_memory.SharedMemory, ref: PackedBatchRef) -> None:
+        self.shm = shm
+        self.ref = ref
+
+
+class SharedMemoryPackedBatchStore:
+    """Own immutable current-format batches in bounded POSIX shared memory."""
+
+    def __init__(self, *, owner_actor_id: str, capacity_bytes: int) -> None:
+        if capacity_bytes <= 0:
+            raise ValueError("capacity_bytes must be > 0")
+        self.owner_actor_id = owner_actor_id
+        self.capacity_bytes = capacity_bytes
+        self._entries: dict[str, _Entry] = {}
+        self._reservations: dict[str, BatchReservation] = {}
+        self._reclaimed: set[str] = set()
+        self._used_bytes = 0
+        self._reserved_bytes = 0
+        self._peak_bytes = 0
+        self._created_bytes = 0
+        self._copied_bytes = 0
+        self._transmitted_bytes = 0
+        self._copy_count = 0
+
+    def create(
+        self,
+        tensors: Any,
+        *,
+        batch_id: str,
+        group_ids: tuple[str, ...] = (),
+        record_ids: tuple[str, ...] = (),
+        min_source_version: int = 0,
+        max_source_version: int = 0,
+    ) -> PackedBatchRef:
+        flat, metadata = _flatten_packed_tensors(tensors)
+        manifest, storage_bytes = _layout(flat)
+        if batch_id in self._reclaimed:
+            raise PackedBatchLeaseError(f"packed batch {batch_id!r} was reclaimed")
+        if batch_id in self._entries or any(
+            reservation.batch_id == batch_id
+            for reservation in self._reservations.values()
+        ):
+            raise ValueError(f"packed batch {batch_id!r} already exists")
+        self._require_capacity(storage_bytes)
+        lease_id = secrets.token_hex(16)
+        shm = shared_memory.SharedMemory(create=True, size=storage_bytes)
+        try:
+            for spec, (_, tensor) in zip(manifest, flat, strict=True):
+                destination = _tensor_from_buffer(_shm_buffer(shm), spec)
+                destination.copy_(tensor)
+            ref = PackedBatchRef(
+                batch_id=batch_id,
+                owner_actor_id=self.owner_actor_id,
+                lease_id=lease_id,
+                shared_memory_name=shm.name,
+                owner_process_id=os.getpid(),
+                tensors=manifest,
+                num_sequences=metadata["num_sequences"],
+                sequence_length=metadata["sequence_length"],
+                byte_count=sum(spec.byte_count for spec in manifest),
+                storage_byte_count=storage_bytes,
+                pixel_values_present=metadata["pixel_values_present"],
+                image_grid_thw_present=metadata["image_grid_thw_present"],
+                moe_routing_replay=metadata["moe_routing_replay"],
+                prefix_tree_packing_stats=metadata["prefix_tree_packing_stats"],
+                group_ids=group_ids,
+                record_ids=record_ids,
+                min_source_version=min_source_version,
+                max_source_version=max_source_version,
+            )
+        except BaseException:
+            shm.close()
+            shm.unlink()
+            raise
+        self._entries[batch_id] = _Entry(shm, ref)
+        self._used_bytes += storage_bytes
+        self._peak_bytes = max(
+            self._peak_bytes, self._used_bytes + self._reserved_bytes
+        )
+        self._created_bytes += storage_bytes
+        self._copied_bytes += ref.byte_count
+        self._copy_count += len(manifest)
+        return ref
+
+    def reserve(self, source: PackedBatchRef) -> BatchReservation:
+        if source.batch_id in self._reclaimed:
+            raise PackedBatchLeaseError(
+                f"packed batch {source.batch_id!r} was reclaimed"
+            )
+        if source.batch_id in self._entries or any(
+            reservation.batch_id == source.batch_id
+            for reservation in self._reservations.values()
+        ):
+            raise ValueError(f"packed batch {source.batch_id!r} already exists")
+        self._require_capacity(source.storage_byte_count)
+        reservation = BatchReservation(
+            reservation_id=secrets.token_hex(16),
+            batch_id=source.batch_id,
+            storage_byte_count=source.storage_byte_count,
+        )
+        self._reservations[reservation.reservation_id] = reservation
+        self._reserved_bytes += reservation.storage_byte_count
+        self._peak_bytes = max(
+            self._peak_bytes, self._used_bytes + self._reserved_bytes
+        )
+        return reservation
+
+    async def commit_stream(
+        self,
+        reservation_id: str,
+        source: PackedBatchRef,
+        transfer: PackedBatchTransfer,
+        *,
+        timeout_s: float,
+    ) -> PackedBatchRef:
+        reservation = self._reservations.get(reservation_id)
+        if reservation is None or reservation.batch_id != source.batch_id:
+            raise PackedBatchLeaseError(
+                "unknown or mismatched packed-batch reservation"
+            )
+        if (
+            transfer.batch_id != source.batch_id
+            or transfer.byte_count != reservation.storage_byte_count
+        ):
+            raise PackedBatchLeaseError(
+                "packed-batch transfer does not match reservation"
+            )
+        shm = shared_memory.SharedMemory(
+            create=True, size=reservation.storage_byte_count
+        )
+        try:
+            from art.utils.lifecycle import complete_to_thread
+
+            _, cancelled = await complete_to_thread(
+                lambda: _receive_stream(transfer, shm, timeout_s)
+            )
+            if cancelled is not None:
+                raise cancelled
+            return self._finish_commit(reservation, source, shm)
+        except BaseException:
+            shm.close()
+            shm.unlink()
+            raise
+
+    def abort(self, reservation_id: str) -> None:
+        reservation = self._reservations.pop(reservation_id, None)
+        if reservation is not None:
+            self._reserved_bytes -= reservation.storage_byte_count
+
+    def drop(self, ref: PackedBatchRef) -> None:
+        """Idempotently reclaim one host-owned packed batch."""
+
+        entry = self._entries.get(ref.batch_id)
+        if entry is None:
+            return
+        if entry.ref.lease_id != ref.lease_id:
+            raise PackedBatchLeaseError("packed-batch reference has a stale lease")
+        self.reclaim(ref.batch_id)
+
+    def reclaim(self, batch_id: str, *, fence: bool = True) -> bool:
+        """Release committed or in-flight storage and optionally fence late writes."""
+
+        if fence:
+            self._reclaimed.add(batch_id)
+        found = False
+        for reservation_id, reservation in tuple(self._reservations.items()):
+            if reservation.batch_id == batch_id:
+                self.abort(reservation_id)
+                found = True
+        entry = self._entries.pop(batch_id, None)
+        if entry is not None:
+            self._used_bytes -= entry.ref.storage_byte_count
+            entry.shm.close()
+            entry.shm.unlink()
+            found = True
+        return found
+
+    def map(self, ref: PackedBatchRef) -> "MappedPackedBatch":
+        entry = self._entries.get(ref.batch_id)
+        if entry is None or entry.ref.lease_id != ref.lease_id:
+            raise PackedBatchLeaseError("packed-batch reference has no active lease")
+        return MappedPackedBatch.open(ref)
+
+    def note_transmitted(self, byte_count: int) -> None:
+        self._transmitted_bytes += byte_count
+
+    def close(self) -> None:
+        batch_ids = set(self._entries)
+        batch_ids.update(
+            reservation.batch_id for reservation in self._reservations.values()
+        )
+        for batch_id in batch_ids:
+            self.reclaim(batch_id, fence=True)
+
+    def stats(self) -> DataPlaneStats:
+        return DataPlaneStats(
+            capacity_bytes=self.capacity_bytes,
+            used_bytes=self._used_bytes,
+            reserved_bytes=self._reserved_bytes,
+            peak_bytes=self._peak_bytes,
+            created_bytes=self._created_bytes,
+            copied_bytes=self._copied_bytes,
+            transmitted_bytes=self._transmitted_bytes,
+            copy_count=self._copy_count,
+            batches=len(self._entries),
+            leases=len(self._entries),
+        )
+
+    def _require_capacity(self, byte_count: int) -> None:
+        if byte_count > self.capacity_bytes:
+            raise PackedBatchCapacityError(
+                f"packed batch requires {byte_count} bytes, capacity is "
+                f"{self.capacity_bytes}"
+            )
+        available = self.capacity_bytes - self._used_bytes - self._reserved_bytes
+        if byte_count > available:
+            raise PackedBatchCapacityError(
+                f"packed batch requires {byte_count} bytes, only {available} available"
+            )
+
+    def _finish_commit(
+        self,
+        reservation: BatchReservation,
+        source: PackedBatchRef,
+        shm: shared_memory.SharedMemory,
+    ) -> PackedBatchRef:
+        if self._reservations.get(reservation.reservation_id) != reservation:
+            raise PackedBatchLeaseError(
+                f"packed batch {source.batch_id!r} was reclaimed during transfer"
+            )
+        lease_id = secrets.token_hex(16)
+        ref = source.model_copy(
+            update={
+                "owner_actor_id": self.owner_actor_id,
+                "lease_id": lease_id,
+                "shared_memory_name": shm.name,
+                "owner_process_id": os.getpid(),
+            }
+        )
+        self._reservations.pop(reservation.reservation_id)
+        self._reserved_bytes -= reservation.storage_byte_count
+        self._entries[ref.batch_id] = _Entry(shm, ref)
+        self._used_bytes += ref.storage_byte_count
+        self._created_bytes += ref.storage_byte_count
+        self._copied_bytes += ref.storage_byte_count
+        self._copy_count += 1
+        return ref
+
+
+class MappedPackedBatch(BaseModel):
+    """Zero-copy consumer view; callers must not mutate its immutable tensors."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    ref: PackedBatchRef
+    tensors: Any
+    _shm: Any = None
+    _closed: bool = False
+
+    @classmethod
+    def open(cls, ref: PackedBatchRef) -> "MappedPackedBatch":
+        shm = shared_memory.SharedMemory(name=ref.shared_memory_name)
+        if ref.owner_process_id != os.getpid():
+            # Python 3.12 has no public `track=False`. The segment belongs to the
+            # host inbox, so an unrelated consumer's tracker must not unlink it.
+            resource_tracker.unregister(cast(Any, shm)._name, "shared_memory")
+        try:
+            flat = {
+                spec.name: _tensor_from_buffer(_shm_buffer(shm), spec)
+                for spec in ref.tensors
+            }
+            tensors = _unflatten_packed_tensors(flat, ref)
+        except BaseException:
+            shm.close()
+            raise
+        mapped = cls(ref=ref, tensors=tensors)
+        mapped._shm = shm
+        return mapped
+
+    def close(self) -> None:
+        if not self._closed:
+            self.tensors = None
+            self._shm.close()
+            self._closed = True
+
+    def __enter__(self) -> "MappedPackedBatch":
+        return self
+
+    def __exit__(self, *_: object) -> None:
+        self.close()
+
+
+class ByteStreamServerLoop:
+    """A process-local I/O loop that cannot be blocked by rollout code."""
+
+    def __init__(self) -> None:
+        self._loop = asyncio.new_event_loop()
+        self._thread = Thread(target=self._run, name="art-byte-stream", daemon=True)
+        self._thread.start()
+
+    def _run(self) -> None:
+        asyncio.set_event_loop(self._loop)
+        self._loop.run_forever()
+
+    async def submit(self, coroutine: Coroutine[Any, Any, T]) -> T:
+        return await asyncio.wrap_future(
+            asyncio.run_coroutine_threadsafe(coroutine, self._loop)
+        )
+
+    async def close(self) -> None:
+        if self._thread.is_alive():
+            self._loop.call_soon_threadsafe(self._loop.stop)
+            await asyncio.to_thread(self._thread.join)
+            self._loop.close()
+
+
+class _AuthenticatedStreamPublisher:
+    def __init__(
+        self, advertise_host: str, server_loop: ByteStreamServerLoop | None = None
+    ) -> None:
+        self.advertise_host = advertise_host
+        self._server_loop = server_loop
+        self._token = secrets.token_bytes(32)
+        self._server: Any = None
+        self._handlers: set[asyncio.Task[None]] = set()
+
+    async def start(self) -> None:
+        if self._server_loop is not None:
+            return await self._server_loop.submit(self._start())
+        await self._start()
+
+    async def _start(self) -> None:
+        family = socket.getaddrinfo(self.advertise_host, 0, type=socket.SOCK_STREAM)[0][
+            0
+        ]
+        bind_host = "::" if family == socket.AF_INET6 else "0.0.0.0"
+        self._server = await asyncio.start_server(
+            self._handle, bind_host, 0, family=family
+        )
+
+    def _port(self) -> int:
+        if self._server is None or not self._server.sockets:
+            raise RuntimeError("byte-stream publisher is not listening")
+        return int(self._server.sockets[0].getsockname()[1])
+
+    async def close(self) -> None:
+        if self._server_loop is not None:
+            return await self._server_loop.submit(self._close())
+        await self._close()
+
+    async def _close(self) -> None:
+        if self._server is None:
+            return
+        self._server.close()
+        await self._server.wait_closed()
+        for task in self._handlers:
+            task.cancel()
+        await asyncio.gather(*self._handlers, return_exceptions=True)
+        self._handlers.clear()
+        self._server = None
+
+    async def _handle(
+        self, reader: asyncio.StreamReader, writer: asyncio.StreamWriter
+    ) -> None:
+        task = cast(asyncio.Task[None], asyncio.current_task())
+        self._handlers.add(task)
+        sent = False
+        try:
+            token = await reader.readexactly(len(self._token))
+            if not secrets.compare_digest(token, self._token):
+                return
+            await self._write(writer)
+            sent = True
+        except (asyncio.IncompleteReadError, ConnectionError):
+            pass
+        finally:
+            try:
+                writer.close()
+                if task.cancelling():
+                    writer.transport.abort()
+                else:
+                    try:
+                        await writer.wait_closed()
+                    except asyncio.CancelledError:
+                        writer.transport.abort()
+                        raise
+                    except Exception:
+                        pass
+            finally:
+                self._handlers.discard(task)
+                if sent and not task.cancelling():
+                    self._sent()
+
+    async def _write(self, writer: asyncio.StreamWriter) -> None:
+        raise NotImplementedError
+
+    def _sent(self) -> None:
+        pass
+
+
+class ByteStreamPublisher(_AuthenticatedStreamPublisher):
+    """Authenticated one-shot transport for immutable byte chunks."""
+
+    def __init__(
+        self,
+        stream_id: str,
+        advertise_host: str,
+        chunks: tuple[bytes, ...],
+        on_sent: Callable[[], None] | None,
+        server_loop: ByteStreamServerLoop | None,
+    ) -> None:
+        super().__init__(advertise_host, server_loop)
+        self.stream_id = stream_id
+        self.chunks = chunks
+        self.on_sent = on_sent
+        self.byte_count = sum(map(len, chunks))
+        if not stream_id or self.byte_count < 1:
+            raise ValueError("byte stream ID and payload must be non-empty")
+
+    @classmethod
+    async def create(
+        cls,
+        stream_id: str,
+        chunks: tuple[bytes, ...],
+        *,
+        advertise_host: str,
+        on_sent: Callable[[], None] | None = None,
+        server_loop: ByteStreamServerLoop | None = None,
+    ) -> "ByteStreamPublisher":
+        publisher = cls(stream_id, advertise_host, chunks, on_sent, server_loop)
+        await publisher.start()
+        return publisher
+
+    @property
+    def transfer(self) -> ByteStreamTransfer:
+        return ByteStreamTransfer(
+            stream_id=self.stream_id,
+            host=self.advertise_host,
+            port=self._port(),
+            token=self._token.hex(),
+            byte_count=self.byte_count,
+        )
+
+    async def _write(self, writer: asyncio.StreamWriter) -> None:
+        for chunk in self.chunks:
+            await _write_stream_chunk(writer, chunk)
+
+    def _sent(self) -> None:
+        if self.on_sent is not None:
+            self.on_sent()
+
+
+class PackedBatchPublisher(_AuthenticatedStreamPublisher):
+    """Batch-scoped authenticated stream over the cluster's routable TCP fabric."""
+
+    def __init__(
+        self,
+        ref: PackedBatchRef,
+        advertise_host: str,
+        shm: shared_memory.SharedMemory,
+    ) -> None:
+        super().__init__(advertise_host)
+        self.ref = ref
+        self.shm = shm
+
+    @classmethod
+    async def create(
+        cls, ref: PackedBatchRef, *, advertise_host: str
+    ) -> "PackedBatchPublisher":
+        shm = shared_memory.SharedMemory(name=ref.shared_memory_name)
+        if ref.owner_process_id != os.getpid():
+            resource_tracker.unregister(cast(Any, shm)._name, "shared_memory")
+        publisher = cls(ref, advertise_host, shm)
+        try:
+            await publisher.start()
+            return publisher
+        except BaseException:
+            shm.close()
+            raise
+
+    @property
+    def transfer(self) -> PackedBatchTransfer:
+        return PackedBatchTransfer(
+            batch_id=self.ref.batch_id,
+            host=self.advertise_host,
+            port=self._port(),
+            token=self._token.hex(),
+            byte_count=self.ref.storage_byte_count,
+        )
+
+    async def close(self) -> None:
+        try:
+            await super().close()
+        finally:
+            self.shm.close()
+
+    async def _write(self, writer: asyncio.StreamWriter) -> None:
+        source = _shm_buffer(self.shm)[: self.ref.storage_byte_count]
+        try:
+            await _write_stream_chunk(writer, source)
+        finally:
+            source.release()
+
+
+class PackedBatchInbox:
+    def __init__(self, *, host_id: str, capacity_bytes: int) -> None:
+        self.host_id = host_id
+        self.store = SharedMemoryPackedBatchStore(
+            owner_actor_id=f"packed_batch_inbox:{host_id}",
+            capacity_bytes=capacity_bytes,
+        )
+
+    async def receive(
+        self, ref: PackedBatchRef, transfer: PackedBatchTransfer, *, timeout_s: float
+    ) -> PackedBatchRef:
+        reservation = self.store.reserve(ref)
+        try:
+            return await self.store.commit_stream(
+                reservation.reservation_id,
+                ref,
+                transfer,
+                timeout_s=timeout_s,
+            )
+        except BaseException:
+            self.store.abort(reservation.reservation_id)
+            raise
+
+    async def drop(self, ref: PackedBatchRef) -> None:
+        self.store.drop(ref)
+
+    async def reclaim(self, batch_id: str, *, fence: bool = True) -> bool:
+        return self.store.reclaim(batch_id, fence=fence)
+
+
+class PackedBatchSourceEndpoint(Protocol):
+    async def publish(self, ref: PackedBatchRef) -> PackedBatchTransfer: ...
+
+    async def drop(self, batch_id: str) -> None: ...
+
+    async def note_transmitted(self, byte_count: int) -> None: ...
+
+
+class PackedBatchInboxEndpoint(Protocol):
+    async def receive(
+        self, ref: PackedBatchRef, transfer: PackedBatchTransfer, *, timeout_s: float
+    ) -> PackedBatchRef: ...
+
+    async def drop(self, ref: PackedBatchRef) -> None: ...
+
+
+async def fanout_packed_batch(
+    *,
+    ref: PackedBatchRef,
+    source_endpoint: PackedBatchSourceEndpoint,
+    inboxes: Mapping[str, PackedBatchInboxEndpoint],
+    timeout_s: float,
+) -> dict[str, PackedBatchRef]:
+    """Publish once, stream once per host, and always drop the source listener."""
+
+    transfer = await source_endpoint.publish(ref)
+    try:
+        tasks = {
+            host_id: asyncio.create_task(
+                inbox.receive(ref, transfer, timeout_s=timeout_s)
+            )
+            for host_id, inbox in inboxes.items()
+        }
+        try:
+            results = await asyncio.gather(*tasks.values(), return_exceptions=True)
+        except BaseException:
+            for task in tasks.values():
+                task.cancel()
+            results = await asyncio.gather(*tasks.values(), return_exceptions=True)
+            await _release_transferred(
+                inboxes,
+                [
+                    result if isinstance(result, BaseException) else (host_id, result)
+                    for host_id, result in zip(tasks, results, strict=True)
+                ],
+            )
+            raise
+        failures = [result for result in results if isinstance(result, BaseException)]
+        if failures:
+            await _release_transferred(
+                inboxes,
+                [
+                    result if isinstance(result, BaseException) else (host_id, result)
+                    for host_id, result in zip(tasks, results, strict=True)
+                ],
+            )
+            raise failures[0]
+        await source_endpoint.note_transmitted(len(inboxes) * ref.storage_byte_count)
+        return dict(zip(tasks, cast(list[PackedBatchRef], results), strict=True))
+    finally:
+        await source_endpoint.drop(ref.batch_id)
+
+
+async def _release_transferred(
+    inboxes: Mapping[str, PackedBatchInboxEndpoint],
+    results: list[tuple[str, PackedBatchRef] | BaseException],
+) -> None:
+    for result in results:
+        if not isinstance(result, BaseException):
+            host_id, destination_ref = result
+            await inboxes[host_id].drop(destination_ref)
+
+
+async def receive_byte_stream(
+    transfer: ByteStreamTransfer, *, timeout_s: float
+) -> bytearray:
+    from art.utils.lifecycle import complete_to_thread
+
+    payload = bytearray(transfer.byte_count)
+    destination = memoryview(payload)
+    try:
+        _, cancelled = await complete_to_thread(
+            lambda: _receive_into_stream(transfer, destination, timeout_s)
+        )
+        if cancelled is not None:
+            raise cancelled
+        return payload
+    finally:
+        destination.release()
+
+
+def _receive_stream(
+    transfer: PackedBatchTransfer,
+    shm: shared_memory.SharedMemory,
+    timeout_s: float,
+) -> None:
+    destination = _shm_buffer(shm)[: transfer.byte_count]
+    try:
+        _receive_into_stream(transfer, destination, timeout_s)
+    finally:
+        destination.release()
+
+
+def _receive_into_stream(
+    transfer: PackedBatchTransfer | ByteStreamTransfer,
+    destination: memoryview,
+    timeout_s: float,
+) -> None:
+    deadline = time.monotonic() + timeout_s
+    with socket.create_connection(
+        (transfer.host, transfer.port), timeout=max(0.001, timeout_s)
+    ) as connection:
+        connection.sendall(bytes.fromhex(transfer.token))
+        offset = 0
+        while offset < len(destination):
+            connection.settimeout(max(0.001, deadline - time.monotonic()))
+            received = connection.recv_into(destination[offset:])
+            if not received:
+                raise ConnectionError(
+                    f"byte stream ended after {offset} of {len(destination)} bytes"
+                )
+            offset += received
+
+
+async def _write_stream_chunk(
+    writer: asyncio.StreamWriter, source: bytes | memoryview
+) -> None:
+    for offset in range(0, len(source), _STREAM_CHUNK_BYTES):
+        writer.write(source[offset : offset + _STREAM_CHUNK_BYTES])
+        await writer.drain()
+
+
+def _flatten_packed_tensors(
+    tensors: Any,
+) -> tuple[list[tuple[str, Any]], dict[str, Any]]:
+    import torch
+
+    required = (
+        "tokens",
+        "group_ids",
+        "parent_ids",
+        "input_pos",
+        "assistant_mask",
+        "logprobs",
+        "advantages",
+        "weights",
+    )
+    flat: list[tuple[str, Any]] = []
+    for name in required:
+        tensor = tensors[name]
+        _validate_tensor(name, tensor, torch)
+        flat.append((name, tensor))
+    shape = tuple(tensors["tokens"].shape)
+    if len(shape) != 2 or any(tuple(tensors[name].shape) != shape for name in required):
+        raise ValueError(
+            "core packed tensors must share [num_sequences, sequence_length]"
+        )
+    for list_name in ("pixel_values", "image_grid_thw"):
+        for index, tensor in enumerate(tensors[list_name]):
+            if tensor is not None:
+                _validate_tensor(f"{list_name}/{index}", tensor, torch)
+                flat.append((f"{list_name}/{index}", tensor))
+    original = tensors.get("original_logprobs")
+    if original is not None:
+        _validate_tensor("original_logprobs", original, torch)
+        if tuple(original.shape) != shape:
+            raise ValueError("original_logprobs must match the core packed shape")
+        flat.append(("original_logprobs", original))
+    replay = tensors.get("moe_routing_replay")
+    replay_spec = None
+    if replay is not None:
+        tensor = replay.expert_indices
+        _validate_tensor("moe_routing_replay/expert_indices", tensor, torch)
+        flat.append(("moe_routing_replay/expert_indices", tensor))
+        replay_spec = MoeRoutingReplaySpec(
+            num_layers=replay.num_layers,
+            topk=replay.topk,
+            num_experts=replay.num_experts,
+            packed_tokens=replay.pack_stats.packed_tokens,
+        )
+    return flat, {
+        "num_sequences": shape[0],
+        "sequence_length": shape[1],
+        "pixel_values_present": tuple(x is not None for x in tensors["pixel_values"]),
+        "image_grid_thw_present": tuple(
+            x is not None for x in tensors["image_grid_thw"]
+        ),
+        "moe_routing_replay": replay_spec,
+        "prefix_tree_packing_stats": tensors.get("prefix_tree_packing_stats"),
+    }
+
+
+def _validate_tensor(name: str, tensor: Any, torch: Any) -> None:
+    if not isinstance(tensor, torch.Tensor):
+        raise TypeError(f"{name} must be a torch.Tensor")
+    if tensor.device.type != "cpu" or not tensor.is_contiguous():
+        raise ValueError(f"{name} must be a contiguous CPU tensor")
+
+
+def _layout(flat: list[tuple[str, Any]]) -> tuple[tuple[TensorSpec, ...], int]:
+    offset = 0
+    specs = []
+    for name, tensor in flat:
+        element_size = tensor.element_size()
+        offset = (offset + element_size - 1) // element_size * element_size
+        byte_count = tensor.numel() * element_size
+        specs.append(
+            TensorSpec(
+                name=name,
+                dtype=str(tensor.dtype).removeprefix("torch."),
+                shape=tuple(tensor.shape),
+                offset=offset,
+                byte_count=byte_count,
+            )
+        )
+        offset += byte_count
+    return tuple(specs), max(offset, 1)
+
+
+def _tensor_from_buffer(buffer: memoryview, spec: TensorSpec) -> Any:
+    import torch
+
+    dtype = getattr(torch, spec.dtype, None)
+    if not isinstance(dtype, torch.dtype):
+        raise ValueError(f"unsupported tensor dtype {spec.dtype!r}")
+    return torch.frombuffer(
+        buffer, dtype=dtype, count=_numel(spec.shape), offset=spec.offset
+    ).reshape(spec.shape)
+
+
+def _shm_buffer(shm: shared_memory.SharedMemory) -> memoryview:
+    buffer = shm.buf
+    if buffer is None:
+        raise RuntimeError("shared-memory buffer is closed")
+    return buffer
+
+
+def _numel(shape: tuple[int, ...]) -> int:
+    result = 1
+    for dimension in shape:
+        if dimension < 0:
+            raise ValueError("tensor dimensions must be non-negative")
+        result *= dimension
+    return result
+
+
+def _logical_ref(ref: PackedBatchRef) -> dict[str, Any]:
+    return ref.model_dump(
+        exclude={
+            "owner_actor_id",
+            "lease_id",
+            "shared_memory_name",
+            "owner_process_id",
+        }
+    )
+
+
+def _unflatten_packed_tensors(flat: dict[str, Any], ref: PackedBatchRef) -> Any:
+    from art.preprocessing.moe_routing import (
+        MoeRoutingPackStats,
+        PackedMoeRoutingReplay,
+    )
+
+    tensors: dict[str, Any] = {
+        name: flat[name]
+        for name in (
+            "tokens",
+            "group_ids",
+            "parent_ids",
+            "input_pos",
+            "assistant_mask",
+            "logprobs",
+            "advantages",
+            "weights",
+        )
+    }
+    for name, present in (
+        ("pixel_values", ref.pixel_values_present),
+        ("image_grid_thw", ref.image_grid_thw_present),
+    ):
+        tensors[name] = [
+            flat[f"{name}/{index}"] if value else None
+            for index, value in enumerate(present)
+        ]
+    replay = ref.moe_routing_replay
+    tensors["moe_routing_replay"] = (
+        PackedMoeRoutingReplay(
+            expert_indices=flat["moe_routing_replay/expert_indices"],
+            num_experts=replay.num_experts,
+            pack_stats=MoeRoutingPackStats(packed_tokens=replay.packed_tokens),
+        )
+        if replay is not None
+        else None
+    )
+    if "original_logprobs" in flat:
+        tensors["original_logprobs"] = flat["original_logprobs"]
+    if ref.prefix_tree_packing_stats is not None:
+        tensors["prefix_tree_packing_stats"] = (
+            ref.prefix_tree_packing_stats.model_dump()
+        )
+    return tensors
diff --git a/src/art/distributed/etcd_runtime.py b/src/art/distributed/etcd_runtime.py
new file mode 100644
index 000000000..1d0bf778b
--- /dev/null
+++ b/src/art/distributed/etcd_runtime.py
@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+import fcntl
+import hashlib
+import json
+import os
+from pathlib import Path
+import shutil
+import signal
+import socket
+import subprocess
+import tarfile
+import tempfile
+import time
+from urllib.request import Request, urlopen
+
+from art.utils.cache_dirs import configure_model_cache_env
+
+from .specs import EndpointSpec
+
+ETCD_VERSION = "3.5.33"
+ETCD_SHA256 = "5025b5b24d81a9616b6e284ccd439b9a3df055ef8fdcdc142af3ec8f6a3b3c95"
+ETCD_URL = (
+    "https://github.com/etcd-io/etcd/releases/download/"
+    f"v{ETCD_VERSION}/etcd-v{ETCD_VERSION}-linux-amd64.tar.gz"
+)
+
+
+def _sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as source:
+        for chunk in iter(lambda: source.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def ensure_etcd() -> Path:
+    root = configure_model_cache_env() / "native" / f"etcd-{ETCD_VERSION}"
+    executable = root / "etcd"
+    root.parent.mkdir(parents=True, exist_ok=True)
+    with (root.parent / f".{root.name}.lock").open("w") as lock:
+        fcntl.flock(lock, fcntl.LOCK_EX)
+        if executable.is_file() and os.access(executable, os.X_OK):
+            return executable
+        if root.exists():
+            raise RuntimeError(f"Refusing to replace invalid etcd cache: {root}")
+        archive = root.parent / f"etcd-{ETCD_VERSION}.tar.gz"
+        if not archive.is_file() or _sha256(archive) != ETCD_SHA256:
+            with tempfile.NamedTemporaryFile(
+                dir=root.parent, prefix=f".{archive.name}.", delete=False
+            ) as output:
+                partial = Path(output.name)
+                try:
+                    request = Request(ETCD_URL, headers={"User-Agent": "openpipe-art"})
+                    with urlopen(request, timeout=60) as response:
+                        shutil.copyfileobj(response, output)
+                except BaseException:
+                    partial.unlink(missing_ok=True)
+                    raise
+            if _sha256(partial) != ETCD_SHA256:
+                partial.unlink(missing_ok=True)
+                raise RuntimeError("Downloaded etcd archive failed checksum validation")
+            partial.replace(archive)
+        stage = Path(tempfile.mkdtemp(prefix=f".{root.name}.", dir=root.parent))
+        try:
+            member_name = f"etcd-v{ETCD_VERSION}-linux-amd64/etcd"
+            with tarfile.open(archive) as tar:
+                member = tar.getmember(member_name)
+                if not member.isfile() or member.size <= 0:
+                    raise RuntimeError("Pinned etcd archive has an invalid executable")
+                source = tar.extractfile(member)
+                if source is None:
+                    raise RuntimeError("Pinned etcd archive is missing its executable")
+                with (stage / "etcd").open("wb") as destination:
+                    shutil.copyfileobj(source, destination)
+            (stage / "etcd").chmod(0o755)
+            stage.rename(root)
+        finally:
+            if stage.exists():
+                shutil.rmtree(stage)
+    return executable
+
+
+def _free_port() -> int:
+    with socket.socket() as listener:
+        listener.bind(("", 0))
+        return int(listener.getsockname()[1])
+
+
+def _healthy(endpoint: EndpointSpec, timeout_s: float) -> bool:
+    try:
+        with urlopen(f"{endpoint.url}/health", timeout=timeout_s) as response:
+            return json.loads(response.read()).get("health") in (True, "true")
+    except (json.JSONDecodeError, OSError):
+        return False
+
+
+class ManagedEtcd:
+    def __init__(
+        self,
+        process: subprocess.Popen[bytes],
+        endpoint: EndpointSpec,
+        data_dir: Path,
+    ) -> None:
+        self.process = process
+        self.endpoint = endpoint
+        self.data_dir = data_dir
+
+    @classmethod
+    def start(
+        cls, *, advertise_host: str, runtime_id: str, timeout_s: float
+    ) -> ManagedEtcd:
+        executable = ensure_etcd()
+        client_port, peer_port = _free_port(), _free_port()
+        while peer_port == client_port:
+            peer_port = _free_port()
+        endpoint = EndpointSpec(host=advertise_host, port=client_port)
+        data_dir = Path(tempfile.mkdtemp(prefix=f"art-etcd-{runtime_id[:12]}-"))
+        name = f"art-{runtime_id[:24]}"
+        peer_url = f"http://127.0.0.1:{peer_port}"
+        try:
+            with (data_dir / "etcd.log").open("wb") as log:
+                process = subprocess.Popen(
+                    [
+                        str(executable),
+                        "--name",
+                        name,
+                        "--data-dir",
+                        str(data_dir / "data"),
+                        "--listen-client-urls",
+                        f"http://0.0.0.0:{client_port}",
+                        "--advertise-client-urls",
+                        endpoint.url,
+                        "--listen-peer-urls",
+                        peer_url,
+                        "--initial-advertise-peer-urls",
+                        peer_url,
+                        "--initial-cluster",
+                        f"{name}={peer_url}",
+                        "--initial-cluster-state",
+                        "new",
+                        "--logger",
+                        "zap",
+                        "--log-level",
+                        "warn",
+                    ],
+                    stdout=log,
+                    stderr=subprocess.STDOUT,
+                    start_new_session=True,
+                )
+        except BaseException:
+            shutil.rmtree(data_dir)
+            raise
+        deadline = time.monotonic() + timeout_s
+        while time.monotonic() < deadline:
+            if process.poll() is not None:
+                detail = (data_dir / "etcd.log").read_text(errors="replace")[-4000:]
+                shutil.rmtree(data_dir)
+                raise RuntimeError(
+                    f"managed etcd exited {process.returncode}:\n{detail}"
+                )
+            if _healthy(endpoint, min(0.2, max(0.01, deadline - time.monotonic()))):
+                return cls(process, endpoint, data_dir)
+            time.sleep(0.05)
+        instance = cls(process, endpoint, data_dir)
+        instance.close()
+        raise TimeoutError(f"managed etcd did not become healthy at {endpoint.url}")
+
+    def close(self) -> None:
+        if self.process.poll() is None:
+            try:
+                os.killpg(self.process.pid, signal.SIGTERM)
+                self.process.wait(timeout=5)
+            except ProcessLookupError:
+                pass
+            except subprocess.TimeoutExpired:
+                os.killpg(self.process.pid, signal.SIGKILL)
+                self.process.wait()
+        shutil.rmtree(self.data_dir, ignore_errors=True)
diff --git a/src/art/distributed/host_admission.py b/src/art/distributed/host_admission.py
new file mode 100644
index 000000000..7eeda07af
--- /dev/null
+++ b/src/art/distributed/host_admission.py
@@ -0,0 +1,449 @@
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+import csv
+import hashlib
+from importlib import metadata
+import json
+import os
+from pathlib import Path
+import platform
+import re
+import shutil
+import socket
+import subprocess
+import sys
+from typing import Annotated, Literal
+from uuid import UUID
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from .specs import CUDA_DEVICE_UUID_PATTERN, GpuId, HostServiceHealth, HostSpec
+
+_SCHEMA = "art-host-runtime-v1"
+_SHA256 = r"^[0-9a-f]{64}$"
+_BOOT_ID_PATH = Path("/proc/sys/kernel/random/boot_id")
+_BASE_PACKAGES = ("openpipe-art", "pydantic", "torchmonarch")
+_TRAINER_PACKAGES = (
+    "flash-attn-4",
+    "megatron-bridge",
+    "megatron-core",
+    "numpy",
+    "torch",
+    "transformer_engine",
+    "transformer_engine_torch",
+    "transformers",
+    "triton",
+)
+_RUNTIME_ENV = {
+    "ART_DISABLE_MEGATRON_COMPILE",
+    "ART_MEGATRON_ALLOW_UNVALIDATED_ARCH",
+    "ART_MEGATRON_ENABLE_MOE_ROUTING_REPLAY",
+    "ART_MEGATRON_OFFLOAD_BETWEEN_JOBS",
+    "ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD",
+    "ART_VLLM_RUNTIME_BIN",
+    "CUDA_DEVICE_MAX_CONNECTIONS",
+    "CUDA_LAUNCH_BLOCKING",
+    "CUDA_MODULE_LOADING",
+    "NCCL_ALGO",
+    "NCCL_DEBUG",
+    "NCCL_IB_DISABLE",
+    "NCCL_IB_GID_INDEX",
+    "NCCL_IB_HCA",
+    "NCCL_NET",
+    "NCCL_NET_PLUGIN",
+    "NCCL_NVLS_ENABLE",
+    "NCCL_P2P_DISABLE",
+    "NCCL_PROTO",
+    "NCCL_SOCKET_IFNAME",
+    "NVTE_ALLOW_NONDETERMINISTIC_ALGO",
+    "NVTE_FLASH_ATTN",
+    "NVTE_FUSED_ATTN",
+    "PYTORCH_ALLOC_CONF",
+    "PYTORCH_CUDA_ALLOC_CONF",
+    "TORCH_CUDA_ARCH_LIST",
+    "TORCH_NCCL_ASYNC_ERROR_HANDLING",
+    "TORCH_NCCL_BLOCKING_WAIT",
+    "VLLM_USE_V1",
+    "VLLM_WORKER_MULTIPROC_METHOD",
+}
+
+
+class _Contract(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class GpuIdentity(_Contract):
+    index: int = Field(ge=0)
+    uuid: str = Field(pattern=CUDA_DEVICE_UUID_PATTERN)
+    parent_uuid: str = Field(
+        pattern=r"^GPU-[0-9A-Fa-f]{8}(?:-[0-9A-Fa-f]{4}){3}-[0-9A-Fa-f]{12}$"
+    )
+    pci_bus_id: str = Field(
+        pattern=r"^(?:[0-9A-F]{4}|[0-9A-F]{8}):[0-9A-F]{2}:[0-9A-F]{2}\.[0-7]$"
+    )
+
+    @property
+    def is_mig(self) -> bool:
+        return self.uuid.startswith("MIG-")
+
+
+class RuntimeFingerprint(_Contract):
+    schema_version: Literal["art-host-runtime-v1"] = _SCHEMA
+    art_build_sha256: str = Field(pattern=_SHA256)
+    python: str = Field(min_length=1)
+    platform: str = Field(min_length=1)
+    packages: tuple[tuple[str, str], ...]
+    environment: tuple[tuple[str, str], ...]
+    sha256: str = Field(pattern=_SHA256)
+
+    @model_validator(mode="after")
+    def _validate_digest(self) -> RuntimeFingerprint:
+        manifest = self.model_dump(mode="json", exclude={"sha256"})
+        if self.sha256 != _json_sha256(manifest):
+            raise ValueError("runtime fingerprint digest does not match its manifest")
+        return self
+
+
+class HostAdmissionRequest(_Contract):
+    host_id: str = Field(min_length=1)
+    node_rank: int = Field(ge=0)
+    expected_gpu_ids: tuple[GpuId, ...]
+    runtime_packages: tuple[Annotated[str, Field(min_length=1)], ...]
+
+
+class HostAdmissionReport(HostServiceHealth):
+    node_rank: int = Field(ge=0)
+    boot_id: str = Field(
+        pattern=r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$"
+    )
+    assigned_gpus: tuple[GpuIdentity, ...]
+    nvidia_driver_version: str | None = Field(
+        default=None, pattern=r"^[0-9]+(?:\.[0-9]+)*$"
+    )
+    runtime: RuntimeFingerprint
+
+
+def runtime_package_names(*, trainer: bool) -> tuple[str, ...]:
+    return tuple(sorted((*_BASE_PACKAGES, *(_TRAINER_PACKAGES if trainer else ()))))
+
+
+def build_runtime_fingerprint(
+    package_names: Sequence[str] = _BASE_PACKAGES,
+) -> RuntimeFingerprint:
+    libc = platform.libc_ver()
+    values = {
+        "schema_version": _SCHEMA,
+        "art_build_sha256": _art_build_sha256(),
+        "python": f"{platform.python_implementation()}-{platform.python_version()}-"
+        f"{sys.implementation.cache_tag}",
+        "platform": f"{platform.system()}-{platform.machine()}-{libc[0]}-{libc[1]}",
+        "packages": tuple((name, metadata.version(name)) for name in package_names),
+        "environment": _runtime_environment(os.environ),
+    }
+    return RuntimeFingerprint(**values, sha256=_json_sha256(values))
+
+
+def inspect_host(request: HostAdmissionRequest) -> HostAdmissionReport:
+    runtime = build_runtime_fingerprint(request.runtime_packages)
+    inventory: dict[int | str, tuple[GpuIdentity, str]] = {}
+    include_mig = any(
+        isinstance(gpu_id, str) and gpu_id.startswith("MIG-")
+        for gpu_id in request.expected_gpu_ids
+    )
+    for gpu, driver in (
+        _query_gpu_inventory(include_mig=include_mig)
+        if request.expected_gpu_ids
+        else ()
+    ):
+        if not gpu.is_mig:
+            inventory[gpu.index] = (gpu, driver)
+        inventory[gpu.uuid.casefold()] = (gpu, driver)
+    expected = tuple(
+        gpu_id.casefold() if isinstance(gpu_id, str) else gpu_id
+        for gpu_id in request.expected_gpu_ids
+    )
+    missing = [gpu_id for gpu_id in expected if gpu_id not in inventory]
+    if missing:
+        raise RuntimeError(
+            f"host {request.host_id!r} is missing configured CUDA devices {missing}; "
+            f"nvidia-smi reported {sorted(map(str, inventory))}"
+        )
+    assigned = tuple(inventory[gpu_id][0] for gpu_id in expected)
+    _require_unique(
+        "assigned CUDA device UUIDs",
+        [
+            (gpu.uuid.casefold(), request.expected_gpu_ids[index])
+            for index, gpu in enumerate(assigned)
+        ],
+    )
+    drivers = {inventory[gpu_id][1] for gpu_id in expected}
+    if len(drivers) > 1:
+        raise RuntimeError(f"host {request.host_id!r} has multiple NVIDIA drivers")
+    hostname = socket.gethostname().strip()
+    if not hostname:
+        raise RuntimeError("host returned an empty hostname")
+    return HostAdmissionReport(
+        host_id=request.host_id,
+        node_rank=request.node_rank,
+        hostname=hostname,
+        boot_id=_read_boot_id(),
+        process_id=os.getpid(),
+        assigned_gpus=assigned,
+        nvidia_driver_version=next(iter(drivers), None),
+        runtime=runtime,
+    )
+
+
+def validate_host_admission(
+    hosts: Sequence[HostSpec],
+    reports: Sequence[HostAdmissionReport],
+    *,
+    expected_runtime: RuntimeFingerprint,
+) -> dict[str, HostAdmissionReport]:
+    expected = {host.host_id: host for host in hosts}
+    actual = {report.host_id: report for report in reports}
+    if len(actual) != len(reports) or actual.keys() != expected.keys():
+        raise RuntimeError(
+            f"host-service membership mismatch: expected={sorted(expected)} "
+            f"actual={sorted(actual)}"
+        )
+    controller_contract = expected_runtime.model_dump(exclude={"environment", "sha256"})
+    for host_id, host in expected.items():
+        report = actual[host_id]
+        if report.node_rank != host.node_rank:
+            raise RuntimeError(f"host {host_id!r} reported an unexpected node rank")
+        if len(report.assigned_gpus) != len(host.gpu_ids) or any(
+            not _matches_gpu_id(expected_gpu, gpu)
+            for expected_gpu, gpu in zip(
+                host.gpu_ids, report.assigned_gpus, strict=True
+            )
+        ):
+            raise RuntimeError(f"host {host_id!r} reported unexpected CUDA devices")
+        host_contract = report.runtime.model_dump(exclude={"environment", "sha256"})
+        if host_contract != controller_contract:
+            fields = sorted(
+                name
+                for name, value in controller_contract.items()
+                if host_contract[name] != value
+            )
+            raise RuntimeError(
+                f"host {host_id!r} runtime contract differs from controller: {fields}"
+            )
+    runtime_digests = {report.runtime.sha256 for report in actual.values()}
+    if len(runtime_digests) > 1:
+        detail = " ".join(
+            f"{host_id}={report.runtime.sha256}" for host_id, report in actual.items()
+        )
+        raise RuntimeError(f"runtime fingerprints differ across hosts: {detail}")
+    drivers = {
+        report.nvidia_driver_version
+        for report in actual.values()
+        if report.nvidia_driver_version is not None
+    }
+    if len(drivers) > 1:
+        raise RuntimeError(f"NVIDIA driver versions differ across hosts: {drivers}")
+    _require_unique(
+        "physical host boot IDs",
+        [(report.boot_id, host_id) for host_id, report in actual.items()],
+    )
+    _require_unique(
+        "GPU UUIDs",
+        [
+            (gpu.uuid.casefold(), f"{host_id}:{gpu.index}")
+            for host_id, report in actual.items()
+            for gpu in report.assigned_gpus
+        ],
+    )
+    _require_unique(
+        "physical GPU PCI identities",
+        [
+            (f"{report.boot_id}/{gpu.pci_bus_id}", f"{host_id}:{gpu.index}")
+            for host_id, report in actual.items()
+            for gpu in report.assigned_gpus
+            if not gpu.is_mig
+        ],
+    )
+    for host_id, report in actual.items():
+        full_gpus = {
+            gpu.uuid.casefold() for gpu in report.assigned_gpus if not gpu.is_mig
+        }
+        if conflicts := [
+            gpu.uuid
+            for gpu in report.assigned_gpus
+            if gpu.is_mig and gpu.parent_uuid.casefold() in full_gpus
+        ]:
+            raise RuntimeError(
+                f"host {host_id!r} assigns both a physical GPU and its MIG device: "
+                f"{conflicts}"
+            )
+    return actual
+
+
+def _query_gpu_inventory(
+    *, include_mig: bool = False
+) -> tuple[tuple[GpuIdentity, str], ...]:
+    executable = shutil.which("nvidia-smi")
+    if executable is None:
+        raise RuntimeError("nvidia-smi is required for GPU host admission")
+    result = _run_nvidia_smi(
+        executable,
+        "--query-gpu=index,uuid,pci.bus_id,driver_version",
+        "--format=csv,noheader,nounits",
+    )
+    rows: list[tuple[GpuIdentity, str]] = []
+    for line_number, row in enumerate(csv.reader(result.stdout.splitlines()), start=1):
+        values = tuple(value.strip() for value in row)
+        try:
+            if len(values) != 4:
+                raise ValueError(f"expected 4 fields, received {len(values)}")
+            gpu = GpuIdentity(
+                index=int(values[0]),
+                uuid=values[1],
+                parent_uuid=values[1],
+                pci_bus_id=values[2].upper(),
+            )
+        except ValueError as error:
+            raise RuntimeError(
+                f"invalid nvidia-smi row {line_number}: {error}"
+            ) from None
+        rows.append((gpu, values[3]))
+    _require_unique(
+        "nvidia-smi GPU indices", [(gpu.index, gpu.uuid) for gpu, _ in rows]
+    )
+    _require_unique(
+        "nvidia-smi GPU UUIDs", [(gpu.uuid.casefold(), gpu.index) for gpu, _ in rows]
+    )
+    _require_unique(
+        "nvidia-smi PCI identities", [(gpu.pci_bus_id, gpu.index) for gpu, _ in rows]
+    )
+    if not include_mig:
+        return tuple(rows)
+    parents = {gpu.index: (gpu, driver) for gpu, driver in rows}
+    listed_parent: tuple[GpuIdentity, str] | None = None
+    for line_number, line in enumerate(
+        _run_nvidia_smi(executable, "-L").stdout.splitlines(), start=1
+    ):
+        if line.startswith("GPU "):
+            match = re.fullmatch(r"GPU ([0-9]+): .* \(UUID: (GPU-[^)]+)\)", line)
+            if match is None:
+                raise RuntimeError(
+                    f"invalid nvidia-smi -L GPU row {line_number}: {line!r}"
+                )
+            listed_parent = parents.get(int(match[1]))
+            if (
+                listed_parent is None
+                or listed_parent[0].uuid.casefold() != match[2].casefold()
+            ):
+                raise RuntimeError(
+                    f"nvidia-smi -L GPU row {line_number} disagrees with inventory"
+                )
+            continue
+        if not line.lstrip().startswith("MIG "):
+            continue
+        match = re.fullmatch(r"\s+MIG .* \(UUID: (MIG-[^)]+)\)", line)
+        if match is None or listed_parent is None:
+            raise RuntimeError(f"invalid nvidia-smi -L MIG row {line_number}: {line!r}")
+        parent, driver = listed_parent
+        try:
+            mig = GpuIdentity(
+                index=parent.index,
+                uuid=match[1],
+                parent_uuid=parent.uuid,
+                pci_bus_id=parent.pci_bus_id,
+            )
+        except ValueError as error:
+            raise RuntimeError(
+                f"invalid nvidia-smi -L MIG row {line_number}: {error}"
+            ) from None
+        rows.append((mig, driver))
+    _require_unique(
+        "nvidia-smi CUDA UUIDs",
+        [(gpu.uuid.casefold(), gpu.index) for gpu, _ in rows],
+    )
+    return tuple(rows)
+
+
+def _matches_gpu_id(gpu_id: GpuId, identity: GpuIdentity) -> bool:
+    if isinstance(gpu_id, int):
+        return not identity.is_mig and identity.index == gpu_id
+    return identity.uuid.casefold() == gpu_id.casefold()
+
+
+def _run_nvidia_smi(
+    executable: str, *arguments: str
+) -> subprocess.CompletedProcess[str]:
+    try:
+        result = subprocess.run(
+            (executable, *arguments),
+            capture_output=True,
+            check=False,
+            text=True,
+            timeout=30,
+        )
+    except (OSError, subprocess.TimeoutExpired) as error:
+        raise RuntimeError(f"nvidia-smi GPU identity query failed: {error}") from None
+    if result.returncode:
+        detail = result.stderr.strip() or result.stdout.strip() or "no output"
+        raise RuntimeError(f"nvidia-smi exited {result.returncode}: {detail}")
+    return result
+
+
+def _art_build_sha256(root: Path | None = None) -> str:
+    root = root or Path(__file__).resolve().parents[1]
+    files = sorted(
+        path
+        for path in root.rglob("*")
+        if path.is_file()
+        and not any(part.startswith(".") for part in path.relative_to(root).parts)
+        and path.suffix not in {".pyc", ".pyo"}
+    )
+    if not files:
+        raise RuntimeError(f"ART package root {root} contains no build files")
+    digest = hashlib.sha256()
+    for path in files:
+        _update_digest(digest, path.relative_to(root).as_posix().encode())
+        with path.open("rb") as handle:
+            _update_digest(digest, handle.read())
+    return digest.hexdigest()
+
+
+def _runtime_environment(
+    environment: Mapping[str, str],
+) -> tuple[tuple[str, str], ...]:
+    return tuple(
+        sorted(
+            (name, environment[name])
+            for name in _RUNTIME_ENV & environment.keys()
+            if environment[name]
+        )
+    )
+
+
+def _read_boot_id() -> str:
+    try:
+        return str(UUID(_BOOT_ID_PATH.read_text(encoding="ascii").strip()))
+    except (OSError, ValueError) as error:
+        raise RuntimeError(
+            f"cannot read Linux physical host boot ID: {error}"
+        ) from None
+
+
+def _require_unique(name: str, values: Sequence[tuple[object, object]]) -> None:
+    owners: dict[object, object] = {}
+    for value, owner in values:
+        if value in owners:
+            raise RuntimeError(
+                f"duplicate {name}: {value!r} belongs to {owners[value]!r} and {owner!r}"
+            )
+        owners[value] = owner
+
+
+def _json_sha256(value: object) -> str:
+    payload = json.dumps(value, separators=(",", ":"), sort_keys=True).encode()
+    return hashlib.sha256(payload).hexdigest()
+
+
+def _update_digest(digest: hashlib._Hash, value: bytes) -> None:
+    digest.update(len(value).to_bytes(8, "big"))
+    digest.update(value)
diff --git a/src/art/distributed/launch.py b/src/art/distributed/launch.py
new file mode 100644
index 000000000..6431f3a9c
--- /dev/null
+++ b/src/art/distributed/launch.py
@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from .specs import (
+    ClusterSpec,
+    GpuId,
+    HostSpec,
+    NcclTransportSpec,
+    NixlTransportSpec,
+)
+
+
+class ArtLaunchContext(BaseModel):
+    """Provider-neutral resources owned by an ``art-monarch`` invocation."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid", frozen=True)
+
+    host_mesh: Any = Field(exclude=True)
+    worker_addresses: tuple[str, ...]
+    controller_rank: int = Field(default=0, ge=0)
+
+    @model_validator(mode="after")
+    def _validate_hosts(self) -> "ArtLaunchContext":
+        if not self.worker_addresses:
+            raise ValueError("worker_addresses must not be empty")
+        if len(set(self.worker_addresses)) != len(self.worker_addresses):
+            raise ValueError("worker_addresses must be unique")
+        if self.controller_rank >= len(self.worker_addresses):
+            raise ValueError("controller_rank must identify a worker address")
+        return self
+
+    @property
+    def host_count(self) -> int:
+        return len(self.worker_addresses)
+
+    def homogeneous_cluster(
+        self,
+        *,
+        cpu_slots: int,
+        gpu_ids: tuple[GpuId, ...] = (),
+        artifact_root: str | None = None,
+        cache_root: str | None = None,
+        nccl_transport: NcclTransportSpec | None = None,
+        nixl_transport: NixlTransportSpec | None = None,
+        startup_timeout_s: float = 600.0,
+        rpc_timeout_s: float = 60.0,
+    ) -> ClusterSpec:
+        host_ids = tuple(f"host{rank}" for rank in range(self.host_count))
+        return ClusterSpec(
+            hosts=tuple(
+                HostSpec(
+                    host_id=host_id,
+                    node_rank=rank,
+                    worker_address=address,
+                    cpu_slots=cpu_slots,
+                    gpu_ids=gpu_ids,
+                )
+                for rank, (host_id, address) in enumerate(
+                    zip(host_ids, self.worker_addresses, strict=True)
+                )
+            ),
+            controller_host_id=host_ids[self.controller_rank],
+            artifact_root=artifact_root,
+            cache_root=cache_root,
+            nccl_transport=nccl_transport,
+            nixl_transport=nixl_transport,
+            startup_timeout_s=startup_timeout_s,
+            rpc_timeout_s=rpc_timeout_s,
+        )
diff --git a/src/art/distributed/monarch_actor.py b/src/art/distributed/monarch_actor.py
new file mode 100644
index 000000000..181872042
--- /dev/null
+++ b/src/art/distributed/monarch_actor.py
@@ -0,0 +1,776 @@
+from __future__ import annotations
+
+import asyncio
+from collections import OrderedDict
+from functools import partial, wraps
+import json
+import os
+from pathlib import Path
+import socket
+import time
+import traceback
+from typing import Any, Literal
+from urllib.request import urlopen
+
+# This module is imported only by explicit distributed runtime construction.
+from monarch.actor import (  # ty: ignore[unresolved-import]
+    Actor,
+    concurrent_endpoint,
+    endpoint,
+)
+
+from art.megatron.runtime.managed import MegatronRuntimeInfo
+from art.utils.lifecycle import complete_task, complete_to_thread
+
+from .adapter_transport import AdapterSnapshotReceiver
+from .artifact_preflight import (
+    ArtifactProbeCommand,
+    ArtifactProbeResult,
+    execute_artifact_probe,
+)
+from .data_plane import (
+    ByteStreamServerLoop,
+    PackedBatchCapacityError,
+    PackedBatchInbox,
+    PackedBatchLeaseError,
+    PackedBatchPublisher,
+    PackedBatchRef,
+    PackedBatchTransfer,
+)
+from .host_admission import (
+    HostAdmissionReport,
+    HostAdmissionRequest,
+    inspect_host,
+)
+from .monarch_runtime import RemoteCallError, RemoteCallResult
+from .nccl_preflight import (
+    NcclPreflightSessionRequest,
+    NcclProbeRequest,
+    NcclProbeResult,
+    NcclRendezvous,
+    NcclRendezvousRequest,
+    NcclRendezvousResult,
+    run_nccl_probe,
+    start_nccl_rendezvous,
+)
+from .packing import PackingRequest, PackingResult
+from .rollout import RolloutInvocation, RolloutResult
+from .specs import HostServiceHealth
+from .trajectory_store import (
+    TrajectoryCapacityError,
+    TrajectoryEnqueueResult,
+    TrajectoryGroupRef,
+    TrajectoryLeaseError,
+    TrajectoryQueueItem,
+    TrajectoryQueuePacking,
+    TrajectoryQueueRelease,
+    TrajectoryQueueResize,
+    TrajectoryQueueSnapshot,
+    TrajectoryQueueStore,
+    TrajectoryQueueTake,
+    publish_trajectory_bundles,
+)
+from .vllm_replica import HostMemberLaunchRequest
+
+
+def _require_etcd_health(url: str, timeout_s: float) -> None:
+    with urlopen(f"{url}/health", timeout=timeout_s) as response:
+        health = json.load(response).get("health")
+    if health not in (True, "true"):
+        raise RuntimeError(f"etcd health check failed: {health!r}")
+
+
+def resilient_endpoint(function: Any = None, *, concurrent: bool = False) -> Any:
+    if function is None:
+        return partial(resilient_endpoint, concurrent=concurrent)
+
+    @wraps(function)
+    async def wrapped(*args: Any, **kwargs: Any) -> RemoteCallResult:
+        try:
+            return RemoteCallResult(value=await function(*args, **kwargs))
+        except (KeyboardInterrupt, SystemExit, GeneratorExit):
+            raise
+        except BaseException as error:
+            from art.errors import LocalServingUnavailableError
+
+            if isinstance(error, asyncio.CancelledError):
+                kind = "cancelled"
+            elif isinstance(error, LocalServingUnavailableError):
+                kind = "serving"
+            elif isinstance(error, PackedBatchCapacityError | TrajectoryCapacityError):
+                kind = "capacity"
+            elif isinstance(error, PackedBatchLeaseError | TrajectoryLeaseError):
+                kind = "lease"
+            elif isinstance(error, (TypeError, ValueError)):
+                kind = "input"
+            else:
+                kind = "internal"
+            return RemoteCallResult(
+                error=RemoteCallError(
+                    kind=kind,
+                    error_type=type(error).__name__,
+                    message=str(error) or type(error).__name__,
+                    traceback=traceback.format_exc(),
+                )
+            )
+
+    return (concurrent_endpoint if concurrent else endpoint)(wrapped)
+
+
+class AdapterTransferHostService(Actor):
+    """Adapter receiver isolated from packing in its own host process."""
+
+    def __init__(self, host_id: str, output_root: str) -> None:
+        self._receiver = AdapterSnapshotReceiver(host_id, output_root)
+
+    @resilient_endpoint
+    async def prepare(
+        self,
+        generation_id: str,
+        template_path: str,
+        timeout_s: float,
+        transport: Literal["local", "nixl"],
+    ):
+        return await asyncio.to_thread(
+            self._receiver.prepare,
+            generation_id,
+            template_path,
+            timeout_s,
+            transport,
+        )
+
+    @resilient_endpoint
+    async def poll(self, generation_id: str):
+        return await asyncio.to_thread(self._receiver.poll, generation_id)
+
+    @resilient_endpoint
+    async def release(self, generation_id: str) -> None:
+        await asyncio.to_thread(self._receiver.release, generation_id)
+
+    @resilient_endpoint
+    async def close(self) -> None:
+        await asyncio.to_thread(self._receiver.close)
+
+
+class ArtHostService(Actor):
+    """One ART control and data-plane service per host."""
+
+    def __init__(
+        self,
+        admission_json: str,
+        packed_batch_capacity_bytes: int,
+        vllm_output_root: str = "/tmp/art-vllm",
+        data_plane_host: str | None = None,
+    ) -> None:
+        admission = HostAdmissionRequest.model_validate_json(admission_json)
+        self.host_id = admission.host_id
+        self._admission = admission
+        self._admission_report: HostAdmissionReport | None = None
+        self._packed_batches = PackedBatchInbox(
+            host_id=self.host_id, capacity_bytes=packed_batch_capacity_bytes
+        )
+        self._batch_publishers: dict[str, PackedBatchPublisher] = {}
+        self._data_plane_host = data_plane_host or socket.gethostbyname(
+            socket.gethostname()
+        )
+        self._trajectory_queues: dict[str, TrajectoryQueueStore] = {}
+        self._packer = None
+        self._packing_lock = asyncio.Lock()
+        self._vllm_output_root = vllm_output_root
+        self._vllm_launcher = None
+        self._nccl_cleanups: dict[str, asyncio.Task[None]] = {}
+        self._nccl_rendezvous: dict[str, NcclRendezvous] = {}
+        self._nccl_sessions: dict[str, tuple[float, asyncio.Task[None]]] = {}
+        self._nccl_tasks: dict[str, asyncio.Task[Any]] = {}
+        self._cancelled_nccl_probes: set[str] = set()
+        self._megatron_runtimes: dict[tuple[bool, bool], MegatronRuntimeInfo] = {}
+        self._managed_etcd = None
+
+    @resilient_endpoint
+    async def admission(self) -> HostAdmissionReport:
+        if self._admission_report is None:
+            self._admission_report = await asyncio.to_thread(
+                inspect_host, self._admission
+            )
+        return self._admission_report
+
+    @resilient_endpoint
+    async def health(self) -> HostServiceHealth:
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        return HostServiceHealth(
+            host_id=self.host_id,
+            hostname=socket.gethostname(),
+            process_id=os.getpid(),
+        )
+
+    @resilient_endpoint
+    async def artifact_root_probe(
+        self, command: ArtifactProbeCommand
+    ) -> ArtifactProbeResult:
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        return await asyncio.to_thread(execute_artifact_probe, self.host_id, command)
+
+    @resilient_endpoint
+    async def nixl_metadata_store_health(self, url: str, timeout_s: float) -> str:
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        try:
+            await asyncio.to_thread(_require_etcd_health, url, timeout_s)
+        except BaseException as error:
+            raise RuntimeError(
+                f"host {self.host_id!r} cannot reach healthy NIXL metadata store {url}"
+            ) from error
+        return self.host_id
+
+    @resilient_endpoint
+    async def ensure_megatron_runtime(
+        self, require_hybrid_ep: bool, multinode: bool
+    ) -> MegatronRuntimeInfo:
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        key = (require_hybrid_ep, multinode)
+        if key not in self._megatron_runtimes:
+            from art.megatron.runtime.managed import ensure_megatron_runtime
+
+            self._megatron_runtimes[key] = await asyncio.to_thread(
+                ensure_megatron_runtime,
+                art_build_sha256=self._admission_report.runtime.art_build_sha256,
+                require_hybrid_ep=require_hybrid_ep,
+                multinode=multinode,
+            )
+        return self._megatron_runtimes[key]
+
+    @resilient_endpoint
+    async def start_nixl_metadata_store(self, runtime_id: str, timeout_s: float):
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        if self._managed_etcd is None:
+            from .etcd_runtime import ManagedEtcd
+
+            self._managed_etcd = await asyncio.to_thread(
+                ManagedEtcd.start,
+                advertise_host=self._data_plane_host,
+                runtime_id=runtime_id,
+                timeout_s=timeout_s,
+            )
+        return self._managed_etcd.endpoint
+
+    @resilient_endpoint
+    async def start_nccl_preflight_session(
+        self, request: NcclPreflightSessionRequest
+    ) -> None:
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        if request.probe_id in self._cancelled_nccl_probes:
+            raise asyncio.CancelledError
+        if request.probe_id in self._nccl_sessions:
+            raise RuntimeError(f"NCCL probe {request.probe_id!r} is already admitted")
+        deadline = time.monotonic() + request.lease_s
+        reaper = asyncio.create_task(
+            self._expire_nccl_preflight_session(request.probe_id, deadline)
+        )
+        self._nccl_sessions[request.probe_id] = (deadline, reaper)
+
+    @resilient_endpoint
+    async def nccl_preflight_rendezvous(
+        self, request: NcclRendezvousRequest
+    ) -> NcclRendezvousResult:
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        deadline = await self._require_nccl_probe(request.probe_id)
+        if request.probe_id in self._nccl_rendezvous:
+            raise RuntimeError(f"NCCL probe {request.probe_id!r} already has a store")
+        task = asyncio.create_task(start_nccl_rendezvous(request, deadline_s=deadline))
+        self._nccl_tasks[request.probe_id] = task
+        try:
+            async with asyncio.timeout(max(0.0, deadline - time.monotonic())):
+                rendezvous = await task
+        finally:
+            if self._nccl_tasks.get(request.probe_id) is task:
+                self._nccl_tasks.pop(request.probe_id)
+        if request.probe_id in self._cancelled_nccl_probes:
+            await rendezvous.close()
+            raise asyncio.CancelledError
+        self._nccl_rendezvous[request.probe_id] = rendezvous
+        return NcclRendezvousResult(host_id=self.host_id, port=rendezvous.port)
+
+    @resilient_endpoint
+    async def nccl_preflight(self, request: NcclProbeRequest) -> NcclProbeResult:
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        deadline = await self._require_nccl_probe(request.probe_id)
+        task = asyncio.create_task(run_nccl_probe(self.host_id, request))
+        self._nccl_tasks[request.probe_id] = task
+        try:
+            async with asyncio.timeout(max(0.0, deadline - time.monotonic())):
+                return await task
+        finally:
+            if self._nccl_tasks.get(request.probe_id) is task:
+                self._nccl_tasks.pop(request.probe_id)
+
+    @resilient_endpoint
+    async def cancel_nccl_preflight(self, probe_id: str) -> None:
+        await self._cancel_nccl_preflight(probe_id)
+
+    @resilient_endpoint
+    async def close(self) -> None:
+        probe_ids = tuple(
+            {
+                *self._nccl_cleanups,
+                *self._nccl_sessions,
+                *self._nccl_tasks,
+                *self._nccl_rendezvous,
+            }
+        )
+        await asyncio.gather(
+            *(self._cancel_nccl_preflight(probe_id) for probe_id in probe_ids)
+        )
+        for queue in self._trajectory_queues.values():
+            queue.close()
+        self._trajectory_queues.clear()
+        for batch_id in tuple(self._batch_publishers):
+            await self._drop_batch(batch_id)
+        async with self._packing_lock:
+            if self._packer is not None:
+                await self._packer.close()
+                self._packer = None
+        if self._vllm_launcher is not None:
+            await self._vllm_launcher.close()
+            self._vllm_launcher = None
+        if self._managed_etcd is not None:
+            await asyncio.to_thread(self._managed_etcd.close)
+            self._managed_etcd = None
+        self._packed_batches.store.close()
+
+    async def _require_nccl_probe(self, probe_id: str) -> float:
+        if probe_id in self._cancelled_nccl_probes:
+            raise asyncio.CancelledError
+        session = self._nccl_sessions.get(probe_id)
+        if session is None:
+            raise RuntimeError(f"NCCL probe {probe_id!r} has no active session")
+        deadline, _ = session
+        if time.monotonic() >= deadline:
+            await self._cancel_nccl_preflight(probe_id)
+            raise TimeoutError(f"NCCL probe {probe_id!r} session expired")
+        if probe_id in self._nccl_tasks:
+            raise RuntimeError(f"NCCL probe {probe_id!r} is already active")
+        return deadline
+
+    async def _cancel_nccl_preflight(self, probe_id: str) -> None:
+        self._cancelled_nccl_probes.add(probe_id)
+        cleanup = self._nccl_cleanups.get(probe_id)
+        if cleanup is None:
+            cleanup = asyncio.create_task(
+                self._cleanup_nccl_preflight(probe_id, asyncio.current_task())
+            )
+            self._nccl_cleanups[probe_id] = cleanup
+        try:
+            _, cancelled = await complete_task(cleanup)
+        finally:
+            if cleanup.done() and self._nccl_cleanups.get(probe_id) is cleanup:
+                self._nccl_cleanups.pop(probe_id)
+        if cancelled is not None:
+            raise cancelled
+
+    async def _cleanup_nccl_preflight(
+        self, probe_id: str, owner: asyncio.Task[Any] | None
+    ) -> None:
+        session = self._nccl_sessions.pop(probe_id, None)
+        if session is not None and session[1] is not owner:
+            session[1].cancel()
+            await asyncio.gather(session[1], return_exceptions=True)
+        task = self._nccl_tasks.pop(probe_id, None)
+        if task is not None:
+            task.cancel()
+            await asyncio.gather(task, return_exceptions=True)
+        rendezvous = self._nccl_rendezvous.pop(probe_id, None)
+        if rendezvous is not None:
+            await rendezvous.close()
+
+    async def _expire_nccl_preflight_session(
+        self, probe_id: str, deadline: float
+    ) -> None:
+        await asyncio.sleep(max(0.0, deadline - time.monotonic()))
+        if self._nccl_sessions.get(probe_id, (None,))[0] != deadline:
+            return
+        self._cancelled_nccl_probes.add(probe_id)
+        if probe_id in self._nccl_cleanups:
+            return
+        cleanup = asyncio.current_task()
+        assert cleanup is not None
+        self._nccl_cleanups[probe_id] = cleanup
+        try:
+            await self._cleanup_nccl_preflight(probe_id, cleanup)
+        finally:
+            if self._nccl_cleanups.get(probe_id) is cleanup:
+                self._nccl_cleanups.pop(probe_id)
+
+    @resilient_endpoint
+    async def create_trajectory_queue(
+        self,
+        queue_id: str,
+        max_ready_groups: int,
+        capacity_records: int,
+        capacity_bytes: int,
+    ) -> None:
+        if queue_id in self._trajectory_queues:
+            raise ValueError(f"trajectory queue {queue_id!r} already exists")
+        self._trajectory_queues[queue_id] = TrajectoryQueueStore(
+            max_ready_groups=max_ready_groups,
+            capacity_records=capacity_records,
+            capacity_bytes=capacity_bytes,
+        )
+
+    @resilient_endpoint
+    async def resize_trajectory_queue(self, operation: TrajectoryQueueResize) -> None:
+        self._trajectory_queue(operation.queue_id).resize(
+            maxsize=operation.maxsize, generation=operation.generation
+        )
+
+    @resilient_endpoint
+    async def enqueue_trajectory(
+        self, queue_id: str, item: TrajectoryQueueItem
+    ) -> TrajectoryEnqueueResult:
+        return self._trajectory_queue(queue_id).enqueue(item)
+
+    @resilient_endpoint
+    async def take_trajectory(
+        self, queue_id: str, consumer_id: str, count: int
+    ) -> TrajectoryQueueTake:
+        return self._trajectory_queue(queue_id).take(consumer_id, count)
+
+    @resilient_endpoint
+    async def mark_trajectories_packed(self, operation: TrajectoryQueuePacking) -> None:
+        self._trajectory_queue(operation.queue_id).mark_packed(operation)
+
+    @resilient_endpoint
+    async def release_trajectory(self, operation: TrajectoryQueueRelease) -> None:
+        self._trajectory_queue(operation.queue_id).release(operation)
+
+    @resilient_endpoint
+    async def finish_trajectory_queue(self, queue_id: str) -> None:
+        self._trajectory_queue(queue_id).finish()
+
+    @resilient_endpoint
+    async def trajectory_queue_snapshot(self, queue_id: str) -> TrajectoryQueueSnapshot:
+        return self._trajectory_queue(queue_id).snapshot()
+
+    @resilient_endpoint
+    async def close_trajectory_queue(
+        self, queue_id: str
+    ) -> tuple[TrajectoryGroupRef, ...]:
+        queue = self._trajectory_queues.pop(queue_id, None)
+        return () if queue is None else queue.close()
+
+    def _trajectory_queue(self, queue_id: str) -> TrajectoryQueueStore:
+        try:
+            return self._trajectory_queues[queue_id]
+        except KeyError:
+            raise ValueError(f"unknown trajectory queue {queue_id!r}") from None
+
+    def _launcher(self):
+        if self._vllm_launcher is None:
+            from .vllm_replica import ManagedVllmHostLauncher
+
+            self._vllm_launcher = ManagedVllmHostLauncher(
+                self._vllm_output_root,
+                install_parent_cleanup=lambda: None,
+            )
+        return self._vllm_launcher
+
+    @resilient_endpoint(concurrent=True)
+    async def start_vllm_member(self, request: HostMemberLaunchRequest):
+        if self._admission_report is None:
+            raise RuntimeError("host has not passed ART runtime admission")
+        return await self._launcher().start_member(request)
+
+    @resilient_endpoint
+    async def vllm_member_state(self, replica_id: str, member_id: str, generation: int):
+        return await self._launcher().member_state(replica_id, member_id, generation)
+
+    @resilient_endpoint
+    async def stop_vllm_member(
+        self, replica_id: str, member_id: str, generation: int
+    ) -> None:
+        if self._vllm_launcher is not None:
+            await self._vllm_launcher.stop_member(replica_id, member_id, generation)
+
+    @resilient_endpoint
+    async def pack_batch(
+        self, request: PackingRequest, batch_id: str, transfer_timeout_s: float
+    ) -> PackingResult:
+        fetch_started = time.monotonic()
+        if request.trajectory_sources:
+            groups = list(
+                await asyncio.gather(
+                    *(
+                        source.receive(timeout_s=transfer_timeout_s)
+                        for source in request.trajectory_sources
+                    )
+                )
+            )
+        elif request.trajectory_transfer is None:
+            groups = [payload.build() for payload in request.trajectory_groups]
+        else:
+            if request.trajectory_groups:
+                raise ValueError("packing request has inline and streamed trajectories")
+            if request.trajectory_transfer.stream.stream_id != batch_id:
+                raise ValueError("packing request has the wrong trajectory stream")
+            groups = list(
+                await request.trajectory_transfer.receive_groups(
+                    timeout_s=transfer_timeout_s
+                )
+            )
+        trajectory_fetch_s = time.monotonic() - fetch_started
+        if request.collect_packing_shapes:
+            for group in groups:
+                group._collect_packing_shape = True
+        log_future = None
+        if request.trajectory_log_path is not None:
+            from art.utils.trajectory_logging import write_trajectory_groups_parquet
+
+            path = Path(request.trajectory_log_path)
+
+            def write_log() -> None:
+                path.parent.mkdir(parents=True, exist_ok=True)
+                write_trajectory_groups_parquet(groups, str(path))
+
+            log_future = asyncio.get_running_loop().run_in_executor(None, write_log)
+        packing_started = time.monotonic()
+        try:
+            async with self._packing_lock:
+                if self._packer is None:
+                    from art.megatron.backend import MegatronBackend
+
+                    self._packer = MegatronBackend(
+                        path=f"/tmp/art-packing-{os.getpid()}",
+                        enable_expert_replay=request.include_moe_routing,
+                    )
+                packer = self._packer
+                assert packer is not None
+                packed, cancelled = await complete_to_thread(
+                    lambda: packer._get_packed_tensors(
+                        request.model.build(),
+                        groups,
+                        advantage_balance=request.advantage_balance,
+                        allow_training_without_logprobs=(
+                            request.allow_training_without_logprobs
+                        ),
+                        scale_rewards=request.scale_rewards,
+                        plot_tensors=request.plot_tensors,
+                        packed_sequence_length=request.packed_sequence_length,
+                        logprob_calculation_chunk_size=(
+                            request.logprob_calculation_chunk_size
+                        ),
+                        include_moe_routing=request.include_moe_routing,
+                    )
+                )
+                if cancelled is not None:
+                    raise cancelled
+        except BaseException as error:
+            if log_future is not None:
+
+                async def finish_log() -> None:
+                    await log_future
+
+                try:
+                    _, cancelled = await complete_task(
+                        asyncio.create_task(finish_log())
+                    )
+                    if cancelled is not None:
+                        error.add_note("trajectory logging observed cancellation")
+                except BaseException as log_error:
+                    error.add_note(
+                        "trajectory logging also failed: "
+                        f"{type(log_error).__name__}: {log_error}"
+                    )
+            raise
+        packing_core_s = time.monotonic() - packing_started
+        log_wait_started = time.monotonic()
+        if log_future is not None:
+            await log_future
+        trajectory_log_wait_s = time.monotonic() - log_wait_started
+        shapes = tuple(group._packed_group_shape for group in groups)
+        if packed is None:
+            if request.trajectory_log_path is not None:
+                await asyncio.to_thread(Path(request.trajectory_log_path).unlink)
+            return PackingResult(
+                ref=None,
+                packed_group_shapes=shapes,
+                generation_id=request.generation_id,
+                trajectory_fetch_s=trajectory_fetch_s,
+                packing_core_s=packing_core_s,
+                trajectory_log_wait_s=trajectory_log_wait_s,
+            )
+        trainable_assistant_tokens = int(packed["assistant_mask"].sum().item())
+        loss_bearing_tokens = int(packed["assistant_mask"][:, 1:].sum().item())
+        non_padding_tokens = int((packed["group_ids"] != -1).sum().item())
+        finalize_started = time.monotonic()
+        ref = self._packed_batches.store.create(
+            packed,
+            batch_id=batch_id,
+            group_ids=request.group_ids,
+            record_ids=request.record_ids,
+            min_source_version=request.min_source_version,
+            max_source_version=request.max_source_version,
+        )
+        packed_batch_finalize_s = time.monotonic() - finalize_started
+        return PackingResult(
+            ref=ref,
+            packed_group_shapes=shapes,
+            generation_id=request.generation_id,
+            trainable_assistant_tokens=trainable_assistant_tokens,
+            loss_bearing_tokens=loss_bearing_tokens,
+            non_padding_tokens=non_padding_tokens,
+            trajectory_log_path=request.trajectory_log_path,
+            trajectory_fetch_s=trajectory_fetch_s,
+            packing_core_s=packing_core_s,
+            trajectory_log_wait_s=trajectory_log_wait_s,
+            packed_batch_finalize_s=packed_batch_finalize_s,
+        )
+
+    @resilient_endpoint
+    async def publish_batch(self, ref: PackedBatchRef) -> PackedBatchTransfer:
+        if ref.batch_id in self._batch_publishers:
+            raise RuntimeError(f"packed batch {ref.batch_id!r} is already published")
+        publisher = await PackedBatchPublisher.create(
+            ref, advertise_host=self._data_plane_host
+        )
+        try:
+            transfer = publisher.transfer
+        except BaseException:
+            await publisher.close()
+            raise
+        self._batch_publishers[ref.batch_id] = publisher
+        return transfer
+
+    @resilient_endpoint
+    async def drop_batch(self, batch_id: str) -> None:
+        await self._drop_batch(batch_id)
+
+    @resilient_endpoint
+    async def note_batch_transmitted(self, byte_count: int) -> None:
+        self._packed_batches.store.note_transmitted(byte_count)
+
+    async def _drop_batch(self, batch_id: str) -> bool:
+        publisher = self._batch_publishers.pop(batch_id, None)
+        if publisher is None:
+            return False
+        await publisher.close()
+        return True
+
+    @resilient_endpoint
+    async def receive_batch(
+        self, ref: PackedBatchRef, transfer: PackedBatchTransfer, timeout_s: float
+    ) -> PackedBatchRef:
+        return await self._packed_batches.receive(ref, transfer, timeout_s=timeout_s)
+
+    @resilient_endpoint
+    async def drop_batch_ref(self, ref: PackedBatchRef) -> None:
+        await self._packed_batches.drop(ref)
+
+    @resilient_endpoint
+    async def reclaim_batch(self, batch_id: str, fence: bool) -> bool:
+        published = False
+        failure: BaseException | None = None
+        try:
+            published = await self._drop_batch(batch_id)
+        except BaseException as error:
+            failure = error
+        reclaimed = self._packed_batches.store.reclaim(batch_id, fence=fence)
+        if failure is not None:
+            raise failure
+        return published or reclaimed
+
+    @resilient_endpoint
+    async def stats(self):
+        return self._packed_batches.store.stats()
+
+
+class RolloutWorkerService(Actor):
+    """One process-isolated CPU rollout slot."""
+
+    def __init__(
+        self, capacity_records: int, capacity_bytes: int, data_plane_host: str
+    ) -> None:
+        from .trajectory_store import TrajectoryRecordStore
+
+        self._models = OrderedDict()
+        self._results = TrajectoryRecordStore(
+            owner_actor_id=f"rollout:{socket.gethostname()}:{os.getpid()}",
+            capacity_records=capacity_records,
+            capacity_bytes=capacity_bytes,
+        )
+        self._data_plane_host = data_plane_host
+        self._byte_stream_loop = ByteStreamServerLoop()
+        self._trajectory_publishers = {}
+
+    @resilient_endpoint
+    async def run(self, invocation: RolloutInvocation):
+        from art.metrics import MetricsBuilder
+
+        key = invocation.model.cache_key
+        model = self._models.get(key)
+        if model is None:
+            model = invocation.model.build()
+            self._models[key] = model
+            if len(self._models) > 16:
+                _, evicted = self._models.popitem(last=False)
+                await evicted._reset_inference_runtime()
+        else:
+            self._models.move_to_end(key)
+        builder = MetricsBuilder(cost_context="train")
+        token = builder.activate()
+        try:
+            value = await invocation.callable.resolve()(
+                model, invocation.scenario, invocation.config
+            )
+        finally:
+            token.var.reset(token)
+        if invocation.store_result:
+            from art import TrajectoryGroup
+
+            if isinstance(value, TrajectoryGroup):
+                ref = self._results.put(value)
+                try:
+                    transfer, publisher = await publish_trajectory_bundles(
+                        (self._results.bundle(ref),),
+                        stream_id=ref.result_id,
+                        advertise_host=self._data_plane_host,
+                        server_loop=self._byte_stream_loop,
+                    )
+                except BaseException:
+                    self._results.drop(ref)
+                    raise
+                self._trajectory_publishers[ref.result_id] = publisher
+                value = ref.model_copy(update={"transfer": transfer})
+        return RolloutResult(value=value, metrics=await builder.drain_pending())
+
+    async def _release_trajectory(self, ref: TrajectoryGroupRef) -> None:
+        self._results.drop(ref)
+        publisher = self._trajectory_publishers.pop(ref.result_id, None)
+        if publisher is not None:
+            await publisher.close()
+
+    @resilient_endpoint
+    async def drop_result(self, ref: TrajectoryGroupRef) -> None:
+        await self._release_trajectory(ref)
+
+    @resilient_endpoint
+    async def close(self) -> None:
+        try:
+            await asyncio.gather(
+                *(
+                    publisher.close()
+                    for publisher in tuple(self._trajectory_publishers.values())
+                )
+            )
+        finally:
+            self._trajectory_publishers.clear()
+            await self._byte_stream_loop.close()
+        for model in self._models.values():
+            await model._reset_inference_runtime()
+        self._models.clear()
+        self._results.close()
diff --git a/src/art/distributed/monarch_bootstrap.py b/src/art/distributed/monarch_bootstrap.py
new file mode 100644
index 000000000..e053bc1d3
--- /dev/null
+++ b/src/art/distributed/monarch_bootstrap.py
@@ -0,0 +1,1438 @@
+from __future__ import annotations
+
+"""Provider-neutral bootstrap for pinned torchmonarch 0.6.
+
+Both worker and controller endpoints must be reachable only on one trusted private
+network because ART currently configures Monarch with ``trust_all_connections``.
+"""
+
+import argparse
+import asyncio
+from collections.abc import Callable, Iterator, Mapping, MutableMapping, Sequence
+from contextlib import contextmanager
+import fcntl
+import hashlib
+import importlib.util
+import ipaddress
+import os
+from pathlib import Path
+import re
+import select
+import shlex
+import signal
+import socket
+import subprocess
+import sys
+import threading
+import time
+from typing import TYPE_CHECKING, Any
+from urllib.parse import urlsplit
+import uuid
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+if TYPE_CHECKING:
+    from .rollout import InstalledAsyncCallable
+
+DEFAULT_MONARCH_PORT = 22222
+DEFAULT_STARTUP_TIMEOUT_S = 600.0
+_INVALID_IDENTIFIER = re.compile(r"\W")
+_MAX_IDENTIFIER_LENGTH = 48
+_SSH_LAUNCH_ID = re.compile(r"^[0-9a-f]{32}$")
+_SSH_READY_PREFIX = b"ART_MONARCH_READY "
+_PROGRAM_PYTHONPATH_ENV = "ART_MONARCH_PROGRAM_PYTHONPATH"
+_MONARCH_TIMEOUT_ENV = (
+    "HYPERACTOR_HOST_SPAWN_READY_TIMEOUT",
+    "HYPERACTOR_MESSAGE_DELIVERY_TIMEOUT",
+    "HYPERACTOR_MESH_ATTACH_CONFIG_TIMEOUT",
+    "HYPERACTOR_MESH_ACTOR_SPAWN_MAX_IDLE",
+    "HYPERACTOR_MESH_PROC_SPAWN_MAX_IDLE",
+)
+_MONARCH_SHUTDOWN_ENV = {
+    "HYPERACTOR_PROCESS_EXIT_TIMEOUT": "2s",
+    "HYPERACTOR_MESH_PROC_STOP_MAX_IDLE": "240s",
+}
+_WORKER_ADDRESS_LOCK = threading.Lock()
+_USED_WORKER_ADDRESSES: set[str] = set()
+_BROKEN_OUTPUT_FLAGS = select.POLLERR | select.POLLHUP | select.POLLNVAL
+_WORKER_CODE = """\
+import ctypes
+import os
+import signal
+import sys
+
+if len(sys.argv) >= 4 and sys.argv[2] == "--parent-pid":
+    expected_parent_pid = int(sys.argv[3])
+    libc = ctypes.CDLL(None, use_errno=True)
+    if libc.prctl(1, signal.SIGKILL, 0, 0, 0):
+        error_number = ctypes.get_errno()
+        raise OSError(error_number, os.strerror(error_number))
+    if os.getppid() != expected_parent_pid:
+        os.kill(os.getpid(), signal.SIGKILL)
+
+from monarch.actor import run_worker_loop_forever
+run_worker_loop_forever(address=sys.argv[1], ca="trust_all_connections")
+"""
+_LEGACY_OWNED_WORKER_CODE = """\
+import sys
+from monarch.actor import run_worker_loop_forever
+run_worker_loop_forever(address=sys.argv[1], ca="trust_all_connections")
+"""
+_WORKER_LOCK_ROOT = Path("/tmp")
+_OWNED_WORKER_SCHEMA = "art.monarch.owned-worker.v1"
+
+
+class _BootstrapContract(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class _OwnedWorkerMetadata(_BootstrapContract):
+    schema_name: str = _OWNED_WORKER_SCHEMA
+    address: str
+    controller_pid: int = Field(gt=0)
+    controller_start_time: int = Field(gt=0)
+    worker_pid: int = Field(gt=0)
+    worker_start_time: int = Field(gt=0)
+    python_executable: str = Field(min_length=1)
+    worker_code_sha256: str = Field(pattern=r"^[0-9a-f]{64}$")
+    ownership_token: str = Field(pattern=r"^[0-9a-f]{32}$")
+
+    @model_validator(mode="after")
+    def _validate_schema(self) -> "_OwnedWorkerMetadata":
+        if self.schema_name != _OWNED_WORKER_SCHEMA:
+            raise ValueError("unsupported owned-worker metadata schema")
+        return self
+
+
+class _WorkerSession(_BootstrapContract):
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+        frozen=True,
+    )
+
+    address: str
+    process: subprocess.Popen[bytes]
+    label: str
+    graceful: bool = False
+    launch_id: str | None = None
+    lease: Any = None
+
+    @property
+    def exitcode(self) -> int | None:
+        return self.process.poll()
+
+    def is_alive(self) -> bool:
+        return self.exitcode is None
+
+    def release(self) -> None:
+        if not self.is_alive():
+            return
+        if self.graceful:
+            assert self.process.stdin is not None
+            self.process.stdin.close()
+        else:
+            os.killpg(self.process.pid, signal.SIGTERM)
+
+    def wait(self) -> None:
+        try:
+            self.process.wait(timeout=15)
+        except subprocess.TimeoutExpired:
+            if self.graceful:
+                self.process.terminate()
+            else:
+                os.killpg(self.process.pid, signal.SIGKILL)
+            self.process.wait()
+            raise RuntimeError(f"{self.label} did not stop in time") from None
+        finally:
+            if self.lease is not None:
+                self.lease.close()
+        if self.graceful and self.process.returncode:
+            raise RuntimeError(f"{self.label} exited {self.process.returncode}")
+
+
+class ExplicitHostBootstrap(_BootstrapContract):
+    worker_addresses: tuple[str, ...]
+    controller_rank: int = Field(default=0, ge=0)
+
+    @model_validator(mode="after")
+    def _validate_workers(self) -> "ExplicitHostBootstrap":
+        if not self.worker_addresses:
+            raise ValueError("worker_addresses must not be empty")
+        if len(set(self.worker_addresses)) != len(self.worker_addresses):
+            raise ValueError("worker_addresses must be unique")
+        if self.controller_rank >= len(self.worker_addresses):
+            raise ValueError("controller_rank must identify a worker address")
+        return self
+
+
+class SkyPilotBootstrap(_BootstrapContract):
+    node_rank: int = Field(ge=0)
+    node_ips: tuple[str, ...]
+    port: int = Field(default=DEFAULT_MONARCH_PORT, ge=1, le=65534)
+
+    @classmethod
+    def from_environ(
+        cls,
+        environ: Mapping[str, str] | None = None,
+        *,
+        port: int = DEFAULT_MONARCH_PORT,
+    ) -> "SkyPilotBootstrap":
+        environ = os.environ if environ is None else environ
+        try:
+            node_rank = int(environ["SKYPILOT_NODE_RANK"])
+            node_ips = tuple(environ["SKYPILOT_NODE_IPS"].replace(",", "\n").split())
+            declared_nodes = int(environ["SKYPILOT_NUM_NODES"])
+        except KeyError as error:
+            raise RuntimeError(
+                f"missing SkyPilot environment variable {error.args[0]}"
+            ) from None
+        if declared_nodes != len(node_ips):
+            raise ValueError(
+                f"SKYPILOT_NUM_NODES={declared_nodes} but received {len(node_ips)} IPs"
+            )
+        return cls(node_rank=node_rank, node_ips=node_ips, port=port)
+
+    @model_validator(mode="after")
+    def _validate_rank(self) -> "SkyPilotBootstrap":
+        if not self.node_ips or self.node_rank >= len(self.node_ips):
+            raise ValueError("SkyPilot node rank must identify a node IP")
+        if len(set(self.node_ips)) != len(self.node_ips):
+            raise ValueError("SKYPILOT_NODE_IPS must be unique")
+        for node_ip in self.node_ips:
+            try:
+                ipaddress.ip_address(node_ip)
+            except ValueError:
+                raise ValueError(
+                    f"SKYPILOT_NODE_IPS contains invalid IP address {node_ip!r}"
+                ) from None
+        return self
+
+    @property
+    def worker_addresses(self) -> tuple[str, ...]:
+        return tuple(_tcp_address(ip, self.port) for ip in self.node_ips)
+
+    @property
+    def lifecycle_port(self) -> int:
+        return self.port + 1
+
+
+class SshHost(_BootstrapContract):
+    target: str = Field(min_length=1)
+    worker_host: str = Field(min_length=1)
+
+
+class SshBootstrap(_BootstrapContract):
+    hosts: tuple[SshHost, ...]
+    python_executable: str = Field(min_length=1)
+    port: int = Field(default=DEFAULT_MONARCH_PORT, ge=1, le=65535)
+    ssh_args: tuple[str, ...] = ()
+
+    @model_validator(mode="after")
+    def _validate_hosts(self) -> "SshBootstrap":
+        if not self.hosts:
+            raise ValueError("hosts must not be empty")
+        if len({host.target for host in self.hosts}) != len(self.hosts):
+            raise ValueError("SSH targets must be unique")
+        if len({host.worker_host for host in self.hosts}) != len(self.hosts):
+            raise ValueError("worker hosts must be unique")
+        return self
+
+    @property
+    def worker_addresses(self) -> tuple[str, ...]:
+        return tuple(_tcp_address(host.worker_host, self.port) for host in self.hosts)
+
+
+def _tcp_address(host: str, port: int) -> str:
+    host = host.removeprefix("[").removesuffix("]")
+    return f"tcp://[{host}]:{port}" if ":" in host else f"tcp://{host}:{port}"
+
+
+def require_local_worker_address(worker_addresses: Sequence[str]) -> str:
+    error = "local ART runtime requires exactly one loopback tcp worker address"
+    if len(worker_addresses) != 1:
+        raise ValueError(error)
+    address = worker_addresses[0]
+    try:
+        parsed = urlsplit(address)
+        host = parsed.hostname
+        port = parsed.port
+    except ValueError:
+        raise ValueError(error) from None
+    if (
+        parsed.scheme != "tcp"
+        or host is None
+        or port is None
+        or port < 0
+        or parsed.path
+        or parsed.query
+        or parsed.fragment
+        or parsed.username is not None
+        or parsed.password is not None
+    ):
+        raise ValueError(error)
+    try:
+        is_loopback = (
+            host.casefold() == "localhost" or ipaddress.ip_address(host).is_loopback
+        )
+    except ValueError:
+        is_loopback = False
+    if not is_loopback:
+        raise ValueError(error)
+    return address
+
+
+def _parse_ssh_host(value: str) -> SshHost:
+    target, separator, worker_host = value.partition("=")
+    target = target.strip()
+    if not separator:
+        worker_host = target.rsplit("@", 1)[-1]
+    return SshHost(target=target, worker_host=worker_host.strip())
+
+
+def _positive_float(value: str) -> float:
+    parsed = float(value)
+    if parsed <= 0:
+        raise argparse.ArgumentTypeError("must be greater than zero")
+    return parsed
+
+
+def _program_reference(value: str) -> tuple[str, str]:
+    target, separator, qualname = value.partition(":")
+    if not target or not separator or not qualname:
+        raise ValueError("--program must use module:qualname or path.py:qualname")
+    if Path(target).suffix != ".py":
+        return target, qualname
+
+    script = Path(target).expanduser().resolve()
+    if not script.is_file():
+        raise ValueError(f"program script does not exist: {script}")
+    module = script.stem
+    if not module.isidentifier():
+        raise ValueError(
+            f"program script name must be a Python identifier: {script.name}"
+        )
+
+    root = str(script.parent)
+    sys.path[:] = [root, *(path for path in sys.path if path != root)]
+    inherited = os.environ.get("PYTHONPATH", "").split(os.pathsep)
+    os.environ["PYTHONPATH"] = os.pathsep.join(
+        dict.fromkeys((root, *filter(None, inherited)))
+    )
+    os.environ[_PROGRAM_PYTHONPATH_ENV] = root
+    importlib.invalidate_caches()
+    spec = importlib.util.find_spec(module)
+    if spec is None or spec.origin is None or Path(spec.origin).resolve() != script:
+        raise ValueError(f"program module {module!r} does not resolve to {script}")
+    return module, qualname
+
+
+def monarch_identifier(value: str) -> str:
+    """Return a stable valid Monarch mesh, proc, or actor identifier."""
+
+    identifier = _INVALID_IDENTIFIER.sub("_", value)
+    if not identifier or identifier[0].isdigit():
+        identifier = f"art_{identifier}"
+    if identifier == value and len(identifier) <= _MAX_IDENTIFIER_LENGTH:
+        return identifier
+    suffix = hashlib.sha256(value.encode()).hexdigest()[:8]
+    prefix_length = _MAX_IDENTIFIER_LENGTH - len(suffix) - 1
+    return f"{identifier[:prefix_length]}_{suffix}"
+
+
+def _prepare_child_environment(
+    *,
+    worker: bool = False,
+    environ: MutableMapping[str, str] | None = None,
+) -> None:
+    environ = os.environ if environ is None else environ
+    # Monarch's spawned interpreter may resolve outside the active uv venv. Make
+    # the controller's import roots explicit for ART and installed user code.
+    roots = [path for path in sys.path if path and os.path.isabs(path)]
+    roots.extend(environ.get("PYTHONPATH", "").split(os.pathsep))
+    environ["PYTHONPATH"] = os.pathsep.join(dict.fromkeys(filter(None, roots)))
+    if os.path.isfile(os.path.join(sys.prefix, "pyvenv.cfg")):
+        environ.setdefault("ART_VIRTUAL_ENV", sys.prefix)
+    if worker:
+        environ.pop("CUDA_VISIBLE_DEVICES", None)
+        allocator_config = "expandable_segments:True"
+        environ["PYTORCH_ALLOC_CONF"] = allocator_config
+        environ["PYTORCH_CUDA_ALLOC_CONF"] = allocator_config
+        nvidia_libs = (
+            str(path)
+            for root in roots
+            for path in (Path(root) / "nvidia").glob("*/lib")
+            if path.is_dir()
+        )
+        inherited = environ.get("LD_LIBRARY_PATH", "").split(os.pathsep)
+        environ["LD_LIBRARY_PATH"] = os.pathsep.join(
+            dict.fromkeys((*nvidia_libs, *filter(None, inherited)))
+        )
+    for name in _MONARCH_TIMEOUT_ENV:
+        environ.setdefault(name, "600s")
+    for name, value in _MONARCH_SHUTDOWN_ENV.items():
+        environ.setdefault(name, value)
+    # INFO launch records include the inherited environment and may expose secrets.
+    environ.setdefault("MONARCH_FILE_LOG", "warn")
+
+
+def _stabilize_child_stdio() -> None:
+    fds = (sys.stdout.fileno(), sys.stderr.fileno())
+    poller = select.poll()
+    for fd in fds:
+        poller.register(fd, select.POLLOUT)
+    if not any(flags & _BROKEN_OUTPUT_FLAGS for _, flags in poller.poll(0)):
+        return
+    log_dir = Path(
+        os.environ.get("ART_MONARCH_CHILD_LOG_DIR") or "/tmp/art-monarch-child-logs"
+    )
+    log_dir.mkdir(mode=0o700, parents=True, exist_ok=True)
+    with (log_dir / f"{socket.gethostname()}-{os.getpid()}.log").open("ab") as log:
+        for fd in fds:
+            os.dup2(log.fileno(), fd)
+
+
+def activate_child_virtualenv() -> None:
+    """Restore venv identity lost when Monarch resolves the Python executable."""
+
+    _stabilize_child_stdio()
+    if virtual_env := os.environ.get("ART_VIRTUAL_ENV"):
+        sys.prefix = sys.exec_prefix = virtual_env
+
+
+def activate_trainer_child_virtualenv() -> None:
+    threads = os.environ.get("MKL_NUM_THREADS", os.environ.get("OMP_NUM_THREADS", "1"))
+    for name in ("OMP_NUM_THREADS", "MKL_NUM_THREADS", "OPENBLAS_NUM_THREADS"):
+        os.environ.setdefault(name, threads)
+    executable_env = Path(sys.executable).parent.parent
+    if (executable_env / "pyvenv.cfg").is_file():
+        os.environ["ART_VIRTUAL_ENV"] = str(executable_env)
+    activate_child_virtualenv()
+
+
+def activate_cuda_device(gpu_id: int | str) -> int:
+    """Bind a clean trainer process to one physical ordinal or CUDA UUID."""
+
+    if isinstance(gpu_id, str):
+        os.environ["CUDA_VISIBLE_DEVICES"] = gpu_id
+        return 0
+    if "CUDA_VISIBLE_DEVICES" in os.environ:
+        raise RuntimeError(
+            "physical GPU placement requires an unmasked Monarch worker process"
+        )
+    return gpu_id
+
+
+def activate_cpu_child_virtualenv() -> None:
+    os.environ["CUDA_VISIBLE_DEVICES"] = ""
+    activate_child_virtualenv()
+
+
+def _owns_tcp_listener(pid: int, port: int) -> bool:
+    socket_inodes = {
+        target[8:-1]
+        for descriptor in Path(f"/proc/{pid}/fd").iterdir()
+        if (target := os.readlink(descriptor)).startswith("socket:[")
+    }
+    for table in ("tcp", "tcp6"):
+        for line in Path(f"/proc/{pid}/net/{table}").read_text().splitlines()[1:]:
+            fields = line.split()
+            if (
+                len(fields) > 9
+                and fields[3] == "0A"
+                and int(fields[1].rsplit(":", 1)[1], 16) == port
+                and fields[9] in socket_inodes
+            ):
+                return True
+    return False
+
+
+def _stop_worker_process(process: subprocess.Popen[bytes]) -> None:
+    if process.poll() is None:
+        os.killpg(process.pid, signal.SIGTERM)
+    try:
+        process.wait(timeout=10)
+    except subprocess.TimeoutExpired:
+        os.killpg(process.pid, signal.SIGKILL)
+        process.wait()
+
+
+def _run_ssh_worker_session(
+    address: str,
+    launch_id: str,
+    startup_timeout_s: float,
+) -> None:
+    port = urlsplit(address).port
+    if port is None or not _SSH_LAUNCH_ID.fullmatch(launch_id):
+        raise ValueError("invalid SSH worker launch identity")
+    worker = subprocess.Popen(
+        [sys.executable, "-c", _WORKER_CODE, address, launch_id],
+        stdin=subprocess.DEVNULL,
+        stdout=sys.stderr,
+        stderr=sys.stderr,
+        start_new_session=True,
+    )
+
+    def terminate(_signum: int, _frame: Any) -> None:
+        raise SystemExit
+
+    previous = {
+        signum: signal.signal(signum, terminate)
+        for signum in (signal.SIGTERM, signal.SIGHUP)
+    }
+    try:
+        deadline = time.monotonic() + startup_timeout_s
+        while time.monotonic() < deadline and worker.poll() is None:
+            try:
+                if _owns_tcp_listener(worker.pid, port):
+                    print((_SSH_READY_PREFIX + launch_id.encode()).decode(), flush=True)
+                    break
+            except FileNotFoundError:
+                pass
+            time.sleep(0.05)
+        else:
+            if worker.poll() is not None:
+                raise RuntimeError(
+                    f"Monarch worker exited {worker.returncode} before ready"
+                )
+            raise TimeoutError(f"Monarch worker did not own {address} in time")
+        while worker.poll() is None:
+            readable, _, _ = select.select((sys.stdin,), (), (), 0.1)
+            if readable and not os.read(sys.stdin.fileno(), 1):
+                return
+        raise RuntimeError(f"Monarch worker exited {worker.returncode}")
+    finally:
+        _stop_worker_process(worker)
+        for signum, handler in previous.items():
+            signal.signal(signum, handler)
+
+
+def run_worker(
+    address: str,
+    *,
+    launch_id: str | None = None,
+    startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S,
+) -> None:
+    """Run a pinned Monarch worker on a trusted private network.
+
+    ART's trust-all mode must never be exposed to an untrusted or public network.
+    """
+
+    _prepare_child_environment(worker=True)
+    if launch_id is not None:
+        _run_ssh_worker_session(address, launch_id, startup_timeout_s)
+        return
+    # Importing ``art`` initializes enough third-party state to break Monarch's
+    # spawned interpreter bootstrap. Replace this process with a clean worker.
+    os.execv(sys.executable, [sys.executable, "-c", _WORKER_CODE, address])
+
+
+async def attach_controller(
+    worker_addresses: Sequence[str],
+    *,
+    name: str = "art",
+    startup_timeout_s: float | None = None,
+    owned_workers: Sequence[_WorkerSession] = (),
+) -> Any:
+    """Attach a controller to already-started workers on a trusted network."""
+
+    _prepare_child_environment()
+    from monarch.actor import (  # ty: ignore[unresolved-import]
+        attach_to_workers,
+        enable_transport,
+    )
+
+    enable_transport("tcp")
+    hosts = attach_to_workers(
+        workers=list(worker_addresses),
+        ca="trust_all_connections",
+        name=monarch_identifier(name),
+    )
+    initialized = asyncio.ensure_future(hosts.initialized)
+    deadline = (
+        None
+        if startup_timeout_s is None
+        else asyncio.get_running_loop().time() + startup_timeout_s
+    )
+    try:
+        while not initialized.done():
+            exited = [worker for worker in owned_workers if not worker.is_alive()]
+            if exited:
+                raise RuntimeError(
+                    "owned Monarch worker exited during attach: "
+                    + ", ".join(
+                        f"{worker.address} code={worker.exitcode}" for worker in exited
+                    )
+                )
+            timeout = 0.05
+            if deadline is not None:
+                timeout = min(
+                    timeout, max(0.0, deadline - asyncio.get_running_loop().time())
+                )
+                if timeout == 0:
+                    raise TimeoutError("timed out attaching to Monarch workers")
+            await asyncio.wait((initialized,), timeout=timeout)
+        await initialized
+        exited = [worker for worker in owned_workers if not worker.is_alive()]
+        if exited:
+            raise RuntimeError("owned Monarch worker exited as attach completed")
+    except BaseException as startup_error:
+        initialized.cancel()
+        try:
+            await asyncio.wait_for(
+                hosts.shutdown(),
+                startup_timeout_s or DEFAULT_STARTUP_TIMEOUT_S,
+            )
+        except BaseException as cleanup_error:
+            raise BaseExceptionGroup(
+                "Monarch attach and cleanup failed",
+                [startup_error, cleanup_error],
+            ) from None
+        raise
+    return hosts
+
+
+async def run_explicit_controller(
+    spec: ExplicitHostBootstrap,
+    program: "InstalledAsyncCallable",
+    *,
+    startup_timeout_s: float | None = None,
+    owned_workers: Sequence[_WorkerSession] = (),
+) -> Any:
+    from .launch import ArtLaunchContext
+
+    hosts = await attach_controller(
+        spec.worker_addresses,
+        startup_timeout_s=startup_timeout_s,
+        owned_workers=owned_workers,
+    )
+    program_task = asyncio.ensure_future(
+        program.resolve()(
+            ArtLaunchContext(
+                host_mesh=hosts,
+                worker_addresses=spec.worker_addresses,
+                controller_rank=spec.controller_rank,
+            )
+        )
+    )
+    try:
+        while not program_task.done():
+            exited = [worker for worker in owned_workers if not worker.is_alive()]
+            if exited:
+                raise RuntimeError(
+                    "owned Monarch worker exited during controller program: "
+                    + ", ".join(
+                        f"{worker.address} code={worker.exitcode}" for worker in exited
+                    )
+                )
+            await asyncio.wait((program_task,), timeout=0.05)
+        result = await program_task
+        if any(not worker.is_alive() for worker in owned_workers):
+            raise RuntimeError("owned Monarch worker exited as program completed")
+    except BaseException as program_error:
+        if not program_task.done():
+            program_task.cancel()
+            await asyncio.gather(program_task, return_exceptions=True)
+        try:
+            await asyncio.wait_for(
+                hosts.shutdown(),
+                startup_timeout_s or DEFAULT_STARTUP_TIMEOUT_S,
+            )
+        except BaseException as cleanup_error:
+            raise BaseExceptionGroup(
+                "Monarch program and controller cleanup failed",
+                [program_error, cleanup_error],
+            ) from None
+        raise
+    await asyncio.wait_for(
+        hosts.shutdown(),
+        startup_timeout_s or DEFAULT_STARTUP_TIMEOUT_S,
+    )
+    return result
+
+
+def _require_bindable_worker_address(address: str) -> None:
+    parsed = urlsplit(address)
+    assert parsed.hostname is not None and parsed.port is not None
+    error: OSError | None = None
+    for family, socktype, proto, _, sockaddr in socket.getaddrinfo(
+        parsed.hostname, parsed.port, type=socket.SOCK_STREAM
+    ):
+        probe = socket.socket(family, socktype, proto)
+        try:
+            probe.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+            probe.bind(sockaddr)
+            return
+        except OSError as exc:
+            error = exc
+        finally:
+            probe.close()
+    raise RuntimeError(
+        f"Monarch worker address is already in use: {address}"
+    ) from error
+
+
+def _resolve_ephemeral_worker_address(address: str) -> str:
+    parsed = urlsplit(address)
+    if parsed.port != 0:
+        return address
+    assert parsed.hostname is not None
+    error: OSError | None = None
+    for family, socktype, proto, _, sockaddr in socket.getaddrinfo(
+        parsed.hostname, 0, type=socket.SOCK_STREAM
+    ):
+        probe = socket.socket(family, socktype, proto)
+        try:
+            probe.bind(sockaddr)
+            candidate = _tcp_address(parsed.hostname, probe.getsockname()[1])
+            if candidate not in _USED_WORKER_ADDRESSES:
+                return candidate
+        except OSError as exc:
+            error = exc
+        finally:
+            probe.close()
+    raise RuntimeError("could not allocate a fresh local worker address") from error
+
+
+def _worker_lock_path(address: str) -> Path:
+    digest = hashlib.sha256(address.encode()).hexdigest()[:16]
+    return _WORKER_LOCK_ROOT / f"art-monarch-worker-{digest}.lock"
+
+
+def _process_identity(pid: int) -> tuple[int, int, int, str] | None:
+    try:
+        stat = (Path("/proc") / str(pid) / "stat").read_text()
+    except OSError:
+        return None
+    fields = stat.rsplit(")", 1)[1].split()
+    return int(fields[19]), int(fields[1]), int(fields[3]), fields[0]
+
+
+def _process_command(pid: int) -> tuple[str, ...] | None:
+    try:
+        command = (Path("/proc") / str(pid) / "cmdline").read_bytes()
+    except OSError:
+        return None
+    return tuple(os.fsdecode(value) for value in command.rstrip(b"\0").split(b"\0"))
+
+
+def _write_owned_worker_metadata(
+    lease: Any,
+    address: str,
+    process: subprocess.Popen[bytes],
+    ownership_token: str,
+) -> None:
+    controller = _process_identity(os.getpid())
+    worker = _process_identity(process.pid)
+    if controller is None or worker is None:
+        raise RuntimeError("owned Monarch worker process identity disappeared")
+    metadata = _OwnedWorkerMetadata(
+        address=address,
+        controller_pid=os.getpid(),
+        controller_start_time=controller[0],
+        worker_pid=process.pid,
+        worker_start_time=worker[0],
+        python_executable=os.path.realpath(sys.executable),
+        worker_code_sha256=hashlib.sha256(_WORKER_CODE.encode()).hexdigest(),
+        ownership_token=ownership_token,
+    )
+    lease.seek(0)
+    lease.truncate()
+    lease.write(metadata.model_dump_json().encode())
+    lease.flush()
+    os.fsync(lease.fileno())
+
+
+def _metadata_owned_orphan(
+    metadata: _OwnedWorkerMetadata, lock_path: Path
+) -> tuple[int, int] | None:
+    if _worker_lock_path(
+        metadata.address
+    ) != lock_path or metadata.python_executable != os.path.realpath(sys.executable):
+        return None
+    worker = _process_identity(metadata.worker_pid)
+    if worker is None or worker[0] != metadata.worker_start_time or worker[3] == "Z":
+        return None
+    controller = _process_identity(metadata.controller_pid)
+    if controller is not None and controller[0] == metadata.controller_start_time:
+        return None
+    command = _process_command(metadata.worker_pid)
+    if command is None or len(command) != 8:
+        return None
+    expected_tail = (
+        metadata.address,
+        "--parent-pid",
+        str(metadata.controller_pid),
+        "--ownership-token",
+        metadata.ownership_token,
+    )
+    if os.path.realpath(command[0]) != metadata.python_executable:
+        return None
+    if (
+        command[1] != "-c"
+        or hashlib.sha256(command[2].encode()).hexdigest()
+        != metadata.worker_code_sha256
+        or command[3:] != expected_tail
+        or worker[2] != metadata.worker_pid
+    ):
+        return None
+    return metadata.worker_pid, metadata.worker_start_time
+
+
+def _legacy_owned_orphans() -> dict[Path, tuple[int, int]]:
+    matches: dict[Path, tuple[int, int]] = {}
+    for process_dir in Path("/proc").iterdir():
+        if not process_dir.name.isdigit():
+            continue
+        pid = int(process_dir.name)
+        identity = _process_identity(pid)
+        command = _process_command(pid)
+        if identity is None or command is None or len(command) != 4:
+            continue
+        start_time, parent_pid, session_id, state = identity
+        if parent_pid != 1 or session_id != pid or state == "Z":
+            continue
+        if os.path.realpath(command[0]) != os.path.realpath(sys.executable):
+            continue
+        if command[1:3] != ("-c", _LEGACY_OWNED_WORKER_CODE):
+            continue
+        address = command[3]
+        parsed = urlsplit(address)
+        try:
+            loopback = (
+                parsed.hostname is not None
+                and ipaddress.ip_address(parsed.hostname).is_loopback
+            )
+        except ValueError:
+            loopback = False
+        if not loopback or parsed.port in (None, 0):
+            continue
+        lock_path = _worker_lock_path(address)
+        if lock_path in matches:
+            raise RuntimeError(f"multiple legacy workers match owned lease {lock_path}")
+        matches[lock_path] = (pid, start_time)
+    return matches
+
+
+def _terminate_owned_orphan(pid: int, start_time: int) -> None:
+    try:
+        pidfd = os.pidfd_open(pid)
+    except ProcessLookupError:
+        return
+    try:
+        identity = _process_identity(pid)
+        if identity is None or identity[0] != start_time or identity[3] == "Z":
+            return
+        signal.pidfd_send_signal(pidfd, signal.SIGKILL)
+        exited = select.poll()
+        exited.register(pidfd, select.POLLIN)
+        if not exited.poll(5000):
+            raise RuntimeError(f"owned Monarch worker {pid} did not exit")
+    finally:
+        os.close(pidfd)
+
+
+def _reconcile_orphaned_workers() -> None:
+    legacy_owned: dict[Path, tuple[int, int]] | None = None
+    for lock_path in sorted(_WORKER_LOCK_ROOT.glob("art-monarch-worker-*.lock")):
+        try:
+            lease = open(lock_path, "r+b")
+        except FileNotFoundError:
+            continue
+        try:
+            try:
+                fcntl.flock(lease.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+            except BlockingIOError:
+                continue
+            payload = lease.read().strip()
+            owned: tuple[int, int] | None
+            if payload:
+                try:
+                    metadata = _OwnedWorkerMetadata.model_validate_json(payload)
+                except ValueError:
+                    continue
+                if _worker_lock_path(
+                    metadata.address
+                ) != lock_path or metadata.python_executable != os.path.realpath(
+                    sys.executable
+                ):
+                    continue
+                identity = _process_identity(metadata.worker_pid)
+                if (
+                    identity is None
+                    or identity[0] != metadata.worker_start_time
+                    or identity[3] == "Z"
+                ):
+                    lock_path.unlink(missing_ok=True)
+                    continue
+                owned = _metadata_owned_orphan(metadata, lock_path)
+            else:
+                if legacy_owned is None:
+                    legacy_owned = _legacy_owned_orphans()
+                owned = legacy_owned.get(lock_path)
+            if owned is None:
+                continue
+            _terminate_owned_orphan(*owned)
+            lock_path.unlink(missing_ok=True)
+        finally:
+            lease.close()
+
+
+def _wait_for_worker_listener(
+    process: subprocess.Popen[bytes], address: str, timeout_s: float
+) -> None:
+    port = urlsplit(address).port
+    assert port is not None
+    deadline = time.monotonic() + timeout_s
+    while True:
+        if (exitcode := process.poll()) is not None:
+            raise RuntimeError(
+                f"Monarch worker exited {exitcode} before listening on {address}"
+            )
+        try:
+            if _owns_tcp_listener(process.pid, port):
+                return
+        except FileNotFoundError:
+            pass
+        if time.monotonic() >= deadline:
+            raise TimeoutError(f"Monarch worker did not listen on {address} in time")
+        time.sleep(0.05)
+
+
+def _start_worker(
+    address: str, *, startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S
+) -> _WorkerSession:
+    with _WORKER_ADDRESS_LOCK:
+        _reconcile_orphaned_workers()
+        address = _resolve_ephemeral_worker_address(address)
+        lease = open(_worker_lock_path(address), "a+b")
+        process: subprocess.Popen[bytes] | None = None
+        ownership_token = uuid.uuid4().hex
+        try:
+            fcntl.flock(lease.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+            if address in _USED_WORKER_ADDRESSES:
+                raise RuntimeError(
+                    "Monarch 0.5 requires a fresh owned-worker address per "
+                    f"generation; use port 0 instead of reusing {address}"
+                )
+            _require_bindable_worker_address(address)
+            environment = os.environ.copy()
+            _prepare_child_environment(worker=True, environ=environment)
+            process = subprocess.Popen(
+                [
+                    sys.executable,
+                    "-c",
+                    _WORKER_CODE,
+                    address,
+                    "--parent-pid",
+                    str(os.getpid()),
+                    "--ownership-token",
+                    ownership_token,
+                ],
+                env=environment,
+                stdin=subprocess.DEVNULL,
+                start_new_session=True,
+            )
+            _wait_for_worker_listener(process, address, startup_timeout_s)
+            _write_owned_worker_metadata(lease, address, process, ownership_token)
+            _USED_WORKER_ADDRESSES.add(address)
+            return _WorkerSession(
+                address=address,
+                process=process,
+                label=f"Monarch worker {address}",
+                lease=lease,
+            )
+        except BaseException:
+            if process is not None:
+                _stop_worker_process(process)
+            lease.close()
+            raise
+
+
+def _stop_worker_sessions(workers: Sequence[_WorkerSession]) -> None:
+    failures: list[BaseException] = []
+    for worker in workers:
+        try:
+            worker.release()
+        except BaseException as error:
+            failures.append(error)
+    for worker in workers:
+        try:
+            worker.wait()
+        except BaseException as error:
+            failures.append(error)
+    if failures:
+        raise BaseExceptionGroup("Monarch worker cleanup failed", failures)
+
+
+def _stop_worker(worker: _WorkerSession) -> None:
+    _stop_worker_sessions((worker,))
+    with _WORKER_ADDRESS_LOCK:
+        _reconcile_orphaned_workers()
+
+
+def run_local(
+    program: "InstalledAsyncCallable",
+    *,
+    port: int = DEFAULT_MONARCH_PORT,
+    startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S,
+) -> None:
+    """Own one clean loopback worker and controller for one local program."""
+
+    address = require_local_worker_address((_tcp_address("127.0.0.1", port),))
+    worker = _start_worker(address, startup_timeout_s=startup_timeout_s)
+    try:
+        asyncio.run(
+            run_explicit_controller(
+                ExplicitHostBootstrap(worker_addresses=(worker.address,)),
+                program,
+                startup_timeout_s=startup_timeout_s,
+                owned_workers=(worker,),
+            )
+        )
+    except BaseException as program_error:
+        try:
+            _stop_worker(worker)
+        except BaseException as cleanup_error:
+            raise BaseExceptionGroup(
+                "local controller and worker cleanup failed",
+                [program_error, cleanup_error],
+            ) from None
+        raise
+    _stop_worker(worker)
+
+
+def _lifecycle_listener(spec: SkyPilotBootstrap) -> socket.socket:
+    # Task parents use this channel to leave together independently of worker exit.
+    family = (
+        socket.AF_INET6
+        if ipaddress.ip_address(spec.node_ips[0]).version == 6
+        else socket.AF_INET
+    )
+    listener = socket.socket(family, socket.SOCK_STREAM)
+    try:
+        listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        listener.bind((spec.node_ips[0], spec.lifecycle_port))
+        listener.listen(len(spec.node_ips) - 1)
+        return listener
+    except BaseException:
+        listener.close()
+        raise
+
+
+def _accept_sky_peers(
+    spec: SkyPilotBootstrap,
+    listener: socket.socket,
+    worker: _WorkerSession,
+    startup_timeout_s: float,
+) -> list[socket.socket]:
+    peers: list[socket.socket] = []
+    deadline = time.monotonic() + startup_timeout_s
+    try:
+        while len(peers) < len(spec.node_ips) - 1:
+            if not worker.is_alive():
+                raise RuntimeError(f"Monarch worker exited with code {worker.exitcode}")
+            remaining = deadline - time.monotonic()
+            if remaining <= 0:
+                missing = len(spec.node_ips) - 1 - len(peers)
+                raise TimeoutError(f"timed out waiting for {missing} SkyPilot rank(s)")
+            listener.settimeout(min(1.0, remaining))
+            try:
+                connection, _ = listener.accept()
+            except TimeoutError:
+                continue
+            connection.settimeout(None)
+            peers.append(connection)
+        return peers
+    except BaseException:
+        for connection in peers:
+            connection.close()
+        raise
+
+
+def _wait_for_sky_controller(
+    spec: SkyPilotBootstrap,
+    worker: _WorkerSession,
+    startup_timeout_s: float,
+) -> None:
+    deadline = time.monotonic() + startup_timeout_s
+    last_error: OSError | None = None
+    while time.monotonic() < deadline:
+        if not worker.is_alive():
+            raise RuntimeError(f"Monarch worker exited with code {worker.exitcode}")
+        try:
+            connection = socket.create_connection(
+                (spec.node_ips[0], spec.lifecycle_port), timeout=1
+            )
+            break
+        except OSError as error:
+            last_error = error
+            time.sleep(0.2)
+    else:
+        raise TimeoutError("timed out connecting to SkyPilot rank 0") from last_error
+    with connection:
+        connection.settimeout(None)
+        status = connection.recv(1)
+    if status != b"\x00":
+        detail = "failed" if status == b"\x01" else "disconnected"
+        raise RuntimeError(f"SkyPilot rank-0 ART controller {detail}")
+
+
+def _notify_sky_peers(peers: Sequence[socket.socket], success: bool) -> None:
+    status = b"\x00" if success else b"\x01"
+    for connection in peers:
+        try:
+            connection.sendall(status)
+        except OSError:
+            pass
+        finally:
+            connection.close()
+
+
+def run_skypilot(
+    program_module: str,
+    program_qualname: str,
+    *,
+    port: int = DEFAULT_MONARCH_PORT,
+    startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S,
+) -> None:
+    """Translate SkyPilot topology and own one worker process per task rank."""
+
+    spec = SkyPilotBootstrap.from_environ(port=port)
+    worker = _start_worker(
+        spec.worker_addresses[spec.node_rank], startup_timeout_s=startup_timeout_s
+    )
+    if spec.node_rank != 0:
+        try:
+            _wait_for_sky_controller(spec, worker, startup_timeout_s)
+        finally:
+            _stop_worker(worker)
+        return
+
+    peers: list[socket.socket] = []
+    listener: socket.socket | None = None
+    success = False
+    try:
+        from .rollout import InstalledAsyncCallable
+
+        program = InstalledAsyncCallable(
+            module=program_module,
+            qualname=program_qualname,
+        )
+        if len(spec.node_ips) > 1:
+            listener = _lifecycle_listener(spec)
+            peers = _accept_sky_peers(spec, listener, worker, startup_timeout_s)
+        asyncio.run(
+            run_explicit_controller(
+                ExplicitHostBootstrap(worker_addresses=spec.worker_addresses),
+                program,
+                startup_timeout_s=startup_timeout_s,
+                owned_workers=(worker,),
+            )
+        )
+        success = True
+    finally:
+        _notify_sky_peers(peers, success)
+        if listener is not None:
+            listener.close()
+        _stop_worker(worker)
+
+
+def _require_unused_ssh_addresses(spec: SshBootstrap) -> None:
+    for host in spec.hosts:
+        try:
+            connection = socket.create_connection(
+                (host.worker_host.strip("[]"), spec.port),
+                timeout=0.2,
+            )
+        except OSError:
+            continue
+        connection.close()
+        raise RuntimeError(
+            "refusing to reuse a pre-existing Monarch worker listener at "
+            f"{host.worker_host}:{spec.port}"
+        )
+
+
+def _start_ssh_workers(
+    spec: SshBootstrap,
+    startup_timeout_s: float,
+) -> list[_WorkerSession]:
+    workers: list[_WorkerSession] = []
+    environment = os.environ.copy()
+    environment.pop("ART_VIRTUAL_ENV", None)
+    environment.pop("PYTHONPATH", None)
+    try:
+        for host, address in zip(spec.hosts, spec.worker_addresses, strict=True):
+            launch_id = uuid.uuid4().hex
+            python_path = os.environ.get(_PROGRAM_PYTHONPATH_ENV)
+            command_prefix = ("env", f"PYTHONPATH={python_path}") if python_path else ()
+            command = "exec " + shlex.join(
+                (
+                    *command_prefix,
+                    spec.python_executable,
+                    "-m",
+                    "art.distributed.monarch_bootstrap",
+                    "worker",
+                    "--address",
+                    address,
+                    "--launch-id",
+                    launch_id,
+                    "--startup-timeout",
+                    str(startup_timeout_s),
+                )
+            )
+            workers.append(
+                _WorkerSession(
+                    address=address,
+                    process=subprocess.Popen(
+                        (
+                            "ssh",
+                            "-o",
+                            "BatchMode=yes",
+                            *spec.ssh_args,
+                            host.target,
+                            command,
+                        ),
+                        stdin=subprocess.PIPE,
+                        stdout=subprocess.PIPE,
+                        env=environment,
+                    ),
+                    label=f"SSH worker {host.target!r}",
+                    graceful=True,
+                    launch_id=launch_id,
+                )
+            )
+        return workers
+    except BaseException as startup_error:
+        try:
+            _stop_ssh_workers(spec, workers)
+        except BaseException as cleanup_error:
+            raise BaseExceptionGroup(
+                "SSH worker startup and cleanup failed",
+                [startup_error, cleanup_error],
+            ) from None
+        raise
+
+
+def _wait_for_ssh_workers(
+    spec: SshBootstrap, workers: Sequence[_WorkerSession], timeout_s: float
+) -> None:
+    pending = {
+        host.target: (host, worker)
+        for host, worker in zip(spec.hosts, workers, strict=True)
+    }
+    streams = {}
+    for target, (_, worker) in pending.items():
+        assert worker.process.stdout is not None and worker.launch_id is not None
+        streams[worker.process.stdout] = target
+    ready: set[str] = set()
+    deadline = time.monotonic() + timeout_s
+    while pending:
+        for target, (_, worker) in tuple(pending.items()):
+            if (code := worker.exitcode) is not None:
+                raise RuntimeError(f"SSH worker {target!r} exited {code} before ready")
+        wait = max(0.0, min(0.05, deadline - time.monotonic()))
+        readable, _, _ = select.select(tuple(streams), (), (), wait)
+        for stream in readable:
+            target = streams.pop(stream)
+            _, worker = pending[target]
+            assert worker.launch_id is not None
+            expected = _SSH_READY_PREFIX + worker.launch_id.encode() + b"\n"
+            if stream.readline() != expected:
+                raise RuntimeError(
+                    f"SSH worker {target!r} did not prove launch identity"
+                )
+            ready.add(target)
+        for target in tuple(ready):
+            host, _ = pending[target]
+            try:
+                with socket.create_connection(
+                    (host.worker_host.strip("[]"), spec.port),
+                    timeout=0.2,
+                ):
+                    pending.pop(target)
+                    ready.remove(target)
+            except OSError:
+                pass
+        if time.monotonic() >= deadline:
+            raise TimeoutError(f"timed out waiting for SSH workers {tuple(pending)}")
+
+
+def _stop_ssh_workers(
+    _spec: SshBootstrap,
+    workers: Sequence[_WorkerSession],
+) -> None:
+    _stop_worker_sessions(workers)
+
+
+@contextmanager
+def _ssh_termination_signals() -> Iterator[Callable[[], None]]:
+    received = False
+
+    def terminate(signum: int, _frame: Any) -> None:
+        nonlocal received
+        if not received:
+            received = True
+            raise SystemExit(128 + signum)
+
+    managed = (signal.SIGTERM, signal.SIGHUP)
+    previous = {signum: signal.signal(signum, terminate) for signum in managed}
+
+    def ignore() -> None:
+        for signum in managed:
+            signal.signal(signum, signal.SIG_IGN)
+
+    try:
+        yield ignore
+    finally:
+        for signum, handler in previous.items():
+            signal.signal(signum, handler)
+
+
+def run_ssh(
+    spec: SshBootstrap,
+    program: "InstalledAsyncCallable",
+    *,
+    startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S,
+) -> None:
+    """Start workers on passwordless SSH hosts and own them for one ART run."""
+
+    with _ssh_termination_signals() as ignore_termination:
+        _require_unused_ssh_addresses(spec)
+        workers = _start_ssh_workers(spec, startup_timeout_s)
+        try:
+            _wait_for_ssh_workers(spec, workers, startup_timeout_s)
+            asyncio.run(
+                run_explicit_controller(
+                    ExplicitHostBootstrap(worker_addresses=spec.worker_addresses),
+                    program,
+                    startup_timeout_s=startup_timeout_s,
+                    owned_workers=workers,
+                )
+            )
+        except BaseException as program_error:
+            ignore_termination()
+            try:
+                _stop_ssh_workers(spec, workers)
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "SSH controller and worker cleanup failed",
+                    [program_error, cleanup_error],
+                ) from None
+            raise
+        ignore_termination()
+        _stop_ssh_workers(spec, workers)
+
+
+def main(argv: Sequence[str] | None = None) -> None:
+    parser = argparse.ArgumentParser(
+        description="ART Monarch bootstrap (trusted private networks only)",
+        epilog=(
+            "ART uses Monarch trust-all transport; never expose worker addresses "
+            "to a public or untrusted network."
+        ),
+    )
+    subparsers = parser.add_subparsers(dest="command", required=True)
+    worker = subparsers.add_parser("worker")
+    worker.add_argument("--address", required=True)
+    worker.add_argument("--launch-id")
+    worker.add_argument(
+        "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S
+    )
+    controller = subparsers.add_parser(
+        "controller", help="attach to worker commands managed by the caller"
+    )
+    controller.add_argument("--worker", action="append", required=True)
+    program_help = "module:qualname or path.py:qualname"
+    controller.add_argument("--program", required=True, help=program_help)
+    controller.add_argument(
+        "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S
+    )
+    local = subparsers.add_parser("local", help="own one loopback worker")
+    local.add_argument("--program", required=True, help=program_help)
+    local.add_argument("--port", type=int, default=DEFAULT_MONARCH_PORT)
+    local.add_argument(
+        "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S
+    )
+    sky = subparsers.add_parser(
+        "skypilot", help="consume the nodes in one SkyPilot task"
+    )
+    sky.add_argument("--program", required=True, help=program_help)
+    sky.add_argument("--port", type=int, default=DEFAULT_MONARCH_PORT)
+    sky.add_argument(
+        "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S
+    )
+    ssh = subparsers.add_parser(
+        "ssh", help="start and own workers on preallocated SSH hosts"
+    )
+    ssh.add_argument(
+        "--host",
+        action="append",
+        required=True,
+        help="[USER@]SSH_TARGET[=WORKER_HOST]",
+    )
+    ssh.add_argument("--program", required=True, help=program_help)
+    ssh.add_argument("--python", default=sys.executable, dest="python_executable")
+    ssh.add_argument("--port", type=int, default=DEFAULT_MONARCH_PORT)
+    ssh.add_argument(
+        "--ssh-arg",
+        action="append",
+        default=[],
+        help="argument passed to ssh; use --ssh-arg=VALUE",
+    )
+    ssh.add_argument(
+        "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S
+    )
+    args = parser.parse_args(argv)
+    if args.command == "worker":
+        run_worker(
+            args.address,
+            launch_id=args.launch_id,
+            startup_timeout_s=args.startup_timeout,
+        )
+        return
+    try:
+        module, qualname = _program_reference(args.program)
+    except ValueError as error:
+        parser.error(str(error))
+    if args.command == "skypilot":
+        run_skypilot(
+            module,
+            qualname,
+            port=args.port,
+            startup_timeout_s=args.startup_timeout,
+        )
+        return
+    from .rollout import InstalledAsyncCallable
+
+    program = InstalledAsyncCallable(module=module, qualname=qualname)
+    if args.command == "local":
+        run_local(
+            program,
+            port=args.port,
+            startup_timeout_s=args.startup_timeout,
+        )
+    elif args.command == "ssh":
+        run_ssh(
+            SshBootstrap(
+                hosts=tuple(_parse_ssh_host(host) for host in args.host),
+                python_executable=args.python_executable,
+                port=args.port,
+                ssh_args=tuple(args.ssh_arg),
+            ),
+            program,
+            startup_timeout_s=args.startup_timeout,
+        )
+    else:
+        asyncio.run(
+            run_explicit_controller(
+                ExplicitHostBootstrap(worker_addresses=tuple(args.worker)),
+                program,
+                startup_timeout_s=args.startup_timeout,
+            )
+        )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/art/distributed/monarch_runtime.py b/src/art/distributed/monarch_runtime.py
new file mode 100644
index 000000000..c1f0a0c70
--- /dev/null
+++ b/src/art/distributed/monarch_runtime.py
@@ -0,0 +1,255 @@
+from __future__ import annotations
+
+import asyncio
+from typing import Any, Literal
+
+from pydantic import BaseModel, ConfigDict
+
+from art.trajectories import TrajectoryGroup
+
+from .adapter_transport import AdapterReceiveResult, AdapterTransferTarget
+from .data_plane import PackedBatchRef, PackedBatchTransfer
+from .packing import PackingRequest, PackingResult
+from .rollout import (
+    RolloutInvocation,
+    RolloutResult,
+    RolloutWorkerEndpoint,
+)
+from .trajectory_store import (
+    TrajectoryEnqueueResult,
+    TrajectoryGroupRef,
+    TrajectoryQueueItem,
+    TrajectoryQueuePacking,
+    TrajectoryQueueRelease,
+    TrajectoryQueueResize,
+    TrajectoryQueueSnapshot,
+    TrajectoryQueueTake,
+)
+from .vllm_replica import HostMemberLaunchRequest, HostMemberState
+
+
+class RemoteCallError(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    kind: Literal["cancelled", "capacity", "input", "lease", "serving", "internal"]
+    error_type: str
+    message: str
+    traceback: str
+
+
+class RemoteCallResult(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid", frozen=True)
+
+    value: Any = None
+    error: RemoteCallError | None = None
+
+
+def unwrap_remote_call(result: RemoteCallResult) -> Any:
+    if result.error is None:
+        return result.value
+    error = result.error
+    message = f"remote {error.error_type}: {error.message}\n{error.traceback}"
+    if error.kind == "cancelled":
+        raise asyncio.CancelledError(message)
+    if error.kind == "serving":
+        from art.errors import LocalServingUnavailableError
+
+        raise LocalServingUnavailableError(message)
+    if error.kind == "capacity":
+        from .data_plane import PackedBatchCapacityError
+
+        raise PackedBatchCapacityError(message)
+    if error.kind == "lease":
+        from .data_plane import PackedBatchLeaseError
+
+        raise PackedBatchLeaseError(message)
+    if error.kind == "input":
+        raise ValueError(message)
+    raise RuntimeError(message)
+
+
+async def call_remote(endpoint: Any, *args: Any) -> Any:
+    return unwrap_remote_call(await endpoint.call_one(*args))
+
+
+class MonarchRolloutWorkerEndpoint(RolloutWorkerEndpoint):
+    def __init__(
+        self, actor: Any, *, timeout_s: float, owns_actor: bool = False
+    ) -> None:
+        self.actor = actor
+        self.timeout_s = timeout_s
+        self.owns_actor = owns_actor
+
+    async def run(self, invocation: RolloutInvocation) -> RolloutResult:
+        await self.actor.initialized
+        return await call_remote(self.actor.run, invocation)
+
+    async def materialize(self, ref: TrajectoryGroupRef) -> TrajectoryGroup:
+        transfer = ref.transfer
+        if transfer is None:
+            raise RuntimeError("remote trajectory has no data-plane transfer")
+        if transfer.stream.stream_id != ref.result_id:
+            raise RuntimeError("trajectory owner returned the wrong result ID")
+        if transfer.stream.byte_count != ref.descriptor.byte_count:
+            raise RuntimeError("trajectory owner returned the wrong byte count")
+        groups = await transfer.receive_groups(timeout_s=self.timeout_s)
+        if len(groups) != 1:
+            raise RuntimeError("trajectory owner returned the wrong group count")
+        return groups[0]
+
+    async def drop(self, ref: TrajectoryGroupRef) -> None:
+        await call_remote(self.actor.drop_result, ref)
+
+    async def close(self) -> None:
+        if self.owns_actor:
+            await call_remote(self.actor.close)
+
+
+class MonarchTrajectoryQueueEndpoint:
+    def __init__(self, actor: Any) -> None:
+        self.actor = actor
+
+    async def create(
+        self,
+        queue_id: str,
+        max_ready_groups: int,
+        capacity_records: int,
+        capacity_bytes: int,
+    ) -> None:
+        await call_remote(
+            self.actor.create_trajectory_queue,
+            queue_id,
+            max_ready_groups,
+            capacity_records,
+            capacity_bytes,
+        )
+
+    async def enqueue(
+        self, queue_id: str, item: TrajectoryQueueItem
+    ) -> TrajectoryEnqueueResult:
+        return await call_remote(self.actor.enqueue_trajectory, queue_id, item)
+
+    async def resize(self, operation: TrajectoryQueueResize) -> None:
+        await call_remote(self.actor.resize_trajectory_queue, operation)
+
+    async def take(
+        self, queue_id: str, consumer_id: str, count: int
+    ) -> TrajectoryQueueTake:
+        return await call_remote(
+            self.actor.take_trajectory, queue_id, consumer_id, count
+        )
+
+    async def mark_packed(self, operation: TrajectoryQueuePacking) -> None:
+        await call_remote(self.actor.mark_trajectories_packed, operation)
+
+    async def release(self, operation: TrajectoryQueueRelease) -> None:
+        await call_remote(self.actor.release_trajectory, operation)
+
+    async def finish(self, queue_id: str) -> None:
+        await call_remote(self.actor.finish_trajectory_queue, queue_id)
+
+    async def snapshot(self, queue_id: str) -> TrajectoryQueueSnapshot:
+        return await call_remote(self.actor.trajectory_queue_snapshot, queue_id)
+
+    async def close(self, queue_id: str) -> tuple[TrajectoryGroupRef, ...]:
+        return await call_remote(self.actor.close_trajectory_queue, queue_id)
+
+
+class MonarchVllmHostLauncher:
+    def __init__(self, actor: Any, adapter_actor: Any) -> None:
+        self.actor = actor
+        self.adapter_actor = adapter_actor
+
+    async def start_member(self, request: HostMemberLaunchRequest) -> HostMemberState:
+        return await call_remote(self.actor.start_vllm_member, request)
+
+    async def member_state(
+        self, replica_id: str, member_id: str, generation: int
+    ) -> HostMemberState:
+        return await call_remote(
+            self.actor.vllm_member_state, replica_id, member_id, generation
+        )
+
+    async def stop_member(
+        self, replica_id: str, member_id: str, generation: int
+    ) -> None:
+        await call_remote(
+            self.actor.stop_vllm_member, replica_id, member_id, generation
+        )
+
+    async def prepare_adapter_receive(
+        self,
+        generation_id: str,
+        template_path: str,
+        timeout_s: float,
+        transport: Literal["local", "nixl"],
+    ) -> AdapterTransferTarget:
+        return await call_remote(
+            self.adapter_actor.prepare,
+            generation_id,
+            template_path,
+            timeout_s,
+            transport,
+        )
+
+    async def wait_adapter_receive(
+        self, generation_id: str, timeout_s: float
+    ) -> AdapterReceiveResult:
+        deadline = asyncio.get_running_loop().time() + timeout_s
+        while True:
+            result = await call_remote(self.adapter_actor.poll, generation_id)
+            if result is not None:
+                return result
+            remaining_s = deadline - asyncio.get_running_loop().time()
+            if remaining_s <= 0:
+                raise TimeoutError(f"Adapter transfer timed out: {generation_id}")
+            await asyncio.sleep(min(0.01, remaining_s))
+
+    async def release_adapter_receive(self, generation_id: str) -> None:
+        await call_remote(self.adapter_actor.release, generation_id)
+
+
+class MonarchPackedBatchInbox:
+    def __init__(self, actor: Any) -> None:
+        self.actor = actor
+
+    async def receive(
+        self, ref: PackedBatchRef, transfer: PackedBatchTransfer, *, timeout_s: float
+    ) -> PackedBatchRef:
+        return await call_remote(self.actor.receive_batch, ref, transfer, timeout_s)
+
+    async def drop(self, ref: PackedBatchRef) -> None:
+        await call_remote(self.actor.drop_batch_ref, ref)
+
+    async def reclaim(self, batch_id: str, *, fence: bool) -> bool:
+        return await call_remote(self.actor.reclaim_batch, batch_id, fence)
+
+
+class MonarchPackedBatchSource:
+    def __init__(self, actor: Any) -> None:
+        self.actor = actor
+
+    async def publish(self, ref: PackedBatchRef) -> PackedBatchTransfer:
+        return await call_remote(self.actor.publish_batch, ref)
+
+    async def drop(self, batch_id: str) -> None:
+        await call_remote(self.actor.drop_batch, batch_id)
+
+    async def note_transmitted(self, byte_count: int) -> None:
+        await call_remote(self.actor.note_batch_transmitted, byte_count)
+
+
+class MonarchPackingEndpoint:
+    def __init__(self, actor: Any) -> None:
+        self.actor = actor
+
+    async def pack(
+        self,
+        request: PackingRequest,
+        batch_id: str,
+        *,
+        transfer_timeout_s: float,
+    ) -> PackingResult:
+        return await call_remote(
+            self.actor.pack_batch, request, batch_id, transfer_timeout_s
+        )
diff --git a/src/art/distributed/nccl_preflight.py b/src/art/distributed/nccl_preflight.py
new file mode 100644
index 000000000..574c78901
--- /dev/null
+++ b/src/art/distributed/nccl_preflight.py
@@ -0,0 +1,332 @@
+from __future__ import annotations
+
+import asyncio
+import os
+from pathlib import Path
+import re
+import signal
+import sys
+import tempfile
+import time
+from typing import Literal
+import uuid
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from art.utils.lifecycle import complete_task
+
+from .specs import GpuId
+
+
+class _NcclRuntimeRequest(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    probe_id: str = Field(min_length=1)
+    runtime_kind: Literal["trainer", "vllm"]
+    master_addr: str = Field(min_length=1)
+    timeout_s: float = Field(gt=0)
+    runtime_python: str | None = Field(default=None, min_length=1)
+
+    @model_validator(mode="after")
+    def _validate_runtime_python(self) -> _NcclRuntimeRequest:
+        if (self.runtime_kind == "trainer") != (self.runtime_python is not None):
+            raise ValueError("trainer NCCL probes require their managed runtime Python")
+        return self
+
+
+class NcclPreflightSessionRequest(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    probe_id: str = Field(min_length=1)
+    lease_s: float = Field(gt=0)
+
+
+class NcclRendezvousRequest(_NcclRuntimeRequest):
+    pass
+
+
+class NcclRendezvousResult(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    host_id: str
+    port: int = Field(ge=1, le=65535)
+
+
+class NcclProbeRequest(_NcclRuntimeRequest):
+    rank: int = Field(ge=0)
+    world_size: int = Field(ge=2)
+    master_port: int = Field(ge=1, le=65535)
+    gpu_id: GpuId
+    net_name: str = Field(min_length=1)
+
+    @model_validator(mode="after")
+    def _validate_rank(self) -> "NcclProbeRequest":
+        if self.rank >= self.world_size:
+            raise ValueError("NCCL probe rank must be smaller than world_size")
+        return self
+
+
+class NcclProbeResult(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    host_id: str
+    rank: int = Field(ge=0)
+    net_name: str
+    duration_s: float = Field(ge=0)
+
+
+_PARENT_DEATH = r"""
+import ctypes
+import os
+import signal
+
+parent = os.getppid()
+libc = ctypes.CDLL(None, use_errno=True)
+if libc.prctl(1, signal.SIGTERM) != 0:
+    raise OSError(ctypes.get_errno(), "prctl(PR_SET_PDEATHSIG) failed")
+if os.getppid() != parent:
+    os.kill(os.getpid(), signal.SIGTERM)
+"""
+
+_RENDEZVOUS_SCRIPT = (
+    _PARENT_DEATH
+    + r"""
+from datetime import timedelta
+import select
+import sys
+import time
+
+import torch.distributed as dist
+
+store = dist.TCPStore(
+    os.environ["MASTER_ADDR"],
+    0,
+    None,
+    True,
+    timedelta(seconds=float(os.environ["ART_NCCL_TIMEOUT_S"])),
+    wait_for_workers=False,
+)
+print(f"ART_NCCL_RENDEZVOUS_PORT={store.port}", flush=True)
+remaining = max(0.0, float(os.environ["ART_NCCL_DEADLINE_S"]) - time.monotonic())
+select.select([sys.stdin.buffer], [], [], remaining)
+"""
+)
+
+_CHILD_SCRIPT = (
+    _PARENT_DEATH
+    + r"""
+from datetime import timedelta
+
+import torch
+import torch.distributed as dist
+
+rank = int(os.environ["RANK"])
+world_size = int(os.environ["WORLD_SIZE"])
+timeout = timedelta(seconds=float(os.environ["ART_NCCL_TIMEOUT_S"]))
+device = torch.device("cuda", 0)
+torch.cuda.set_device(device)
+store = dist.TCPStore(
+    os.environ["MASTER_ADDR"],
+    int(os.environ["MASTER_PORT"]),
+    None,
+    False,
+    timeout,
+)
+options = dist.ProcessGroupNCCL.Options()
+options.config.net_name = os.environ["ART_NCCL_EXPECTED_NET"]
+try:
+    dist.init_process_group(
+        "nccl",
+        store=store,
+        rank=rank,
+        world_size=world_size,
+        timeout=timeout,
+        pg_options=options,
+        device_id=device,
+    )
+    value = torch.tensor(rank + 1, device=device, dtype=torch.int64)
+    dist.all_reduce(value)
+    torch.cuda.synchronize(device)
+    expected = world_size * (world_size + 1) // 2
+    if value.item() != expected:
+        raise RuntimeError(f"NCCL preflight reduced {value.item()}, expected {expected}")
+finally:
+    if dist.is_initialized():
+        dist.destroy_process_group()
+"""
+)
+
+_VLLM_EXEC_SCRIPT = r"""
+import os
+import sys
+
+from art.vllm_runtime import (
+    RUNTIME_SERVER,
+    _runtime_python_for_nccl_discovery,
+    _vllm_runtime_subprocess_cwd,
+    _vllm_runtime_subprocess_env,
+)
+
+try:
+    python = _runtime_python_for_nccl_discovery()
+except RuntimeError as error:
+    raise RuntimeError(
+        "Cannot derive the Python environment behind ART_VLLM_RUNTIME_BIN; "
+        "point it directly to a .venv/bin/art-vllm-runtime-server executable"
+    ) from error
+server = str(python.parent / RUNTIME_SERVER)
+environment = _vllm_runtime_subprocess_env([server])
+os.chdir(_vllm_runtime_subprocess_cwd([server]))
+os.execve(str(python), [str(python), "-c", sys.argv[1]], environment)
+"""
+
+_RENDEZVOUS_PREFIX = b"ART_NCCL_RENDEZVOUS_PORT="
+_SELECTED_NETWORK = re.compile(r"NCCL INFO Using network ([^\r\n]+)$", re.MULTILINE)
+
+
+class NcclRendezvous:
+    def __init__(self, process: asyncio.subprocess.Process, port: int) -> None:
+        self.process = process
+        self.port = port
+
+    async def close(self) -> None:
+        await complete_task(asyncio.create_task(_stop_process(self.process)))
+
+
+def parse_selected_network(log: str, expected: str) -> str:
+    selected = tuple(value.strip() for value in _SELECTED_NETWORK.findall(log))
+    if selected != (expected,):
+        raise RuntimeError(
+            f"NCCL selected-network proof mismatch: expected={expected!r}, "
+            f"reported={selected!r}"
+        )
+    return selected[0]
+
+
+async def start_nccl_rendezvous(
+    request: NcclRendezvousRequest, *, deadline_s: float
+) -> NcclRendezvous:
+    command, environment = _runtime_launch(request, _RENDEZVOUS_SCRIPT)
+    environment.update(
+        {
+            "ART_NCCL_DEADLINE_S": str(deadline_s),
+            "ART_NCCL_TIMEOUT_S": str(request.timeout_s),
+            "CUDA_VISIBLE_DEVICES": "",
+            "MASTER_ADDR": request.master_addr,
+        }
+    )
+    process = await asyncio.create_subprocess_exec(
+        *command,
+        env=environment,
+        start_new_session=True,
+        stdin=asyncio.subprocess.PIPE,
+        stdout=asyncio.subprocess.PIPE,
+        stderr=asyncio.subprocess.STDOUT,
+    )
+    try:
+        async with asyncio.timeout(
+            min(request.timeout_s, max(0.0, deadline_s - time.monotonic()))
+        ):
+            port = await _read_rendezvous_port(process)
+        return NcclRendezvous(process, port)
+    except BaseException:
+        await complete_task(asyncio.create_task(_stop_process(process)))
+        raise
+
+
+async def run_nccl_probe(host_id: str, request: NcclProbeRequest) -> NcclProbeResult:
+    command, environment = _runtime_launch(request, _CHILD_SCRIPT)
+    log_path = Path(tempfile.gettempdir()) / (
+        f"art-nccl-{request.probe_id}-{request.rank}-{uuid.uuid4().hex}.log"
+    )
+    environment.update(
+        {
+            "ART_NCCL_EXPECTED_NET": request.net_name,
+            "ART_NCCL_TIMEOUT_S": str(request.timeout_s),
+            "CUDA_VISIBLE_DEVICES": str(request.gpu_id),
+            "MASTER_ADDR": request.master_addr,
+            "MASTER_PORT": str(request.master_port),
+            "NCCL_DEBUG": "INFO",
+            "NCCL_DEBUG_FILE": str(log_path),
+            "NCCL_DEBUG_SUBSYS": "INIT,NET",
+            "NCCL_NET": request.net_name,
+            "RANK": str(request.rank),
+            "WORLD_SIZE": str(request.world_size),
+        }
+    )
+    started = time.monotonic()
+    process: asyncio.subprocess.Process | None = None
+    try:
+        process = await asyncio.create_subprocess_exec(
+            *command,
+            env=environment,
+            start_new_session=True,
+            stdout=asyncio.subprocess.PIPE,
+            stderr=asyncio.subprocess.STDOUT,
+        )
+        async with asyncio.timeout(request.timeout_s):
+            output, _ = await process.communicate()
+        log = log_path.read_text(errors="replace") if log_path.exists() else ""
+        detail = output.decode(errors="replace")[-4000:]
+        if process.returncode:
+            raise RuntimeError(
+                f"NCCL {request.runtime_kind} preflight rank {request.rank} exited "
+                f"{process.returncode}:\n{detail}\n{log[-4000:]}"
+            )
+        selected = parse_selected_network(log, request.net_name)
+        return NcclProbeResult(
+            host_id=host_id,
+            rank=request.rank,
+            net_name=selected,
+            duration_s=time.monotonic() - started,
+        )
+    except BaseException:
+        if process is not None:
+            await complete_task(asyncio.create_task(_stop_process(process)))
+        raise
+    finally:
+        log_path.unlink(missing_ok=True)
+
+
+def _runtime_launch(
+    request: _NcclRuntimeRequest, script: str
+) -> tuple[tuple[str, ...], dict[str, str]]:
+    if request.runtime_kind == "trainer":
+        assert request.runtime_python is not None
+        return (request.runtime_python, "-c", script), os.environ.copy()
+    return (
+        _art_python(),
+        "-c",
+        _VLLM_EXEC_SCRIPT,
+        script,
+    ), os.environ.copy()
+
+
+def _art_python() -> str:
+    candidate = Path(os.environ.get("ART_VIRTUAL_ENV", sys.prefix)) / "bin/python"
+    return str(candidate if candidate.exists() else Path(sys.executable))
+
+
+async def _read_rendezvous_port(process: asyncio.subprocess.Process) -> int:
+    assert process.stdout is not None
+    output = bytearray()
+    while line := await process.stdout.readline():
+        if line.startswith(_RENDEZVOUS_PREFIX):
+            return int(line.removeprefix(_RENDEZVOUS_PREFIX))
+        output.extend(line)
+        del output[:-4000]
+    await process.wait()
+    raise RuntimeError(
+        f"NCCL rendezvous exited {process.returncode} before binding a port:\n"
+        f"{output.decode(errors='replace')}"
+    )
+
+
+async def _stop_process(process: asyncio.subprocess.Process) -> None:
+    if process.returncode is not None:
+        return
+    try:
+        os.killpg(process.pid, signal.SIGKILL)
+    except ProcessLookupError:
+        return
+    await process.wait()
diff --git a/src/art/distributed/nixl_runtime.py b/src/art/distributed/nixl_runtime.py
new file mode 100644
index 000000000..32a7742ea
--- /dev/null
+++ b/src/art/distributed/nixl_runtime.py
@@ -0,0 +1,117 @@
+from __future__ import annotations
+
+from collections.abc import MutableMapping
+from ctypes.util import find_library
+import importlib.util
+import os
+from pathlib import Path
+import platform
+import shutil
+
+from pydantic import BaseModel, ConfigDict
+
+
+class NixlRuntimePaths(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    module: str
+    library_dir: Path
+    dependency_library_dir: Path
+    plugin_dir: Path
+    ucx_module_dir: Path
+
+
+def discover_nixl_runtime() -> NixlRuntimePaths:
+    for module in ("nixl_cu13", "nixl_cu12", "nixl"):
+        spec = importlib.util.find_spec(module)
+        if spec is None or not spec.submodule_search_locations:
+            continue
+        site_packages = Path(next(iter(spec.submodule_search_locations))).parent
+        core = site_packages / f".{module}.mesonpy.libs"
+        dependencies = site_packages / f"{module}.libs"
+        plugin = dependencies / "nixl"
+        ucx = dependencies / "ucx"
+        if not (core / "libnixl.so").is_file():
+            raise RuntimeError(f"{module} is missing its bundled libnixl.so")
+        if not (plugin / "libplugin_UCX.so").is_file():
+            raise RuntimeError(f"{module} is missing its bundled UCX plugin")
+        if not (ucx / "libuct_ib_mlx5_gda.so").is_file():
+            raise RuntimeError(f"{module} is missing its UCX GDA transport")
+        return NixlRuntimePaths(
+            module=module,
+            library_dir=core,
+            dependency_library_dir=dependencies,
+            plugin_dir=plugin,
+            ucx_module_dir=ucx,
+        )
+    raise RuntimeError(
+        "NIXL is unavailable; install ART with the megatron or megatron-cu130 extra"
+    )
+
+
+def configure_nixl_environment(
+    environment: MutableMapping[str, str] | None = None,
+) -> NixlRuntimePaths:
+    environment = os.environ if environment is None else environment
+    paths = discover_nixl_runtime()
+    environment["NIXL_LIBRARY_DIR"] = str(paths.library_dir)
+    environment["NIXL_DEPENDENCY_LIBRARY_DIR"] = str(paths.dependency_library_dir)
+    environment["NIXL_PLUGIN_DIR"] = str(paths.plugin_dir)
+    environment["UCX_MODULE_DIR"] = str(paths.ucx_module_dir)
+    environment.setdefault("UCX_NET_DEVICES", "all")
+    environment.setdefault("UCX_TLS", "rc,rc_gda,cuda_copy")
+    environment.setdefault("UCX_IB_GDA_RETAIN_INACTIVE_CTX", "yes")
+    libraries = (str(paths.library_dir), str(paths.dependency_library_dir))
+    inherited = environment.get("LD_LIBRARY_PATH", "").split(os.pathsep)
+    environment["LD_LIBRARY_PATH"] = os.pathsep.join(
+        dict.fromkeys((*libraries, *filter(None, inherited)))
+    )
+    return paths
+
+
+def validate_nixl_host() -> NixlRuntimePaths:
+    """Fail before compilation when the image cannot support HybridEP GDA."""
+
+    if platform.system() != "Linux" or platform.machine() != "x86_64":
+        raise RuntimeError("multi-node HybridEP requires Linux x86_64")
+    missing_commands = [
+        command for command in ("c++", "gcc", "ninja") if shutil.which(command) is None
+    ]
+    if missing_commands:
+        raise RuntimeError(
+            f"HybridEP image is missing build tools: {', '.join(missing_commands)}"
+        )
+    required_files = (
+        Path("/usr/include/infiniband/verbs.h"),
+        Path("/dev/infiniband/rdma_cm"),
+        Path("/dev/infiniband/uverbs0"),
+    )
+    if missing := [str(path) for path in required_files if not path.exists()]:
+        raise RuntimeError(
+            f"HybridEP image is missing RDMA/GDA capabilities: {missing}"
+        )
+    try:
+        driver = Path("/proc/driver/nvidia/version").read_text()
+        modules = Path("/proc/modules").read_text()
+        parameters = Path("/proc/driver/nvidia/params").read_text()
+    except OSError as error:
+        raise RuntimeError(
+            "HybridEP cannot inspect NVIDIA kernel capabilities"
+        ) from error
+    if "Open Kernel Module" not in driver:
+        raise RuntimeError("HybridEP GDA requires the NVIDIA open kernel module")
+    if not any(line.startswith("nvidia_peermem ") for line in modules.splitlines()):
+        raise RuntimeError("HybridEP GDA requires loaded kernel module nvidia_peermem")
+    for setting in ("EnableStreamMemOPs: 1", "PeerMappingOverride=1"):
+        if setting not in parameters:
+            raise RuntimeError(f"HybridEP GDA requires NVIDIA setting {setting}")
+    missing_libraries = [
+        library for library in ("ibverbs", "mlx5") if find_library(library) is None
+    ]
+    if missing_libraries:
+        raise RuntimeError(
+            f"HybridEP image is missing RDMA libraries: {', '.join(missing_libraries)}"
+        )
+    if not any(Path("/sys/class/infiniband").glob("*")):
+        raise RuntimeError("HybridEP image exposes no InfiniBand device")
+    return configure_nixl_environment()
diff --git a/src/art/distributed/packing.py b/src/art/distributed/packing.py
new file mode 100644
index 000000000..1e2eae797
--- /dev/null
+++ b/src/art/distributed/packing.py
@@ -0,0 +1,336 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+import secrets
+from typing import TYPE_CHECKING, Any, Literal
+
+import numpy as np
+from openai.types.chat.chat_completion import Choice
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from art.pipeline_tuner.config import PackedGroupShape
+from art.preprocessing.moe_routing import (
+    ART_MOE_ROUTING_METADATA_KEY,
+    NUM_EXPERTS_KEY,
+    ROUTED_EXPERTS_KEY,
+    MoeRouteArray,
+    moe_route_dtype,
+)
+from art.trajectories import (
+    MetadataValue,
+    PydanticException,
+    Trajectory,
+    TrajectoryGroup,
+)
+
+from .data_plane import PackedBatchRef
+from .rollout import RolloutModelSpec
+from .trajectory_store import (
+    TrajectoryBatchTransfer,
+    TrajectoryGroupBundle,
+    TrajectoryQueueItem,
+)
+
+if TYPE_CHECKING:
+    from art.model import TrainableModel
+
+
+class _ChoiceRoutingPayload(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    metadata: dict[str, Any]
+    dtype: Literal["uint8", "uint16"]
+    shape: tuple[int, int, int]
+    data: bytes
+
+    @classmethod
+    def from_metadata(cls, metadata: dict[str, Any]) -> "_ChoiceRoutingPayload":
+        routes = metadata[ROUTED_EXPERTS_KEY]
+        if not isinstance(routes, np.ndarray) or routes.dtype not in {
+            np.dtype(np.uint8),
+            np.dtype(np.uint16),
+        }:
+            raise RuntimeError("routed experts must be a uint8 or uint16 array")
+        if routes.ndim != 3:
+            raise RuntimeError(f"routed experts must have rank 3, got {routes.shape}")
+        num_experts = int(metadata.get(NUM_EXPERTS_KEY, 0))
+        if routes.dtype != moe_route_dtype(num_experts):
+            raise RuntimeError("routed experts do not match exact expert count")
+        dtype: Literal["uint8", "uint16"] = (
+            "uint8" if routes.dtype == np.dtype(np.uint8) else "uint16"
+        )
+        return cls(
+            metadata={
+                key: value
+                for key, value in metadata.items()
+                if key != ROUTED_EXPERTS_KEY
+            },
+            dtype=dtype,
+            shape=routes.shape,
+            data=routes.tobytes(),
+        )
+
+    def build(self) -> dict[str, Any]:
+        num_experts = int(self.metadata[NUM_EXPERTS_KEY])
+        routes = MoeRouteArray(
+            np.frombuffer(self.data, dtype=self.dtype).reshape(self.shape),
+            num_experts=num_experts,
+        )
+        return {**self.metadata, ROUTED_EXPERTS_KEY: routes}
+
+
+class TrajectoryPayload(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    payload: dict[str, Any]
+    choice_positions: tuple[int, ...] = ()
+    additional_history_choice_positions: tuple[tuple[int, ...], ...] = ()
+    choice_routing_metadata: dict[int, _ChoiceRoutingPayload] = Field(
+        default_factory=dict
+    )
+    additional_history_choice_routing_metadata: tuple[
+        dict[int, _ChoiceRoutingPayload], ...
+    ] = ()
+    exchange_choice_routing_metadata: tuple[dict[int, _ChoiceRoutingPayload], ...] = ()
+
+    @classmethod
+    def from_trajectory(cls, trajectory: Trajectory) -> "TrajectoryPayload":
+        choice_routing = _choice_routing_metadata(trajectory.messages_and_choices)
+        history_routing = tuple(
+            _choice_routing_metadata(history.messages_and_choices)
+            for history in trajectory.additional_histories
+        )
+        exchange_routing = tuple(
+            _choice_routing_metadata(exchange.response.choices)
+            for exchange in trajectory.exchanges.chat_completions
+        )
+        exclude: dict[str, Any] = {
+            "messages_and_choices": _routing_exclude(choice_routing),
+            "additional_histories": {
+                index: {
+                    "messages_and_choices": _routing_exclude(routing),
+                }
+                for index, routing in enumerate(history_routing)
+            },
+        }
+        return cls(
+            payload=trajectory.model_dump(mode="json", exclude=exclude),
+            choice_positions=tuple(
+                index
+                for index, item in enumerate(trajectory.messages_and_choices)
+                if isinstance(item, Choice)
+            ),
+            additional_history_choice_positions=tuple(
+                tuple(
+                    index
+                    for index, item in enumerate(history.messages_and_choices)
+                    if isinstance(item, Choice)
+                )
+                for history in trajectory.additional_histories
+            ),
+            choice_routing_metadata=choice_routing,
+            additional_history_choice_routing_metadata=history_routing,
+            exchange_choice_routing_metadata=exchange_routing,
+        )
+
+    def build(self) -> Trajectory:
+        payload = dict(self.payload)
+        messages = list(payload.get("messages_and_choices", []))
+        for index in self.choice_positions:
+            messages[index] = _build_choice(
+                messages[index], self.choice_routing_metadata.get(index)
+            )
+        payload["messages_and_choices"] = messages
+        histories = [
+            dict(history) for history in payload.get("additional_histories", [])
+        ]
+        for history, positions, routing in zip(
+            histories,
+            self.additional_history_choice_positions,
+            self.additional_history_choice_routing_metadata,
+            strict=True,
+        ):
+            messages = list(history["messages_and_choices"])
+            for index in positions:
+                messages[index] = _build_choice(messages[index], routing.get(index))
+            history["messages_and_choices"] = messages
+        payload["additional_histories"] = histories
+        exchanges = dict(payload.get("exchanges", {}))
+        chat_exchanges = [
+            dict(exchange) for exchange in exchanges.get("chat_completions", [])
+        ]
+        for exchange, routing in zip(
+            chat_exchanges,
+            self.exchange_choice_routing_metadata,
+            strict=True,
+        ):
+            response = dict(exchange["response"])
+            choices = list(response["choices"])
+            for index, metadata in routing.items():
+                choices[index] = _build_choice(choices[index], metadata)
+            response["choices"] = choices
+            exchange["response"] = response
+        exchanges["chat_completions"] = chat_exchanges
+        payload["exchanges"] = exchanges
+        return Trajectory.model_validate(payload)
+
+
+def _choice_routing_metadata(items: list[Any]) -> dict[int, _ChoiceRoutingPayload]:
+    return {
+        index: _ChoiceRoutingPayload.from_metadata(metadata)
+        for index, item in enumerate(items)
+        if isinstance(item, Choice)
+        and isinstance(
+            metadata := (item.model_extra or {}).get(ART_MOE_ROUTING_METADATA_KEY),
+            dict,
+        )
+    }
+
+
+def _routing_exclude(
+    routing: dict[int, _ChoiceRoutingPayload],
+) -> dict[int, set[str]]:
+    return {index: {ART_MOE_ROUTING_METADATA_KEY} for index in routing}
+
+
+def _build_choice(payload: Any, routing: _ChoiceRoutingPayload | None) -> Choice:
+    choice = Choice.model_validate(payload)
+    if routing is not None:
+        if choice.model_extra is None:
+            raise RuntimeError("OpenAI Choice.model_extra is unavailable")
+        choice.model_extra[ART_MOE_ROUTING_METADATA_KEY] = routing.build()
+    return choice
+
+
+class TrajectoryGroupPayload(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    trajectories: tuple[TrajectoryPayload, ...]
+    exceptions: tuple[dict[str, str], ...] = ()
+    metadata: dict[str, MetadataValue] = Field(default_factory=dict)
+    metrics: dict[str, float | int | bool] = Field(default_factory=dict)
+    logs: tuple[str, ...] = ()
+    collect_packing_shape: bool = False
+
+    @classmethod
+    def from_group(cls, group: TrajectoryGroup) -> "TrajectoryGroupPayload":
+        return cls(
+            trajectories=tuple(
+                TrajectoryPayload.from_trajectory(trajectory)
+                for trajectory in group.trajectories
+            ),
+            exceptions=tuple(
+                exception.model_dump(mode="json") for exception in group.exceptions
+            ),
+            metadata=group.metadata,
+            metrics=group.metrics,
+            logs=tuple(group.logs),
+            collect_packing_shape=group._collect_packing_shape,
+        )
+
+    def build(self) -> TrajectoryGroup:
+        group = TrajectoryGroup(
+            (payload.build() for payload in self.trajectories),
+            metadata=self.metadata,
+            metrics=self.metrics,
+            logs=list(self.logs),
+        )
+        group.exceptions = [
+            PydanticException.model_validate(payload) for payload in self.exceptions
+        ]
+        group._collect_packing_shape = self.collect_packing_shape
+        return group
+
+
+class PackingRequest(BaseModel):
+    """Current ART packing inputs; generalized loss programs are intentionally absent."""
+
+    model_config = ConfigDict(extra="forbid", frozen=True, arbitrary_types_allowed=True)
+
+    model: RolloutModelSpec
+    generation_id: str = Field(min_length=1)
+    trajectory_groups: tuple[TrajectoryGroupBundle, ...] = ()
+    trajectory_transfer: TrajectoryBatchTransfer | None = None
+    trajectory_sources: tuple[TrajectoryQueueItem, ...] = ()
+    trajectory_log_path: str | None = None
+    advantage_balance: float = 0.0
+    allow_training_without_logprobs: bool = False
+    scale_rewards: bool = True
+    plot_tensors: bool = False
+    packed_sequence_length: int = Field(ge=1)
+    logprob_calculation_chunk_size: int = Field(default=1024, ge=1)
+    include_moe_routing: bool = False
+    collect_packing_shapes: bool = False
+    group_ids: tuple[str, ...] = ()
+    record_ids: tuple[str, ...] = ()
+    min_source_version: int = Field(default=0, ge=0)
+    max_source_version: int = Field(default=0, ge=0)
+
+    @model_validator(mode="after")
+    def _validate_trajectory_input(self) -> "PackingRequest":
+        inputs = (
+            bool(self.trajectory_groups),
+            self.trajectory_transfer is not None,
+            bool(self.trajectory_sources),
+        )
+        if sum(inputs) != 1:
+            raise ValueError("packing requires exactly one trajectory input")
+        return self
+
+    @classmethod
+    def from_groups(
+        cls,
+        model: TrainableModel,
+        trajectory_groups: Iterable[TrajectoryGroup],
+        *,
+        packed_sequence_length: int,
+        advantage_balance: float = 0.0,
+        allow_training_without_logprobs: bool = False,
+        scale_rewards: bool = True,
+        plot_tensors: bool = False,
+        logprob_calculation_chunk_size: int = 1024,
+        include_moe_routing: bool = False,
+        group_ids: tuple[str, ...] = (),
+        record_ids: tuple[str, ...] = (),
+        min_source_version: int = 0,
+        max_source_version: int = 0,
+    ) -> "PackingRequest":
+        """Build a serializable packing request from public ART objects."""
+
+        return cls(
+            model=RolloutModelSpec.from_model(model),
+            generation_id=secrets.token_hex(16),
+            trajectory_groups=tuple(
+                TrajectoryGroupBundle.from_group(group) for group in trajectory_groups
+            ),
+            advantage_balance=advantage_balance,
+            allow_training_without_logprobs=allow_training_without_logprobs,
+            scale_rewards=scale_rewards,
+            plot_tensors=plot_tensors,
+            packed_sequence_length=packed_sequence_length,
+            logprob_calculation_chunk_size=logprob_calculation_chunk_size,
+            include_moe_routing=include_moe_routing,
+            collect_packing_shapes=any(
+                group._collect_packing_shape for group in trajectory_groups
+            ),
+            group_ids=group_ids,
+            record_ids=record_ids,
+            min_source_version=min_source_version,
+            max_source_version=max_source_version,
+        )
+
+
+class PackingResult(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True, arbitrary_types_allowed=True)
+
+    ref: PackedBatchRef | None
+    packed_group_shapes: tuple[PackedGroupShape | None, ...]
+    trainable_assistant_tokens: int = Field(default=0, ge=0)
+    loss_bearing_tokens: int = Field(default=0, ge=0)
+    non_padding_tokens: int = Field(default=0, ge=0)
+    trajectory_log_path: str | None = None
+    trajectory_fetch_s: float = Field(default=0.0, ge=0)
+    packing_core_s: float = Field(default=0.0, ge=0)
+    trajectory_log_wait_s: float = Field(default=0.0, ge=0)
+    packed_batch_finalize_s: float = Field(default=0.0, ge=0)
+    generation_id: str = Field(min_length=1)
diff --git a/src/art/distributed/rollout.py b/src/art/distributed/rollout.py
new file mode 100644
index 000000000..0bbd376ea
--- /dev/null
+++ b/src/art/distributed/rollout.py
@@ -0,0 +1,1126 @@
+from __future__ import annotations
+
+import asyncio
+from collections import deque
+from collections.abc import Awaitable, Callable, Mapping, Sequence
+from functools import lru_cache
+import hashlib
+import importlib
+import inspect
+import json
+from pathlib import Path
+import time
+from typing import Any, Literal, Protocol
+import uuid
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from art.model import TrainableModel
+from art.serving_capabilities import ServingCapabilities
+from art.trajectories import (
+    MetadataValue,
+    PydanticException,
+    Trajectory,
+    TrajectoryGroup,
+)
+
+from .trajectory_store import (
+    TrajectoryCapacityError,
+    TrajectoryEnqueueResult,
+    TrajectoryGroupAnnotations,
+    TrajectoryGroupRef,
+    TrajectoryLeaseError,
+    TrajectoryQueueItem,
+    TrajectoryQueueLease,
+    TrajectoryQueuePacking,
+    TrajectoryQueueRelease,
+    TrajectoryQueueResize,
+    TrajectoryQueueSnapshot,
+    TrajectoryQueueStore,
+    TrajectoryQueueTake,
+    TrajectoryRecordStore,
+)
+
+
+class InstalledAsyncCallable(BaseModel):
+    """Import path for installed user code; functions and closures are never shipped."""
+
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    module: str = Field(min_length=1)
+    qualname: str = Field(min_length=1)
+    source_sha256: str | None = Field(default=None, pattern=r"^[0-9a-f]{64}$")
+
+    @model_validator(mode="after")
+    def _validate_import_path(self) -> "InstalledAsyncCallable":
+        if self.qualname == "<lambda>" or "<locals>" in self.qualname.split("."):
+            raise ValueError(
+                "distributed rollout callable must be a top-level function"
+            )
+        if self.source_sha256 is None:
+            object.__setattr__(
+                self, "source_sha256", _callable_source_sha256(self._resolve())
+            )
+        return self
+
+    @classmethod
+    def from_callable(
+        cls, function: Callable[..., Awaitable[Any]]
+    ) -> "InstalledAsyncCallable":
+        module = getattr(function, "__module__", None)
+        qualname = getattr(function, "__qualname__", None)
+        if not module or not qualname:
+            raise ValueError(
+                "distributed rollout callable requires module and qualname"
+            )
+        reference = cls(module=module, qualname=qualname)
+        if not inspect.iscoroutinefunction(function):
+            raise TypeError("distributed rollout callable must be async")
+        if reference.resolve() is not function:
+            raise ValueError(
+                "distributed rollout callable must resolve from installed code"
+            )
+        return reference
+
+    def resolve(self) -> Callable[..., Awaitable[Any]]:
+        assert self.source_sha256 is not None
+        return _verified_callable(self.module, self.qualname, self.source_sha256)
+
+    def _resolve(self) -> Callable[..., Awaitable[Any]]:
+        value: Any = importlib.import_module(self.module)
+        for component in self.qualname.split("."):
+            value = getattr(value, component)
+        if not inspect.iscoroutinefunction(value):
+            raise TypeError(f"{self.module}:{self.qualname} is not an async function")
+        return value
+
+
+@lru_cache(maxsize=128)
+def _verified_callable(
+    module: str, qualname: str, source_sha256: str
+) -> Callable[..., Awaitable[Any]]:
+    value: Any = importlib.import_module(module)
+    for component in qualname.split("."):
+        value = getattr(value, component)
+    if not inspect.iscoroutinefunction(value):
+        raise TypeError(f"{module}:{qualname} is not an async function")
+    if _callable_source_sha256(value) != source_sha256:
+        raise RuntimeError(f"installed callable source differs for {module}:{qualname}")
+    return value
+
+
+def _callable_source_sha256(function: Callable[..., Awaitable[Any]]) -> str:
+    source = inspect.getsourcefile(function)
+    if source is None:
+        raise ValueError("distributed callable must come from a source-backed module")
+    try:
+        payload = Path(source).read_bytes()
+    except OSError as error:
+        raise RuntimeError(
+            f"cannot read distributed callable source {source}: {error}"
+        ) from None
+    return hashlib.sha256(payload).hexdigest()
+
+
+class RolloutModelSpec(BaseModel):
+    """Serializable inference-only view of a registered trainable model."""
+
+    model_config = ConfigDict(extra="forbid", frozen=True, arbitrary_types_allowed=True)
+
+    payload: dict[str, Any]
+    user_config: Any = None
+    internal_config: dict[str, Any] | None = None
+    serving_capabilities: ServingCapabilities | None = None
+    binary_routes_base_url: str | None = None
+
+    @classmethod
+    def from_model(cls, model: TrainableModel) -> "RolloutModelSpec":
+        payload = model.model_dump(mode="json")
+        payload["config"] = None
+        payload["inference_model_name"] = model.get_inference_name()
+        return cls(
+            payload=payload,
+            user_config=model.config,
+            internal_config=(
+                dict(model._internal_config)
+                if model._internal_config is not None
+                else None
+            ),
+            serving_capabilities=model._serving_capabilities,
+            binary_routes_base_url=model._art_binary_routes_base_url,
+        )
+
+    @property
+    def cache_key(self) -> str:
+        payload = {
+            "model": self.payload,
+            "internal_config": self.internal_config,
+            "capabilities": (
+                self.serving_capabilities.model_dump(mode="json")
+                if self.serving_capabilities is not None
+                else None
+            ),
+            "binary_routes_base_url": self.binary_routes_base_url,
+        }
+        return hashlib.sha256(
+            json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
+        ).hexdigest()
+
+    def build(self) -> TrainableModel:
+        model = TrainableModel.model_validate(self.payload)
+        object.__setattr__(model, "config", self.user_config)
+        object.__setattr__(model, "_internal_config", self.internal_config)
+        object.__setattr__(model, "_serving_capabilities", self.serving_capabilities)
+        object.__setattr__(
+            model, "_art_binary_routes_base_url", self.binary_routes_base_url
+        )
+        return model
+
+
+class RolloutInvocation(BaseModel):
+    model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
+
+    callable: InstalledAsyncCallable
+    model: RolloutModelSpec
+    scenario: Any
+    config: Any
+    store_result: bool = False
+
+
+class RolloutResult(BaseModel):
+    model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
+
+    value: Any
+    metrics: dict[str, float] = Field(default_factory=dict)
+
+
+class RolloutExecutor(Protocol):
+    @property
+    def max_workers(self) -> int | None: ...
+
+    def set_target(self, target_workers: int) -> None: ...
+
+    def set_workers(self, worker_ids: tuple[int, ...]) -> None: ...
+
+    async def run(
+        self,
+        worker_id: int,
+        rollout_fn: Callable[..., Awaitable[Any]],
+        model: Any,
+        scenario: Any,
+        config: Any,
+    ) -> Any: ...
+
+
+class LocalRolloutExecutor:
+    max_workers: int | None = None
+
+    def __init__(
+        self,
+        *,
+        trajectory_capacity_records: int = 16_384,
+        trajectory_capacity_bytes: int = 4 << 30,
+    ) -> None:
+        self._owner = InProcessRolloutWorker(
+            capacity_records=trajectory_capacity_records,
+            capacity_bytes=trajectory_capacity_bytes,
+        )
+        self._owner_endpoints: dict[str, RolloutWorkerEndpoint] = {
+            self._owner.owner_actor_id: self._owner
+        }
+        self._trajectory_capacity_records = trajectory_capacity_records
+        self._trajectory_capacity_bytes = trajectory_capacity_bytes
+        self._result_queue: DistributedTrajectoryQueue | None = None
+
+    def create_result_queue(self, maxsize: int) -> DistributedTrajectoryQueue:
+        if self._result_queue is not None:
+            raise RuntimeError("local rollout result queue already exists")
+        self._result_queue = DistributedTrajectoryQueue(
+            endpoint=_InProcessTrajectoryQueueEndpoint(),
+            owner_endpoints=self._owner_endpoints,
+            maxsize=maxsize,
+            capacity_records=self._trajectory_capacity_records,
+            capacity_bytes=self._trajectory_capacity_bytes,
+        )
+        return self._result_queue
+
+    def set_target(self, target_workers: int) -> None:
+        if target_workers < 1:
+            raise ValueError("target_workers must be >= 1")
+
+    def set_workers(self, worker_ids: tuple[int, ...]) -> None:
+        del worker_ids
+
+    async def run(
+        self,
+        worker_id: int,
+        rollout_fn: Callable[..., Awaitable[Any]],
+        model: Any,
+        scenario: Any,
+        config: Any,
+    ) -> Any:
+        del worker_id
+        result = await rollout_fn(model, scenario, config)
+        if self._result_queue is not None and isinstance(result, TrajectoryGroup):
+            return self._owner.store(result)
+        return result
+
+
+class RolloutWorkerEndpoint(Protocol):
+    async def run(self, invocation: RolloutInvocation) -> RolloutResult: ...
+
+    async def materialize(self, ref: TrajectoryGroupRef) -> TrajectoryGroup: ...
+
+    async def drop(self, ref: TrajectoryGroupRef) -> None: ...
+
+    async def close(self) -> None: ...
+
+
+class TrajectoryQueueEndpoint(Protocol):
+    async def create(
+        self,
+        queue_id: str,
+        max_ready_groups: int,
+        capacity_records: int,
+        capacity_bytes: int,
+    ) -> None: ...
+
+    async def enqueue(
+        self, queue_id: str, item: TrajectoryQueueItem
+    ) -> TrajectoryEnqueueResult: ...
+
+    async def resize(self, operation: TrajectoryQueueResize) -> None: ...
+
+    async def take(
+        self, queue_id: str, consumer_id: str, count: int
+    ) -> TrajectoryQueueTake: ...
+
+    async def mark_packed(self, operation: TrajectoryQueuePacking) -> None: ...
+
+    async def release(self, operation: TrajectoryQueueRelease) -> None: ...
+
+    async def finish(self, queue_id: str) -> None: ...
+
+    async def snapshot(self, queue_id: str) -> TrajectoryQueueSnapshot: ...
+
+    async def close(self, queue_id: str) -> tuple[TrajectoryGroupRef, ...]: ...
+
+
+class _InProcessTrajectoryQueueEndpoint:
+    def __init__(self) -> None:
+        self._queues: dict[str, TrajectoryQueueStore] = {}
+
+    async def create(
+        self,
+        queue_id: str,
+        max_ready_groups: int,
+        capacity_records: int,
+        capacity_bytes: int,
+    ) -> None:
+        if queue_id in self._queues:
+            raise ValueError(f"trajectory queue {queue_id!r} already exists")
+        self._queues[queue_id] = TrajectoryQueueStore(
+            max_ready_groups=max_ready_groups,
+            capacity_records=capacity_records,
+            capacity_bytes=capacity_bytes,
+        )
+
+    async def enqueue(
+        self, queue_id: str, item: TrajectoryQueueItem
+    ) -> TrajectoryEnqueueResult:
+        return self._queue(queue_id).enqueue(item)
+
+    async def resize(self, operation: TrajectoryQueueResize) -> None:
+        self._queue(operation.queue_id).resize(
+            maxsize=operation.maxsize, generation=operation.generation
+        )
+
+    async def take(
+        self, queue_id: str, consumer_id: str, count: int
+    ) -> TrajectoryQueueTake:
+        return self._queue(queue_id).take(consumer_id, count)
+
+    async def mark_packed(self, operation: TrajectoryQueuePacking) -> None:
+        self._queue(operation.queue_id).mark_packed(operation)
+
+    async def release(self, operation: TrajectoryQueueRelease) -> None:
+        self._queue(operation.queue_id).release(operation)
+
+    async def finish(self, queue_id: str) -> None:
+        self._queue(queue_id).finish()
+
+    async def snapshot(self, queue_id: str) -> TrajectoryQueueSnapshot:
+        return self._queue(queue_id).snapshot()
+
+    async def close(self, queue_id: str) -> tuple[TrajectoryGroupRef, ...]:
+        queue = self._queues.pop(queue_id, None)
+        return () if queue is None else queue.close()
+
+    def _queue(self, queue_id: str) -> TrajectoryQueueStore:
+        try:
+            return self._queues[queue_id]
+        except KeyError:
+            raise ValueError(f"unknown trajectory queue {queue_id!r}") from None
+
+
+class DistributedTrajectoryQueue:
+    def __init__(
+        self,
+        *,
+        endpoint: TrajectoryQueueEndpoint,
+        owner_endpoints: dict[str, RolloutWorkerEndpoint],
+        maxsize: int,
+        capacity_records: int,
+        capacity_bytes: int,
+    ) -> None:
+        if maxsize < 1:
+            raise ValueError("trajectory queue maxsize must be positive")
+        self.endpoint = endpoint
+        self.owner_endpoints = owner_endpoints
+        self.maxsize = maxsize
+        self._effective_maxsize = maxsize
+        self._minimum_take_size = 0
+        self.capacity_records = capacity_records
+        self.capacity_bytes = capacity_bytes
+        self.queue_id = uuid.uuid4().hex
+        self.consumer_id = f"pipeline:{uuid.uuid4().hex}"
+        self.put_waiters = 0
+        self._started = False
+        self._finished = False
+        self._closed = False
+        self._cleanup_refs: tuple[TrajectoryGroupRef, ...] = ()
+        self._resize_generation = 0
+        self._resize_tasks: set[asyncio.Task[None]] = set()
+        self._space_waiters: set[asyncio.Future[None]] = set()
+        self._item_waiters: set[asyncio.Future[None]] = set()
+        self._take_lock = asyncio.Lock()
+        self._owner_cleanup_refs: dict[str, deque[TrajectoryGroupRef]] = {}
+        self._owner_cleanup_tasks: dict[str, asyncio.Task[None]] = {}
+        self._owner_cleanup_failure: BaseException | None = None
+
+    async def start(self) -> None:
+        if self._started:
+            return
+        created_maxsize = self._effective_maxsize
+        await self.endpoint.create(
+            self.queue_id,
+            created_maxsize,
+            self.capacity_records,
+            self.capacity_bytes,
+        )
+        self._started = True
+        if self._required_maxsize() != created_maxsize:
+            self._effective_maxsize = created_maxsize
+            self._sync_maxsize()
+
+    def set_maxsize(self, maxsize: int) -> None:
+        if maxsize < 1:
+            raise ValueError("trajectory queue maxsize must be positive")
+        if maxsize == self.maxsize:
+            return
+        self.maxsize = maxsize
+        self._sync_maxsize()
+
+    async def put(
+        self,
+        ref: TrajectoryGroupRef,
+        *,
+        metadata: dict[str, MetadataValue],
+        initial_policy_version: int,
+        final_policy_version: int,
+        rollout_wall_s: float,
+        actor_idle_s: float,
+    ) -> tuple[bool, float]:
+        started = time.monotonic()
+        transferred = False
+        self.put_waiters += 1
+        try:
+            while not self._closed:
+                wait_s = time.monotonic() - started
+                space_available = asyncio.get_running_loop().create_future()
+                self._space_waiters.add(space_available)
+                request = asyncio.create_task(
+                    self.endpoint.enqueue(
+                        self.queue_id,
+                        TrajectoryQueueItem(
+                            ref=ref,
+                            annotations=TrajectoryGroupAnnotations(
+                                metadata=metadata,
+                                initial_policy_version=initial_policy_version,
+                                final_policy_version=final_policy_version,
+                                rollout_wall_s=rollout_wall_s,
+                                actor_idle_s=actor_idle_s + wait_s,
+                                queue_wait_s=wait_s,
+                            ),
+                        ),
+                    )
+                )
+                try:
+                    try:
+                        result = await asyncio.shield(request)
+                    except asyncio.CancelledError:
+                        result = await request
+                        transferred = result.status == "accepted"
+                        if transferred:
+                            self._notify_items()
+                        raise
+                    if result.status == "accepted":
+                        transferred = True
+                        self._notify_items()
+                        return True, time.monotonic() - started
+                    if result.status in ("oversize", "minimum_unreachable"):
+                        self._notify_items()
+                        raise TrajectoryCapacityError(
+                            result.reason or "oversize result"
+                        )
+                    if result.status == "closed":
+                        self._notify_items()
+                        return False, time.monotonic() - started
+                    await space_available
+                finally:
+                    self._space_waiters.discard(space_available)
+                    if not space_available.done():
+                        space_available.cancel()
+            return False, time.monotonic() - started
+        finally:
+            self.put_waiters -= 1
+            if not transferred:
+                await self._owner(ref).drop(ref)
+
+    async def get(self) -> TrajectoryGroup | None:
+        groups, _ = await self.get_many(1, wait=True)
+        return groups[0] if groups else None
+
+    async def get_nowait(self) -> tuple[bool, TrajectoryGroup | None]:
+        groups, closed = await self.get_many(1, wait=False)
+        return bool(groups) or closed, groups[0] if groups else None
+
+    async def get_many(
+        self, count: int, *, wait: bool
+    ) -> tuple[list[TrajectoryGroup], bool]:
+        if count < 1:
+            raise ValueError("trajectory queue get count must be positive")
+        self._raise_owner_cleanup_failure()
+        async with self._take_lock:
+            minimum_reserved = wait and count <= self.maxsize
+            if minimum_reserved:
+                self._minimum_take_size = count
+                self._sync_maxsize()
+            try:
+                if wait:
+                    await self._flush_resizes()
+                closed = self._closed
+                while not closed:
+                    item_available = asyncio.get_running_loop().create_future()
+                    self._item_waiters.add(item_available)
+                    try:
+                        # Negative counts retain best-effort bulk reads above the minimum.
+                        take = await self._take_trajectories(count if wait else -count)
+                        if take.leases:
+                            return await self._resolve_many(take.leases), take.closed
+                        closed = take.closed
+                        if closed or not wait:
+                            break
+                        self._notify_space()
+                        try:
+                            await item_available
+                        except asyncio.CancelledError:
+                            if not self._closed:
+                                await self.endpoint.take(
+                                    self.queue_id, self.consumer_id, 0
+                                )
+                            raise
+                        closed = self._closed
+                    finally:
+                        self._item_waiters.discard(item_available)
+                        if not item_available.done():
+                            item_available.cancel()
+                return [], closed
+            finally:
+                if minimum_reserved:
+                    self._minimum_take_size = 0
+                    self._sync_maxsize()
+
+    async def discard_group(self, group: TrajectoryGroup) -> None:
+        selection = group._distributed_lease
+        if not isinstance(selection, DistributedTrajectorySelection):
+            return
+        await self.release_selection(selection, disposition="discarded")
+
+    async def mark_packed(
+        self,
+        selections: Sequence[DistributedTrajectorySelection],
+        generation_id: str,
+    ) -> None:
+        if any(selection.queue is not self for selection in selections):
+            raise TrajectoryLeaseError("trajectory selection belongs to another queue")
+        await self.endpoint.mark_packed(
+            TrajectoryQueuePacking(
+                queue_id=self.queue_id,
+                leases=tuple(selection.lease for selection in selections),
+                generation_id=generation_id,
+            )
+        )
+
+    async def release_selection(
+        self,
+        selection: DistributedTrajectorySelection,
+        *,
+        disposition: Literal["consumed", "discarded"],
+        generation_id: str | None = None,
+    ) -> None:
+        await self.release_selections(
+            (selection,),
+            disposition=disposition,
+            generation_id=generation_id,
+        )
+
+    async def release_selections(
+        self,
+        selections: Sequence[DistributedTrajectorySelection],
+        *,
+        disposition: Literal["consumed", "discarded"],
+        generation_id: str | None = None,
+    ) -> None:
+        if any(selection.queue is not self for selection in selections):
+            raise TrajectoryLeaseError("trajectory selection belongs to another queue")
+        cleanup = await self._release_many(
+            tuple(selection.lease for selection in selections),
+            tuple(self._owner(selection.lease.item.ref) for selection in selections),
+            disposition=disposition,
+            generation_id=generation_id,
+        )
+        if cleanup:
+            raise BaseExceptionGroup("trajectory selection release failed", cleanup)
+        self._raise_owner_cleanup_failure()
+
+    async def finish(self) -> None:
+        if self._started and not self._finished and not self._closed:
+            await self.endpoint.finish(self.queue_id)
+            self._finished = True
+            self._notify_space()
+            self._notify_items()
+
+    async def discard(self, ref: TrajectoryGroupRef) -> None:
+        await self._owner(ref).drop(ref)
+
+    async def snapshot(self) -> TrajectoryQueueSnapshot:
+        if not self._started or self._closed:
+            return TrajectoryQueueSnapshot(
+                items=(),
+                max_ready_groups=self._effective_maxsize,
+                generation=self._resize_generation,
+                capacity_records=self.capacity_records,
+                capacity_bytes=self.capacity_bytes,
+                used_records=0,
+                used_bytes=0,
+                leased_groups=0,
+                ready_groups=0,
+                packing_groups=0,
+                packed_groups=0,
+                released_leases=0,
+                lease_lifetime_s=0.0,
+                max_lease_lifetime_s=0.0,
+            )
+        while True:
+            await self._flush_resizes()
+            snapshot = await self.endpoint.snapshot(self.queue_id)
+            if snapshot.generation >= self._resize_generation:
+                return snapshot
+
+    async def close(self) -> None:
+        failures: list[BaseException] = []
+        try:
+            await self._flush_resizes()
+        except BaseException as error:
+            failures.append(error)
+        if not self._closed:
+            self._closed = True
+            self._notify_space()
+            self._notify_items()
+            if self._started:
+                try:
+                    self._cleanup_refs += await self.endpoint.close(self.queue_id)
+                except BaseException as error:
+                    failures.append(error)
+        if self._owner_cleanup_tasks:
+            await asyncio.gather(*tuple(self._owner_cleanup_tasks.values()))
+        refs = self._cleanup_refs
+        self._owner_cleanup_failure = None
+        results = await asyncio.gather(
+            *(self._owner(ref).drop(ref) for ref in refs), return_exceptions=True
+        )
+        self._cleanup_refs = tuple(
+            ref
+            for ref, result in zip(refs, results, strict=True)
+            if isinstance(result, BaseException)
+        )
+        failures.extend(
+            result for result in results if isinstance(result, BaseException)
+        )
+        if failures:
+            raise BaseExceptionGroup("trajectory queue cleanup failed", failures)
+
+    def _required_maxsize(self) -> int:
+        return max(self.maxsize, self._minimum_take_size)
+
+    def _sync_maxsize(self) -> None:
+        maxsize = self._required_maxsize()
+        if maxsize == self._effective_maxsize:
+            return
+        self._effective_maxsize = maxsize
+        if self._started and not self._closed:
+            self._schedule_resize(maxsize)
+
+    def _schedule_resize(self, maxsize: int) -> None:
+        self._resize_generation += 1
+        operation = TrajectoryQueueResize(
+            queue_id=self.queue_id,
+            maxsize=maxsize,
+            generation=self._resize_generation,
+        )
+
+        async def resize() -> None:
+            await self.endpoint.resize(operation)
+            self._notify_space()
+            self._notify_items()
+
+        task = asyncio.create_task(resize())
+        self._resize_tasks.add(task)
+
+    async def _flush_resizes(self) -> None:
+        while self._resize_tasks:
+            tasks = tuple(self._resize_tasks)
+            self._resize_tasks.difference_update(tasks)
+            results = await asyncio.gather(*tasks, return_exceptions=True)
+            failures = [
+                result for result in results if isinstance(result, BaseException)
+            ]
+            if failures:
+                if len(failures) == 1:
+                    raise failures[0]
+                raise BaseExceptionGroup("trajectory queue resize failed", failures)
+
+    async def _consume(self, lease: TrajectoryQueueLease) -> TrajectoryGroup:
+        item = lease.item
+        owner = self._owner(item.ref)
+        try:
+            group = await owner.materialize(item.ref)
+        except BaseException as error:
+            cleanup = await self._release(lease, owner)
+            if cleanup:
+                raise BaseExceptionGroup(
+                    "trajectory materialization and release failed", [error, *cleanup]
+                ) from None
+            raise
+        cleanup = await self._release(lease, owner)
+        if cleanup:
+            raise BaseExceptionGroup("trajectory result release failed", cleanup)
+        return item.apply_annotations(group)
+
+    async def _resolve_many(
+        self, leases: Sequence[TrajectoryQueueLease]
+    ) -> list[TrajectoryGroup]:
+        return [self._summary_group(lease) for lease in leases]
+
+    async def _take_trajectories(self, count: int) -> TrajectoryQueueTake:
+        request = asyncio.create_task(
+            self.endpoint.take(self.queue_id, self.consumer_id, count)
+        )
+        try:
+            return await asyncio.shield(request)
+        except asyncio.CancelledError as cancelled:
+            take = await request
+            if take.leases:
+                cleanup = await self._release_many(
+                    take.leases,
+                    tuple(self._owner(lease.item.ref) for lease in take.leases),
+                    disposition="discarded",
+                    generation_id=None,
+                )
+                if cleanup:
+                    raise BaseExceptionGroup(
+                        "trajectory acquisition cancellation cleanup failed",
+                        [cancelled, *cleanup],
+                    ) from None
+            elif count > 0 and not take.closed and not self._closed:
+                await self.endpoint.take(self.queue_id, self.consumer_id, 0)
+            raise
+
+    async def materialize_selection(
+        self, selection: DistributedTrajectorySelection
+    ) -> TrajectoryGroup:
+        if selection.queue is not self:
+            raise TrajectoryLeaseError("trajectory selection belongs to another queue")
+        item = selection.lease.item
+        return item.apply_annotations(await self._owner(item.ref).materialize(item.ref))
+
+    def _summary_group(self, lease: TrajectoryQueueLease) -> TrajectoryGroup:
+        item = lease.item
+        descriptor = item.ref.descriptor
+        trajectories = []
+        for reward, initial, final, counts, metrics, metadata in zip(
+            descriptor.rewards,
+            descriptor.trajectory_initial_policy_versions,
+            descriptor.trajectory_final_policy_versions,
+            descriptor.trajectory_policy_token_counts,
+            descriptor.trajectory_metrics,
+            descriptor.trajectory_metadata,
+            strict=True,
+        ):
+            trajectory = Trajectory(
+                reward=reward,
+                initial_policy_version=(
+                    initial
+                    if initial is not None
+                    else item.annotations.initial_policy_version
+                ),
+                final_policy_version=(
+                    final
+                    if final is not None
+                    else item.annotations.final_policy_version
+                ),
+                metrics=dict(metrics),
+                metadata=dict(metadata),
+            )
+            trajectory._policy_token_counts = dict(counts)
+            trajectories.append(trajectory)
+        group = TrajectoryGroup(
+            trajectories,
+            metadata={**descriptor.group_metadata, **item.annotations.metadata},
+            metrics=dict(descriptor.group_metrics),
+        )
+        group.exceptions = [
+            PydanticException(type=kind, message=message, traceback="")
+            for kind, message in descriptor.exceptions
+        ]
+        group.metadata["_art_rollout_wall_s"] = item.annotations.rollout_wall_s
+        group.metadata["_art_actor_idle_s"] = item.annotations.actor_idle_s
+        group.metadata["_art_queue_wait_s"] = item.annotations.queue_wait_s
+        group._distributed_lease = DistributedTrajectorySelection(self, lease)
+        return group
+
+    async def _release(
+        self,
+        lease: TrajectoryQueueLease,
+        owner: RolloutWorkerEndpoint,
+        *,
+        disposition: Literal["consumed", "discarded"] = "discarded",
+        generation_id: str | None = None,
+    ) -> list[BaseException]:
+        return await self._release_many(
+            (lease,),
+            (owner,),
+            disposition=disposition,
+            generation_id=generation_id,
+        )
+
+    async def _release_many(
+        self,
+        leases: tuple[TrajectoryQueueLease, ...],
+        owners: tuple[RolloutWorkerEndpoint, ...],
+        *,
+        disposition: Literal["consumed", "discarded"],
+        generation_id: str | None,
+    ) -> list[BaseException]:
+        if not leases:
+            return []
+        try:
+            await self.endpoint.release(
+                TrajectoryQueueRelease(
+                    queue_id=self.queue_id,
+                    leases=leases,
+                    generation_id=generation_id,
+                    disposition=disposition,
+                )
+            )
+        except BaseException as error:
+            return [error]
+        self._notify_space()
+        for lease, owner in zip(leases, owners, strict=True):
+            self._schedule_owner_cleanup(owner, lease.item.ref)
+        return []
+
+    def _notify_space(self) -> None:
+        for waiter in tuple(self._space_waiters):
+            if not waiter.done():
+                waiter.set_result(None)
+
+    def _notify_items(self) -> None:
+        for waiter in tuple(self._item_waiters):
+            if not waiter.done():
+                waiter.set_result(None)
+
+    def _schedule_owner_cleanup(
+        self, owner: RolloutWorkerEndpoint, ref: TrajectoryGroupRef
+    ) -> None:
+        owner_id = ref.owner_actor_id
+        self._owner_cleanup_refs.setdefault(owner_id, deque()).append(ref)
+        if owner_id not in self._owner_cleanup_tasks:
+            self._owner_cleanup_tasks[owner_id] = asyncio.create_task(
+                self._drain_owner_cleanup(owner_id, owner)
+            )
+
+    async def _drain_owner_cleanup(
+        self, owner_id: str, owner: RolloutWorkerEndpoint
+    ) -> None:
+        refs = self._owner_cleanup_refs[owner_id]
+        while refs:
+            ref = refs.popleft()
+            try:
+                await owner.drop(ref)
+            except Exception as error:
+                self._cleanup_refs += (ref,)
+                self._owner_cleanup_failure = self._owner_cleanup_failure or error
+        del self._owner_cleanup_refs[owner_id]
+        del self._owner_cleanup_tasks[owner_id]
+
+    def _raise_owner_cleanup_failure(self) -> None:
+        error = self._owner_cleanup_failure
+        self._owner_cleanup_failure = None
+        if error is not None:
+            raise error
+
+    def _owner(self, ref: TrajectoryGroupRef) -> RolloutWorkerEndpoint:
+        try:
+            return self.owner_endpoints[ref.owner_actor_id]
+        except KeyError:
+            raise RuntimeError(
+                f"trajectory owner {ref.owner_actor_id!r} is unavailable"
+            ) from None
+
+
+class DistributedTrajectorySelection:
+    __slots__ = ("lease", "queue")
+
+    def __init__(
+        self, queue: DistributedTrajectoryQueue, lease: TrajectoryQueueLease
+    ) -> None:
+        self.queue = queue
+        self.lease = lease
+
+
+def apportion_rollout_workers(
+    target_workers: int, host_slots: Mapping[str, int]
+) -> dict[str, int]:
+    """Deterministically assign one global exact target without host-local policy."""
+
+    if target_workers < 1:
+        raise ValueError("target_workers must be >= 1")
+    if not host_slots or any(slots < 1 for slots in host_slots.values()):
+        raise ValueError("rollout hosts must each provide at least one CPU slot")
+    allocation = dict.fromkeys(host_slots, 0)
+    for _ in range(target_workers):
+        candidates = [
+            host for host, slots in host_slots.items() if allocation[host] < slots
+        ]
+        if not candidates:
+            raise ValueError(
+                f"global rollout-worker target {target_workers} exceeds host capacity "
+                f"{sum(host_slots.values())}"
+            )
+        host_id = min(
+            candidates, key=lambda host: (allocation[host] / host_slots[host], host)
+        )
+        allocation[host_id] += 1
+    return allocation
+
+
+class DistributedRolloutExecutor:
+    def __init__(
+        self,
+        *,
+        callable: InstalledAsyncCallable,
+        hosts: Mapping[str, Sequence[RolloutWorkerEndpoint]],
+        target_workers: int,
+        queue_endpoint: TrajectoryQueueEndpoint | None = None,
+        trajectory_capacity_records: int = 16_384,
+        trajectory_capacity_bytes: int = 4 << 30,
+    ) -> None:
+        if not hosts or any(not endpoints for endpoints in hosts.values()):
+            raise ValueError("rollout hosts must each provide at least one endpoint")
+        self.callable = callable
+        self.hosts = {host: tuple(endpoints) for host, endpoints in hosts.items()}
+        self.max_workers = sum(len(endpoints) for endpoints in self.hosts.values())
+        self._worker_endpoints: tuple[RolloutWorkerEndpoint, ...] = ()
+        self._endpoint_by_worker: dict[int, RolloutWorkerEndpoint] = {}
+        self._queue_endpoint = queue_endpoint
+        self._trajectory_capacity_records = trajectory_capacity_records
+        self._trajectory_capacity_bytes = trajectory_capacity_bytes
+        self._endpoint_by_owner: dict[str, RolloutWorkerEndpoint] = {}
+        self._result_queue: DistributedTrajectoryQueue | None = None
+        self.set_target(target_workers)
+
+    def create_result_queue(self, maxsize: int) -> DistributedTrajectoryQueue:
+        if self._result_queue is not None:
+            raise RuntimeError("distributed rollout result queue already exists")
+        queue_endpoint = self._queue_endpoint
+        if queue_endpoint is None:
+            endpoints = next(iter(self.hosts.values()))
+            if len(self.hosts) != 1 or not all(
+                isinstance(endpoint, InProcessRolloutWorker) for endpoint in endpoints
+            ):
+                raise RuntimeError(
+                    "queue_endpoint is required unless one host uses only "
+                    "in-process rollout workers"
+                )
+            queue_endpoint = _InProcessTrajectoryQueueEndpoint()
+            self._queue_endpoint = queue_endpoint
+        self._result_queue = DistributedTrajectoryQueue(
+            endpoint=queue_endpoint,
+            owner_endpoints=self._endpoint_by_owner,
+            maxsize=maxsize,
+            capacity_records=self._trajectory_capacity_records,
+            capacity_bytes=self._trajectory_capacity_bytes,
+        )
+        return self._result_queue
+
+    def set_target(self, target_workers: int) -> None:
+        allocation = apportion_rollout_workers(
+            target_workers,
+            {host: len(endpoints) for host, endpoints in self.hosts.items()},
+        )
+        self._worker_endpoints = tuple(
+            endpoint
+            for host_id in sorted(allocation)
+            for endpoint in self.hosts[host_id][: allocation[host_id]]
+        )
+
+    def set_workers(self, worker_ids: tuple[int, ...]) -> None:
+        workers = tuple(sorted(worker_ids))
+        drained = len(workers) <= len(self._worker_endpoints)
+        assignments = {
+            worker_id: self._endpoint_by_worker[worker_id]
+            for worker_id in workers
+            if worker_id in self._endpoint_by_worker
+            and (
+                not drained
+                or self._endpoint_by_worker[worker_id] in self._worker_endpoints
+            )
+        }
+        available = [
+            endpoint
+            for endpoint in self._worker_endpoints
+            if endpoint not in assignments.values()
+        ]
+        unassigned = [
+            worker_id for worker_id in workers if worker_id not in assignments
+        ]
+        if len(unassigned) > len(available):
+            raise ValueError("new rollout workers exceed the global target")
+        assignments.update(zip(unassigned, available, strict=False))
+        self._endpoint_by_worker = assignments
+
+    async def run(
+        self,
+        worker_id: int,
+        rollout_fn: Callable[..., Awaitable[Any]],
+        model: Any,
+        scenario: Any,
+        config: Any,
+    ) -> Any:
+        if InstalledAsyncCallable.from_callable(rollout_fn) != self.callable:
+            raise ValueError(
+                "PipelineTrainer rollout_fn differs from distributed callable"
+            )
+        try:
+            endpoint = self._endpoint_by_worker[worker_id]
+        except KeyError:
+            raise RuntimeError(
+                f"rollout worker {worker_id} has no host assignment"
+            ) from None
+        result = await endpoint.run(
+            RolloutInvocation(
+                callable=self.callable,
+                model=RolloutModelSpec.from_model(model),
+                scenario=scenario,
+                config=config,
+                store_result=self._result_queue is not None,
+            )
+        )
+        if result.metrics:
+            from art.metrics import MetricsBuilder
+
+            try:
+                builder = MetricsBuilder.get_active()
+            except LookupError:
+                raise RuntimeError(
+                    "distributed rollout produced metrics without an active ART metrics context"
+                ) from None
+            for key, value in result.metrics.items():
+                builder.add_metric(key, value)
+        if isinstance(result.value, TrajectoryGroupRef):
+            existing = self._endpoint_by_owner.setdefault(
+                result.value.owner_actor_id, endpoint
+            )
+            if existing is not endpoint:
+                raise RuntimeError(
+                    f"trajectory owner {result.value.owner_actor_id!r} changed endpoint"
+                )
+        return result.value
+
+    async def close(self) -> None:
+        failures: list[BaseException] = []
+        if self._result_queue is not None:
+            try:
+                await self._result_queue.close()
+            except BaseException as error:
+                failures.append(error)
+        results = await asyncio.gather(
+            *(
+                endpoint.close()
+                for endpoints in self.hosts.values()
+                for endpoint in endpoints
+            ),
+            return_exceptions=True,
+        )
+        failures.extend(
+            result for result in results if isinstance(result, BaseException)
+        )
+        if failures:
+            raise BaseExceptionGroup("distributed rollout cleanup failed", failures)
+
+
+class InProcessRolloutWorker:
+    """One in-process rollout execution slot used by local collapse and tests."""
+
+    def __init__(
+        self, *, capacity_records: int = 16_384, capacity_bytes: int = 4 << 30
+    ) -> None:
+        self._results = TrajectoryRecordStore(
+            owner_actor_id=f"in-process:{uuid.uuid4().hex}",
+            capacity_records=capacity_records,
+            capacity_bytes=capacity_bytes,
+        )
+
+    @property
+    def owner_actor_id(self) -> str:
+        return self._results.owner_actor_id
+
+    def store(self, group: TrajectoryGroup) -> TrajectoryGroupRef:
+        return self._results.put(group)
+
+    async def run(self, invocation: RolloutInvocation) -> RolloutResult:
+        from art.metrics import MetricsBuilder
+
+        function = invocation.callable.resolve()
+        builder = MetricsBuilder(cost_context="train")
+        token = builder.activate()
+        try:
+            value = await function(
+                invocation.model.build(), invocation.scenario, invocation.config
+            )
+        finally:
+            token.var.reset(token)
+        if invocation.store_result and isinstance(value, TrajectoryGroup):
+            value = self._results.put(value)
+        return RolloutResult(value=value, metrics=await builder.drain_pending())
+
+    async def materialize(self, ref: TrajectoryGroupRef) -> TrajectoryGroup:
+        return self._results.materialize(ref)
+
+    async def drop(self, ref: TrajectoryGroupRef) -> None:
+        self._results.drop(ref)
+
+    async def close(self) -> None:
+        self._results.close()
diff --git a/src/art/distributed/specs.py b/src/art/distributed/specs.py
new file mode 100644
index 000000000..7bfacca33
--- /dev/null
+++ b/src/art/distributed/specs.py
@@ -0,0 +1,407 @@
+from __future__ import annotations
+
+from collections import Counter
+from collections.abc import Sequence
+from ipaddress import ip_address
+from typing import Annotated, Literal, TypeAlias
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from ..types import MegatronTopologyConfig
+
+CUDA_DEVICE_UUID_PATTERN = (
+    r"^(?:GPU-[0-9A-Fa-f]{8}(?:-[0-9A-Fa-f]{4}){3}-[0-9A-Fa-f]{12}"
+    r"|MIG-(?:[0-9A-Fa-f]{8}(?:-[0-9A-Fa-f]{4}){3}-[0-9A-Fa-f]{12}"
+    r"|GPU-[0-9A-Fa-f]{8}(?:-[0-9A-Fa-f]{4}){3}-[0-9A-Fa-f]{12}/[0-9]+/[0-9]+))$"
+)
+GpuId: TypeAlias = (
+    Annotated[int, Field(ge=0)]
+    | Annotated[str, Field(pattern=CUDA_DEVICE_UUID_PATTERN)]
+)
+
+
+class _Spec(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+def _gpu_identities(gpu_ids: tuple[GpuId, ...]) -> tuple[int | str, ...]:
+    return tuple(
+        gpu_id.casefold() if isinstance(gpu_id, str) else gpu_id for gpu_id in gpu_ids
+    )
+
+
+class HostSpec(_Spec):
+    host_id: str = Field(min_length=1)
+    node_rank: int = Field(ge=0)
+    worker_address: str = Field(min_length=1)
+    cpu_slots: int = Field(ge=1)
+    gpu_ids: tuple[GpuId, ...] = ()
+
+    @model_validator(mode="after")
+    def _validate_gpu_ids(self) -> "HostSpec":
+        identities = _gpu_identities(self.gpu_ids)
+        if len(set(identities)) != len(identities):
+            raise ValueError("gpu_ids must be unique within a host")
+        return self
+
+
+class NcclTransportSpec(_Spec):
+    net_name: str = Field(min_length=1, pattern=r"^[^\x00\r\n]+$")
+
+    @model_validator(mode="after")
+    def _validate_net_name(self) -> "NcclTransportSpec":
+        if self.net_name != self.net_name.strip():
+            raise ValueError(
+                "NCCL network name must not contain surrounding whitespace"
+            )
+        if self.net_name.casefold() == "socket":
+            raise ValueError("multi-host GPU workloads may not use NCCL Socket")
+        return self
+
+
+class EndpointSpec(_Spec):
+    host: str = Field(min_length=1)
+    port: int = Field(ge=1, le=65535)
+
+    @property
+    def url(self) -> str:
+        return f"http://{self.host}:{self.port}"
+
+    @property
+    def is_loopback(self) -> bool:
+        if self.host.lower() == "localhost":
+            return True
+        try:
+            return ip_address(self.host.strip("[]")).is_loopback
+        except ValueError:
+            return False
+
+    @property
+    def is_routable(self) -> bool:
+        if self.host.lower() == "localhost":
+            return False
+        try:
+            address = ip_address(self.host.strip("[]"))
+        except ValueError:
+            return self.host not in {"0.0.0.0", "::"}
+        return not (
+            address.is_loopback
+            or address.is_unspecified
+            or address.is_link_local
+            or address.is_multicast
+        )
+
+
+class NixlTransportSpec(_Spec):
+    metadata_store: EndpointSpec | None = None
+    nixl_home: str | None = Field(default=None, min_length=1)
+    ucx_home: str | None = Field(default=None, min_length=1)
+    nixl_plugin_dir: str | None = Field(default=None, min_length=1)
+    ucx_module_dir: str | None = Field(default=None, min_length=1)
+    ucx_net_devices: str = Field(default="all", min_length=1)
+    ucx_tls: str = Field(default="rc,rc_gda,cuda_copy", min_length=1)
+    enable_cuda_fabric: bool = False
+
+    @model_validator(mode="after")
+    def _validate_metadata_store(self) -> "NixlTransportSpec":
+        if self.metadata_store is not None and not self.metadata_store.is_routable:
+            raise ValueError("NIXL metadata store must be routable across hosts")
+        return self
+
+
+class ClusterSpec(_Spec):
+    hosts: tuple[HostSpec, ...]
+    controller_host_id: str
+    artifact_root: str | None = None
+    cache_root: str | None = Field(default=None, min_length=1)
+    nccl_transport: NcclTransportSpec | None = None
+    nixl_transport: NixlTransportSpec | None = None
+    startup_timeout_s: float = Field(default=600.0, gt=0)
+    rpc_timeout_s: float = Field(default=60.0, gt=0)
+
+    @model_validator(mode="after")
+    def _validate_hosts(self) -> "ClusterSpec":
+        if not self.hosts:
+            raise ValueError("hosts must not be empty")
+        host_ids = [host.host_id for host in self.hosts]
+        node_ranks = [host.node_rank for host in self.hosts]
+        addresses = [host.worker_address for host in self.hosts]
+        if len(set(host_ids)) != len(host_ids):
+            raise ValueError("host_id values must be unique")
+        if node_ranks != list(range(len(self.hosts))):
+            raise ValueError("hosts must be ordered by contiguous node_rank from zero")
+        if len(set(addresses)) != len(addresses):
+            raise ValueError("worker_address values must be unique")
+        if self.controller_host_id not in host_ids:
+            raise ValueError("controller_host_id must identify a configured host")
+        return self
+
+    @property
+    def host_ids(self) -> tuple[str, ...]:
+        return tuple(host.host_id for host in self.hosts)
+
+    def gpu_placements(
+        self, host_ids: Sequence[str] | None = None
+    ) -> tuple[GpuPlacement, ...]:
+        selected = set(self.host_ids if host_ids is None else host_ids)
+        unknown = selected.difference(self.host_ids)
+        if unknown:
+            raise ValueError(f"unknown GPU placement hosts: {sorted(unknown)}")
+        return tuple(
+            GpuPlacement(host_id=host.host_id, gpu_id=gpu_id)
+            for host in self.hosts
+            if host.host_id in selected
+            for gpu_id in host.gpu_ids
+        )
+
+
+class GpuPlacement(_Spec):
+    host_id: str = Field(min_length=1)
+    gpu_id: GpuId
+
+
+class TrainerMeshSpec(_Spec):
+    ranks: tuple[GpuPlacement, ...]
+    topology: MegatronTopologyConfig
+    coordinator_rank: Literal[0] = 0
+
+    @model_validator(mode="after")
+    def _validate_world(self) -> "TrainerMeshSpec":
+        if not self.ranks:
+            raise ValueError("trainer ranks must not be empty")
+        if len(set(self.ranks)) != len(self.ranks):
+            raise ValueError("trainer GPU placements must be unique")
+        world_size = len(self.ranks)
+        topology = self.topology
+        if world_size % (topology.tp * topology.cp * topology.pp):
+            raise ValueError("trainer world size must be divisible by TP * CP * PP")
+        if world_size % (topology.etp * topology.ep * topology.pp):
+            raise ValueError("trainer world size must be divisible by ETP * EP * PP")
+        return self
+
+
+class VllmParallelSpec(_Spec):
+    tp: int = Field(default=1, ge=1)
+    pp: int = Field(default=1, ge=1)
+    dp: int = Field(default=1, ge=1)
+    enable_expert_parallel: bool = False
+
+    @property
+    def world_size(self) -> int:
+        return self.tp * self.pp * self.dp
+
+
+class ModelServiceMemberSpec(_Spec):
+    member_id: str = Field(min_length=1)
+    host_id: str = Field(min_length=1)
+    node_rank: int = Field(ge=0)
+    gpu_ids: tuple[GpuId, ...]
+
+    @model_validator(mode="after")
+    def _validate_gpu_ids(self) -> "ModelServiceMemberSpec":
+        if not self.gpu_ids:
+            raise ValueError("model-service members require at least one GPU")
+        identities = _gpu_identities(self.gpu_ids)
+        if len(set(identities)) != len(identities):
+            raise ValueError("member gpu_ids must be unique")
+        return self
+
+
+class ModelServiceSpec(_Spec):
+    name: str = Field(min_length=1)
+    capabilities: frozenset[str] = frozenset()
+    members: tuple[ModelServiceMemberSpec, ...]
+    leader_endpoint: EndpointSpec
+    rendezvous: EndpointSpec
+    base_model: str = Field(min_length=1)
+    model_revision: str | None = Field(default=None, min_length=1)
+    runtime_fingerprint: str = Field(min_length=1)
+    parallel: VllmParallelSpec
+    temporal_gpu_sharing: bool = False
+
+    @model_validator(mode="after")
+    def _validate_members(self) -> "ModelServiceSpec":
+        if not self.members:
+            raise ValueError("model service members must not be empty")
+        member_ids = [member.member_id for member in self.members]
+        node_ranks = [member.node_rank for member in self.members]
+        if len(set(member_ids)) != len(member_ids):
+            raise ValueError("member_id values must be unique within a model service")
+        if node_ranks != list(range(len(self.members))):
+            raise ValueError(
+                "members must be ordered by contiguous node_rank from zero"
+            )
+        if len({member.host_id for member in self.members}) != len(self.members):
+            raise ValueError("native vLLM members must occupy distinct hosts")
+        local_world_sizes = {len(member.gpu_ids) for member in self.members}
+        if len(local_world_sizes) != 1:
+            raise ValueError("native vLLM members must have equal local world sizes")
+        if (
+            sum(len(member.gpu_ids) for member in self.members)
+            != self.parallel.world_size
+        ):
+            raise ValueError("vLLM TP * PP * DP must equal the service GPU count")
+        if len(self.members) > 1 and not self.rendezvous.is_routable:
+            raise ValueError("multi-host vLLM rendezvous must be routable")
+        local_world_size = len(self.members[0].gpu_ids)
+        world_size_within_dp = self.parallel.tp * self.parallel.pp
+        if (
+            local_world_size >= world_size_within_dp
+            and local_world_size % world_size_within_dp
+        ) or (
+            local_world_size < world_size_within_dp
+            and world_size_within_dp % local_world_size
+        ):
+            raise ValueError(
+                "native vLLM DP groups must pack evenly within or span whole members"
+            )
+        if self.leader_endpoint.port == self.rendezvous.port:
+            raise ValueError("model-service API and rendezvous ports must not overlap")
+        return self
+
+    @property
+    def gpu_placements(self) -> tuple[GpuPlacement, ...]:
+        return tuple(
+            GpuPlacement(host_id=member.host_id, gpu_id=gpu_id)
+            for member in self.members
+            for gpu_id in member.gpu_ids
+        )
+
+
+class RuntimeTopology(_Spec):
+    cluster: ClusterSpec
+    rollout_host_ids: tuple[str, ...]
+    trainer: TrainerMeshSpec | None = None
+    model_services: tuple[ModelServiceSpec, ...] = ()
+
+    @model_validator(mode="after")
+    def _validate_runtime(self) -> "RuntimeTopology":
+        hosts = {host.host_id: host for host in self.cluster.hosts}
+        if len(set(self.rollout_host_ids)) != len(self.rollout_host_ids):
+            raise ValueError("rollout_host_ids must be unique")
+        unknown_rollout_hosts = sorted(set(self.rollout_host_ids) - hosts.keys())
+        if unknown_rollout_hosts:
+            raise ValueError(
+                f"rollout_host_ids references unknown hosts: {unknown_rollout_hosts}"
+            )
+
+        placements: list[tuple[str, GpuId, str]] = []
+        if self.trainer is not None:
+            trainer_hosts = tuple(rank.host_id for rank in self.trainer.ranks)
+            unknown_trainer_hosts = sorted(set(trainer_hosts) - hosts.keys())
+            if unknown_trainer_hosts:
+                raise ValueError(
+                    f"trainer references unknown hosts: {unknown_trainer_hosts}"
+                )
+            counts = Counter(trainer_hosts)
+            if len(set(counts.values())) != 1:
+                raise ValueError("Monarch trainer hosts require equal ranks per host")
+            selected_hosts = tuple(
+                host.host_id for host in self.cluster.hosts if host.host_id in counts
+            )
+            selected_indices = tuple(
+                index
+                for index, host in enumerate(self.cluster.hosts)
+                if host.host_id in counts
+            )
+            if selected_indices != tuple(
+                range(selected_indices[0], selected_indices[-1] + 1)
+            ):
+                raise ValueError("trainer hosts must be contiguous in the cluster mesh")
+            ranks_per_host = next(iter(counts.values()))
+            expected_rank_hosts = tuple(
+                host_id for host_id in selected_hosts for _ in range(ranks_per_host)
+            )
+            if trainer_hosts != expected_rank_hosts:
+                raise ValueError(
+                    "trainer ranks must be host-major in cluster host order"
+                )
+            placements.extend(
+                (rank.host_id, rank.gpu_id, "trainer") for rank in self.trainer.ranks
+            )
+
+        service_names = [service.name for service in self.model_services]
+        if len(set(service_names)) != len(service_names):
+            raise ValueError("model service names must be unique")
+
+        endpoints: list[tuple[str, int, str]] = []
+        for service in self.model_services:
+            placements.extend(
+                (placement.host_id, placement.gpu_id, service.name)
+                for placement in service.gpu_placements
+            )
+            endpoints.extend(
+                (
+                    (
+                        service.members[0].host_id,
+                        service.leader_endpoint.port,
+                        "leader",
+                    ),
+                    (
+                        service.members[0].host_id,
+                        service.rendezvous.port,
+                        "rendezvous",
+                    ),
+                )
+            )
+        spans_hosts = (
+            self.trainer is not None
+            and len({rank.host_id for rank in self.trainer.ranks}) > 1
+        ) or any(len(service.members) > 1 for service in self.model_services)
+        if spans_hosts and self.cluster.nccl_transport is None:
+            raise ValueError("multi-host GPU workloads require nccl_transport")
+        for host_id, gpu_id, owner in placements:
+            host = hosts.get(host_id)
+            if host is None:
+                raise ValueError(f"{owner} references unknown host {host_id!r}")
+            if gpu_id not in host.gpu_ids:
+                raise ValueError(f"{owner} requests unavailable GPU {host_id}:{gpu_id}")
+        temporal_services = {
+            service.name
+            for service in self.model_services
+            if service.temporal_gpu_sharing
+        }
+        overlapping = {
+            placement: tuple(
+                owner
+                for host_id, gpu_id, owner in placements
+                if (host_id, gpu_id) == placement
+            )
+            for placement, count in Counter(
+                (host_id, gpu_id) for host_id, gpu_id, _ in placements
+            ).items()
+            if count > 1
+        }
+        invalid_overlap = {
+            placement: owners
+            for placement, owners in overlapping.items()
+            if len(owners) != 2
+            or "trainer" not in owners
+            or next(owner for owner in owners if owner != "trainer")
+            not in temporal_services
+        }
+        if invalid_overlap:
+            raise ValueError(f"GPU placements overlap: {invalid_overlap}")
+        seen: dict[tuple[str, int], str] = {}
+        for host_id, port, kind in endpoints:
+            key = (host_id, port)
+            if previous := seen.get(key):
+                raise ValueError(
+                    f"model-service port {host_id}:{port} overlaps "
+                    f"{previous} and {kind}"
+                )
+            seen[key] = kind
+        return self
+
+
+class ArtRuntimeConfig(_Spec):
+    packed_batch_capacity_bytes: int = Field(default=2 << 30, ge=1)
+    trajectory_capacity_records: int = Field(default=16_384, ge=1)
+    trajectory_capacity_bytes: int = Field(default=4 << 30, ge=1)
+    vllm_output_root: str = "/tmp/art-vllm"
+
+
+class HostServiceHealth(_Spec):
+    host_id: str = Field(min_length=1)
+    hostname: str = Field(min_length=1)
+    process_id: int = Field(ge=1)
diff --git a/src/art/distributed/topology.py b/src/art/distributed/topology.py
new file mode 100644
index 000000000..9f959773d
--- /dev/null
+++ b/src/art/distributed/topology.py
@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+from .specs import (
+    ClusterSpec,
+    ModelServiceSpec,
+    RuntimeTopology,
+    TrainerMeshSpec,
+)
+
+
+def compile_topology(
+    *,
+    cluster: ClusterSpec,
+    rollout_host_ids: tuple[str, ...] | None = None,
+    trainer: TrainerMeshSpec | None = None,
+    model_services: tuple[ModelServiceSpec, ...] = (),
+) -> RuntimeTopology:
+    return RuntimeTopology(
+        cluster=cluster,
+        rollout_host_ids=(
+            tuple(host.host_id for host in cluster.hosts)
+            if rollout_host_ids is None
+            else rollout_host_ids
+        ),
+        trainer=trainer,
+        model_services=model_services,
+    )
diff --git a/src/art/distributed/trajectory_store.py b/src/art/distributed/trajectory_store.py
new file mode 100644
index 000000000..36c191fed
--- /dev/null
+++ b/src/art/distributed/trajectory_store.py
@@ -0,0 +1,797 @@
+from __future__ import annotations
+
+import asyncio
+from collections import deque
+from collections.abc import Callable, Mapping
+import secrets
+import time
+from typing import TYPE_CHECKING, Any, Literal
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from art.preprocessing.policy_spans import PolicyTokenSpan
+from art.trajectories import MetadataValue, Trajectory, TrajectoryGroup
+
+from .data_plane import (
+    ByteStreamPublisher,
+    ByteStreamServerLoop,
+    ByteStreamTransfer,
+    receive_byte_stream,
+)
+
+if TYPE_CHECKING:
+    from .packing import TrajectoryGroupPayload
+
+TRAJECTORY_FORMAT = "art_trajectory_v1"
+
+
+class _Contract(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class TrajectoryRecordRef(_Contract):
+    record_id: str = Field(min_length=1)
+    owner_actor_id: str = Field(min_length=1)
+    byte_count: int = Field(ge=0)
+
+
+class TrajectoryGroupDescriptor(_Contract):
+    grouping_key: str = Field(min_length=1)
+    trajectory_count: int = Field(ge=0)
+    exception_count: int = Field(ge=0)
+    rewards: tuple[float, ...]
+    initial_policy_versions: tuple[int, ...]
+    completion_tokens: tuple[float, ...]
+    policy_token_counts: dict[int, int]
+    trajectory_initial_policy_versions: tuple[int | None, ...]
+    trajectory_final_policy_versions: tuple[int | None, ...]
+    trajectory_policy_token_counts: tuple[dict[int, int], ...]
+    trajectory_metrics: tuple[dict[str, float | int | bool], ...]
+    trajectory_metadata: tuple[dict[str, MetadataValue], ...]
+    group_metadata: dict[str, MetadataValue]
+    group_metrics: dict[str, float | int | bool]
+    exceptions: tuple[tuple[str, str], ...]
+    byte_count: int = Field(ge=0)
+
+    @model_validator(mode="after")
+    def _validate_trajectory_summaries(self) -> "TrajectoryGroupDescriptor":
+        aligned = (
+            self.rewards,
+            self.completion_tokens,
+            self.trajectory_initial_policy_versions,
+            self.trajectory_final_policy_versions,
+            self.trajectory_policy_token_counts,
+            self.trajectory_metrics,
+            self.trajectory_metadata,
+        )
+        if any(len(values) != self.trajectory_count for values in aligned):
+            raise ValueError("trajectory descriptor summaries are not aligned")
+        if len(self.exceptions) != self.exception_count:
+            raise ValueError("trajectory descriptor exceptions are not aligned")
+        return self
+
+
+class TrajectoryGroupBundle(_Contract):
+    """Binary trajectory records for bulk transport across actor boundaries."""
+
+    header: bytes
+    records: tuple[bytes, ...]
+
+    @classmethod
+    def from_payload(cls, payload: TrajectoryGroupPayload) -> "TrajectoryGroupBundle":
+        from msgspec import msgpack
+
+        return cls(
+            header=msgpack.encode(
+                payload.model_copy(update={"trajectories": ()}).model_dump(
+                    mode="python"
+                )
+            ),
+            records=tuple(
+                msgpack.encode(record.model_dump(mode="python"))
+                for record in payload.trajectories
+            ),
+        )
+
+    @classmethod
+    def from_group(cls, group: TrajectoryGroup) -> "TrajectoryGroupBundle":
+        from .packing import TrajectoryGroupPayload
+
+        return cls.from_payload(TrajectoryGroupPayload.from_group(group))
+
+    def payload(self) -> TrajectoryGroupPayload:
+        from msgspec import msgpack
+
+        from .packing import TrajectoryGroupPayload
+
+        header = msgpack.decode(self.header)
+        header["trajectories"] = tuple(
+            msgpack.decode(record) for record in self.records
+        )
+        return TrajectoryGroupPayload.model_validate(header)
+
+    def build(self) -> TrajectoryGroup:
+        return self.payload().build()
+
+
+class TrajectoryGroupLayout(_Contract):
+    header_byte_count: int = Field(ge=1)
+    record_byte_counts: tuple[int, ...]
+
+
+class TrajectoryBatchTransfer(_Contract):
+    stream: ByteStreamTransfer
+    groups: tuple[TrajectoryGroupLayout, ...]
+
+    @model_validator(mode="after")
+    def _validate_layout(self) -> "TrajectoryBatchTransfer":
+        if any(
+            byte_count < 0
+            for group in self.groups
+            for byte_count in group.record_byte_counts
+        ):
+            raise ValueError("trajectory record byte counts must be non-negative")
+        byte_count = sum(
+            group.header_byte_count + sum(group.record_byte_counts)
+            for group in self.groups
+        )
+        if not self.groups or byte_count != self.stream.byte_count:
+            raise ValueError("trajectory stream layout does not match its payload")
+        return self
+
+    async def receive_groups(self, *, timeout_s: float) -> tuple[TrajectoryGroup, ...]:
+        payload = await receive_byte_stream(self.stream, timeout_s=timeout_s)
+        return await asyncio.to_thread(self._build_groups, payload)
+
+    def _build_groups(self, payload: bytearray) -> tuple[TrajectoryGroup, ...]:
+        from msgspec import msgpack
+
+        from .packing import TrajectoryGroupPayload
+
+        view = memoryview(payload)
+        offset = 0
+        groups = []
+        try:
+            for layout in self.groups:
+                end = offset + layout.header_byte_count
+                header = msgpack.decode(view[offset:end])
+                offset = end
+                records = []
+                for byte_count in layout.record_byte_counts:
+                    end = offset + byte_count
+                    records.append(msgpack.decode(view[offset:end]))
+                    offset = end
+                header["trajectories"] = tuple(records)
+                groups.append(TrajectoryGroupPayload.model_validate(header).build())
+        finally:
+            view.release()
+        return tuple(groups)
+
+
+class TrajectoryGroupRef(_Contract):
+    result_id: str = Field(min_length=1)
+    owner_actor_id: str = Field(min_length=1)
+    lease_id: str = Field(min_length=1)
+    format: Literal["art_trajectory_v1"] = TRAJECTORY_FORMAT
+    records: tuple[TrajectoryRecordRef, ...]
+    descriptor: TrajectoryGroupDescriptor
+    transfer: TrajectoryBatchTransfer | None = None
+
+
+async def publish_trajectory_bundles(
+    bundles: tuple[TrajectoryGroupBundle, ...],
+    *,
+    stream_id: str,
+    advertise_host: str,
+    on_sent: Callable[[], None] | None = None,
+    server_loop: ByteStreamServerLoop | None = None,
+) -> tuple[TrajectoryBatchTransfer, ByteStreamPublisher]:
+    publisher = await ByteStreamPublisher.create(
+        stream_id,
+        tuple(
+            chunk for bundle in bundles for chunk in (bundle.header, *bundle.records)
+        ),
+        advertise_host=advertise_host,
+        on_sent=on_sent,
+        server_loop=server_loop,
+    )
+    try:
+        transfer = TrajectoryBatchTransfer(
+            stream=publisher.transfer,
+            groups=tuple(
+                TrajectoryGroupLayout(
+                    header_byte_count=len(bundle.header),
+                    record_byte_counts=tuple(map(len, bundle.records)),
+                )
+                for bundle in bundles
+            ),
+        )
+    except BaseException:
+        await publisher.close()
+        raise
+    return transfer, publisher
+
+
+class TrajectoryGroupAnnotations(_Contract):
+    metadata: dict[str, MetadataValue] = Field(default_factory=dict)
+    initial_policy_version: int = Field(ge=0)
+    final_policy_version: int = Field(ge=0)
+    rollout_wall_s: float = Field(default=0.0, ge=0)
+    actor_idle_s: float = Field(default=0.0, ge=0)
+    queue_wait_s: float = Field(default=0.0, ge=0)
+
+
+class TrajectoryQueueItem(_Contract):
+    ref: TrajectoryGroupRef
+    annotations: TrajectoryGroupAnnotations
+
+    async def receive(self, *, timeout_s: float) -> TrajectoryGroup:
+        transfer = self.ref.transfer
+        if transfer is None:
+            raise RuntimeError("remote trajectory has no data-plane transfer")
+        if transfer.stream.stream_id != self.ref.result_id:
+            raise RuntimeError("trajectory owner returned the wrong result ID")
+        if transfer.stream.byte_count != self.ref.descriptor.byte_count:
+            raise RuntimeError("trajectory owner returned the wrong byte count")
+        groups = await transfer.receive_groups(timeout_s=timeout_s)
+        if len(groups) != 1:
+            raise RuntimeError("trajectory owner returned the wrong group count")
+        return self.apply_annotations(groups[0])
+
+    def apply_annotations(self, group: TrajectoryGroup) -> TrajectoryGroup:
+        annotations = self.annotations
+        group.metadata.update(annotations.metadata)
+        group.metadata["_art_rollout_wall_s"] = annotations.rollout_wall_s
+        group.metadata["_art_actor_idle_s"] = annotations.actor_idle_s
+        group.metadata["_art_queue_wait_s"] = annotations.queue_wait_s
+        for trajectory in group.trajectories:
+            if trajectory.initial_policy_version is None:
+                trajectory.initial_policy_version = annotations.initial_policy_version
+            if trajectory.final_policy_version is None:
+                trajectory.final_policy_version = annotations.final_policy_version
+        return group
+
+
+class TrajectoryQueueResize(_Contract):
+    queue_id: str = Field(min_length=1)
+    maxsize: int = Field(ge=1)
+    generation: int = Field(ge=1)
+
+
+class TrajectoryEnqueueResult(_Contract):
+    status: Literal["accepted", "full", "oversize", "minimum_unreachable", "closed"]
+    reason: str | None = None
+
+
+class TrajectoryQueueTake(_Contract):
+    leases: tuple[TrajectoryQueueLease, ...] = ()
+    closed: bool = False
+
+
+class TrajectoryQueueLease(_Contract):
+    claim_id: str = Field(min_length=1)
+    consumer_id: str = Field(min_length=1)
+    generation: int = Field(ge=1)
+    item: TrajectoryQueueItem
+
+
+class TrajectoryQueuePacking(_Contract):
+    queue_id: str = Field(min_length=1)
+    leases: tuple[TrajectoryQueueLease, ...]
+    generation_id: str = Field(min_length=1)
+
+
+class TrajectoryQueueRelease(_Contract):
+    queue_id: str = Field(min_length=1)
+    leases: tuple[TrajectoryQueueLease, ...] = Field(min_length=1)
+    generation_id: str | None = None
+    disposition: Literal["consumed", "discarded"]
+
+
+class TrajectoryQueueSnapshot(_Contract):
+    items: tuple[TrajectoryQueueItem, ...]
+    max_ready_groups: int = Field(ge=1)
+    generation: int = Field(ge=0)
+    capacity_records: int = Field(ge=1)
+    capacity_bytes: int = Field(ge=1)
+    used_records: int = Field(ge=0)
+    used_bytes: int = Field(ge=0)
+    leased_groups: int = Field(ge=0)
+    ready_groups: int = Field(ge=0)
+    packing_groups: int = Field(ge=0)
+    packed_groups: int = Field(ge=0)
+    released_leases: int = Field(ge=0)
+    lease_lifetime_s: float = Field(ge=0)
+    max_lease_lifetime_s: float = Field(ge=0)
+
+
+class TrajectoryCapacityError(RuntimeError):
+    pass
+
+
+class TrajectoryLeaseError(RuntimeError):
+    pass
+
+
+class _StoredGroup:
+    def __init__(self, header: bytes, ref: TrajectoryGroupRef) -> None:
+        self.header = header
+        self.ref = ref
+
+
+class TrajectoryRecordStore:
+    """Own typed trajectory records until their rollout-result lease is released."""
+
+    def __init__(
+        self, *, owner_actor_id: str, capacity_records: int, capacity_bytes: int
+    ) -> None:
+        if capacity_records < 1 or capacity_bytes < 1:
+            raise ValueError("trajectory store capacities must be positive")
+        self.owner_actor_id = owner_actor_id
+        self.capacity_records = capacity_records
+        self.capacity_bytes = capacity_bytes
+        self._records: dict[str, bytes] = {}
+        self._groups: dict[str, _StoredGroup] = {}
+        self._used_bytes = 0
+
+    def put(self, group: TrajectoryGroup) -> TrajectoryGroupRef:
+        from .packing import TrajectoryGroupPayload
+
+        payload = TrajectoryGroupPayload.from_group(group)
+        bundle = TrajectoryGroupBundle.from_payload(payload)
+        record_sizes = tuple(len(record) for record in bundle.records)
+        byte_count = sum(record_sizes) + len(bundle.header)
+        record_count = len(payload.trajectories)
+        if record_count > self.capacity_records or byte_count > self.capacity_bytes:
+            raise TrajectoryCapacityError(
+                f"trajectory group requires {record_count} records/{byte_count} bytes; "
+                f"store capacity is {self.capacity_records}/{self.capacity_bytes}"
+            )
+        if (
+            len(self._records) + record_count > self.capacity_records
+            or self._used_bytes + byte_count > self.capacity_bytes
+        ):
+            raise TrajectoryCapacityError("trajectory record store capacity exhausted")
+
+        result_id = secrets.token_hex(16)
+        records = tuple(
+            TrajectoryRecordRef(
+                record_id=secrets.token_hex(16),
+                owner_actor_id=self.owner_actor_id,
+                byte_count=size,
+            )
+            for size in record_sizes
+        )
+        for record_ref, record in zip(records, bundle.records, strict=True):
+            self._records[record_ref.record_id] = record
+        descriptor = TrajectoryGroupDescriptor(
+            grouping_key=_grouping_key(group, result_id),
+            trajectory_count=len(group.trajectories),
+            exception_count=len(group.exceptions),
+            rewards=tuple(trajectory.reward for trajectory in group.trajectories),
+            initial_policy_versions=tuple(
+                trajectory.initial_policy_version
+                for trajectory in group.trajectories
+                if trajectory.initial_policy_version is not None
+            ),
+            completion_tokens=tuple(
+                float(value)
+                if not isinstance(value, bool) and isinstance(value, int | float)
+                else 0.0
+                for trajectory in group.trajectories
+                for value in (trajectory.metrics.get("completion_tokens"),)
+            ),
+            policy_token_counts=_policy_token_counts(group.trajectories),
+            trajectory_initial_policy_versions=tuple(
+                trajectory.initial_policy_version for trajectory in group.trajectories
+            ),
+            trajectory_final_policy_versions=tuple(
+                trajectory.final_policy_version for trajectory in group.trajectories
+            ),
+            trajectory_policy_token_counts=tuple(
+                _trajectory_policy_token_counts(trajectory)
+                for trajectory in group.trajectories
+            ),
+            trajectory_metrics=tuple(
+                trajectory.metrics for trajectory in group.trajectories
+            ),
+            trajectory_metadata=tuple(
+                trajectory.metadata for trajectory in group.trajectories
+            ),
+            group_metadata=group.metadata,
+            group_metrics=group.metrics,
+            exceptions=tuple(
+                (exception.type, exception.message) for exception in group.exceptions
+            ),
+            byte_count=byte_count,
+        )
+        ref = TrajectoryGroupRef(
+            result_id=result_id,
+            owner_actor_id=self.owner_actor_id,
+            lease_id=secrets.token_hex(16),
+            records=records,
+            descriptor=descriptor,
+        )
+        self._groups[result_id] = _StoredGroup(bundle.header, ref)
+        self._used_bytes += byte_count
+        return ref
+
+    def bundle(self, ref: TrajectoryGroupRef) -> TrajectoryGroupBundle:
+        stored = self._entry(ref)
+        return TrajectoryGroupBundle(
+            header=stored.header,
+            records=tuple(
+                self._records[record.record_id] for record in stored.ref.records
+            ),
+        )
+
+    def payload(self, ref: TrajectoryGroupRef) -> TrajectoryGroupPayload:
+        return self.bundle(ref).payload()
+
+    def materialize(self, ref: TrajectoryGroupRef) -> TrajectoryGroup:
+        return self.payload(ref).build()
+
+    def drop(self, ref: TrajectoryGroupRef) -> None:
+        stored = self._groups.get(ref.result_id)
+        if stored is None:
+            return
+        self._require_same_lease(stored.ref, ref)
+        self._groups.pop(ref.result_id)
+        for record in stored.ref.records:
+            self._records.pop(record.record_id)
+        self._used_bytes -= stored.ref.descriptor.byte_count
+
+    def close(self) -> None:
+        self._groups.clear()
+        self._records.clear()
+        self._used_bytes = 0
+
+    def _entry(self, ref: TrajectoryGroupRef) -> _StoredGroup:
+        try:
+            stored = self._groups[ref.result_id]
+        except KeyError:
+            raise TrajectoryLeaseError(
+                f"unknown trajectory result {ref.result_id!r}"
+            ) from None
+        self._require_same_lease(stored.ref, ref)
+        return stored
+
+    @staticmethod
+    def _require_same_lease(
+        expected: TrajectoryGroupRef, received: TrajectoryGroupRef
+    ) -> None:
+        if (
+            expected.owner_actor_id != received.owner_actor_id
+            or expected.lease_id != received.lease_id
+            or expected.records != received.records
+        ):
+            raise TrajectoryLeaseError("trajectory result lease does not match storage")
+
+
+class _QueueEntry:
+    def __init__(self, item: TrajectoryQueueItem) -> None:
+        self.item = item
+        self.phase: Literal["ready", "packing", "packed"] = "ready"
+        self.consumer_id: str | None = None
+        self.claim_id: str | None = None
+        self.claim_generation: int | None = None
+        self.packing_generation_id: str | None = None
+        self.acquired_at: float | None = None
+
+
+class TrajectoryQueueStore:
+    """Bounded FIFO and consumer-lease owner for trajectory-group references."""
+
+    def __init__(
+        self,
+        *,
+        max_ready_groups: int,
+        capacity_records: int,
+        capacity_bytes: int,
+    ) -> None:
+        if min(max_ready_groups, capacity_records, capacity_bytes) < 1:
+            raise ValueError("trajectory queue capacities must be positive")
+        self.max_ready_groups = max_ready_groups
+        self.capacity_records = capacity_records
+        self.capacity_bytes = capacity_bytes
+        self._entries: dict[str, _QueueEntry] = {}
+        self._ready: deque[str] = deque()
+        self._used_records = 0
+        self._used_bytes = 0
+        self._finished = False
+        self._pending_minimum: tuple[str, int] | None = None
+        self._minimum_error: str | None = None
+        self.generation = 0
+        self._claim_generation = 0
+        self._released_leases = 0
+        self._lease_lifetime_s = 0.0
+        self._max_lease_lifetime_s = 0.0
+
+    def resize(self, *, maxsize: int, generation: int) -> None:
+        if maxsize < 1 or generation < 1:
+            raise ValueError("trajectory queue resize values must be positive")
+        if generation < self.generation:
+            return
+        if generation == self.generation:
+            if maxsize != self.max_ready_groups:
+                raise ValueError("trajectory queue resize generation conflicts")
+            return
+        self.max_ready_groups = maxsize
+        self.generation = generation
+
+    def enqueue(self, item: TrajectoryQueueItem) -> TrajectoryEnqueueResult:
+        item = _resolve_grouping(item)
+        ref = item.ref
+        records = len(ref.records)
+        byte_count = ref.descriptor.byte_count
+        if records > self.capacity_records or byte_count > self.capacity_bytes:
+            reason = f"result requires {records} records/{byte_count} bytes"
+            if self._pending_minimum is not None:
+                return self._fail_pending_minimum(item, reason)
+            return TrajectoryEnqueueResult(
+                status="oversize",
+                reason=reason,
+            )
+        if self._finished:
+            return TrajectoryEnqueueResult(status="closed")
+        existing = self._entries.get(ref.result_id)
+        if existing is not None:
+            if existing.item.ref == ref:
+                return TrajectoryEnqueueResult(status="accepted")
+            raise TrajectoryLeaseError("trajectory result lease changed while queued")
+        if self._minimum_error is not None:
+            return TrajectoryEnqueueResult(
+                status="minimum_unreachable", reason=self._minimum_error
+            )
+        blockers = []
+        if len(self._entries) >= self.max_ready_groups:
+            blockers.append("group capacity")
+        if self._used_records + records > self.capacity_records:
+            blockers.append("record capacity")
+        if self._used_bytes + byte_count > self.capacity_bytes:
+            blockers.append("byte capacity")
+        if blockers:
+            pending = self._pending_minimum
+            if (
+                pending is not None
+                and len(self._ready) < pending[1]
+                and self._minimum_cannot_make_progress()
+            ):
+                return self._fail_pending_minimum(item, ", ".join(blockers))
+            return TrajectoryEnqueueResult(status="full")
+        self._entries[ref.result_id] = _QueueEntry(item)
+        self._ready.append(ref.result_id)
+        self._used_records += records
+        self._used_bytes += byte_count
+        return TrajectoryEnqueueResult(status="accepted")
+
+    def take(self, consumer_id: str, count: int) -> TrajectoryQueueTake:
+        """Acquire a positive minimum, take up to a negative limit, or cancel at zero."""
+        if not consumer_id:
+            raise ValueError("consumer_id must not be empty")
+        if count == 0:
+            pending = self._pending_minimum
+            if pending is not None and pending[0] != consumer_id:
+                raise TrajectoryLeaseError(
+                    "trajectory minimum acquisition belongs to another consumer"
+                )
+            self._pending_minimum = None
+            return TrajectoryQueueTake(closed=self._finished and not self._ready)
+        if self._minimum_error is not None:
+            raise TrajectoryCapacityError(self._minimum_error)
+
+        best_effort = count < 0
+        limit = abs(count)
+        request = (consumer_id, limit)
+        if self._pending_minimum not in (None, request):
+            raise TrajectoryLeaseError(
+                "trajectory queue already has a pending minimum acquisition"
+            )
+        if best_effort:
+            if self._pending_minimum is not None:
+                raise TrajectoryLeaseError(
+                    "best-effort take cannot replace a pending minimum acquisition"
+                )
+            take_count = min(limit, len(self._ready))
+        elif not self._finished and limit > self.max_ready_groups:
+            raise TrajectoryCapacityError(
+                f"minimum acquisition requires {limit} trajectory groups; shared "
+                f"queue capacity is {self.max_ready_groups} groups"
+            )
+        elif not self._finished and len(self._ready) < limit:
+            self._pending_minimum = request
+            return TrajectoryQueueTake()
+        else:
+            self._pending_minimum = None
+            take_count = min(limit, len(self._ready))
+
+        leases: list[TrajectoryQueueLease] = []
+        while len(leases) < take_count:
+            result_id = self._ready.popleft()
+            entry = self._entries[result_id]
+            self._claim_generation += 1
+            entry.phase = "packing"
+            entry.consumer_id = consumer_id
+            entry.claim_id = secrets.token_hex(16)
+            entry.claim_generation = self._claim_generation
+            entry.acquired_at = time.monotonic()
+            leases.append(
+                TrajectoryQueueLease(
+                    claim_id=entry.claim_id,
+                    consumer_id=consumer_id,
+                    generation=self._claim_generation,
+                    item=entry.item,
+                )
+            )
+        return TrajectoryQueueTake(
+            leases=tuple(leases), closed=self._finished and not self._ready
+        )
+
+    def mark_packed(self, operation: TrajectoryQueuePacking) -> None:
+        entries = [self._leased_entry(lease) for lease in operation.leases]
+        if any(entry.phase != "packing" for entry in entries):
+            raise TrajectoryLeaseError("trajectory claim is not being packed")
+        for entry in entries:
+            entry.phase = "packed"
+            entry.packing_generation_id = operation.generation_id
+
+    def release(self, operation: TrajectoryQueueRelease) -> None:
+        entries = [self._leased_entry(lease) for lease in operation.leases]
+        for entry in entries:
+            if entry.phase == "packing":
+                if operation.disposition != "discarded" or operation.generation_id:
+                    raise TrajectoryLeaseError(
+                        "unpacked trajectory can only be discarded"
+                    )
+            elif entry.phase == "packed":
+                if operation.generation_id != entry.packing_generation_id:
+                    raise TrajectoryLeaseError(
+                        "trajectory packing generation does not match"
+                    )
+            else:
+                raise TrajectoryLeaseError("trajectory claim was not acquired")
+        now = time.monotonic()
+        for lease, entry in zip(operation.leases, entries, strict=True):
+            assert entry.acquired_at is not None
+            lifetime = now - entry.acquired_at
+            self._released_leases += 1
+            self._lease_lifetime_s += lifetime
+            self._max_lease_lifetime_s = max(self._max_lease_lifetime_s, lifetime)
+            self._remove(lease.item.ref.result_id)
+
+    def finish(self) -> None:
+        self._finished = True
+
+    def close(self) -> tuple[TrajectoryGroupRef, ...]:
+        refs = tuple(entry.item.ref for entry in self._entries.values())
+        self._entries.clear()
+        self._ready.clear()
+        self._used_records = 0
+        self._used_bytes = 0
+        self._finished = True
+        self._pending_minimum = None
+        self._minimum_error = None
+        return refs
+
+    def snapshot(self) -> TrajectoryQueueSnapshot:
+        return TrajectoryQueueSnapshot(
+            items=tuple(entry.item for entry in self._entries.values()),
+            max_ready_groups=self.max_ready_groups,
+            generation=self.generation,
+            capacity_records=self.capacity_records,
+            capacity_bytes=self.capacity_bytes,
+            used_records=self._used_records,
+            used_bytes=self._used_bytes,
+            leased_groups=sum(
+                entry.phase != "ready" for entry in self._entries.values()
+            ),
+            ready_groups=sum(
+                entry.phase == "ready" for entry in self._entries.values()
+            ),
+            packing_groups=sum(
+                entry.phase == "packing" for entry in self._entries.values()
+            ),
+            packed_groups=sum(
+                entry.phase == "packed" for entry in self._entries.values()
+            ),
+            released_leases=self._released_leases,
+            lease_lifetime_s=self._lease_lifetime_s,
+            max_lease_lifetime_s=self._max_lease_lifetime_s,
+        )
+
+    def _leased_entry(self, lease: TrajectoryQueueLease) -> _QueueEntry:
+        entry = self._entries.get(lease.item.ref.result_id)
+        if (
+            entry is None
+            or entry.item != lease.item
+            or entry.consumer_id != lease.consumer_id
+            or entry.claim_id != lease.claim_id
+            or entry.claim_generation != lease.generation
+        ):
+            raise TrajectoryLeaseError("trajectory result has no matching claim")
+        return entry
+
+    def _remove(self, result_id: str) -> None:
+        entry = self._entries.pop(result_id)
+        self._used_records -= len(entry.item.ref.records)
+        self._used_bytes -= entry.item.ref.descriptor.byte_count
+
+    def _fail_pending_minimum(
+        self, item: TrajectoryQueueItem, blocker: str
+    ) -> TrajectoryEnqueueResult:
+        assert self._pending_minimum is not None
+        count = self._pending_minimum[1]
+        ref = item.ref
+        packing = sum(entry.phase == "packing" for entry in self._entries.values())
+        self._minimum_error = (
+            f"minimum acquisition of {count} trajectory groups is unreachable: "
+            f"{len(self._ready)} ready/{packing} packing groups use "
+            f"{self._used_records}/{self.capacity_records} records and "
+            f"{self._used_bytes}/{self.capacity_bytes} bytes; result "
+            f"{ref.result_id!r} requires {len(ref.records)} records/"
+            f"{ref.descriptor.byte_count} bytes ({blocker})"
+        )
+        return TrajectoryEnqueueResult(
+            status="minimum_unreachable", reason=self._minimum_error
+        )
+
+    def _minimum_cannot_make_progress(self) -> bool:
+        return all(entry.phase == "ready" for entry in self._entries.values())
+
+
+def _grouping_key(group: TrajectoryGroup, fallback: str) -> str:
+    value = group.metadata.get("grouping_tag", group.metadata.get("scenario_id"))
+    return fallback if value is None else str(value)
+
+
+def _resolve_grouping(item: TrajectoryQueueItem) -> TrajectoryQueueItem:
+    ref = item.ref
+    scenario_id = item.annotations.metadata.get("scenario_id")
+    if ref.descriptor.grouping_key != ref.result_id or scenario_id is None:
+        return item
+    descriptor = ref.descriptor.model_copy(update={"grouping_key": str(scenario_id)})
+    return item.model_copy(
+        update={"ref": ref.model_copy(update={"descriptor": descriptor})}
+    )
+
+
+def _policy_token_counts(trajectories: list[Trajectory]) -> dict[int, int]:
+    counts: dict[int, int] = {}
+    for trajectory in trajectories:
+        for version, tokens in _trajectory_policy_token_counts(trajectory).items():
+            counts[version] = counts.get(version, 0) + tokens
+    return counts
+
+
+def _trajectory_policy_token_counts(trajectory: Trajectory) -> dict[int, int]:
+    counts: dict[int, int] = {}
+    items: list[Any] = [
+        choice
+        for exchange in trajectory.exchanges.chat_completions
+        for choice in exchange.response.choices
+    ]
+    items.extend(trajectory.messages_and_choices)
+    for history in trajectory.additional_histories:
+        items.extend(history.messages_and_choices)
+    for item in items:
+        extra = getattr(item, "model_extra", None)
+        if not isinstance(extra, Mapping):
+            continue
+        spans = extra.get("policy_token_spans")
+        if spans is None:
+            continue
+        if not isinstance(spans, list):
+            raise RuntimeError("policy_token_spans must be a list")
+        cursor = 0
+        for span in spans:
+            parsed = PolicyTokenSpan.model_validate(span)
+            if parsed.start_token != cursor:
+                raise RuntimeError(
+                    "policy_token_spans must be a contiguous completion partition"
+                )
+            tokens = parsed.end_token - parsed.start_token
+            counts[parsed.policy_version] = (
+                counts.get(parsed.policy_version, 0) + tokens
+            )
+            cursor = parsed.end_token
+    return counts
diff --git a/src/art/distributed/vllm_replica.py b/src/art/distributed/vllm_replica.py
new file mode 100644
index 000000000..15073176d
--- /dev/null
+++ b/src/art/distributed/vllm_replica.py
@@ -0,0 +1,644 @@
+from __future__ import annotations
+
+import asyncio
+from collections.abc import Awaitable, Callable, Mapping
+import hashlib
+import json
+from pathlib import Path
+from typing import Literal, Protocol
+import uuid
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from ..utils.lifecycle import ChildProcessSupervisor
+from ..vllm_runtime import ManagedVllmRuntime, VllmRuntimeLaunchConfig
+from .adapter_transport import AdapterReceiveResult, AdapterTransferTarget
+from .specs import ModelServiceMemberSpec, ModelServiceSpec
+
+
+class _Message(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+MemberPhase = Literal["starting", "ready", "stopped", "failed"]
+ReplicaPhase = Literal[
+    "stopped", "starting", "ready", "updating", "quarantined", "closing"
+]
+
+
+class ReplicaLaunchTemplate(_Message):
+    served_model_name: str = Field(min_length=1)
+    lora_path: str | None = None
+    initial_policy_version: int | None = Field(default=None, ge=0)
+    engine_args: dict[str, object] = Field(default_factory=dict)
+    server_args: dict[str, object] = Field(default_factory=dict)
+
+
+class HostMemberLaunchRequest(_Message):
+    replica_id: str
+    member: ModelServiceMemberSpec
+    generation: int = Field(ge=0)
+    generation_digest: str = Field(min_length=1)
+    process_uuid: str = Field(min_length=1)
+    startup_timeout_s: float = Field(gt=0)
+    launch_config: VllmRuntimeLaunchConfig
+
+
+class HostMemberState(_Message):
+    replica_id: str
+    member_id: str
+    generation: int = Field(ge=0)
+    generation_digest: str = Field(min_length=1)
+    process_uuid: str = Field(min_length=1)
+    phase: MemberPhase
+    detail: str | None = None
+
+
+class ReplicaUpdateReport(_Message):
+    replica_id: str
+    generation: int = Field(ge=0)
+    generation_digest: str = Field(min_length=1)
+    policy_version: str = Field(min_length=1)
+    policy_digest: str = Field(min_length=1)
+    update_identity: str = Field(min_length=1)
+    ambiguous: bool = False
+
+
+class ReplicaState(_Message):
+    replica_id: str
+    generation: int = Field(ge=0)
+    generation_digest: str = Field(min_length=1)
+    phase: ReplicaPhase
+    members: tuple[HostMemberState, ...] = ()
+    committed_version: str | None = None
+    policy_digest: str | None = None
+    update_identity: str | None = None
+    quarantine_reason: str | None = None
+
+
+class ReplicaFailure(_Message):
+    replica_id: str
+    generation: int = Field(ge=0)
+    generation_digest: str = Field(min_length=1)
+    reason: str = Field(min_length=1)
+
+
+class ReplicaHostLauncher(Protocol):
+    async def start_member(
+        self, request: HostMemberLaunchRequest
+    ) -> HostMemberState: ...
+
+    async def member_state(
+        self, replica_id: str, member_id: str, generation: int
+    ) -> HostMemberState: ...
+
+    async def stop_member(
+        self, replica_id: str, member_id: str, generation: int
+    ) -> None: ...
+
+    async def prepare_adapter_receive(
+        self,
+        generation_id: str,
+        template_path: str,
+        timeout_s: float,
+        transport: Literal["local", "nixl"],
+    ) -> AdapterTransferTarget: ...
+
+    async def wait_adapter_receive(
+        self, generation_id: str, timeout_s: float
+    ) -> AdapterReceiveResult: ...
+
+    async def release_adapter_receive(self, generation_id: str) -> None: ...
+
+
+class _ManagedMember:
+    def __init__(self, request: HostMemberLaunchRequest) -> None:
+        self.request = request
+        self.runtime = ManagedVllmRuntime(host=request.launch_config.host)
+        self.failure: RuntimeError | None = None
+        self.supervisor = ChildProcessSupervisor(self._failed)
+
+    def _failed(self, error: RuntimeError) -> None:
+        self.failure = error
+
+
+class ManagedVllmHostLauncher:
+    """Host-local implementation of the serializable member launch protocol."""
+
+    def __init__(
+        self,
+        output_root: str,
+        *,
+        install_parent_cleanup: Callable[[], None] = lambda: None,
+        startup_timeout_s: float | None = None,
+    ) -> None:
+        self._output_root = Path(output_root)
+        self._install_parent_cleanup = install_parent_cleanup
+        self._startup_timeout_s = startup_timeout_s
+        self._members: dict[tuple[str, str, int], _ManagedMember] = {}
+
+    async def start_member(self, request: HostMemberLaunchRequest) -> HostMemberState:
+        key = (request.replica_id, request.member.member_id, request.generation)
+        if key in self._members:
+            raise RuntimeError(f"vLLM member already exists: {key}")
+        managed = _ManagedMember(request)
+        self._members[key] = managed
+        output_dir = self._output_root / request.process_uuid / request.replica_id
+        output_dir /= str(request.generation)
+        output_dir /= request.member.member_id
+        try:
+            await managed.runtime.start(
+                launch_config=request.launch_config,
+                output_dir=str(output_dir),
+                child_processes=managed.supervisor,
+                install_parent_cleanup=self._install_parent_cleanup,
+                timeout=self._startup_timeout_s or request.startup_timeout_s,
+            )
+        except BaseException:
+            await self.stop_member(*key)
+            raise
+        return self._state(managed, "ready")
+
+    async def member_state(
+        self, replica_id: str, member_id: str, generation: int
+    ) -> HostMemberState:
+        managed = self._members.get((replica_id, member_id, generation))
+        if managed is None:
+            raise RuntimeError(
+                f"unknown vLLM member {replica_id}/{member_id}/{generation}"
+            )
+        process = managed.runtime.process
+        failed = managed.failure
+        if failed is None and process is not None and process.poll() is not None:
+            failed = RuntimeError(f"process exited with code {process.returncode}")
+        return self._state(
+            managed,
+            "failed" if failed is not None else "ready",
+            detail=str(failed) if failed is not None else None,
+        )
+
+    async def stop_member(
+        self, replica_id: str, member_id: str, generation: int
+    ) -> None:
+        key = (replica_id, member_id, generation)
+        managed = self._members.get(key)
+        if managed is None:
+            return
+        managed.supervisor.close()
+        await asyncio.to_thread(managed.runtime.close)
+        self._members.pop(key, None)
+
+    async def close(self) -> None:
+        keys = tuple(self._members)
+        results = await asyncio.gather(
+            *(self.stop_member(*key) for key in keys), return_exceptions=True
+        )
+        failures = [result for result in results if isinstance(result, BaseException)]
+        if failures:
+            raise BaseExceptionGroup("failed to stop vLLM host members", failures)
+
+    @staticmethod
+    def _state(
+        managed: _ManagedMember, phase: MemberPhase, detail: str | None = None
+    ) -> HostMemberState:
+        request = managed.request
+        return HostMemberState(
+            replica_id=request.replica_id,
+            member_id=request.member.member_id,
+            generation=request.generation,
+            generation_digest=request.generation_digest,
+            process_uuid=request.process_uuid,
+            phase=phase,
+            detail=detail,
+        )
+
+
+class ReplicaManager:
+    """Owns one native vLLM serving group as an indivisible failure domain."""
+
+    def __init__(
+        self,
+        spec: ModelServiceSpec,
+        launchers: Mapping[str, ReplicaHostLauncher],
+        template: ReplicaLaunchTemplate,
+        *,
+        on_failure: Callable[[ReplicaFailure], Awaitable[None]] | None = None,
+        startup_timeout_s: float = 600.0,
+        rpc_timeout_s: float = 60.0,
+        monitor_interval_s: float = 0.25,
+    ) -> None:
+        if min(startup_timeout_s, rpc_timeout_s, monitor_interval_s) <= 0:
+            raise ValueError("replica timeouts must be positive")
+        missing = {member.host_id for member in spec.members} - launchers.keys()
+        if missing:
+            raise ValueError(f"replica launchers missing hosts: {sorted(missing)}")
+        executor = template.engine_args.get("distributed_executor_backend")
+        if executor not in (None, "mp", "multiprocessing"):
+            raise ValueError("ART-managed replicas require vLLM multiprocessing")
+        for key in ("revision", "tokenizer_revision"):
+            configured = template.engine_args.get(key)
+            if configured is not None and configured != spec.model_revision:
+                raise ValueError(f"{key} conflicts with the replica model revision")
+        self._spec = spec
+        self._launchers = launchers
+        self._host_launchers = tuple(
+            launchers[host_id]
+            for host_id in dict.fromkeys(member.host_id for member in spec.members)
+        )
+        self._template = template
+        self._on_failure = on_failure
+        self._startup_timeout_s = startup_timeout_s
+        self._rpc_timeout_s = rpc_timeout_s
+        self._monitor_interval_s = monitor_interval_s
+        self._lock = asyncio.Lock()
+        self._monitor_task: asyncio.Task[None] | None = None
+        digest = self._generation_digest(spec, 0)
+        self._state = ReplicaState(
+            replica_id=spec.name,
+            generation=0,
+            generation_digest=digest,
+            phase="stopped",
+        )
+
+    @property
+    def spec(self) -> ModelServiceSpec:
+        return self._spec
+
+    @property
+    def state(self) -> ReplicaState:
+        return self._state
+
+    async def start(self) -> ReplicaState:
+        async with self._lock:
+            return await self._start_locked()
+
+    async def _start_locked(self) -> ReplicaState:
+        if self._state.phase != "stopped":
+            raise RuntimeError(f"cannot start replica in {self._state.phase} state")
+        self._state = self._state.model_copy(update={"phase": "starting"})
+        requests = tuple(self._launch_request(member) for member in self._spec.members)
+        tasks = [
+            asyncio.create_task(
+                self._launchers[request.member.host_id].start_member(request)
+            )
+            for request in requests
+        ]
+        try:
+            async with asyncio.timeout(self._startup_timeout_s + self._rpc_timeout_s):
+                members = await asyncio.gather(*tasks)
+            if any(member.phase != "ready" for member in members):
+                raise RuntimeError(f"vLLM gang was not ready: {members!r}")
+        except BaseException as error:
+            for task in tasks:
+                task.cancel()
+            await asyncio.gather(*tasks, return_exceptions=True)
+            try:
+                await self._stop_members(requests)
+            except BaseException as cleanup_error:
+                error = BaseExceptionGroup(
+                    "vLLM gang startup and teardown failed",
+                    [error, cleanup_error],
+                )
+            self._state = self._state.model_copy(
+                update={
+                    "phase": "quarantined",
+                    "quarantine_reason": f"gang startup failed: {error}",
+                }
+            )
+            raise error from None
+        self._state = self._state.model_copy(
+            update={"phase": "ready", "members": tuple(members)}
+        )
+        self._monitor_task = asyncio.create_task(self._monitor())
+        return self._state
+
+    async def stop(self) -> ReplicaState:
+        async with self._lock:
+            return await self._stop_locked()
+
+    async def _stop_locked(self) -> ReplicaState:
+        await self._cancel_monitor()
+        self._state = self._state.model_copy(update={"phase": "closing"})
+        try:
+            await self._stop_current_members()
+        except BaseException as error:
+            self._state = self._state.model_copy(
+                update={
+                    "phase": "quarantined",
+                    "quarantine_reason": f"replica teardown failed: {error}",
+                }
+            )
+            raise
+        self._state = self._state.model_copy(update={"phase": "stopped", "members": ()})
+        return self._state
+
+    async def restart(
+        self,
+        *,
+        served_model_name: str,
+        lora_path: str | None,
+        initial_policy_version: int | None,
+    ) -> ReplicaState:
+        async with self._lock:
+            await self._stop_locked()
+            self._template = self._template.model_copy(
+                update={
+                    "served_model_name": served_model_name,
+                    "lora_path": lora_path,
+                    "initial_policy_version": initial_policy_version,
+                }
+            )
+            generation = self._state.generation + 1
+            self._state = ReplicaState(
+                replica_id=self._spec.name,
+                generation=generation,
+                generation_digest=self._generation_digest(self._spec, generation),
+                phase="stopped",
+            )
+            return await self._start_locked()
+
+    def prepare_update(self, *, update_identity: str) -> ReplicaState:
+        if self._state.phase != "ready":
+            raise RuntimeError(f"cannot update replica in {self._state.phase} state")
+        self._state = self._state.model_copy(
+            update={"phase": "updating", "update_identity": update_identity}
+        )
+        return self._state
+
+    async def prepare_adapter_transfer(
+        self,
+        generation_id: str,
+        template_path: str,
+        *,
+        transport: Literal["local", "nixl"] = "nixl",
+    ) -> tuple[AdapterTransferTarget, ...]:
+        return tuple(
+            await asyncio.gather(
+                *(
+                    asyncio.wait_for(
+                        launcher.prepare_adapter_receive(
+                            generation_id,
+                            template_path,
+                            max(1.0, self._rpc_timeout_s - 1.0),
+                            transport,
+                        ),
+                        self._rpc_timeout_s,
+                    )
+                    for launcher in self._host_launchers
+                )
+            )
+        )
+
+    async def wait_adapter_transfer(
+        self, generation_id: str
+    ) -> tuple[AdapterReceiveResult, ...]:
+        return tuple(
+            await asyncio.gather(
+                *(
+                    asyncio.wait_for(
+                        launcher.wait_adapter_receive(
+                            generation_id, self._rpc_timeout_s
+                        ),
+                        self._rpc_timeout_s,
+                    )
+                    for launcher in self._host_launchers
+                )
+            )
+        )
+
+    async def release_adapter_transfer(self, generation_id: str) -> None:
+        await asyncio.gather(
+            *(
+                asyncio.wait_for(
+                    launcher.release_adapter_receive(generation_id),
+                    self._rpc_timeout_s,
+                )
+                for launcher in self._host_launchers
+            )
+        )
+
+    def verify_update(self, report: ReplicaUpdateReport) -> ReplicaState:
+        expected = self._state
+        valid = (
+            expected.phase == "updating"
+            and report.replica_id == expected.replica_id
+            and report.generation == expected.generation
+            and report.generation_digest == expected.generation_digest
+            and report.update_identity == expected.update_identity
+            and not report.ambiguous
+        )
+        if not valid:
+            return self.quarantine(f"ambiguous update report: {report.model_dump()}")
+        self._state = expected.model_copy(
+            update={
+                "phase": "ready",
+                "committed_version": report.policy_version,
+                "policy_digest": report.policy_digest,
+                "quarantine_reason": None,
+            }
+        )
+        return self._state
+
+    def quarantine(self, reason: str) -> ReplicaState:
+        self._state = self._state.model_copy(
+            update={"phase": "quarantined", "quarantine_reason": reason}
+        )
+        return self._state
+
+    async def poll(self) -> ReplicaState:
+        failure_event: ReplicaFailure | None = None
+        async with self._lock:
+            if self._state.phase not in {"ready", "updating"}:
+                return self._state
+            states = await asyncio.gather(
+                *(
+                    asyncio.wait_for(
+                        self._launchers[member.host_id].member_state(
+                            self._spec.name,
+                            member.member_id,
+                            self._state.generation,
+                        ),
+                        self._rpc_timeout_s,
+                    )
+                    for member in self._spec.members
+                ),
+                return_exceptions=True,
+            )
+            failure = next(
+                (
+                    state
+                    for state in states
+                    if isinstance(state, BaseException) or state.phase != "ready"
+                ),
+                None,
+            )
+            if failure is None:
+                self._state = self._state.model_copy(
+                    update={"members": tuple(states)}  # type: ignore[arg-type]
+                )
+                return self._state
+            reason = f"member failure: {failure}"
+            generation = self._state.generation
+            generation_digest = self._state.generation_digest
+            self.quarantine(reason)
+            failure_event = ReplicaFailure(
+                replica_id=self._spec.name,
+                generation=generation,
+                generation_digest=generation_digest,
+                reason=reason,
+            )
+            try:
+                await self._stop_current_members()
+            except BaseException as error:
+                reason += f"; teardown failure: {error}"
+                self.quarantine(reason)
+                failure_event = failure_event.model_copy(update={"reason": reason})
+        if self._on_failure is not None:
+            await self._on_failure(failure_event)
+        return self._state
+
+    async def _monitor(self) -> None:
+        current = asyncio.current_task()
+        try:
+            while self._monitor_task is current and self._state.phase in {
+                "ready",
+                "updating",
+            }:
+                await asyncio.sleep(self._monitor_interval_s)
+                await self.poll()
+        except asyncio.CancelledError:
+            pass
+
+    async def _cancel_monitor(self) -> None:
+        task, self._monitor_task = self._monitor_task, None
+        if task is None or task is asyncio.current_task():
+            return
+        task.cancel()
+        await asyncio.gather(task, return_exceptions=True)
+
+    async def _stop_current_members(self) -> None:
+        await self._stop_calls(
+            tuple(
+                (
+                    self._launchers[member.host_id],
+                    self._spec.name,
+                    member.member_id,
+                    self._state.generation,
+                )
+                for member in self._spec.members
+            )
+        )
+
+    async def _stop_members(
+        self, requests: tuple[HostMemberLaunchRequest, ...]
+    ) -> None:
+        await self._stop_calls(
+            tuple(
+                (
+                    self._launchers[request.member.host_id],
+                    request.replica_id,
+                    request.member.member_id,
+                    request.generation,
+                )
+                for request in requests
+            )
+        )
+
+    async def _stop_calls(
+        self,
+        calls: tuple[tuple[ReplicaHostLauncher, str, str, int], ...],
+    ) -> None:
+        pending = calls
+        failures: list[BaseException] = []
+        for _attempt in range(2):
+            results = await asyncio.gather(
+                *(
+                    asyncio.wait_for(
+                        launcher.stop_member(replica_id, member_id, generation),
+                        self._rpc_timeout_s,
+                    )
+                    for launcher, replica_id, member_id, generation in pending
+                ),
+                return_exceptions=True,
+            )
+            failures = [
+                result for result in results if isinstance(result, BaseException)
+            ]
+            if not failures:
+                return
+            pending = tuple(
+                call
+                for call, result in zip(pending, results, strict=True)
+                if isinstance(result, BaseException)
+            )
+        raise BaseExceptionGroup("failed to stop vLLM replica members", failures)
+
+    def _launch_request(
+        self, member: ModelServiceMemberSpec
+    ) -> HostMemberLaunchRequest:
+        parallel = self._spec.parallel
+        engine_args = {
+            **self._template.engine_args,
+            "tensor_parallel_size": parallel.tp,
+            "pipeline_parallel_size": parallel.pp,
+            "data_parallel_size": parallel.dp,
+            "enable_expert_parallel": parallel.enable_expert_parallel,
+        }
+        if self._spec.model_revision is not None:
+            engine_args.update(
+                revision=self._spec.model_revision,
+                tokenizer_revision=self._spec.model_revision,
+            )
+        process_uuid = uuid.uuid4().hex
+        physical_ids = all(isinstance(gpu_id, int) for gpu_id in member.gpu_ids)
+        launch = VllmRuntimeLaunchConfig(
+            base_model=self._spec.base_model,
+            port=self._spec.leader_endpoint.port,
+            host=(
+                self._spec.leader_endpoint.host
+                if member.node_rank == 0
+                else "127.0.0.1"
+            ),
+            cuda_visible_devices=(
+                None if physical_ids else ",".join(map(str, member.gpu_ids))
+            ),
+            local_gpu_ids=(
+                tuple(gpu_id for gpu_id in member.gpu_ids if isinstance(gpu_id, int))
+                if physical_ids
+                else None
+            ),
+            lora_path=self._template.lora_path,
+            served_model_name=self._template.served_model_name,
+            engine_args=engine_args,
+            server_args=self._template.server_args,
+            nnodes=len(self._spec.members),
+            node_rank=member.node_rank,
+            master_addr=self._spec.rendezvous.host
+            if len(self._spec.members) > 1
+            else None,
+            master_port=self._spec.rendezvous.port
+            if len(self._spec.members) > 1
+            else None,
+            headless=member.node_rank != 0,
+            replica_generation=self._state.generation,
+            process_uuid=process_uuid,
+            update_identity=self._state.update_identity,
+            initial_policy_version=self._template.initial_policy_version,
+        )
+        return HostMemberLaunchRequest(
+            replica_id=self._spec.name,
+            member=member,
+            generation=self._state.generation,
+            generation_digest=self._state.generation_digest,
+            process_uuid=process_uuid,
+            startup_timeout_s=self._startup_timeout_s,
+            launch_config=launch,
+        )
+
+    @staticmethod
+    def _generation_digest(spec: ModelServiceSpec, generation: int) -> str:
+        payload = json.dumps(
+            {"generation": generation, "spec": spec.model_dump(mode="json")},
+            sort_keys=True,
+        ).encode()
+        return hashlib.sha256(payload).hexdigest()
diff --git a/src/art/local/adapter_leases.py b/src/art/local/adapter_leases.py
index f790e5a36..f8f6f9a8d 100644
--- a/src/art/local/adapter_leases.py
+++ b/src/art/local/adapter_leases.py
@@ -32,3 +32,8 @@ async def lease(self, step: int) -> AsyncIterator[None]:
 
     def active_steps(self) -> set[int]:
         return set(self._counts)
+
+    @asynccontextmanager
+    async def prune_guard(self) -> AsyncIterator[set[int]]:
+        async with self._condition:
+            yield self.active_steps()
diff --git a/src/art/local/backend.py b/src/art/local/backend.py
index 7d8b71b5d..2de2c77bf 100644
--- a/src/art/local/backend.py
+++ b/src/art/local/backend.py
@@ -22,6 +22,8 @@
 )
 from art.utils.lifecycle import (
     PROCESS_SHUTDOWN_TIMEOUT_SECONDS,
+    complete_task,
+    complete_to_thread,
     process_shutdown_timeout,
 )
 
@@ -35,7 +37,7 @@
 
 import httpx
 import numpy as np
-import polars as pl
+from pydantic import BaseModel, ConfigDict
 import torch
 from tqdm import auto as tqdm
 from transformers import AutoTokenizer
@@ -93,7 +95,7 @@
     tokenize_sft_batch,
     tokenize_trajectory_groups,
 )
-from ..serving_capabilities import ServingCapabilities
+from ..serving_capabilities import FastMetricsSnapshot, ServingCapabilities
 from ..trajectories import Trajectory, TrajectoryGroup
 from ..trajectories._selection import automatic_training_model_selector
 from ..types import (
@@ -118,59 +120,41 @@
 from .service import ModelService
 
 
-def _prometheus_values(text: str, name: str) -> list[float]:
-    values: list[float] = []
-    for line in text.splitlines():
-        if not line or line.startswith("#"):
-            continue
-        try:
-            sample, raw_value = line.rsplit(None, 1)
-        except ValueError:
-            continue
-        sample_name = sample.split("{", 1)[0]
-        if sample_name != name:
-            continue
-        try:
-            values.append(float(raw_value))
-        except ValueError:
-            continue
-    return values
-
+class _PackedTrainingBatch(BaseModel):
+    model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
 
-def _prometheus_sum(text: str, name: str) -> float | None:
-    values = _prometheus_values(text, name)
-    if not values:
-        return None
-    return math.fsum(values)
+    payload: Any
+    num_sequences: int
+    sequence_length: int
+    trainable_assistant_tokens: int
+    loss_bearing_tokens: int
+    non_padding_tokens: int
+    logical_tokens: int
+    physical_tokens: int
+    include_moe_routing: bool
 
 
-def _prometheus_sum_with_label(
-    text: str, name: str, label: str, value: str
-) -> float | None:
-    values: list[float] = []
-    needle = f'{label}="{value}"'
-    for line in text.splitlines():
-        if not line or line.startswith("#"):
-            continue
-        try:
-            sample, raw_value = line.rsplit(None, 1)
-        except ValueError:
-            continue
-        sample_name = sample.split("{", 1)[0]
-        if sample_name != name or needle not in sample:
-            continue
-        try:
-            values.append(float(raw_value))
-        except ValueError:
-            continue
-    return math.fsum(values) if values else None
+class _TrainStepVllmMetricsCollector:
+    def __init__(self, backend: "LocalBackend", model: Model) -> None:
+        self._backend = backend
+        self._model = model
+        self._client = httpx.AsyncClient(
+            timeout=1.0,
+            limits=httpx.Limits(max_connections=1, max_keepalive_connections=1),
+        )
+        self._snapshots: dict[
+            tuple[str, str, str, int], tuple[float, dict[str, float]]
+        ] = {}
 
+    async def collect(self) -> dict[str, float]:
+        return await self._backend._collect_train_step_vllm_metrics(
+            self._model,
+            client=self._client,
+            snapshots=self._snapshots,
+        )
 
-def _prometheus_mean(text: str, name: str) -> float | None:
-    values = _prometheus_values(text, name)
-    if not values:
-        return None
-    return math.fsum(values) / len(values)
+    async def aclose(self) -> None:
+        await self._client.aclose()
 
 
 def _configured_chat_template_value(
@@ -355,8 +339,9 @@ def __init__(
         self._grad_accumulation_sequences_by_service: dict[int, int] = {}
         self._provenance_update_tasks: set[asyncio.Task[None]] = set()
         self._vllm_metric_snapshots: dict[
-            tuple[str, str], tuple[float, dict[str, float]]
+            tuple[str, str, str, int], tuple[float, dict[str, float]]
         ] = {}
+        self._vllm_metrics_client: httpx.AsyncClient | None = None
         self._image_processors: dict[str, BaseImageProcessor | None] = {}
         self._requires_explicit_packed_sequence_length = False
         self._packed_sequence_length_requires_chunk_alignment = True
@@ -414,6 +399,13 @@ def _model_max_sequence_length(self, model: AnyTrainableModel) -> int:
     def supports_automatic_train_step_metrics(self) -> bool:
         return True
 
+    def _supports_concurrent_training_and_inference(
+        self, model: AnyTrainableModel
+    ) -> bool:
+        from ..dev.validate import is_dedicated_mode
+
+        return is_dedicated_mode(model._internal_config or dev.InternalModelConfig())
+
     def automatic_gpu_cost_per_hour_usd(self, model: Model) -> float | None:
         per_gpu_cost = self._resolve_gpu_cost_per_hour_usd()
         if per_gpu_cost is None:
@@ -424,65 +416,80 @@ def automatic_gpu_cost_per_hour_usd(self, model: Model) -> float | None:
             return None
         return per_gpu_cost * gpu_count
 
+    def create_train_step_vllm_metrics_collector(
+        self, model: Model
+    ) -> _TrainStepVllmMetricsCollector:
+        return _TrainStepVllmMetricsCollector(self, model)
+
     async def collect_train_step_vllm_metrics(self, model: Model) -> dict[str, float]:
+        client = self._vllm_metrics_client
+        if client is None:
+            client = self._vllm_metrics_client = httpx.AsyncClient(
+                timeout=1.0,
+                limits=httpx.Limits(max_connections=4, max_keepalive_connections=4),
+            )
+        return await self._collect_train_step_vllm_metrics(
+            model,
+            client=client,
+            snapshots=self._vllm_metric_snapshots,
+        )
+
+    async def _collect_train_step_vllm_metrics(
+        self,
+        model: Model,
+        *,
+        client: httpx.AsyncClient,
+        snapshots: dict[tuple[str, str, str, int], tuple[float, dict[str, float]]],
+    ) -> dict[str, float]:
         capabilities = model._serving_capabilities
         if capabilities is None:
             raise RuntimeError("vLLM serving capabilities have not been discovered")
         capabilities.require("fast_metrics", operation="ART vLLM metrics collection")
-        base_url = model.inference_base_url
-        if not base_url or not base_url.startswith(("http://", "https://")):
-            raise RuntimeError(
-                "ART vLLM metrics require model.inference_base_url to point to the "
-                "dedicated ART vLLM runtime."
-            )
-
-        metrics_root = base_url.rstrip("/")
-        if metrics_root.endswith("/v1"):
-            metrics_root = metrics_root[: -len("/v1")]
-        headers = (
-            {"Authorization": f"Bearer {model.inference_api_key}"}
-            if model.inference_api_key
-            else None
-        )
+        endpoint = capabilities.fast_metrics
+        assert endpoint is not None
+        metrics_url = str(endpoint.url)
         try:
-            async with httpx.AsyncClient(timeout=1.0) as client:
-                response = await client.get(
-                    f"{metrics_root}/art/metrics",
-                    headers=headers,
-                )
-                response.raise_for_status()
-                payload = response.json()
+            response = await client.get(
+                metrics_url,
+                headers=(
+                    {"Authorization": f"Bearer {model.inference_api_key}"}
+                    if model.inference_api_key
+                    else None
+                ),
+            )
+            response.raise_for_status()
+            payload = FastMetricsSnapshot.model_validate(response.json())
         except httpx.TimeoutException:
             raise ArtVllmMetricsTimeoutError(
-                f"Timed out collecting ART vLLM metrics from {metrics_root}."
+                f"Timed out collecting ART vLLM metrics from {metrics_url}."
             )
         except (httpx.HTTPError, ValueError) as exc:
             raise RuntimeError(
-                "ART vLLM metrics require the dedicated ART runtime endpoint at "
-                f"{metrics_root}/art/metrics."
+                f"ART vLLM metrics endpoint returned an invalid response from "
+                f"{metrics_url}."
             ) from exc
 
-        raw_metrics = payload.get("metrics") if isinstance(payload, dict) else None
-        if not isinstance(raw_metrics, dict):
-            raise RuntimeError(
-                "ART vLLM metrics endpoint returned an invalid payload: expected "
-                "a top-level metrics object."
-            )
+        raw_metrics = payload.metrics
+        process_uuid = payload.process_uuid
+        generation = payload.generation
 
         def required_metric(name: str) -> float:
-            raw_value = raw_metrics.get(name)
-            if not isinstance(raw_value, (int, float)):
+            try:
+                return raw_metrics[name]
+            except KeyError:
                 raise RuntimeError(
                     f"ART vLLM metrics endpoint did not provide numeric {name!r}."
-                )
-            return float(raw_value)
+                ) from None
 
         def optional_metric(name: str) -> float | None:
-            raw_value = raw_metrics.get(name)
-            if not isinstance(raw_value, (int, float)):
-                return None
-            return float(raw_value)
+            return raw_metrics.get(name)
 
+        counter_names = (
+            "prompt_tokens_total",
+            "generation_tokens_total",
+            "prefix_cache_queries_total",
+            "prefix_cache_hits_total",
+        )
         snapshot = {
             "prompt_tokens_total": required_metric("prompt_tokens_total"),
             "generation_tokens_total": required_metric("generation_tokens_total"),
@@ -490,8 +497,7 @@ def optional_metric(name: str) -> float | None:
             "prefix_cache_hits_total": required_metric("prefix_cache_hits_total"),
             "num_preemptions_total": required_metric("num_preempted_reqs_total"),
         }
-        metrics: dict[str, float] = {}
-        gauges = {
+        metrics: dict[str, float] = {
             "vllm/num_requests_running": required_metric("num_requests_running"),
             "vllm/num_requests_waiting": required_metric("num_requests_waiting"),
             "vllm/num_requests_waiting_capacity": required_metric(
@@ -500,9 +506,6 @@ def optional_metric(name: str) -> float | None:
             "vllm/kv_cache_usage_perc": required_metric("kv_cache_usage_perc"),
             "vllm/num_preemptions_total": snapshot["num_preemptions_total"],
         }
-        for key, value in gauges.items():
-            if value is not None:
-                metrics[key] = value
         for name in (
             "max_num_seqs",
             "max_num_batched_tokens",
@@ -514,52 +517,52 @@ def optional_metric(name: str) -> float | None:
             if value is not None:
                 metrics[f"vllm/{name}"] = value
 
+        current = {name: snapshot[name] for name in counter_names}
         now = time.monotonic()
-        storage_key = self._model_storage_key(model)
-        previous = self._vllm_metric_snapshots.get(storage_key)
+        model_key = self._model_storage_key(model)
+        key = (*model_key, process_uuid, generation)
+        previous = snapshots.get(key)
+        snapshots[key] = (now, current)
+        for stale in tuple(snapshots):
+            if stale[:2] == model_key and stale != key:
+                del snapshots[stale]
+        delta_queries = 0.0
         if previous is not None:
             previous_time, previous_snapshot = previous
-            elapsed = max(0.0, now - previous_time)
+            elapsed = now - previous_time
             if elapsed > 0:
-                prompt_tokens = snapshot["prompt_tokens_total"]
-                previous_prompt_tokens = previous_snapshot.get("prompt_tokens_total")
-                if prompt_tokens is not None and previous_prompt_tokens is not None:
-                    metrics["vllm/prompt_tok_per_s"] = max(
-                        0.0, (prompt_tokens - previous_prompt_tokens) / elapsed
+                metrics["vllm/prompt_tok_per_s"] = max(
+                    0.0,
+                    (
+                        current["prompt_tokens_total"]
+                        - previous_snapshot["prompt_tokens_total"]
                     )
-                generation_tokens = snapshot["generation_tokens_total"]
-                previous_generation_tokens = previous_snapshot.get(
-                    "generation_tokens_total"
+                    / elapsed,
                 )
-                if (
-                    generation_tokens is not None
-                    and previous_generation_tokens is not None
-                ):
-                    metrics["vllm/completion_tok_per_s"] = max(
-                        0.0, (generation_tokens - previous_generation_tokens) / elapsed
+                metrics["vllm/completion_tok_per_s"] = max(
+                    0.0,
+                    (
+                        current["generation_tokens_total"]
+                        - previous_snapshot["generation_tokens_total"]
                     )
-
-            prefix_queries = snapshot["prefix_cache_queries_total"]
-            previous_prefix_queries = previous_snapshot.get(
-                "prefix_cache_queries_total"
+                    / elapsed,
+                )
+            delta_queries = max(
+                0.0,
+                current["prefix_cache_queries_total"]
+                - previous_snapshot["prefix_cache_queries_total"],
             )
-            prefix_hits = snapshot["prefix_cache_hits_total"]
-            previous_prefix_hits = previous_snapshot.get("prefix_cache_hits_total")
-            if (
-                prefix_queries is not None
-                and previous_prefix_queries is not None
-                and prefix_hits is not None
-                and previous_prefix_hits is not None
-            ):
-                delta_queries = prefix_queries - previous_prefix_queries
-                if delta_queries > 0:
-                    metrics["vllm/prefix_cache_hit_rate"] = max(
-                        0.0,
-                        min(1.0, (prefix_hits - previous_prefix_hits) / delta_queries),
-                    )
-        elif (
-            snapshot["prefix_cache_queries_total"] is not None
-            and snapshot["prefix_cache_hits_total"] is not None
+            delta_hits = max(
+                0.0,
+                current["prefix_cache_hits_total"]
+                - previous_snapshot["prefix_cache_hits_total"],
+            )
+            if delta_queries > 0:
+                metrics["vllm/prefix_cache_hit_rate"] = min(
+                    1.0, delta_hits / delta_queries
+                )
+        if (
+            "vllm/prefix_cache_hit_rate" not in metrics
             and snapshot["prefix_cache_queries_total"] > 0
         ):
             metrics["vllm/prefix_cache_hit_rate"] = max(
@@ -571,10 +574,6 @@ def optional_metric(name: str) -> float | None:
                 ),
             )
 
-        self._vllm_metric_snapshots[storage_key] = (
-            now,
-            {key: value for key, value in snapshot.items() if value is not None},
-        )
         return metrics
 
     def _resolve_gpu_cost_per_hour_usd(self) -> float | None:
@@ -660,10 +659,18 @@ async def __aexit__(
         await self.close()
 
     async def close(self) -> None:
+        task = asyncio.create_task(self._close_local_backend())
+        _, cancelled = await complete_task(task)
+        if cancelled is not None:
+            raise cancelled
+
+    async def _close_local_backend(self) -> None:
         """
         If running vLLM in a separate process, this will kill that process and close the communication threads.
         """
+        failures: list[Exception] = []
         for service in self._services.values():
+            propagate = bool(getattr(service, "propagate_close_errors", False))
             try:
                 aclose = getattr(service, "aclose", None)
                 if aclose is None:
@@ -672,24 +679,40 @@ async def close(self) -> None:
                         close()
                 else:
                     await asyncio.wait_for(
-                        aclose(), timeout=_SERVICE_CLOSE_TIMEOUT_SECONDS
+                        aclose(),
+                        timeout=float(
+                            getattr(
+                                service,
+                                "close_timeout_s",
+                                _SERVICE_CLOSE_TIMEOUT_SECONDS,
+                            )
+                        ),
                     )
-            except TimeoutError:
-                logger.warning("Timed out while closing local backend service.")
-            except Exception:
-                logger.exception("Failed to close local backend service.")
+            except Exception as error:
+                if propagate:
+                    failures.append(error)
+                else:
+                    logger.exception("Failed to close local backend service.")
             finally:
                 try:
                     close_proxy(service)
-                except Exception:
-                    logger.exception("Failed to close local backend service proxy.")
+                except Exception as error:
+                    if propagate:
+                        failures.append(error)
+                    else:
+                        logger.exception("Failed to close local backend service proxy.")
         self._services.clear()
         self._adapter_leases.clear()
+        client, self._vllm_metrics_client = self._vllm_metrics_client, None
+        if client is not None:
+            await client.aclose()
         await self._drain_provenance_update_tasks()
         gc.collect()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
             torch.cuda.ipc_collect()
+        if failures:
+            raise ExceptionGroup("distributed backend close failed", failures)
 
     def _close(self) -> None:
         self._cancel_provenance_update_tasks()
@@ -707,6 +730,14 @@ def _close(self) -> None:
                     logger.exception("Failed to close local backend service proxy.")
         self._services.clear()
         self._adapter_leases.clear()
+        client, self._vllm_metrics_client = self._vllm_metrics_client, None
+        if client is not None:
+            try:
+                loop = asyncio.get_running_loop()
+            except RuntimeError:
+                asyncio.run(client.aclose())
+            else:
+                loop.create_task(client.aclose())
         gc.collect()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
@@ -901,11 +932,14 @@ async def prune_model_adapters(
         if service is None:
             return
         manager = self._adapter_leases.get(storage_key)
-        if manager is not None:
-            retain_steps = set(retain_steps) | manager.active_steps()
         prune_loaded_adapters = getattr(service, "prune_loaded_adapters", None)
-        if prune_loaded_adapters is not None:
+        if prune_loaded_adapters is None:
+            return
+        if manager is None:
             await prune_loaded_adapters(retain_steps=retain_steps)
+            return
+        async with manager.prune_guard() as leased_steps:
+            await prune_loaded_adapters(retain_steps=set(retain_steps) | leased_steps)
 
     async def _get_service(self, model: TrainableModel) -> ModelService:
         from ..dev.get_model_config import get_model_config
@@ -940,11 +974,14 @@ async def _get_service(self, model: TrainableModel) -> ModelService:
                     str(g) for g in config["trainer_gpu_ids"]
                 )
 
-            self._services[storage_key] = service_class(
-                model_name=model.name,
-                base_model=model.base_model,
-                config=config,
-                output_dir=get_model_dir(model=model, art_path=self._path),
+            self._services[storage_key] = cast(
+                ModelService,
+                service_class(
+                    model_name=model.name,
+                    base_model=model.base_model,
+                    config=config,
+                    output_dir=get_model_dir(model=model, art_path=self._path),
+                ),
             )
             if not dedicated and not self._in_process:
                 self._services[storage_key] = move_to_child_process(
@@ -1091,7 +1128,7 @@ def _get_packed_tensors(
             not allow_training_without_logprobs
             and np.isnan(packed_tensors["logprobs"]).all()
         ):
-            print(
+            logger.warning(
                 "There are no assistant logprobs to train on. Did you forget to include at least one Choice in Trajectory.messages_and_choices?"
             )
             return None
@@ -1100,7 +1137,7 @@ def _get_packed_tensors(
                 packed_tensors, get_model_dir(model=model, art_path=self._path)
             )
         else:
-            print(
+            logger.info(
                 f"Packed {len(tokenized_results)} trajectories into {packed_tensors['tokens'].shape[0]} sequences of length {packed_tensors['tokens'].shape[1]}"
             )
         return packed_tensors
@@ -1150,6 +1187,8 @@ async def _delete_checkpoint_files(
         """Delete checkpoint files, keeping only the specified steps."""
 
         output_dir = get_model_dir(model=model, art_path=self._path)
+        from ..megatron.optimizer_state import optimizer_retention_lease
+
         service = await self._get_service(model)
         try:
             from ..tinker.service import TinkerService
@@ -1159,7 +1198,12 @@ async def _delete_checkpoint_files(
                 return
         except ImportError:
             pass
-        delete_checkpoints(output_dir, steps_to_keep)
+
+        def delete_retained() -> None:
+            with optimizer_retention_lease(output_dir, set(steps_to_keep)) as protected:
+                delete_checkpoints(output_dir, sorted(protected))
+
+        await asyncio.to_thread(delete_retained)
 
     async def _prepare_backend_for_training(
         self,
@@ -1280,6 +1324,8 @@ async def train(  # type: ignore[override]
         # Checkpoint behavior
         save_checkpoint: bool = True,
         optimizer_save_interval: int = 5,
+        final_training_step: int | None = None,
+        grad_accumulation_sequences: int | None = None,
         # Verbosity
         verbose: bool = False,
     ) -> LocalTrainResult:
@@ -1416,6 +1462,8 @@ async def train(  # type: ignore[override]
             num_trajectories_learning_rate_multiplier_power=num_trajectories_learning_rate_multiplier_power,
             kl_ref_adapter_path=resolved_kl_ref_adapter_path,
             optimizer_save_interval=optimizer_save_interval,
+            final_training_step=final_training_step,
+            grad_accumulation_sequences=grad_accumulation_sequences,
         )
 
         # Collect metrics from training
@@ -1477,6 +1525,57 @@ async def update() -> None:
         self._provenance_update_tasks.add(task)
         task.add_done_callback(self._provenance_update_tasks.discard)
 
+    async def _advance_skipped_step(
+        self,
+        model: TrainableModel,
+        service: ModelService,
+        current_step: int,
+        next_step: int,
+    ) -> dict[str, float]:
+        model_dir = get_model_dir(model=model, art_path=self._path)
+        current = get_step_checkpoint_dir(model_dir, current_step)
+        if not os.path.exists(current):
+            return {}
+        checkpoint = get_step_checkpoint_dir(model_dir, next_step)
+        if os.path.exists(checkpoint):
+            raise RuntimeError(f"Refusing to replace checkpoint {checkpoint}")
+        registration_started = False
+        try:
+            _, cancelled = await complete_to_thread(
+                lambda: shutil.copytree(current, checkpoint)
+            )
+            if cancelled is not None:
+                raise cancelled
+            registration_started = True
+            await service.register_lora_for_step(next_step, checkpoint)
+        except BaseException as error:
+            failures: list[BaseException] = [error]
+            if registration_started:
+                self._services.pop(model.name, None)
+                try:
+                    _, close_cancelled = await complete_task(
+                        asyncio.create_task(service.aclose())
+                    )
+                    if close_cancelled is not None:
+                        failures.append(close_cancelled)
+                except BaseException as close_error:
+                    failures.append(close_error)
+            if os.path.exists(checkpoint):
+                try:
+                    _, remove_cancelled = await complete_to_thread(
+                        lambda: shutil.rmtree(checkpoint)
+                    )
+                    if remove_cancelled is not None:
+                        failures.append(remove_cancelled)
+                except BaseException as remove_error:
+                    failures.append(remove_error)
+            if len(failures) > 1:
+                raise BaseExceptionGroup(
+                    "skipped-step publication and rollback failed", failures
+                ) from None
+            raise
+        return {}
+
     async def _train_model(
         self,
         model: TrainableModel,
@@ -1498,22 +1597,13 @@ async def _train_model(
             include_trainable_groups=True,
         )
         include_moe_routing = self._model_uses_expert_replay(model)
-        packed_tensors = self._get_packed_tensors(
+        packed_batch = await self._prepare_training_batch(
             model,
             trajectory_groups,
-            advantage_balance=dev_config.get("advantage_balance", 0.0),
-            allow_training_without_logprobs=dev_config.get(
-                "allow_training_without_logprobs", False
-            ),
-            scale_rewards=dev_config.get("scale_rewards", True),
-            plot_tensors=dev_config.get("plot_tensors", False),
-            packed_sequence_length=dev_config.get("packed_sequence_length"),
-            logprob_calculation_chunk_size=dev_config.get(
-                "logprob_calculation_chunk_size", 1024
-            ),
+            dev_config,
             include_moe_routing=include_moe_routing,
         )
-        if packed_tensors is None:
+        if packed_batch is None:
             print(
                 "Skipping tuning as there is no suitable data. "
                 "This can happen when all the trajectories in the same group "
@@ -1521,94 +1611,221 @@ async def _train_model(
             )
 
             # Still advance the step by renaming the checkpoint directory
-            current_step = self.__get_step(model)
+            current_step = await self._get_step(model)
             next_step = current_step + 1
             logger.info(
                 f"[BACKEND] _train_model SKIP: current_step={current_step} "
                 f"next_step={next_step} (all rewards equal)"
             )
-            current_checkpoint_dir = get_step_checkpoint_dir(
-                get_model_dir(model=model, art_path=self._path), current_step
+            advance_metrics = await self._advance_skipped_step(
+                model, service, current_step, next_step
             )
-            next_checkpoint_dir = get_step_checkpoint_dir(
-                get_model_dir(model=model, art_path=self._path), next_step
+            logger.info(
+                f"[BACKEND] _train_model SKIP: advanced checkpoint "
+                f"{current_step} -> {next_step}"
             )
 
-            # If the current checkpoint exists, copy it to the next step
-            if os.path.exists(current_checkpoint_dir):
-                shutil.copytree(
-                    current_checkpoint_dir,
-                    next_checkpoint_dir,
-                    dirs_exist_ok=True,
-                )
-                logger.info(
-                    f"[BACKEND] _train_model SKIP: copied checkpoint "
-                    f"{current_step} -> {next_step}, calling register_lora_for_step..."
-                )
-
-                try:
-                    # Register the copied checkpoint as a new LoRA adapter
-                    # so it's available for inference at the new step
-                    register_lora_for_step = getattr(
-                        service, "register_lora_for_step", None
-                    )
-                    if callable(register_lora_for_step):
-                        await register_lora_for_step(next_step, next_checkpoint_dir)
-                    logger.info(
-                        f"[BACKEND] _train_model SKIP: register_lora_for_step "
-                        f"completed for step {next_step}"
-                    )
-                except ModuleNotFoundError:
-                    pass  # Unsloth is not installed
-
             # Yield metrics showing no groups were trainable
             # (the frontend will handle logging)
             yield {
                 **base_metrics,
                 "data/step_num_groups_trainable": 0.0,
                 "data/step_trainable_assistant_tokens": 0.0,
+                "data/step_nonpadding_logical_tokens": 0.0,
+                "data/step_loss_bearing_tokens": 0.0,
+                "data/step_executed_token_equivalents": 0.0,
+                "data/step_nominal_schedule_capacity_tokens": 0.0,
+                "data/step_dummy_executed_token_equivalents": 0.0,
+                "data/step_dummy_schedule_capacity_tokens": 0.0,
+                "data/step_unused_packed_capacity_tokens": 0.0,
+                "data/step_unused_and_dummy_ratio": 0.0,
                 TRAIN_GRADIENT_STEPS_KEY: 0.0,
+                **advance_metrics,
             }
             return
-        base_metrics["data/step_trainable_assistant_tokens"] = float(
-            packed_tensors["assistant_mask"].sum().item()
-        )
-        packed_sequences, packed_sequence_length = packed_tensors["tokens"].shape
-        non_padding_tokens = int((packed_tensors["group_ids"] != -1).sum().item())
-        packing_stats = packed_tensors["prefix_tree_packing_stats"]
-        disk_packed_tensors = packed_tensors_to_dir(
-            packed_tensors, f"{get_model_dir(model=model, art_path=self._path)}/tensors"
-        )
-        service_dev_config = cast(dev.TrainConfig, {**dev_config})
-        grad_accumulation_sequences = await self._resolve_grad_accumulation_sequences(
-            service,
-            config,
-        )
-        fallback_gradient_steps = math.ceil(
-            packed_sequences / grad_accumulation_sequences
+        async with self._training_batch_lifecycle(packed_batch):
+            base_metrics["data/step_trainable_assistant_tokens"] = float(
+                packed_batch.trainable_assistant_tokens
+            )
+            packed_sequences = packed_batch.num_sequences
+            packed_sequence_length = packed_batch.sequence_length
+            non_padding_tokens = packed_batch.non_padding_tokens
+            service_dev_config = cast(dev.TrainConfig, {**dev_config})
+            grad_accumulation_sequences = (
+                await self._resolve_grad_accumulation_sequences(service, config)
+            )
+            fallback_gradient_steps = math.ceil(
+                packed_sequences / grad_accumulation_sequences
+            )
+            packed_train_tokens = int(
+                fallback_gradient_steps
+                * grad_accumulation_sequences
+                * packed_sequence_length
+            )
+            base_metrics.update(
+                {
+                    "data/step_packed_sequences": float(packed_sequences),
+                    "data/step_nonpadding_logical_tokens": float(non_padding_tokens),
+                    "data/step_loss_bearing_tokens": float(
+                        packed_batch.loss_bearing_tokens
+                    ),
+                    "data/step_executed_token_equivalents": float(packed_train_tokens),
+                    "data/step_nominal_schedule_capacity_tokens": float(
+                        packed_train_tokens
+                    ),
+                    "data/step_dummy_executed_token_equivalents": 0.0,
+                    "data/step_dummy_schedule_capacity_tokens": 0.0,
+                    "data/step_unused_packed_capacity_tokens": float(
+                        packed_train_tokens - non_padding_tokens
+                    ),
+                    "data/step_unused_and_dummy_ratio": (
+                        float(packed_train_tokens - non_padding_tokens)
+                        / packed_train_tokens
+                    ),
+                    "prefix_tree/logical_tokens": float(packed_batch.logical_tokens),
+                    "prefix_tree/physical_tokens": float(packed_batch.physical_tokens),
+                    "prefix_tree/compression_ratio": (
+                        packed_batch.logical_tokens / packed_batch.physical_tokens
+                    ),
+                }
+            )
+            # The frontend applies reward scaling and logs the resulting metrics.
+            pbar = tqdm.tqdm(
+                total=fallback_gradient_steps,
+                desc="train",
+                disable=not verbose,
+            )
+            reported_gradient_steps: int | None = None
+            try:
+                async for result in self._stream_prepared_training(
+                    model,
+                    service,
+                    packed_batch,
+                    config,
+                    service_dev_config,
+                    grad_accumulation_sequences,
+                    verbose,
+                ):
+                    raw_num_gradient_steps = result.pop(TRAIN_GRADIENT_STEPS_KEY, None)
+                    if raw_num_gradient_steps is not None:
+                        num_gradient_steps = int(raw_num_gradient_steps)
+                        if reported_gradient_steps is None:
+                            reported_gradient_steps = num_gradient_steps
+                            if pbar.total != num_gradient_steps:
+                                pbar.total = num_gradient_steps
+                                pbar.refresh()
+                        else:
+                            assert num_gradient_steps == reported_gradient_steps, (
+                                f"num_gradient_steps {num_gradient_steps} != "
+                                f"reported_gradient_steps {reported_gradient_steps}"
+                            )
+                    else:
+                        num_gradient_steps = (
+                            reported_gradient_steps or fallback_gradient_steps
+                        )
+                    yield {
+                        **base_metrics,
+                        **result,
+                        TRAIN_GRADIENT_STEPS_KEY: float(num_gradient_steps),
+                    }
+                    if verbose:
+                        pbar.update(1)
+                        pbar.set_postfix(result)
+            finally:
+                pbar.close()
+            if verbose:
+                print("_train_model complete")
+
+    @asynccontextmanager
+    async def _training_batch_lifecycle(
+        self, batch: _PackedTrainingBatch
+    ) -> AsyncIterator[None]:
+        primary: BaseException | None = None
+        try:
+            yield
+        except BaseException as error:
+            primary = error
+            raise
+        finally:
+            try:
+                _, cancelled = await complete_task(
+                    asyncio.create_task(
+                        self._finish_training_batch(batch, failed=primary is not None)
+                    )
+                )
+                if cancelled is not None:
+                    raise cancelled
+            except BaseException as release_error:
+                if primary is None:
+                    raise
+                if release_error is not primary:
+                    primary.add_note(
+                        "training-batch release also failed: "
+                        f"{type(release_error).__name__}: {release_error}"
+                    )
+
+    async def _finish_training_batch(
+        self, batch: _PackedTrainingBatch, *, failed: bool
+    ) -> None:
+        await self._release_training_batch(batch)
+
+    async def _release_training_batch(self, batch: _PackedTrainingBatch) -> None:
+        pass
+
+    async def _prepare_training_batch(
+        self,
+        model: TrainableModel,
+        trajectory_groups: list[TrajectoryGroup],
+        dev_config: dev.TrainConfig,
+        *,
+        include_moe_routing: bool,
+    ) -> _PackedTrainingBatch | None:
+        packed = self._get_packed_tensors(
+            model,
+            trajectory_groups,
+            advantage_balance=dev_config.get("advantage_balance", 0.0),
+            allow_training_without_logprobs=dev_config.get(
+                "allow_training_without_logprobs", False
+            ),
+            scale_rewards=dev_config.get("scale_rewards", True),
+            plot_tensors=dev_config.get("plot_tensors", False),
+            packed_sequence_length=dev_config.get("packed_sequence_length"),
+            logprob_calculation_chunk_size=dev_config.get(
+                "logprob_calculation_chunk_size", 1024
+            ),
+            include_moe_routing=include_moe_routing,
         )
-        packed_train_tokens = int(
-            fallback_gradient_steps
-            * grad_accumulation_sequences
-            * packed_sequence_length
+        if packed is None:
+            return None
+        num_sequences, sequence_length = packed["tokens"].shape
+        packing_stats = packed["prefix_tree_packing_stats"]
+        return _PackedTrainingBatch(
+            payload=packed,
+            num_sequences=num_sequences,
+            sequence_length=sequence_length,
+            trainable_assistant_tokens=int(packed["assistant_mask"].sum().item()),
+            loss_bearing_tokens=int(packed["assistant_mask"][:, 1:].sum().item()),
+            non_padding_tokens=int((packed["group_ids"] != -1).sum().item()),
+            logical_tokens=packing_stats["logical_tokens"],
+            physical_tokens=packing_stats["physical_tokens"],
+            include_moe_routing=include_moe_routing,
         )
-        base_metrics.update(
-            {
-                "data/step_packed_sequences": float(packed_sequences),
-                "data/step_packed_train_tokens": float(packed_train_tokens),
-                "data/step_non_padding_train_tokens": float(non_padding_tokens),
-                "data/step_padding_ratio": (
-                    float(packed_train_tokens - non_padding_tokens)
-                    / packed_train_tokens
-                ),
-                "prefix_tree/logical_tokens": float(packing_stats["logical_tokens"]),
-                "prefix_tree/physical_tokens": float(packing_stats["physical_tokens"]),
-                "prefix_tree/compression_ratio": (
-                    packing_stats["logical_tokens"] / packing_stats["physical_tokens"]
-                ),
-            }
+
+    async def _stream_prepared_training(
+        self,
+        model: TrainableModel,
+        service: ModelService,
+        batch: _PackedTrainingBatch,
+        config: TrainConfig,
+        service_dev_config: dev.TrainConfig,
+        grad_accumulation_sequences: int,
+        verbose: bool,
+    ) -> AsyncIterator[dict[str, float]]:
+        packed = cast(PackedTensors, batch.payload)
+        disk = packed_tensors_to_dir(
+            packed, f"{get_model_dir(model=model, art_path=self._path)}/tensors"
         )
-        if include_moe_routing:
+        if batch.include_moe_routing:
             from ..megatron.routing_replay import (
                 build_moe_routing_replay_bundle_from_packed_tensors,
             )
@@ -1618,42 +1835,13 @@ async def _train_model(
                 "moe_routing_replay"
             )
             build_moe_routing_replay_bundle_from_packed_tensors(
-                packed_tensors=packed_tensors,
+                packed_tensors=packed,
                 global_grad_accumulation_sequences=grad_accumulation_sequences,
             ).to_dir(routing_replay_dir)
             service_dev_config["moe_routing_replay_path"] = routing_replay_dir
             service_dev_config["moe_routing_replay_strict"] = True
-        # Note: scale_learning_rate_by_reward_std_dev is now handled by the frontend (Model.train())
-        pbar = tqdm.tqdm(total=fallback_gradient_steps, desc="train")
-        reported_gradient_steps: int | None = None
-        async for result in service.train(
-            disk_packed_tensors, config, service_dev_config, verbose
-        ):
-            raw_num_gradient_steps = result.pop(TRAIN_GRADIENT_STEPS_KEY, None)
-            if raw_num_gradient_steps is not None:
-                num_gradient_steps = int(raw_num_gradient_steps)
-                if reported_gradient_steps is None:
-                    reported_gradient_steps = num_gradient_steps
-                    if pbar.total != num_gradient_steps:
-                        pbar.total = num_gradient_steps
-                        pbar.refresh()
-                else:
-                    assert num_gradient_steps == reported_gradient_steps, (
-                        f"num_gradient_steps {num_gradient_steps} != reported_gradient_steps {reported_gradient_steps}"
-                    )
-            else:
-                num_gradient_steps = reported_gradient_steps or fallback_gradient_steps
-            yield {
-                **base_metrics,
-                **result,
-                TRAIN_GRADIENT_STEPS_KEY: float(num_gradient_steps),
-            }
-            pbar.update(1)
-            pbar.set_postfix(result)
-        pbar.close()
-        # Note: Metrics logging is now handled by the frontend (Model.train())
-        if verbose:
-            print("_train_model complete")
+        async for result in service.train(disk, config, service_dev_config, verbose):
+            yield result
 
     async def _resolve_grad_accumulation_sequences(
         self,
@@ -1661,27 +1849,16 @@ async def _resolve_grad_accumulation_sequences(
         config: TrainConfig,
     ) -> int:
         if config.grad_accumulation_sequences is not None:
-            return max(1, int(config.grad_accumulation_sequences))
+            return int(await service.resolve_global_grad_accumulation_sequences(config))
 
         service_key = id(service)
         if service_key in self._grad_accumulation_sequences_by_service:
             return self._grad_accumulation_sequences_by_service[service_key]
 
-        resolver = getattr(
-            cast(Any, service),
-            "resolve_global_grad_accumulation_sequences",
-            None,
-        )
-        if callable(resolver):
-            resolved = max(1, int(await resolver(config)))
-        else:
-            resolved = 1
+        resolved = int(await service.resolve_global_grad_accumulation_sequences(config))
         self._grad_accumulation_sequences_by_service[service_key] = resolved
         return resolved
 
-    # Note: _get_reward_std_dev_learning_rate_multiplier and _log_metrics
-    # have been moved to the Model class (frontend)
-
     async def _train_sft(
         self,
         model: AnyTrainableModel,
@@ -1778,19 +1955,20 @@ async def _train_sft(
         # Get the service and train
         service = await self._get_service(model)
 
-        pbar = tqdm.tqdm(total=len(batches), desc="sft train")
+        pbar = tqdm.tqdm(total=len(batches), desc="sft train", disable=not verbose)
         total_trainable_tokens = sum(batch.num_trainable_tokens for batch in batches)
         total_trajectories = len(trajectory_list)
         batch_count = 0
 
         async for result in service.train_sft(batches, service_config, verbose):
-            pbar.update(1)
-            postfix: dict[str, str | int] = {
-                "loss": f"{result.get('loss/train', 0):.4f}"
-            }
-            if total_dropped_trajectories:
-                postfix["dropped"] = total_dropped_trajectories
-            pbar.set_postfix(postfix)
+            if verbose:
+                pbar.update(1)
+                postfix: dict[str, str | int] = {
+                    "loss": f"{result.get('loss/train', 0):.4f}"
+                }
+                if total_dropped_trajectories:
+                    postfix["dropped"] = total_dropped_trajectories
+                pbar.set_postfix(postfix)
             batch_count += 1
             yield {
                 **result,
@@ -2095,11 +2273,6 @@ async def _experimental_fork_checkpoint(
 
         # If S3 bucket is provided, pull from S3 first
         if from_s3_bucket is not None:
-            if verbose:
-                print(
-                    f"DEBUG: Fork checkpoint - from_s3_bucket={from_s3_bucket}, not_after_step={not_after_step}"
-                )
-
             # Determine which checkpoint to pull
             if not_after_step is None:
                 # Pull only the latest checkpoint
@@ -2162,12 +2335,6 @@ async def _experimental_fork_checkpoint(
                 f"No checkpoints found for model {from_model} in project {from_project}"
             )
 
-        if verbose:
-            print(f"DEBUG: Checkpoint base dir: {checkpoint_base_dir}")
-            print(
-                f"DEBUG: Contents: {os.listdir(checkpoint_base_dir) if os.path.exists(checkpoint_base_dir) else 'Does not exist'}"
-            )
-
         # Get all available checkpoint steps
         available_steps = sorted(
             int(d)
@@ -2206,21 +2373,11 @@ async def _experimental_fork_checkpoint(
             print(
                 f"Copying checkpoint from {source_checkpoint_dir} to {dest_checkpoint_dir}"
             )
-            print(f"DEBUG: Source dir exists: {os.path.exists(source_checkpoint_dir)}")
-            if os.path.exists(source_checkpoint_dir):
-                print(
-                    f"DEBUG: Source dir contents: {os.listdir(source_checkpoint_dir)}"
-                )
-                print(
-                    f"DEBUG: Source dir is empty: {len(os.listdir(source_checkpoint_dir)) == 0}"
-                )
 
         import shutil
 
         # Remove destination if it already exists (empty directory from previous attempts)
         if os.path.exists(dest_checkpoint_dir):
-            if verbose:
-                print("DEBUG: Destination already exists, removing it first")
             shutil.rmtree(dest_checkpoint_dir)
 
         shutil.copytree(source_checkpoint_dir, dest_checkpoint_dir)
diff --git a/src/art/local/service.py b/src/art/local/service.py
index 6417ed9d4..2d65eef0c 100644
--- a/src/art/local/service.py
+++ b/src/art/local/service.py
@@ -26,6 +26,14 @@ async def acquire_exact_adapter(self, step: int, checkpoint_path: str) -> str: .
 
     async def release_exact_adapter(self, step: int) -> None: ...
 
+    async def resolve_global_grad_accumulation_sequences(
+        self, config: types.TrainConfig
+    ) -> int: ...
+
+    async def register_lora_for_step(self, step: int, checkpoint_dir: str) -> None: ...
+
+    async def aclose(self) -> None: ...
+
     def train(
         self,
         disk_packed_tensors: DiskPackedTensors,
diff --git a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh
index ad7f94f8a..a4d550b08 100644
--- a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh
+++ b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh
@@ -716,7 +716,8 @@ inline __device__ void N2N_warp_group_device_function(const int node_rank,
       const int remote_idx = (idx + node_rank) % (NUM_OF_NODES - 1);
       const int actual_remote_node_rank = remote_idx < node_rank ? remote_idx : (remote_idx + 1);
       const int my_node_rank_in_remote = (node_rank < actual_remote_node_rank) ? node_rank : (node_rank - 1);
-      const size_t flag_offset = (my_node_rank_in_remote * NUM_OF_CHUNKS_PER_RANK + chunk_idx) * sizeof(uint64_t);
+      const size_t flag_offset =
+          (my_node_rank_in_remote * (MAX_NUM_OF_TOKENS_PER_RANK / NUM_OF_TOKENS_PER_CHUNK) + chunk_idx) * sizeof(uint64_t);
 
       // Quick density probe: check first warp-width of tokens.
       // On 4+ nodes, per-remote density is ~70%, so this almost always fails,
@@ -861,7 +862,9 @@ inline __device__ void N2N_warp_group_device_function(const int node_rank,
       }
 
       __syncwarp();
-      if (total_tokens > 0 && INTER_NODE_GROUP::thread_rank() == 0) {
+      if (INTER_NODE_GROUP::thread_rank() == 0) {
+        // The receiver waits on every source chunk before reading its routing
+        // map, including chunks with no payload.
         const unsigned channel_id = blockIdx.x % nixl_ctx->num_channels;
         nixlMemViewElem sig{nixl_ctx->remote_signal_mvh, (size_t)remote_idx, flag_offset};
         assert(nixlAtomicAdd<nixl_gpu_level_t::THREAD>(1, sig, channel_id, 0 /* NODELAY: flush all pending */) >= NIXL_SUCCESS);
@@ -1004,8 +1007,11 @@ inline __device__ void inter_node_N2N_warp_group_device_function(
     }
 
     __syncwarp();
-    if (total_tokens > 0 && INTER_NODE_RDMA_GROUP::thread_rank() == 0) {
-      const size_t flag_offset = (my_node_rank_in_remote * NUM_OF_CHUNKS_PER_RANK + chunk_id) * sizeof(uint64_t);
+    if (INTER_NODE_RDMA_GROUP::thread_rank() == 0) {
+      // Advance every chunk's epoch so a later non-empty combine does not wait
+      // on a completion counter left behind by an earlier empty chunk.
+      const size_t flag_offset =
+          (my_node_rank_in_remote * (MAX_NUM_OF_TOKENS_PER_RANK / NUM_OF_TOKENS_PER_CHUNK) + chunk_id) * sizeof(uint64_t);
       const unsigned channel_id = blockIdx.x % nixl_ctx->num_channels;
       nixlMemViewElem sig{nixl_ctx->remote_signal_mvh, (size_t)remote_idx, flag_offset};
       assert(nixlAtomicAdd<nixl_gpu_level_t::THREAD>(1, sig, channel_id, 0 /* NODELAY: flush all pending */) >= NIXL_SUCCESS);
diff --git a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/config.cuh b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/config.cuh
index 230d61f0c..850af28f0 100644
--- a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/config.cuh
+++ b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/config.cuh
@@ -21,6 +21,9 @@ static constexpr int64_t HYBRID_EP_DISPATCH_TX_DEPTH_EXTRA = 1;
 
 static inline bool hybrid_ep_token_capacity_is_valid(
     int max_num_of_tokens_per_rank, int num_of_nodes, const char* config_name) {
+#ifdef USE_NIXL
+  return true;
+#else
   if (num_of_nodes <= 1) {
     return true;
   }
@@ -47,6 +50,7 @@ static inline bool hybrid_ep_token_capacity_is_valid(
           static_cast<long>(HYBRID_EP_IB_QP_MAX_TX_DEPTH));
   fflush(stderr);
   return false;
+#endif
 }
 
 static inline int hybrid_ep_pad_num_of_tokens_per_rank(
diff --git a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/jit/compiler.cu b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/jit/compiler.cu
index cc9214a4c..8d8a5f74f 100644
--- a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/jit/compiler.cu
+++ b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/jit/compiler.cu
@@ -64,16 +64,19 @@ NVCCCompiler::NVCCCompiler(std::string base_path, std::string cuda_home, std::st
     flags += " -DHYBRID_EP_BUILD_MULTINODE_ENABLE";
 #ifdef USE_NIXL
     flags += " -DUSE_NIXL";
-    std::string nixl_home = get_env("NIXL_HOME");
-    if (nixl_home.empty()) nixl_home = "/usr/local/nixl";
-    std::string ucx_home = get_env("UCX_HOME");
-    if (ucx_home.empty()) ucx_home = "/usr";
-    include += " -I" + nixl_home + "/include ";
-    include += " -I" + nixl_home + "/include/gpu/ucx ";
-    include += " -I" + ucx_home + "/include ";
-    std::string nixl_lib = nixl_home + "/lib/x86_64-linux-gnu";
+    std::string nixl_include = get_env("NIXL_INCLUDE_DIR");
+    std::string nixl_gpu_include = get_env("NIXL_GPU_INCLUDE_DIR");
+    std::string ucx_include = get_env("UCX_INCLUDE_DIR");
+    std::string nixl_lib = get_env("NIXL_LIBRARY_DIR");
+    std::string nixl_deps = get_env("NIXL_DEPENDENCY_LIBRARY_DIR");
+    if (nixl_include.empty() || nixl_gpu_include.empty() || ucx_include.empty() ||
+        nixl_lib.empty() || nixl_deps.empty()) {
+        throw std::runtime_error("NIXL HybridEP runtime paths are not configured");
+    }
+    include += " -I" + nixl_include + " -I" + nixl_gpu_include + " -I" + ucx_include + " ";
     library += " -L" + nixl_lib + " -lnixl -lnixl_build -lnixl_common ";
     library += " -Xlinker -rpath -Xlinker " + nixl_lib + " ";
+    library += " -Xlinker -rpath -Xlinker " + nixl_deps + " ";
 #else
     std::string rdma_core_home = RDMA_CORE_HOME;
     if (!rdma_core_home.empty()) {
diff --git a/src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_toolchain.py b/src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_toolchain.py
index fac089c76..689e751e0 100644
--- a/src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_toolchain.py
+++ b/src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_toolchain.py
@@ -6,21 +6,34 @@
 import subprocess
 
 import hybrid_ep_cpp
+import torch
+
+
+def _cuda_paths() -> tuple[Path, Path]:
+    from torch.utils.cpp_extension import CUDA_HOME
+
+    cuda_home = Path(os.environ.get("CUDA_HOME") or CUDA_HOME or "")
+    if torch.version.cuda and torch.version.cuda.startswith("12."):
+        return cuda_home, Path(str(files("nvidia.cuda_cccl") / "include"))
+    if torch.version.cuda and torch.version.cuda.startswith("13."):
+        for include in [cuda_home / "include", *cuda_home.glob("targets/*/include")]:
+            if (include / "cccl/cuda/ptx").is_file():
+                return cuda_home, include / "cccl"
+    raise RuntimeError(f"HybridEP cannot find headers for torch CUDA {torch.version.cuda}")
 
 
 def runtime_paths() -> tuple[str, str, str]:
-    cuda_home = Path(os.environ.get("CUDA_HOME", "/usr/local/cuda-12.8"))
+    cuda_home, cccl_include = _cuda_paths()
     nvcc = cuda_home / "bin" / "nvcc"
     if not nvcc.is_file():
         raise RuntimeError(f"HybridEP requires CUDA nvcc at {nvcc}")
 
-    cccl_include = Path(str(files("nvidia.cuda_cccl") / "include"))
     if not (cccl_include / "cuda" / "ptx").is_file():
         raise RuntimeError(f"HybridEP CCCL headers are missing from {cccl_include}")
 
     digest = sha256()
     digest.update(version("art-deep-ep").encode())
-    digest.update(version("nvidia-cuda-cccl-cu12").encode())
+    digest.update((cccl_include / "cuda/std/__cccl/version.h").read_bytes())
     digest.update(str(hybrid_ep_cpp.SM_ARCH).encode())
     digest.update(subprocess.check_output([nvcc, "--version"]))
     digest.update(Path(hybrid_ep_cpp.__file__).read_bytes())
diff --git a/src/art/megatron/_hybrid_ep/pyproject.toml b/src/art/megatron/_hybrid_ep/pyproject.toml
index 6f2a37027..15e7f65fd 100644
--- a/src/art/megatron/_hybrid_ep/pyproject.toml
+++ b/src/art/megatron/_hybrid_ep/pyproject.toml
@@ -2,8 +2,6 @@
 requires = [
     "setuptools>=78.1.0",
     "torch==2.11.0",
-    "nvidia-cuda-cccl-cu12==12.9.27",
-    "nvidia-nvtx-cu12>=12.8,<13",
 ]
 build-backend = "setuptools.build_meta"
 
diff --git a/src/art/megatron/_hybrid_ep/setup.py b/src/art/megatron/_hybrid_ep/setup.py
index 277b1ab21..741695f58 100644
--- a/src/art/megatron/_hybrid_ep/setup.py
+++ b/src/art/megatron/_hybrid_ep/setup.py
@@ -6,6 +6,7 @@
 import shutil
 import re
 
+import torch
 from pathlib import Path
 from setuptools.command.build_py import build_py
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension
@@ -24,6 +25,22 @@ def package_dir(module: str) -> Path:
     return Path(next(iter(spec.submodule_search_locations)))
 
 
+def cuda_includes() -> tuple[Path, Path]:
+    if torch.version.cuda and torch.version.cuda.startswith("12."):
+        return (
+            package_dir("nvidia.cuda_cccl") / "include",
+            package_dir("nvidia.nvtx") / "include",
+        )
+    if torch.version.cuda and torch.version.cuda.startswith("13."):
+        cuda_home = Path(os.environ["CUDA_HOME"])
+        for include in [cuda_home / "include", *cuda_home.glob("targets/*/include")]:
+            if (include / "cccl/cuda/ptx").is_file() and (
+                include / "nvtx3/nvToolsExt.h"
+            ).is_file():
+                return include / "cccl", include
+    raise RuntimeError(f"HybridEP cannot find headers for torch CUDA {torch.version.cuda}")
+
+
 def collect_package_files(package: str, relative_dir: str):
     base_path = Path(package) / relative_dir
     if not base_path.exists():
@@ -51,8 +68,7 @@ def to_nvcc_gencode(s: str) -> str:
 
 def get_extension_hybrid_ep_cpp():
     current_dir = os.path.dirname(os.path.abspath(__file__))
-    cccl_include = package_dir("nvidia.cuda_cccl") / "include"
-    nvtx_include = package_dir("nvidia.nvtx") / "include"
+    cccl_include, nvtx_include = cuda_includes()
     enable_multinode = os.getenv("HYBRID_EP_MULTINODE", "0").strip().lower() in {"1", "true", "t", "yes", "y", "on"}
     # NIXL is opt-in and disabled by default; the DOCA/NCCL path is the default when multinode is enabled.
     use_nixl = os.getenv("USE_NIXL", "0").strip().lower() in {"1", "true", "t", "yes", "y", "on"}
@@ -120,22 +136,20 @@ def get_extension_hybrid_ep_cpp():
                 "csrc/hybrid_ep/buffer/internode_nixl.cu",
                 "csrc/hybrid_ep/buffer/nixl_connector.cu",
             ])
-            nixl_home = os.getenv("NIXL_HOME", "/usr/local/nixl")
-            ucx_home = os.getenv("UCX_HOME", "/usr")
-            nixl_include = os.path.join(nixl_home, "include")
-            nixl_gpu_include = os.path.join(nixl_home, "include/gpu/ucx")
-            import platform
-            machine = platform.machine()
-            if machine == "aarch64":
-                nixl_lib_suffix = "lib/aarch64-linux-gnu"
-            else:
-                nixl_lib_suffix = "lib/x86_64-linux-gnu"
-            nixl_lib = os.path.join(nixl_home, nixl_lib_suffix)
-            include_dirs.extend([nixl_include, nixl_gpu_include, os.path.join(ucx_home, "include")])
+            nixl_include = os.environ["NIXL_INCLUDE_DIR"]
+            nixl_gpu_include = os.environ["NIXL_GPU_INCLUDE_DIR"]
+            ucx_include = os.environ["UCX_INCLUDE_DIR"]
+            nixl_lib = os.environ["NIXL_LIBRARY_DIR"]
+            nixl_deps = os.environ["NIXL_DEPENDENCY_LIBRARY_DIR"]
+            include_dirs.extend([nixl_include, nixl_gpu_include, ucx_include])
             library_dirs.append(nixl_lib)
-            runtime_library_dirs.append(nixl_lib)
             libraries.extend(["nixl", "nixl_build", "nixl_common"])
-            extra_link_args.extend([f"-Wl,-rpath,{nixl_lib}"])
+            extra_link_args.extend(
+                [
+                    f"-Wl,-rpath,$ORIGIN/{Path(nixl_lib).name}",
+                    f"-Wl,-rpath,$ORIGIN/{Path(nixl_deps).name}",
+                ]
+            )
             extra_link_args.append("-l:libnvidia-ml.so.1")
             libraries.extend(["mlx5", "ibverbs"])
             doca_home = os.getenv("DOCA_HOME", "")
@@ -236,7 +250,6 @@ def get_extension_hybrid_ep_cpp():
             include=['deep_ep', 'deep_ep.*']
         ),
         install_requires=[
-            'nvidia-cuda-cccl-cu12==12.9.27',
             'torch==2.11.0',
         ],
         ext_modules=[extension],
diff --git a/src/art/megatron/backend.py b/src/art/megatron/backend.py
index 61e5398f6..86d8f67b9 100644
--- a/src/art/megatron/backend.py
+++ b/src/art/megatron/backend.py
@@ -1,47 +1,249 @@
 import asyncio
-from typing import Any, Iterable, cast
+from contextlib import asynccontextmanager
+from pathlib import Path
+import secrets
+import sys
+import time
+from typing import Any, AsyncIterator, Iterable, Literal, cast
+import uuid
 
-from mp_actors import move_to_child_process
+from pydantic import BaseModel, ConfigDict, Field
 
+from .. import dev, types
 from ..backend import AnyTrainableModel
-from ..local.backend import LocalBackend
+from ..distributed.art_runtime import ArtRuntime
+from ..local.backend import LocalBackend, _PackedTrainingBatch
 from ..local.service import ModelService
 from ..model import Model, TrainableModel
 from ..trajectories import TrajectoryGroup
 from ..types import LocalTrainResult
-from ..utils.lifecycle import process_shutdown_timeout
-from ..utils.output_dirs import get_model_dir
-from .migrations import apply_megatron_migrations, optimizer_state_path
-from .optimizer_state import (
-    format_megatron_resume_message,
-    prepare_megatron_resume_state,
-    read_optimizer_commit,
-)
+from ..utils.lifecycle import complete_task
+from ..utils.output_dirs import get_model_dir, get_step_checkpoint_dir
+from ..vllm_runtime import get_external_vllm_runtime_config
+from .migrations import apply_megatron_migrations
+from .runtime.specs import ResidentLoraInspectionResult, ResidentScoreResult
 from .runtime_config import get_megatron_runtime_config
 
 
+class _DistributedBatchPayload(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+    packed: Any
+    selections: tuple[Any, ...]
+    generation_id: str = Field(min_length=1)
+    runtime: Any
+
+
+class _PackingConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    advantage_balance: float
+    allow_training_without_logprobs: bool
+    scale_rewards: bool
+    plot_tensors: bool
+    packed_sequence_length: int = Field(ge=1)
+    logprob_calculation_chunk_size: int = Field(ge=1)
+    include_moe_routing: bool
+    collect_packing_shapes: bool
+
+    @classmethod
+    def from_dev_config(
+        cls,
+        config: Any,
+        *,
+        include_moe_routing: bool,
+        collect_packing_shapes: bool,
+    ) -> "_PackingConfig":
+        return cls(
+            advantage_balance=config.get("advantage_balance", 0.0),
+            allow_training_without_logprobs=config.get(
+                "allow_training_without_logprobs", False
+            ),
+            scale_rewards=config.get("scale_rewards", True),
+            plot_tensors=config.get("plot_tensors", False),
+            packed_sequence_length=config["packed_sequence_length"],
+            logprob_calculation_chunk_size=config.get(
+                "logprob_calculation_chunk_size", 1024
+            ),
+            include_moe_routing=include_moe_routing,
+            collect_packing_shapes=collect_packing_shapes,
+        )
+
+
+class _PipelinePreparedBatch(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+    batch: Any
+    groups: tuple[Any, ...]
+    packing_config: _PackingConfig
+    metrics: dict[str, float]
+
+
 class MegatronBackend(LocalBackend):
+    supports_pipeline_train_dispatch_fence = True
+
     def __init__(
         self,
         *,
         in_process: bool = False,
         path: str | None = None,
         enable_expert_replay: bool = True,
+        runtime: ArtRuntime | None = None,
     ) -> None:
+        if in_process:
+            raise ValueError(
+                "MegatronBackend(in_process=True) belonged to the removed "
+                "filesystem service proxy and cannot represent a multi-rank typed "
+                "trainer. Use the default Monarch executor."
+            )
+        if runtime is not None:
+            artifact_root = runtime.topology.cluster.artifact_root
+            if artifact_root is None:
+                raise ValueError("distributed Megatron requires cluster.artifact_root")
+            if (
+                path is not None
+                and Path(path).resolve() != Path(artifact_root).resolve()
+            ):
+                raise ValueError("backend path must match cluster.artifact_root")
+            path = artifact_root
         super().__init__(
-            in_process=in_process,
+            in_process=False,
             path=path,
             enable_expert_replay=enable_expert_replay,
         )
         self._requires_explicit_packed_sequence_length = True
         self._packed_sequence_length_requires_chunk_alignment = False
         self._supports_result_packing = True
-        self._resume_prepared_models: set[tuple[str, str]] = set()
+        self._runtime = runtime
+        self._owns_runtime = runtime is None
+        self._runtime_lock = asyncio.Lock()
+        self._service_lock = asyncio.Lock()
+        self._owned_runtimes: dict[tuple[str, str], ArtRuntime] = {}
+        from .runtime.local import LocalEndpointAllocator
+
+        self._local_endpoints = LocalEndpointAllocator()
+        self._owned_runtime_ports: dict[tuple[str, str], tuple[int, int]] = {}
+        self._managed_api_key = secrets.token_urlsafe(32)
+        self._batch_release_tasks: set[asyncio.Task[None]] = set()
+        self._batch_release_failures: list[BaseException] = []
+        self._adapter_prune_requests: dict[
+            tuple[str, str], tuple[AnyTrainableModel, set[int]]
+        ] = {}
+        self._adapter_prune_task: asyncio.Task[None] | None = None
+        self._adapter_prune_failures: list[BaseException] = []
+
+    def __enter__(self) -> "MegatronBackend":
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            return self
+        raise RuntimeError(
+            "Use 'async with MegatronBackend()' inside an async event loop"
+        )
+
+    def _close(self) -> None:
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            asyncio.run(self.close())
+            return
+        raise RuntimeError(
+            "MegatronBackend synchronous close cannot run inside an async event loop"
+        )
+
+    async def __aenter__(self) -> "MegatronBackend":
+        return self
+
+    def _compile_local_topology(
+        self,
+        model: TrainableModel,
+        config: Any,
+        *,
+        service_ports: tuple[int, int] | None = None,
+    ) -> Any:
+        import torch
+
+        from .runtime.local import compile_local_runtime_topology
+
+        return compile_local_runtime_topology(
+            config,
+            model_name=model.name,
+            base_model=model.base_model,
+            artifact_root=str(Path(self._path).resolve()),
+            visible_gpu_count=int(torch.cuda.device_count()),
+            service_ports=service_ports,
+        )
+
+    def _model_runtime_topology(self, model: TrainableModel) -> Any:
+        storage_key = self._model_storage_key(model)
+        runtime = self._runtime or self._owned_runtimes.get(storage_key)
+        if runtime is not None:
+            return runtime.topology
+        return self._compile_local_topology(model, model._internal_config or {})
+
+    async def _ensure_runtime(self, model: TrainableModel, config: Any) -> ArtRuntime:
+        if self._runtime is not None:
+            return self._runtime
+        storage_key = self._model_storage_key(model)
+        if runtime := self._owned_runtimes.get(storage_key):
+            return runtime
+        async with self._runtime_lock:
+            if storage_key not in self._owned_runtimes:
+                ports = self._local_endpoints.reserve()
+                try:
+                    topology = self._compile_local_topology(
+                        model, config, service_ports=ports
+                    )
+                    if not topology.model_services:
+                        self._local_endpoints.release(ports)
+                        ports = None
+                    placements = _topology_gpu_placements(topology)
+                    conflicts = {
+                        key: placements & _topology_gpu_placements(runtime.topology)
+                        for key, runtime in self._owned_runtimes.items()
+                        if placements & _topology_gpu_placements(runtime.topology)
+                    }
+                    if conflicts:
+                        raise ValueError(
+                            "backend-owned per-model runtimes require disjoint GPU "
+                            f"placements; {storage_key!r} conflicts with {conflicts}"
+                        )
+                    runtime = await ArtRuntime.start_local(topology)
+                except BaseException:
+                    if ports is not None:
+                        self._local_endpoints.release(ports)
+                    raise
+                self._owned_runtimes[storage_key] = runtime
+                if ports is not None:
+                    self._owned_runtime_ports[storage_key] = ports
+        return self._owned_runtimes[storage_key]
+
+    async def _configure_owned_api_port(self, model: TrainableModel, port: int) -> None:
+        storage_key = self._model_storage_key(model)
+        async with self._runtime_lock:
+            runtime = self._owned_runtimes.get(storage_key)
+            ports = self._owned_runtime_ports.get(storage_key)
+            if runtime is None or ports is None:
+                raise RuntimeError("owned model service runtime has not started")
+            configured = self._local_endpoints.replace_api_port(ports, port)
+            try:
+                from .runtime.local import with_local_serving_port
+
+                topology = with_local_serving_port(
+                    runtime.topology,
+                    model_name=model.name,
+                    port=configured[0],
+                    rendezvous_port=configured[1],
+                )
+            except BaseException:
+                self._local_endpoints.replace_api_port(configured, ports[0])
+                raise
+            runtime.topology = topology
+            self._owned_runtime_ports[storage_key] = configured
 
     async def register(self, model: Model) -> None:
         await super().register(model)
         if model.trainable:
-            # Keep durable Megatron state migrations centralized behind this call.
             apply_megatron_migrations(get_model_dir(model=model, art_path=self._path))
 
     async def train(
@@ -56,91 +258,1044 @@ async def train(
                     f"MegatronBackend.train gets {removed_kwarg} from "
                     "art.init_megatron_runtime_config(...)."
                 )
-        return await super().train(
+        dispatch_event = kwargs.pop("_pipeline_train_dispatch_event", None)
+        if dispatch_event is not None and not isinstance(dispatch_event, asyncio.Event):
+            raise TypeError("pipeline train dispatch fence must be an asyncio.Event")
+        groups = list(trajectory_groups)
+        pipeline_call = bool(
+            groups
+            and isinstance(groups[0]._prepared_training_batch, _PipelinePreparedBatch)
+        )
+        from .distributed_service import DistributedMegatronService
+
+        dispatch_armed = dispatch_event is not None and not dispatch_event.is_set()
+        service: DistributedMegatronService | None = None
+        if dispatch_armed:
+            if not pipeline_call:
+                raise RuntimeError("trainer dispatch fencing requires a prepared batch")
+            assert dispatch_event is not None
+            service = cast(DistributedMegatronService, await self._get_service(model))
+            service.arm_pipeline_train_dispatch(dispatch_event)
+        try:
+            result = await super().train(
+                model,
+                groups,
+                packed_sequence_length=(
+                    get_megatron_runtime_config().packed_sequence_length
+                ),
+                **kwargs,
+            )
+        finally:
+            if dispatch_armed:
+                assert dispatch_event is not None
+                assert service is not None
+                service.cancel_pipeline_train_dispatch(dispatch_event)
+        if service is None:
+            service = cast(DistributedMegatronService, await self._get_service(model))
+        final_step = kwargs.get("final_training_step")
+        if final_step is not None and result.step >= final_step:
+            result.metrics.update(
+                await service.finalize_publication_metrics(result.step)
+            )
+        if not pipeline_call:
+            await service.wait_for_serving(result.step)
+            result.metrics.update(service.drain_publication_metrics())
+        if not kwargs.get("save_checkpoint", True):
+            return result
+        result.checkpoint_path = get_step_checkpoint_dir(
+            get_model_dir(model=model, art_path=self._path), result.step
+        )
+        if not Path(result.checkpoint_path).exists():
+            result.checkpoint_ready = service.checkpoint_materialization(result.step)
+        return result
+
+    async def finalize_training_session(
+        self, model: AnyTrainableModel
+    ) -> dict[str, float]:
+        from .distributed_service import DistributedMegatronService
+
+        service = cast(DistributedMegatronService, await self._get_service(model))
+        return await service.finalize_publication_metrics(await self._get_step(model))
+
+    async def inspect_resident_lora(
+        self,
+        model: AnyTrainableModel,
+        *,
+        expected_learner_version: int,
+    ) -> ResidentLoraInspectionResult:
+        from .distributed_service import DistributedMegatronService
+
+        service = cast(DistributedMegatronService, await self._get_service(model))
+        service.prefetch_trainer()
+        return await service.inspect_resident_lora(
+            expected_learner_version=expected_learner_version
+        )
+
+    async def score_resident(
+        self,
+        model: AnyTrainableModel,
+        trajectory_groups: Iterable[TrajectoryGroup],
+        *,
+        expected_learner_version: int,
+        top_k: int = 20,
+        grad_accumulation_sequences: int | None = None,
+    ) -> ResidentScoreResult:
+        groups = list(trajectory_groups)
+        if not groups or not any(group.trajectories for group in groups):
+            raise ValueError("resident scoring requires at least one trajectory")
+        stale = [
+            (group_index, trajectory_index, initial, final)
+            for group_index, group in enumerate(groups)
+            for trajectory_index, trajectory in enumerate(group.trajectories)
+            for initial, final in (
+                (
+                    trajectory.initial_policy_version,
+                    trajectory.final_policy_version,
+                ),
+            )
+            if initial != expected_learner_version or final != expected_learner_version
+        ]
+        if stale:
+            raise ValueError(
+                "resident score trajectories must have exact initial/final learner "
+                f"provenance {expected_learner_version}; mismatches={stale[:8]}"
+            )
+
+        include_moe_routing = self._model_uses_expert_replay(model)
+        dev_config = {
+            "advantage_balance": 0.0,
+            "allow_training_without_logprobs": False,
+            "scale_rewards": True,
+            "plot_tensors": False,
+            "packed_sequence_length": (
+                get_megatron_runtime_config().packed_sequence_length
+            ),
+            "logprob_calculation_chunk_size": 1024,
+        }
+        batch = await self._prepare_training_batch(
             model,
-            trajectory_groups,
-            packed_sequence_length=get_megatron_runtime_config().packed_sequence_length,
-            **kwargs,
+            groups,
+            dev_config,
+            include_moe_routing=include_moe_routing,
         )
+        if batch is None:
+            raise RuntimeError("resident scoring produced no packed batch")
+
+        try:
+            from ..distributed.art_runtime import DistributedPackedBatch
+            from .distributed_service import DistributedMegatronService
+
+            payload = batch.payload
+            if not isinstance(payload, _DistributedBatchPayload):
+                raise RuntimeError("resident scoring did not use the typed data plane")
+            distributed_batch = cast(DistributedPackedBatch, payload.packed)
+            service = cast(
+                DistributedMegatronService,
+                await self._get_service(model),
+            )
+            accumulation = await service.resolve_global_grad_accumulation_sequences(
+                types.TrainConfig(
+                    grad_accumulation_sequences=grad_accumulation_sequences
+                )
+            )
+            return await service.score_resident_packed(
+                distributed_batch,
+                expected_learner_version=expected_learner_version,
+                global_grad_accumulation_sequences=accumulation,
+                top_k=top_k,
+            )
+        finally:
+            primary = sys.exception()
+            paths = {
+                group._prepared_log_path
+                for group in groups
+                if group._prepared_log_path is not None
+            }
+            try:
+                results = await asyncio.gather(
+                    self._release_distributed_batch(
+                        batch,
+                        disposition="discarded",
+                    ),
+                    *(
+                        asyncio.to_thread(Path(path).unlink, missing_ok=True)
+                        for path in paths
+                    ),
+                    return_exceptions=True,
+                )
+                for group in groups:
+                    group._prepared_log_path = None
+                failures = [
+                    result for result in results if isinstance(result, BaseException)
+                ]
+                if failures:
+                    raise BaseExceptionGroup(
+                        "resident score batch release failed", failures
+                    )
+            except BaseException as cleanup_error:
+                if primary is None:
+                    raise
+                raise BaseExceptionGroup(
+                    "resident score and batch release failed",
+                    [primary, cleanup_error],
+                ) from None
+
+    def _supports_concurrent_training_and_inference(
+        self, model: AnyTrainableModel
+    ) -> bool:
+        topology = self._model_runtime_topology(cast(TrainableModel, model))
+        services = tuple(
+            service for service in topology.model_services if service.name == model.name
+        )
+        if len(services) == 1:
+            return not services[0].temporal_gpu_sharing
+        if (
+            not services
+            and get_external_vllm_runtime_config(model._internal_config or {})
+            is not None
+        ):
+            return True
+        raise ValueError(
+            f"runtime topology must define one model service named {model.name!r}"
+        )
+
+    def supports_async_pipeline_packing(self, model: AnyTrainableModel) -> bool:
+        return True
+
+    @asynccontextmanager
+    async def adapter_lease(
+        self,
+        model: AnyTrainableModel,
+        step: int,
+    ) -> AsyncIterator[None]:
+        from .distributed_service import DistributedMegatronService
+
+        service = cast(DistributedMegatronService, await self._get_service(model))
+        await service.wait_for_serving(step)
+        async with super().adapter_lease(model, step):
+            yield
+
+    @asynccontextmanager
+    async def exact_adapter_lease(
+        self,
+        model: AnyTrainableModel,
+        step: int,
+    ) -> AsyncIterator[None]:
+        from .distributed_service import DistributedMegatronService
+
+        service = cast(DistributedMegatronService, await self._get_service(model))
+        await service.wait_for_serving(step)
+        async with super().exact_adapter_lease(model, step):
+            yield
 
     async def _get_service(self, model: TrainableModel) -> ModelService:
         from ..dev.get_model_config import get_model_config
-        from .service import MegatronService
 
         storage_key = self._model_storage_key(model)
-        if storage_key not in self._services:
-            output_dir = get_model_dir(model=model, art_path=self._path)
+        if service := self._services.get(storage_key):
+            return service
+        async with self._service_lock:
+            if service := self._services.get(storage_key):
+                return service
             config = get_model_config(
                 base_model=model.base_model,
-                output_dir=output_dir,
+                output_dir=get_model_dir(model=model, art_path=self._path),
                 config=model._internal_config,
                 lora_config=model.lora_config,
             )
-            self._services[storage_key] = MegatronService(
-                model_name=model.name,
-                base_model=model.base_model,
-                config=config,
-                output_dir=output_dir,
-                enable_expert_replay=self._enable_expert_replay,
+            config["init_args"]["model_name"] = (
+                (model._internal_config or {})
+                .get("init_args", {})
+                .get("model_name", model.base_model)
             )
-            if not self._in_process:
-                self._services[storage_key] = move_to_child_process(
-                    self._services[storage_key],
-                    process_name="megatron-service",
+            runtime = await self._ensure_runtime(model, config)
+            from .distributed_service import DistributedMegatronService
+
+            service = cast(
+                ModelService,
+                DistributedMegatronService(
+                    model_name=model.name,
+                    base_model=model.base_model,
+                    config=config,
+                    output_dir=get_model_dir(model=model, art_path=self._path),
+                    runtime=runtime,
+                    enable_expert_replay=self._enable_expert_replay,
+                ),
+            )
+            if not self._owns_runtime:
+                runtime.register_closeable(service)
+            self._services[storage_key] = service
+            return service
+
+    async def _prepare_backend_for_training(
+        self,
+        model: AnyTrainableModel,
+        config: dev.OpenAIServerConfig | None = None,
+    ) -> tuple[str, str]:
+        from .distributed_service import DistributedMegatronService
+
+        service = cast(DistributedMegatronService, await self._get_service(model))
+        if get_external_vllm_runtime_config(model._internal_config or {}) is not None:
+            service.prefetch_trainer()
+            return await super()._prepare_backend_for_training(model, config)
+        config_dict = dict(config or {})
+        server_args = dict(config_dict.get("server_args", {}))
+        server_args.setdefault("api_key", self._managed_api_key)
+        if self._owns_runtime and "port" in server_args:
+            port = server_args["port"]
+            if isinstance(port, bool) or not isinstance(port, int):
+                raise TypeError("OpenAI server port must be an integer")
+            if (
+                service._managed_service_name is not None
+                and service.openai_server_port != port
+            ):
+                raise RuntimeError("cannot change a running OpenAI server port")
+            await self._configure_owned_api_port(cast(TrainableModel, model), port)
+        if "port" not in server_args and not self._owns_runtime:
+            server_args["port"] = service.openai_server_port
+        config_dict["server_args"] = server_args
+        service.prefetch_trainer()
+        return await super()._prepare_backend_for_training(
+            model, cast(dev.OpenAIServerConfig, config_dict)
+        )
+
+    async def _prepare_training_batch(
+        self,
+        model: TrainableModel,
+        trajectory_groups: list[TrajectoryGroup],
+        dev_config: Any,
+        *,
+        include_moe_routing: bool,
+    ) -> _PackedTrainingBatch | None:
+        prepared = tuple(group._prepared_training_batch for group in trajectory_groups)
+        collect_packing_shapes = any(
+            group._collect_packing_shape for group in trajectory_groups
+        )
+        if any(value is not None for value in prepared):
+            first = prepared[0]
+            if (
+                not isinstance(first, _PipelinePreparedBatch)
+                or any(value is not first for value in prepared)
+                or first.groups != tuple(trajectory_groups)
+            ):
+                raise RuntimeError("pipeline prepared batch does not match training")
+            packing_config = _PackingConfig.from_dev_config(
+                dev_config,
+                include_moe_routing=include_moe_routing,
+                collect_packing_shapes=collect_packing_shapes,
+            )
+            if first.packing_config != packing_config:
+                mismatch = RuntimeError(
+                    "pipeline prepared batch packing configuration does not match "
+                    "training"
                 )
-        return self._services[storage_key]
+                try:
+                    await self.discard_pipeline_batch(trajectory_groups)
+                except BaseException as cleanup_error:
+                    raise BaseExceptionGroup(
+                        "prepared batch mismatch cleanup failed",
+                        [mismatch, cleanup_error],
+                    ) from None
+                raise mismatch
+            for group in trajectory_groups:
+                group._prepared_training_batch = None
+            return cast(_PackedTrainingBatch, first.batch)
+        from ..distributed.packing import PackingRequest
+        from ..distributed.rollout import (
+            DistributedTrajectorySelection,
+            RolloutModelSpec,
+        )
+        from ..distributed.trajectory_store import TrajectoryGroupBundle
 
-    async def _get_step(self, model: AnyTrainableModel) -> int:
-        if not model.trainable:
-            return 0
-        storage_key = self._model_storage_key(model)
-        if storage_key in self._resume_prepared_models:
-            return await super()._get_step(model)
-        output_dir = get_model_dir(model=model, art_path=self._path)
-        info = prepare_megatron_resume_state(
-            output_dir=output_dir,
-            optimizer_state_path=optimizer_state_path(output_dir),
+        selections = tuple(group._distributed_lease for group in trajectory_groups)
+        selected = tuple(
+            selection
+            for selection in selections
+            if isinstance(selection, DistributedTrajectorySelection)
         )
-        print(format_megatron_resume_message(info))
-        self._resume_prepared_models.add(storage_key)
-        return await super()._get_step(model)
+        for group, selection in zip(trajectory_groups, selections, strict=True):
+            if isinstance(selection, DistributedTrajectorySelection):
+                group._distributed_lease = None
 
-    async def finalize_training_session(self, model: AnyTrainableModel) -> None:
-        service = self._services.get(self._model_storage_key(model))
-        if service is not None:
-            await cast(Any, service).finalize_training_session()
+        generation_id = uuid.uuid4().hex
+        trajectory_log_path: str | None = None
+        runtime: ArtRuntime | None = None
+        packed: Any = None
+        marked_packed = False
+        transferred = False
+        try:
+            packing_config = _PackingConfig.from_dev_config(
+                dev_config,
+                include_moe_routing=include_moe_routing,
+                collect_packing_shapes=collect_packing_shapes,
+            )
+            if selected and len(selected) != len(trajectory_groups):
+                raise RuntimeError(
+                    "distributed batch mixes owned and controller groups"
+                )
+            queue = selected[0].queue if selected else None
+            if queue is not None and any(
+                selection.queue is not queue for selection in selected
+            ):
+                raise RuntimeError("distributed batch spans trajectory queues")
 
-    async def _delete_checkpoint_files(
+            from .distributed_service import DistributedMegatronService
+
+            service = cast(DistributedMegatronService, await self._get_service(model))
+            runtime = service.runtime
+            versions = [
+                version
+                for group in trajectory_groups
+                for trajectory in group.trajectories
+                for version in (
+                    trajectory.initial_policy_version,
+                    trajectory.final_policy_version,
+                )
+                if version is not None
+            ]
+            current_step = min(versions) if versions else await self._get_step(model)
+            if selected:
+                group_ids = tuple(
+                    selection.lease.item.ref.result_id for selection in selected
+                )
+                record_ids = tuple(
+                    record.record_id
+                    for selection in selected
+                    for record in selection.lease.item.ref.records
+                )
+                trajectory_log_path = str(
+                    Path(get_model_dir(model=model, art_path=self._path))
+                    / "trajectories"
+                    / ".staging"
+                    / f"{generation_id}.parquet"
+                )
+            else:
+                group_ids = tuple(
+                    f"{group.metadata.get('scenario_id', 'group')}:{index}"
+                    for index, group in enumerate(trajectory_groups)
+                )
+                record_ids = tuple(
+                    f"{group_id}:{trajectory_index}"
+                    for group_id, group in zip(
+                        group_ids, trajectory_groups, strict=True
+                    )
+                    for trajectory_index, _ in enumerate(group.trajectories)
+                )
+            local_selections = tuple(
+                selection
+                for selection in selected
+                if selection.lease.item.ref.transfer is None
+            )
+            if local_selections and len(local_selections) != len(selected):
+                raise RuntimeError("distributed batch mixes local and remote owners")
+            local_groups = (
+                tuple(
+                    await asyncio.gather(
+                        *(
+                            queue.materialize_selection(selection)
+                            for selection in selected
+                        )
+                    )
+                )
+                if queue is not None and local_selections
+                else ()
+            )
+            request = PackingRequest(
+                model=RolloutModelSpec.from_model(model),
+                generation_id=generation_id,
+                trajectory_groups=tuple(
+                    TrajectoryGroupBundle.from_group(group)
+                    for group in (
+                        local_groups if selected else tuple(trajectory_groups)
+                    )
+                ),
+                trajectory_sources=(
+                    ()
+                    if local_selections
+                    else tuple(selection.lease.item for selection in selected)
+                ),
+                trajectory_log_path=trajectory_log_path,
+                group_ids=group_ids,
+                record_ids=record_ids,
+                min_source_version=min(versions, default=current_step),
+                max_source_version=max(versions, default=current_step),
+                **packing_config.model_dump(),
+            )
+            packed = await runtime.pack(request)
+            if packed is None:
+                return None
+            if queue is not None:
+                _, cancelled = await complete_task(
+                    asyncio.create_task(queue.mark_packed(selected, generation_id))
+                )
+                marked_packed = True
+                if cancelled is not None:
+                    raise cancelled
+            shapes = tuple(packed.packed_group_shapes)
+            if len(shapes) != len(trajectory_groups):
+                raise RuntimeError("packed-group shapes do not match trajectory groups")
+            ref = packed.leases.ref
+            stats = ref.prefix_tree_packing_stats
+            if stats is None:
+                raise RuntimeError(
+                    "distributed packed batch has no prefix-tree statistics"
+                )
+            batch = _PackedTrainingBatch(
+                payload=_DistributedBatchPayload(
+                    packed=packed,
+                    selections=selected,
+                    generation_id=generation_id,
+                    runtime=runtime,
+                ),
+                num_sequences=ref.num_sequences,
+                sequence_length=ref.sequence_length,
+                trainable_assistant_tokens=packed.trainable_assistant_tokens,
+                loss_bearing_tokens=packed.loss_bearing_tokens,
+                non_padding_tokens=packed.non_padding_tokens,
+                logical_tokens=stats.logical_tokens,
+                physical_tokens=stats.physical_tokens,
+                include_moe_routing=include_moe_routing,
+            )
+            for group, shape in zip(trajectory_groups, shapes, strict=True):
+                if shape is not None:
+                    group._packed_group_shape = shape
+                if selected:
+                    group._prepared_log_path = packed.trajectory_log_path
+            transferred = True
+            return batch
+        finally:
+            if not transferred:
+                primary = sys.exception()
+                try:
+                    _, cancelled = await complete_task(
+                        asyncio.create_task(
+                            self._cleanup_packing_ownership(
+                                runtime=runtime,
+                                packed=packed,
+                                selections=selected,
+                                generation_id=(
+                                    generation_id if marked_packed else None
+                                ),
+                                trajectory_log_path=trajectory_log_path,
+                            )
+                        )
+                    )
+                    if cancelled is not None:
+                        raise cancelled
+                except BaseException as cleanup_error:
+                    if primary is None:
+                        raise
+                    raise BaseExceptionGroup(
+                        "packing and source cleanup failed",
+                        [primary, cleanup_error],
+                    ) from None
+
+    async def _cleanup_packing_ownership(
+        self,
+        *,
+        runtime: ArtRuntime | None,
+        packed: Any,
+        selections: tuple[Any, ...],
+        generation_id: str | None,
+        trajectory_log_path: str | None,
+    ) -> None:
+        paths = {
+            path
+            for path in (
+                trajectory_log_path,
+                getattr(packed, "trajectory_log_path", None),
+            )
+            if path is not None
+        }
+        releases = [
+            *(
+                (runtime.release_batch(packed),)
+                if runtime is not None and packed is not None
+                else ()
+            ),
+            *(
+                selection.queue.release_selection(
+                    selection,
+                    disposition="discarded",
+                    generation_id=generation_id,
+                )
+                for selection in selections
+            ),
+            *(asyncio.to_thread(Path(path).unlink, missing_ok=True) for path in paths),
+        ]
+        results = await asyncio.gather(*releases, return_exceptions=True)
+        failures = [result for result in results if isinstance(result, BaseException)]
+        if failures:
+            raise BaseExceptionGroup("packing ownership cleanup failed", failures)
+
+    async def prepare_pipeline_batch(
+        self,
+        model: TrainableModel,
+        trajectory_groups: list[TrajectoryGroup],
+        *,
+        normalize_advantages: bool = True,
+        advantage_balance: float = 0.0,
+        scale_rewards: bool = True,
+        allow_training_without_logprobs: bool = False,
+        plot_tensors: bool = False,
+        logprob_calculation_chunk_size: int = 1024,
+        grad_accumulation_sequences: int | None = None,
+    ) -> dict[str, float] | None:
+        include_moe_routing = self._model_uses_expert_replay(model)
+        dev_config = {
+            "advantage_balance": advantage_balance,
+            "allow_training_without_logprobs": allow_training_without_logprobs,
+            "scale_rewards": scale_rewards and normalize_advantages,
+            "plot_tensors": plot_tensors,
+            "packed_sequence_length": get_megatron_runtime_config().packed_sequence_length,
+            "logprob_calculation_chunk_size": logprob_calculation_chunk_size,
+        }
+        batch = await self._prepare_training_batch(
+            model,
+            trajectory_groups,
+            dev_config,
+            include_moe_routing=include_moe_routing,
+        )
+        if batch is None:
+            return None
+        payload = batch.payload
+        if not isinstance(payload, _DistributedBatchPayload):
+            raise RuntimeError(
+                "Megatron pipeline batch did not use the typed data plane"
+            )
+        distributed = payload.packed
+        metrics = {
+            "time/step_trajectory_fetch_s": distributed.trajectory_fetch_s,
+            "time/step_packing_core_s": distributed.packing_core_s,
+            "time/step_trajectory_log_wait_s": distributed.trajectory_log_wait_s,
+            "time/step_packed_batch_finalize_s": distributed.packed_batch_finalize_s,
+            "time/step_packing_rpc_s": distributed.packing_rpc_s,
+            "time/step_packed_batch_fanout_s": distributed.packed_batch_fanout_s,
+        }
+        from .distributed_service import DistributedMegatronService
+
+        service = cast(
+            DistributedMegatronService,
+            await self._get_service(model),
+        )
+        packing_config = _PackingConfig.from_dev_config(
+            dev_config,
+            include_moe_routing=include_moe_routing,
+            collect_packing_shapes=any(
+                group._collect_packing_shape for group in trajectory_groups
+            ),
+        )
+        try:
+            metrics.update(
+                await service.prepare_cp_lookahead(
+                    distributed,
+                    global_grad_accumulation_sequences=grad_accumulation_sequences,
+                )
+            )
+        except BaseException as primary:
+            cleanup_prepared = _PipelinePreparedBatch(
+                batch=batch,
+                groups=tuple(trajectory_groups),
+                packing_config=packing_config,
+                metrics=metrics,
+            )
+            paths = {
+                group._prepared_log_path
+                for group in trajectory_groups
+                if group._prepared_log_path is not None
+            }
+            try:
+                _, cancelled = await complete_task(
+                    asyncio.create_task(
+                        self._discard_prepared_resources(
+                            cleanup_prepared, trajectory_groups, paths
+                        )
+                    )
+                )
+                if cancelled is not None:
+                    raise cancelled
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "CP lookahead and packed-batch cleanup failed",
+                    [primary, cleanup_error],
+                ) from None
+            raise
+        prepared = _PipelinePreparedBatch(
+            batch=batch,
+            groups=tuple(trajectory_groups),
+            packing_config=packing_config,
+            metrics=metrics,
+        )
+        for group in trajectory_groups:
+            group._prepared_training_batch = prepared
+        return metrics
+
+    async def discard_pipeline_batch(
+        self, trajectory_groups: list[TrajectoryGroup]
+    ) -> None:
+        prepared = trajectory_groups[0]._prepared_training_batch
+        if not isinstance(prepared, _PipelinePreparedBatch) or any(
+            group._prepared_training_batch is not prepared
+            for group in trajectory_groups
+        ):
+            raise RuntimeError("pipeline batch is not prepared")
+        for group in trajectory_groups:
+            group._prepared_training_batch = None
+        paths = {
+            group._prepared_log_path
+            for group in trajectory_groups
+            if group._prepared_log_path is not None
+        }
+        _, cancelled = await complete_task(
+            asyncio.create_task(
+                self._discard_prepared_resources(prepared, trajectory_groups, paths)
+            )
+        )
+        if cancelled is not None:
+            raise cancelled
+
+    async def _discard_prepared_resources(
+        self,
+        prepared: _PipelinePreparedBatch,
+        trajectory_groups: list[TrajectoryGroup],
+        paths: set[str],
+    ) -> None:
+        results = await asyncio.gather(
+            self._release_distributed_batch(prepared.batch, disposition="discarded"),
+            *(asyncio.to_thread(Path(path).unlink, missing_ok=True) for path in paths),
+            return_exceptions=True,
+        )
+        for group in trajectory_groups:
+            group._prepared_log_path = None
+        failures = [result for result in results if isinstance(result, BaseException)]
+        if failures:
+            raise BaseExceptionGroup("prepared batch discard failed", failures)
+
+    async def _stream_prepared_training(
+        self,
+        model: TrainableModel,
+        service: ModelService,
+        batch: _PackedTrainingBatch,
+        config: Any,
+        service_dev_config: Any,
+        grad_accumulation_sequences: int,
+        verbose: bool,
+    ) -> AsyncIterator[dict[str, float]]:
+        self._collect_batch_release_results()
+        self._raise_batch_release_failures()
+        self._collect_adapter_prune_result()
+        self._raise_adapter_prune_failures()
+        from ..distributed.art_runtime import DistributedPackedBatch
+        from .distributed_service import DistributedMegatronService
+
+        payload = batch.payload
+        if not isinstance(payload, _DistributedBatchPayload):
+            raise RuntimeError("Megatron training did not use the typed data plane")
+        distributed_batch = cast(
+            DistributedPackedBatch,
+            payload.packed,
+        )
+        source_release_s = 0.0
+        if payload.selections:
+            ref = distributed_batch.leases.ref
+            expected_groups = tuple(
+                selection.lease.item.ref.result_id for selection in payload.selections
+            )
+            expected_records = tuple(
+                record.record_id
+                for selection in payload.selections
+                for record in selection.lease.item.ref.records
+            )
+            versions = []
+            for selection in payload.selections:
+                item = selection.lease.item
+                descriptor = item.ref.descriptor
+                versions.extend(
+                    version
+                    for initial, final in zip(
+                        descriptor.trajectory_initial_policy_versions,
+                        descriptor.trajectory_final_policy_versions,
+                        strict=True,
+                    )
+                    for version in (
+                        initial
+                        if initial is not None
+                        else item.annotations.initial_policy_version,
+                        final
+                        if final is not None
+                        else item.annotations.final_policy_version,
+                    )
+                )
+            if (
+                distributed_batch.packing_generation_id != payload.generation_id
+                or ref.group_ids != expected_groups
+                or ref.record_ids != expected_records
+                or ref.min_source_version != min(versions)
+                or ref.max_source_version != max(versions)
+            ):
+                raise RuntimeError("packed batch policy provenance does not match")
+            release_started = time.perf_counter()
+            await self._release_trajectory_sources(batch, payload)
+            source_release_s = time.perf_counter() - release_started
+        distributed_service = cast(DistributedMegatronService, service)
+        async for result in distributed_service.train_packed(
+            distributed_batch, config, service_dev_config
+        ):
+            yield {
+                **result,
+                "time/step_source_lease_release_s": source_release_s,
+                **distributed_service.drain_publication_metrics(),
+            }
+
+    async def _release_training_batch(self, batch: _PackedTrainingBatch) -> None:
+        await self._release_distributed_batch(batch, disposition="consumed")
+
+    async def _release_trajectory_sources(
+        self,
+        batch: _PackedTrainingBatch,
+        payload: _DistributedBatchPayload,
+    ) -> None:
+        selections = payload.selections
+        if not selections:
+            return
+        queue = selections[0].queue
+        if any(selection.queue is not queue for selection in selections):
+            raise RuntimeError("packed batch contains selections from multiple queues")
+        await queue.release_selections(
+            selections,
+            disposition="consumed",
+            generation_id=payload.generation_id,
+        )
+        batch.payload = payload.model_copy(update={"selections": ()})
+
+    async def _finish_training_batch(
+        self, batch: _PackedTrainingBatch, *, failed: bool
+    ) -> None:
+        if failed:
+            await super()._finish_training_batch(batch, failed=failed)
+            if self._batch_release_tasks:
+                await asyncio.gather(
+                    *tuple(self._batch_release_tasks), return_exceptions=True
+                )
+            self._collect_batch_release_results()
+            self._raise_batch_release_failures()
+            return
+        self._collect_batch_release_results()
+        self._raise_batch_release_failures()
+        while len(self._batch_release_tasks) >= 2:
+            await asyncio.wait(
+                self._batch_release_tasks, return_when=asyncio.FIRST_COMPLETED
+            )
+            self._collect_batch_release_results()
+            self._raise_batch_release_failures()
+        self._batch_release_tasks.add(
+            asyncio.create_task(self._release_training_batch(batch))
+        )
+
+    def _collect_batch_release_results(self) -> None:
+        for task in tuple(self._batch_release_tasks):
+            if not task.done():
+                continue
+            self._batch_release_tasks.remove(task)
+            try:
+                task.result()
+            except BaseException as error:
+                self._batch_release_failures.append(error)
+
+    def _raise_batch_release_failures(self) -> None:
+        if not self._batch_release_failures:
+            return
+        failures, self._batch_release_failures = self._batch_release_failures, []
+        raise BaseExceptionGroup("distributed training batch release failed", failures)
+
+    async def prune_model_adapters(
         self,
         model: AnyTrainableModel,
-        steps_to_keep: list[int],
+        *,
+        retain_steps: set[int],
     ) -> None:
-        output_dir = get_model_dir(model=model, art_path=self._path)
-        commit = read_optimizer_commit(optimizer_state_path(output_dir))
-        if commit is not None:
-            steps_to_keep = sorted(set(steps_to_keep) | {commit.step})
-        await super()._delete_checkpoint_files(model, steps_to_keep)
+        service = await self._get_service(cast(TrainableModel, model))
+        if getattr(service, "rollout_weight_update_mode", None) == "in_flight_lora":
+            return
+        self._collect_adapter_prune_result()
+        self._raise_adapter_prune_failures()
+        self._adapter_prune_requests[self._model_storage_key(model)] = (
+            model,
+            set(retain_steps),
+        )
+        if self._adapter_prune_task is None:
+            self._adapter_prune_task = asyncio.create_task(self._prune_adapters())
+
+    async def _prune_adapters(self) -> None:
+        while self._adapter_prune_requests:
+            requests, self._adapter_prune_requests = self._adapter_prune_requests, {}
+            for model, retain_steps in requests.values():
+                await super().prune_model_adapters(model, retain_steps=retain_steps)
+
+    def _collect_adapter_prune_result(self) -> None:
+        task = self._adapter_prune_task
+        if task is None or not task.done():
+            return
+        self._adapter_prune_task = None
+        try:
+            task.result()
+        except BaseException as error:
+            self._adapter_prune_failures.append(error)
+
+    def _raise_adapter_prune_failures(self) -> None:
+        if not self._adapter_prune_failures:
+            return
+        failures, self._adapter_prune_failures = self._adapter_prune_failures, []
+        raise BaseExceptionGroup("Megatron adapter pruning failed", failures)
 
     async def close(self) -> None:
+        task = asyncio.create_task(self._close_megatron_backend())
+        _, cancelled = await complete_task(task)
+        if cancelled is not None:
+            raise cancelled
+
+    async def _close_megatron_backend(self) -> None:
         failures: list[BaseException] = []
-        for service in self._services.values():
-            try:
-                await asyncio.wait_for(
-                    cast(Any, service).finalize_training_session(),
-                    timeout=process_shutdown_timeout(1),
+        if self._batch_release_tasks:
+            results = await asyncio.gather(
+                *self._batch_release_tasks, return_exceptions=True
+            )
+            self._batch_release_tasks.clear()
+            failures.extend(
+                result for result in results if isinstance(result, BaseException)
+            )
+        failures.extend(self._batch_release_failures)
+        self._batch_release_failures.clear()
+        if self._adapter_prune_task is not None:
+            result = await asyncio.gather(
+                self._adapter_prune_task, return_exceptions=True
+            )
+            if isinstance(result[0], BaseException):
+                failures.append(result[0])
+            self._adapter_prune_task = None
+        failures.extend(self._adapter_prune_failures)
+        self._adapter_prune_failures.clear()
+        services = dict(self._services)
+        services_closed = True
+        try:
+            await super().close()
+        except BaseException as error:
+            failures.append(error)
+            services_closed = False
+            for key, service in services.items():
+                self._services.setdefault(key, service)
+        if services_closed:
+            runtimes = tuple(self._owned_runtimes.items())
+            results = await asyncio.gather(
+                *(runtime.close() for _, runtime in runtimes),
+                return_exceptions=True,
+            )
+            for (key, runtime), result in zip(runtimes, results, strict=True):
+                if isinstance(result, BaseException):
+                    failures.append(result)
+                elif self._owned_runtimes.get(key) is runtime:
+                    self._owned_runtimes.pop(key)
+                    ports = self._owned_runtime_ports.pop(key, None)
+                    if ports is not None:
+                        self._local_endpoints.release(ports)
+        if failures:
+            raise BaseExceptionGroup(
+                "distributed Megatron backend close failed", failures
+            )
+
+    async def _release_distributed_batch(
+        self,
+        batch: _PackedTrainingBatch,
+        *,
+        disposition: Literal["consumed", "discarded"],
+    ) -> None:
+        from ..distributed.art_runtime import DistributedPackedBatch
+
+        payload = batch.payload
+        if not isinstance(payload, _DistributedBatchPayload):
+            raise RuntimeError("Megatron batch has no owning typed runtime")
+        runtime = cast(ArtRuntime, payload.runtime)
+        distributed_batch = cast(DistributedPackedBatch, payload.packed)
+        releases: list[Any] = [runtime.release_batch(distributed_batch)]
+        if payload.selections:
+            queue = payload.selections[0].queue
+            releases.append(
+                queue.release_selections(
+                    payload.selections,
+                    disposition=disposition,
+                    generation_id=payload.generation_id,
                 )
-            except BaseException as exc:
-                failures.append(exc)
-        await super().close()
+            )
+        results = await asyncio.gather(*releases, return_exceptions=True)
+        failures = [result for result in results if isinstance(result, BaseException)]
         if failures:
             raise BaseExceptionGroup(
-                "Failed to persist Megatron optimizer state during shutdown",
-                failures,
+                "distributed training batch release failed", failures
             )
 
+    async def _delete_checkpoint_files(
+        self,
+        model: AnyTrainableModel,
+        steps_to_keep: list[int],
+    ) -> None:
+        from ..local.checkpoints import delete_checkpoints
+        from .distributed_service import DistributedMegatronService
+        from .optimizer_state import optimizer_retention_lease
+
+        service = cast(DistributedMegatronService, await self._get_service(model))
+        output_dir = get_model_dir(model=model, art_path=self._path)
+        async with service.checkpoint_retention_lease() as active_steps:
+
+            def delete_retained() -> None:
+                retained = set(steps_to_keep) | set(active_steps)
+                with optimizer_retention_lease(output_dir, retained) as protected:
+                    delete_checkpoints(output_dir, sorted(protected))
+
+            await asyncio.to_thread(delete_retained)
+
+    async def _advance_skipped_step(
+        self,
+        model: TrainableModel,
+        service: ModelService,
+        current_step: int,
+        next_step: int,
+    ) -> dict[str, float]:
+        from .distributed_service import DistributedMegatronService
+
+        distributed = cast(DistributedMegatronService, service)
+        return await distributed.advance_without_training(
+            expected_step=current_step,
+            learner_version=next_step,
+        )
+
+    async def _get_step(self, model: AnyTrainableModel) -> int:
+        if not model.trainable:
+            return 0
+        await self._get_service(cast(TrainableModel, model))
+        storage_key = self._model_storage_key(model)
+        if storage_key in self._services:
+            from .distributed_service import DistributedMegatronService
+
+            service = cast(DistributedMegatronService, self._services[storage_key])
+            return await service.prepare_for_packing()
+        raise RuntimeError("Megatron model service was not initialized")
+
     def _default_sft_batch_size(self) -> int:
         import torch
 
         num_gpus = max(int(torch.cuda.device_count()), 1)
         tensor_parallel_size = min(2, num_gpus)
         return max(num_gpus // tensor_parallel_size, 1)
+
+
+def _topology_gpu_placements(topology: Any) -> frozenset[tuple[str, int | str]]:
+    trainer = () if topology.trainer is None else topology.trainer.ranks
+    return frozenset(
+        [(rank.host_id, rank.gpu_id) for rank in trainer]
+        + [
+            (member.host_id, gpu_id)
+            for service in topology.model_services
+            for member in service.members
+            for gpu_id in member.gpu_ids
+        ]
+    )
diff --git a/src/art/megatron/compile_workarounds.py b/src/art/megatron/compile_workarounds.py
index 5ba10e7e3..4c2ec6dd5 100644
--- a/src/art/megatron/compile_workarounds.py
+++ b/src/art/megatron/compile_workarounds.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import os
-from typing import Any, cast
+from typing import Any
 
 import torch
 
@@ -73,106 +73,6 @@ def _install_self_attn_linear_proj_reduce_scatter_workaround() -> None:
     art_lora.reduce_scatter_to_sequence_parallel_region = wrapped  # type: ignore[assignment]
 
 
-class _WeightedSwiGLUNoInnerForwardCast(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx: Any,
-        input: torch.Tensor,
-        weights: torch.Tensor,
-        fp8_input_store: bool,
-    ) -> torch.Tensor:
-        input_for_backward = input.to(torch.float8_e4m3fn) if fp8_input_store else input
-        ctx.save_for_backward(input_for_backward, weights)
-        ctx.ori_input_dtype = input.dtype
-        ctx.fp8_input_store = fp8_input_store
-        x_glu, x_linear = torch.chunk(input, 2, dim=-1)
-        return torch.nn.functional.silu(x_glu) * x_linear * weights
-
-    @staticmethod
-    def backward(
-        ctx: Any,
-        *grad_outputs: Any,
-    ) -> tuple[torch.Tensor, torch.Tensor, None]:
-        from megatron.core.fusions import fused_bias_swiglu
-
-        grad_output = cast(torch.Tensor, grad_outputs[0])
-        input, weights = ctx.saved_tensors
-        input = input.to(ctx.ori_input_dtype) if ctx.fp8_input_store else input
-        input_grad, weights_grad = fused_bias_swiglu.weighted_swiglu_back(
-            grad_output,
-            input,
-            weights,
-        )
-        return input_grad, weights_grad, None
-
-
-def _install_weighted_bias_swiglu_no_inner_forward_cast_workaround() -> None:
-    from megatron.core.fusions import fused_bias_swiglu
-    from megatron.core.transformer import mlp
-    from megatron.core.transformer.moe import experts
-
-    if getattr(
-        fused_bias_swiglu.weighted_bias_swiglu_impl,
-        "__art_no_inner_forward_cast__",
-        False,
-    ):
-        return
-
-    def _empty_weighted_swiglu_output(
-        input: torch.Tensor,
-        bias: torch.Tensor | None,
-        weights: torch.Tensor,
-    ) -> torch.Tensor:
-        output_shape = (*input.shape[:-1], int(input.shape[-1]) // 2)
-        zero = input.sum() * 0.0 + weights.to(dtype=input.dtype).sum() * 0.0
-        if bias is not None:
-            zero = zero + bias.to(dtype=input.dtype).sum() * 0.0
-        return zero.expand(output_shape).clone()
-
-    def _weighted_bias_swiglu_no_inner_forward_cast(
-        input: torch.Tensor,
-        bias: torch.Tensor | None,
-        weights: torch.Tensor,
-        fp8_input_store: bool = False,
-    ) -> torch.Tensor:
-        if int(input.numel()) == 0:
-            return _empty_weighted_swiglu_output(input, bias=bias, weights=weights)
-        if bias is not None:
-            raise NotImplementedError(
-                "Bias is not supported for weighted swiglu fusion"
-            )
-        original_shape = input.shape
-        output = _WeightedSwiGLUNoInnerForwardCast.apply(
-            input.view(-1, original_shape[-1]),
-            weights,
-            fp8_input_store,
-        ).to(input.dtype)
-        return (
-            output
-            if len(original_shape) == 2
-            else output.view(*original_shape[:-1], -1)
-        )
-
-    setattr(
-        _weighted_bias_swiglu_no_inner_forward_cast,
-        "__art_no_inner_forward_cast__",
-        True,
-    )
-    setattr(
-        fused_bias_swiglu,
-        "weighted_bias_swiglu_impl",
-        _weighted_bias_swiglu_no_inner_forward_cast,
-    )
-    setattr(
-        mlp, "weighted_bias_swiglu_impl", _weighted_bias_swiglu_no_inner_forward_cast
-    )
-    setattr(
-        experts,
-        "weighted_bias_swiglu_impl",
-        _weighted_bias_swiglu_no_inner_forward_cast,
-    )
-
-
 def _install_moe_postprocess_workaround(moe_layer: Any) -> None:
     # Routed token counts change across packed RL steps. Megatron's MoE
     # postprocess reshapes through dispatcher-owned shape state, which makes
@@ -241,8 +141,6 @@ def _sync_dealloc_fake(
         _install_context_parallel_attention_workaround()
     if _SELF_ATTN_LINEAR_PROJ_REDUCE_SCATTER_WORKAROUND_FLAG in flags:
         _install_self_attn_linear_proj_reduce_scatter_workaround()
-    if "weighted_bias_swiglu_no_inner_forward_cast" in flags:
-        _install_weighted_bias_swiglu_no_inner_forward_cast_workaround()
     if "moe_postprocess" in flags:
         _install_moe_postprocess_workaround(moe_layer)
     if "gemma4_moe_postprocess" in flags:
@@ -307,6 +205,10 @@ def _sync_dealloc_fake(
         moe_layer.MoELayer.preprocess = _disable(moe_layer.MoELayer.preprocess)
     if "moe_forward" in flags:
         moe_layer.MoELayer.forward = _disable(moe_layer.MoELayer.forward)
+    if "mlp_forward" in flags:
+        from megatron.core.transformer import mlp
+
+        mlp.MLP.forward = _disable(mlp.MLP.forward)
     if "te_grouped_mlp_forward" in flags:
         moe_experts.TEGroupedMLP.forward = _disable(moe_experts.TEGroupedMLP.forward)
     _INSTALLED_CONFIG = installed_config
diff --git a/src/art/megatron/context_parallel/block_mask.py b/src/art/megatron/context_parallel/block_mask.py
index f5255ec35..e9ad55240 100644
--- a/src/art/megatron/context_parallel/block_mask.py
+++ b/src/art/megatron/context_parallel/block_mask.py
@@ -306,112 +306,6 @@ def k_intervals(k_idx: int) -> tuple[tuple[int, int, int], ...]:
         full_blocks[q_idx, k_idx] = bool(is_full)
 
 
-def _refine_sliding_interval_blocks(
-    *,
-    partial_blocks: np.ndarray,
-    full_blocks: np.ndarray,
-    q_abs: np.ndarray,
-    k_abs: np.ndarray,
-    q_enter: np.ndarray,
-    k_enter: np.ndarray,
-    k_exit: np.ndarray,
-    q_pos: np.ndarray,
-    k_pos: np.ndarray,
-    q_block: int,
-    k_block: int,
-    sliding_window: int,
-) -> None:
-    candidates = partial_blocks | full_blocks
-    if not bool(candidates.any()):
-        return
-
-    q_abs_blocks = _block_matrix(
-        q_abs,
-        block_size=q_block,
-        block_count=int(partial_blocks.shape[0]),
-        fill_value=_INVALID_ABS,
-    )
-    q_enter_blocks = _block_matrix(
-        q_enter,
-        block_size=q_block,
-        block_count=int(partial_blocks.shape[0]),
-        fill_value=_INVALID_ENTER,
-    )
-    q_pos_blocks = _block_matrix(
-        q_pos,
-        block_size=q_block,
-        block_count=int(partial_blocks.shape[0]),
-        fill_value=_INVALID_POS,
-    )
-    k_abs_blocks = _block_matrix(
-        k_abs,
-        block_size=k_block,
-        block_count=int(partial_blocks.shape[1]),
-        fill_value=_INVALID_ABS,
-    )
-    k_enter_blocks = _block_matrix(
-        k_enter,
-        block_size=k_block,
-        block_count=int(partial_blocks.shape[1]),
-        fill_value=_INVALID_ENTER,
-    )
-    k_exit_blocks = _block_matrix(
-        k_exit,
-        block_size=k_block,
-        block_count=int(partial_blocks.shape[1]),
-        fill_value=_INVALID_EXIT,
-    )
-    k_pos_blocks = _block_matrix(
-        k_pos,
-        block_size=k_block,
-        block_count=int(partial_blocks.shape[1]),
-        fill_value=_INVALID_POS,
-    )
-
-    q_valid = (
-        (q_abs_blocks >= 0) & (q_enter_blocks >= 0) & (q_pos_blocks != _INVALID_POS)
-    )
-    k_valid = (
-        (k_abs_blocks >= 0)
-        & (k_enter_blocks >= 0)
-        & (k_exit_blocks > k_enter_blocks)
-        & (k_pos_blocks != _INVALID_POS)
-    )
-
-    q_indices, k_indices = np.nonzero(candidates)
-    partial_blocks[q_indices, k_indices] = False
-    full_blocks[q_indices, k_indices] = False
-    for q_idx, k_idx in zip(q_indices, k_indices, strict=True):
-        q_valid_row = q_valid[q_idx]
-        k_valid_row = k_valid[k_idx]
-        if not bool(q_valid_row.any()) or not bool(k_valid_row.any()):
-            continue
-
-        q_abs_row = q_abs_blocks[q_idx][:, None]
-        q_enter_row = q_enter_blocks[q_idx][:, None]
-        q_pos_row = q_pos_blocks[q_idx][:, None]
-        k_abs_row = k_abs_blocks[k_idx][None, :]
-        k_enter_row = k_enter_blocks[k_idx][None, :]
-        k_exit_row = k_exit_blocks[k_idx][None, :]
-        k_pos_row = k_pos_blocks[k_idx][None, :]
-        delta = q_pos_row - k_pos_row
-        allowed = (
-            q_valid_row[:, None]
-            & k_valid_row[None, :]
-            & (q_abs_row >= k_abs_row)
-            & (k_enter_row <= q_enter_row)
-            & (q_enter_row < k_exit_row)
-            & (delta >= 0)
-            & (delta < int(sliding_window))
-        )
-        if not bool(allowed.any()):
-            continue
-        if bool(q_valid_row.all()) and bool(k_valid_row.all()) and bool(allowed.all()):
-            full_blocks[q_idx, k_idx] = True
-        else:
-            partial_blocks[q_idx, k_idx] = True
-
-
 def _is_strictly_increasing(values: np.ndarray) -> bool:
     return int(values.size) <= 1 or bool(np.all(values[1:] > values[:-1]))
 
@@ -657,6 +551,7 @@ def _build_sparse_block_mask(
         full_blocks[q_slice, k_slice] |= is_full
 
     partial_blocks &= ~full_blocks
+    sliding_full_blocks = full_blocks.copy() if sliding_window is not None else None
     needs_refine = full_blocks | ((touch_counts > 1) & partial_blocks)
     if bool(needs_refine.any()):
         refined_partial = partial_blocks & needs_refine
@@ -674,22 +569,12 @@ def _build_sparse_block_mask(
         )
         partial_blocks = (partial_blocks & ~needs_refine) | refined_partial
         full_blocks = (full_blocks & ~needs_refine) | refined_full
-    if sliding_window is not None:
-        assert q_pos is not None and k_pos is not None
-        _refine_sliding_interval_blocks(
-            partial_blocks=partial_blocks,
-            full_blocks=full_blocks,
-            q_abs=q_abs,
-            k_abs=k_abs,
-            q_enter=q_enter,
-            k_enter=k_enter,
-            k_exit=k_exit,
-            q_pos=q_pos,
-            k_pos=k_pos,
-            q_block=q_block,
-            k_block=k_block,
-            sliding_window=int(sliding_window),
-        )
+    if sliding_full_blocks is not None:
+        promoted = full_blocks & ~sliding_full_blocks
+        partial_blocks |= promoted
+        full_blocks &= sliding_full_blocks
+    # Partial blocks retain exact token masking through mask_mod. Keep ancestry
+    # refinement from promoting sliding-window boundary blocks to full blocks.
     kv_num_blocks, kv_indices = _dense_blocks_to_ordered(
         partial_blocks,
         device=device,
diff --git a/src/art/megatron/context_parallel/comm.py b/src/art/megatron/context_parallel/comm.py
index 8ea97067d..72455ce38 100644
--- a/src/art/megatron/context_parallel/comm.py
+++ b/src/art/megatron/context_parallel/comm.py
@@ -89,6 +89,41 @@ def wait_post_process(self) -> tuple[torch.Tensor, torch.Tensor]:
         )
 
 
+@dataclass
+class TensorFetchWork:
+    packed_buffer: torch.Tensor
+    recv_splits: tuple[int, ...]
+    handle: _Waitable | None
+    send_buffer: torch.Tensor | None = None
+    stream: torch.cuda.Stream | None = None
+    output_layout: str = "token_major"
+    _wait_complete: bool = False
+
+    def is_completed(self) -> bool:
+        if self._wait_complete:
+            return True
+        handle_complete = True
+        if self.handle is not None:
+            is_completed = getattr(self.handle, "is_completed", None)
+            if callable(is_completed):
+                handle_complete = bool(is_completed())
+        return handle_complete and (self.stream is None or bool(self.stream.query()))
+
+    def wait_post_process(self) -> torch.Tensor:
+        if not self._wait_complete:
+            if self.handle is not None:
+                self.handle.wait()
+            if self.stream is not None:
+                torch.cuda.current_stream(self.packed_buffer.device).wait_stream(
+                    self.stream
+                )
+            self._wait_complete = True
+        return _unpack_single_tensor(
+            self.packed_buffer,
+            output_layout=self.output_layout,
+        )
+
+
 @dataclass
 class DkvReduceWork:
     packed_buffer: torch.Tensor | None
@@ -166,6 +201,55 @@ def wait_post_process(self) -> tuple[torch.Tensor, torch.Tensor]:
         return self.dk_local, self.dv_local
 
 
+@dataclass
+class TensorReduceWork:
+    packed_buffer: torch.Tensor | None
+    handle: _Waitable | None
+    send_buffer: torch.Tensor | None
+    stream: torch.cuda.Stream | None
+    plan: DkvReducePlan
+    output: torch.Tensor
+    range_meta_cache: dict[Any, Any] | None = None
+    input_layout: str = "token_major"
+    _wait_complete: bool = False
+
+    def wait_post_process(self) -> torch.Tensor:
+        if not self._wait_complete:
+            if self.handle is not None:
+                self.handle.wait()
+            if self.stream is not None and self.packed_buffer is not None:
+                torch.cuda.current_stream(self.packed_buffer.device).wait_stream(
+                    self.stream
+                )
+            self._wait_complete = True
+        if self.packed_buffer is None or int(self.packed_buffer.shape[0]) == 0:
+            return self.output
+        remote = _unpack_single_tensor(
+            self.packed_buffer,
+            output_layout=self.input_layout,
+        )
+        ranges = tuple(
+            range_
+            for peer_ranges in self.plan.recv_ranges_by_peer
+            for range_ in peer_ranges
+            if range_.size() > 0
+        )
+        reduce_fn = (
+            range_reduce_sum_head_major_
+            if self.input_layout == "head_major"
+            else range_reduce_sum_
+        )
+        reduce_fn(
+            remote
+            if remote.dtype == self.output.dtype
+            else remote.to(dtype=self.output.dtype),
+            output_tensor=self.output,
+            ranges=ranges,
+            range_meta_cache=self.range_meta_cache,
+        )
+        return self.output
+
+
 class A2AVCommunicator:
     def __init__(self) -> None:
         self._streams: dict[int, torch.cuda.Stream] = {}
@@ -194,6 +278,7 @@ def _launch_exchange(
         group: Any,
         async_op: bool,
         input_layout: str,
+        row_factor: int = 2,
     ) -> tuple[_Waitable | None, torch.Tensor, torch.cuda.Stream | None]:
         stream = self._get_stream(tensor) if async_op else None
         send_buffer = (
@@ -202,6 +287,7 @@ def _launch_exchange(
                     tensor=tensor,
                     total_rows=0,
                     input_layout=input_layout,
+                    row_factor=row_factor,
                 )
             )
             if total_send_rows <= 0
@@ -302,6 +388,61 @@ def launch_kv_fetch(
             output_layout=output_layout,
         )
 
+    def launch_tensor_fetch(
+        self,
+        *,
+        tensor_local: torch.Tensor,
+        plan: KvFetchPlan,
+        group: Any,
+        async_op: bool,
+        range_meta_cache: dict[Any, Any] | None = None,
+        input_layout: str = "token_major",
+        output_layout: str = "token_major",
+    ) -> TensorFetchWork:
+        """Fetch one stage tensor without duplicating it on the communication wire."""
+        total_send_rows = int(sum(plan.send_splits))
+        total_recv_rows = int(sum(plan.recv_splits))
+        recv_packed = tensor_local.new_empty(
+            _packed_peer_tensor_shape(
+                tensor=tensor_local,
+                total_rows=total_recv_rows,
+                input_layout=input_layout,
+                row_factor=1,
+            )
+        )
+        if group is None or _DIST.get_world_size(group) == 1:
+            return TensorFetchWork(
+                packed_buffer=recv_packed,
+                recv_splits=plan.recv_splits,
+                handle=None,
+                output_layout=output_layout,
+            )
+        handle, send_buffer, stream = self._launch_exchange(
+            tensor=tensor_local,
+            recv_buffer=recv_packed,
+            total_send_rows=total_send_rows,
+            make_send_buffer=lambda: _pack_gathered_tensor_per_peer(
+                tensor=tensor_local,
+                ranges_by_peer=plan.send_ranges_by_peer,
+                range_meta_cache=range_meta_cache,
+                input_layout=input_layout,
+            ),
+            output_split_sizes=list(plan.recv_splits),
+            input_split_sizes=list(plan.send_splits),
+            group=group,
+            async_op=async_op,
+            input_layout=input_layout,
+            row_factor=1,
+        )
+        return TensorFetchWork(
+            packed_buffer=recv_packed,
+            recv_splits=plan.recv_splits,
+            handle=handle,
+            send_buffer=send_buffer,
+            stream=stream,
+            output_layout=output_layout,
+        )
+
     def launch_dkv_reduce(
         self,
         *,
@@ -369,6 +510,64 @@ def launch_dkv_reduce(
             input_layout=input_layout,
         )
 
+    def launch_tensor_reduce(
+        self,
+        *,
+        remote: torch.Tensor,
+        plan: DkvReducePlan,
+        group: Any,
+        async_op: bool,
+        output: torch.Tensor,
+        range_meta_cache: dict[Any, Any] | None = None,
+        input_layout: str = "token_major",
+    ) -> TensorReduceWork:
+        """Return one stage gradient to owners through the plan's inverse ranges."""
+        if group is None or _DIST.get_world_size(group) == 1:
+            return TensorReduceWork(
+                packed_buffer=None,
+                handle=None,
+                send_buffer=None,
+                stream=None,
+                plan=plan,
+                output=output,
+                range_meta_cache=range_meta_cache,
+                input_layout=input_layout,
+            )
+        recv_packed = remote.new_empty(
+            _packed_peer_tensor_shape(
+                tensor=remote,
+                total_rows=int(sum(plan.recv_splits)),
+                input_layout=input_layout,
+                row_factor=1,
+            )
+        )
+        handle, send_buffer, stream = self._launch_exchange(
+            tensor=remote,
+            recv_buffer=recv_packed,
+            total_send_rows=int(sum(plan.send_splits)),
+            make_send_buffer=lambda: _pack_split_tensor_by_peer(
+                tensor=remote,
+                splits=plan.send_splits,
+                input_layout=input_layout,
+            ),
+            output_split_sizes=list(plan.recv_splits),
+            input_split_sizes=list(plan.send_splits),
+            group=group,
+            async_op=async_op,
+            input_layout=input_layout,
+            row_factor=1,
+        )
+        return TensorReduceWork(
+            packed_buffer=recv_packed,
+            handle=handle,
+            send_buffer=send_buffer,
+            stream=stream,
+            plan=plan,
+            output=output,
+            range_meta_cache=range_meta_cache,
+            input_layout=input_layout,
+        )
+
 
 def range_gather_per_peer(
     input_tensor: torch.Tensor,
@@ -435,6 +634,35 @@ def _pack_gathered_tensors_per_peer(
     return packed
 
 
+def _pack_gathered_tensor_per_peer(
+    *,
+    tensor: torch.Tensor,
+    ranges_by_peer: tuple[tuple[TokenRange, ...], ...],
+    range_meta_cache: dict[Any, Any] | None,
+    input_layout: str,
+) -> torch.Tensor:
+    rows = [
+        _gather_peer_rows(
+            tensor,
+            peer_ranges,
+            input_layout=input_layout,
+            range_meta_cache=range_meta_cache,
+        )
+        for peer_ranges in ranges_by_peer
+        if any(range_.size() > 0 for range_ in peer_ranges)
+    ]
+    if not rows:
+        return tensor.new_empty(
+            _packed_peer_tensor_shape(
+                tensor=tensor,
+                total_rows=0,
+                input_layout=input_layout,
+                row_factor=1,
+            )
+        )
+    return torch.cat(rows, dim=0).contiguous()
+
+
 def _pack_split_tensors_by_peer(
     *,
     left_tensor: torch.Tensor,
@@ -472,6 +700,21 @@ def _pack_split_tensors_by_peer(
     return packed
 
 
+def _pack_split_tensor_by_peer(
+    *, tensor: torch.Tensor, splits: tuple[int, ...], input_layout: str
+) -> torch.Tensor:
+    rows = _peer_row_count(tensor, layout=input_layout)
+    if rows != int(sum(splits)):
+        raise RuntimeError(
+            f"Packed split consumed the wrong number of rows: {rows} != {sum(splits)}"
+        )
+    return (
+        tensor.movedim(1, 0).contiguous()
+        if input_layout == "head_major"
+        else tensor.contiguous()
+    )
+
+
 def _validate_peer_layout(layout: str, *, context: str) -> None:
     if layout not in {"token_major", "head_major"}:
         raise ValueError(f"Unsupported {context} layout: {layout}")
@@ -482,11 +725,12 @@ def _packed_peer_tensor_shape(
     tensor: torch.Tensor,
     total_rows: int,
     input_layout: str,
+    row_factor: int = 2,
 ) -> tuple[int, ...]:
     _validate_peer_layout(input_layout, context="peer tensor input")
     if input_layout == "head_major":
-        return (total_rows * 2, int(tensor.shape[0]), int(tensor.shape[2]))
-    return (total_rows * 2, *tuple(int(dim) for dim in tensor.shape[1:]))
+        return (total_rows * row_factor, int(tensor.shape[0]), int(tensor.shape[2]))
+    return (total_rows * row_factor, *tuple(int(dim) for dim in tensor.shape[1:]))
 
 
 def _peer_row_count(tensor: torch.Tensor, *, layout: str) -> int:
@@ -579,6 +823,19 @@ def _unpack_packed_tensor_per_peer(
     return left, right
 
 
+def _unpack_single_tensor(
+    packed_tensor: torch.Tensor,
+    *,
+    output_layout: str,
+) -> torch.Tensor:
+    _validate_peer_layout(output_layout, context="single-tensor output")
+    return (
+        packed_tensor.movedim(0, 1).contiguous()
+        if output_layout == "head_major"
+        else packed_tensor
+    )
+
+
 def _new_unpacked_peer_tensor(
     packed_tensor: torch.Tensor,
     *,
diff --git a/src/art/megatron/context_parallel/executor.py b/src/art/megatron/context_parallel/executor.py
index b595de990..4015011fe 100644
--- a/src/art/megatron/context_parallel/executor.py
+++ b/src/art/megatron/context_parallel/executor.py
@@ -31,7 +31,6 @@
 )
 from .types import (
     ArtContextParallelState,
-    AttnSlice,
     CpBlockMaskVariant,
     DkvReducePlan,
     ExactMaskMetadata,
@@ -671,6 +670,7 @@ def run(
         backend = flex_backend_for_head_dims(
             head_dim=int(q.shape[-1]),
             head_dim_v=int(v.shape[-1]),
+            device=q.device,
         )
         if compile_key is None:
             _q_len, _k_len, compile_key = select_sparse_execution_family(
@@ -688,6 +688,7 @@ def run(
                 head_dim=int(q.shape[-1]),
                 head_dim_v=int(v.shape[-1]),
                 triton_num_stages_2_head_dims=self.triton_num_stages_2_head_dims,
+                device=q.device,
             )
         )
         prepare_sparse_flex_attention(
diff --git a/src/art/megatron/context_parallel/runtime.py b/src/art/megatron/context_parallel/runtime.py
index 42b7585d5..b373e7c99 100644
--- a/src/art/megatron/context_parallel/runtime.py
+++ b/src/art/megatron/context_parallel/runtime.py
@@ -4,11 +4,14 @@
 from dataclasses import dataclass, replace
 import hashlib
 import json
+from threading import Lock
 from typing import Any, cast
 
+from pydantic import BaseModel
 import torch
 
 from art.loss import shift_tensor
+from art.megatron.selective_lm_head import LmHeadTokenSelection
 from art.preprocessing.pack import PackedTensors
 
 from .builder import build_prefix_tree_attention_spec
@@ -18,6 +21,7 @@
     AttnMaskKind,
     AttnSlice,
     ContextParallelConfig,
+    ContextParallelWorkloadProfile,
     CpBlockMaskVariant,
     DispatchedPackedTensors,
     DkvReducePlan,
@@ -30,6 +34,7 @@
     RankRuntimePlan,
     StagePlan,
     TokenRange,
+    TrainingMicrobatchWorkload,
 )
 
 _CHUNK_MASK_STATS_TORCH_THRESHOLD = 1024
@@ -39,18 +44,26 @@
 
 StagePiece = tuple[TokenRange, TokenRange, AttnMaskKind, int | None]
 StageSliceKey = tuple[int, int, int, int, int, str, int]
+ProfiledChunkPiece = tuple[int, int, int, int, int, int, str, int | None]
 
 
 @dataclass(frozen=True)
 class _PlanningBundle:
     spec: PackedBatchAttentionSpec
-    rank_plans: tuple[RankRuntimePlan, ...]
+    row_spec: PackedRowAttentionSpec
+    chunk_ranges: tuple[TokenRange, ...]
+    owners: tuple[int, ...]
+    wave_assignment: tuple[int, ...]
+    token_layout_index: TokenLayoutIndex
     gdn_execution_spec: Any | None = None
 
 
 _PLANNING_BUNDLE_CACHE: dict[str, _PlanningBundle] = {}
 _RUNTIME_PLAN_CACHE: dict[str, tuple[RankRuntimePlan, ...]] = {}
+_RANK_RUNTIME_PLAN_CACHE: dict[tuple[str, int], RankRuntimePlan] = {}
+_GDN_GLOBAL_DECISION_CACHE: dict[tuple[str, str], Any] = {}
 _GDN_RANK_PLAN_CACHE: dict[tuple[str, str, int | None, int, str], Any] = {}
+_PLAN_CACHE_LOCK = Lock()
 
 
 def _json_cache_key(payload: Any) -> str:
@@ -58,9 +71,10 @@ def _json_cache_key(payload: Any) -> str:
 
 
 def _cache_put(cache: dict[Any, Any], key: Any, value: Any) -> None:
-    if key not in cache and len(cache) >= _PLAN_CACHE_MAX_ENTRIES:
-        cache.pop(next(iter(cache)))
-    cache[key] = value
+    with _PLAN_CACHE_LOCK:
+        if key not in cache and len(cache) >= _PLAN_CACHE_MAX_ENTRIES:
+            cache.pop(next(iter(cache)))
+        cache[key] = value
 
 
 def _metadata_tensor_digest(tensor: torch.Tensor) -> str:
@@ -145,13 +159,21 @@ def _get_or_build_planning_bundle(
             group_ids_cpu,
             parent_ids_cpu,
         )
+    row_spec, chunk_ranges, owners, wave_assignment = _runtime_plan_assignment(
+        spec,
+        topology=topology,
+        config=config,
+    )
     bundle = _PlanningBundle(
         spec=spec,
-        rank_plans=get_or_build_runtime_plan(
-            spec,
-            topology=topology,
-            config=config,
-            original_seq_len=original_seq_len,
+        row_spec=row_spec,
+        chunk_ranges=chunk_ranges,
+        owners=owners,
+        wave_assignment=wave_assignment,
+        token_layout_index=_build_runtime_token_layout_index(
+            chunk_ranges=chunk_ranges,
+            owners=owners,
+            cp_size=max(int(topology.cp), 1),
         ),
         gdn_execution_spec=gdn_execution_spec,
     )
@@ -159,6 +181,48 @@ def _get_or_build_planning_bundle(
     return planning_key, bundle, group_ids_cpu, parent_ids_cpu
 
 
+def _get_or_build_bundle_rank_plan(
+    *,
+    planning_key: str,
+    bundle: _PlanningBundle,
+    original_seq_len: int,
+    target_rank: int,
+    block_size: int,
+) -> RankRuntimePlan:
+    """Materialize only the caller's rank plan at the host-ahead boundary.
+
+    Building every rank plan here scales CPU work with CP size, can exceed the
+    planning budget, and exposes planning after lookahead GPU work completes.
+    Each rank plan depends only on the shared assignment, so peer plans are not
+    part of this runtime boundary.
+    """
+    cache_key = (planning_key, int(target_rank))
+    cached = _RANK_RUNTIME_PLAN_CACHE.get(cache_key)
+    if cached is not None:
+        return cached
+    plan = _build_rank_runtime_plan(
+        row_spec=bundle.row_spec,
+        chunk_ranges=bundle.chunk_ranges,
+        owners=bundle.owners,
+        wave_assignment=bundle.wave_assignment,
+        token_layout_index=bundle.token_layout_index,
+        cp_size=len(bundle.token_layout_index.token_counts_by_rank),
+        original_seq_len=original_seq_len,
+        target_rank=target_rank,
+        block_size=block_size,
+    )
+    _cache_put(_RANK_RUNTIME_PLAN_CACHE, cache_key, plan)
+    return plan
+
+
+def _gdn_planner_config_cache_key(gdn_planner_config: Any | None) -> str:
+    return (
+        _json_cache_key(_dataclass_payload(gdn_planner_config))
+        if gdn_planner_config is not None
+        else ""
+    )
+
+
 def _gdn_rank_plan_cache_key(
     *,
     planning_key: str,
@@ -166,20 +230,47 @@ def _gdn_rank_plan_cache_key(
     gdn_planner_config: Any | None,
     device: torch.device,
 ) -> tuple[str, str, int | None, int, str]:
-    config_key = (
-        _json_cache_key(_dataclass_payload(gdn_planner_config))
-        if gdn_planner_config is not None
-        else ""
-    )
     return (
         planning_key,
         device.type,
         device.index,
         int(cp_rank),
-        config_key,
+        _gdn_planner_config_cache_key(gdn_planner_config),
     )
 
 
+def _plan_gdn_global_execution(
+    *,
+    planning_key: str,
+    bundle: _PlanningBundle,
+    topology: ParallelTopology,
+    gdn_planner_config: Any | None,
+) -> Any:
+    """Select one all-rank GDN decision without rank-local tensors."""
+    if bundle.gdn_execution_spec is None:
+        raise RuntimeError("GDN CP planning requires a parsed execution spec")
+    cache_key = (
+        planning_key,
+        _gdn_planner_config_cache_key(gdn_planner_config),
+    )
+    cached = _GDN_GLOBAL_DECISION_CACHE.get(cache_key)
+    if cached is not None:
+        return cached
+
+    from art.megatron.gdn.gdn_prefix_tree import (
+        build_gdn_global_execution_decision,
+    )
+
+    decision = build_gdn_global_execution_decision(
+        bundle.gdn_execution_spec,
+        cp_size=int(topology.cp),
+        attention_token_layout_index=bundle.token_layout_index,
+        planner_config=gdn_planner_config,
+    )
+    _cache_put(_GDN_GLOBAL_DECISION_CACHE, cache_key, decision)
+    return decision
+
+
 def _plan_gdn_rank_execution(
     *,
     planning_key: str,
@@ -201,14 +292,19 @@ def _plan_gdn_rank_execution(
     if cached is not None:
         return cached
 
-    from art.megatron.gdn.gdn_prefix_tree import build_gdn_rank_execution_plan
+    decision = _plan_gdn_global_execution(
+        planning_key=planning_key,
+        bundle=bundle,
+        topology=topology,
+        gdn_planner_config=gdn_planner_config,
+    )
+    from art.megatron.gdn.gdn_prefix_tree import materialize_gdn_rank_execution_plan
 
-    plan = build_gdn_rank_execution_plan(
+    plan = materialize_gdn_rank_execution_plan(
         bundle.gdn_execution_spec,
+        decision,
         device="cpu",
         cp_rank=int(cp_rank),
-        cp_size=int(topology.cp),
-        attention_token_layout_index=bundle.rank_plans[int(cp_rank)].token_layout_index,
         planner_config=gdn_planner_config,
     )
     _cache_put(_GDN_RANK_PLAN_CACHE, cache_key, plan)
@@ -277,24 +373,16 @@ def context_parallel_rank_model_token_counts(
             build_gdn_execution_spec=build_gdn_execution_spec,
         )
     )
-    attention_counts = tuple(
-        sum(int(length) for length in rank_plan.local_valid_lengths)
-        for rank_plan in bundle.rank_plans
-    )
+    attention_counts = bundle.token_layout_index.token_counts_by_rank
     if not build_gdn_execution_spec:
         return attention_counts
-    gdn_counts = tuple(
-        int(
-            _plan_gdn_rank_execution(
-                planning_key=planning_key,
-                bundle=bundle,
-                topology=topology,
-                cp_rank=cp_rank,
-                gdn_planner_config=gdn_planner_config,
-            ).gdn_token_count
-        )
-        for cp_rank in range(int(topology.cp))
+    decision = _plan_gdn_global_execution(
+        planning_key=planning_key,
+        bundle=bundle,
+        topology=topology,
+        gdn_planner_config=gdn_planner_config,
     )
+    gdn_counts = decision.gdn_token_counts_by_rank
     return tuple(
         max(attention_count, gdn_count)
         for attention_count, gdn_count in zip(attention_counts, gdn_counts, strict=True)
@@ -1232,7 +1320,7 @@ def _evaluate_plan(
     }
 
 
-def _search_chunk_assignment(
+def _search_generic_chunk_assignment(
     *,
     chunk_ranges: tuple[TokenRange, ...],
     pair_matrix: list[list[int]] | torch.Tensor,
@@ -1357,6 +1445,403 @@ def _evaluate_candidate(
     return best
 
 
+def _folded_chunk_assignment(
+    *,
+    weights: list[float],
+    cp_size: int,
+) -> tuple[int, ...]:
+    if len(weights) < 2 * cp_size:
+        return tuple()
+    slab_owners = _contiguous_chunk_assignment(
+        q_weights=weights,
+        cp_size=2 * cp_size,
+    )
+    return tuple(min(owner, 2 * cp_size - 1 - owner) for owner in slab_owners)
+
+
+def _ownership_range_counts(
+    owners: tuple[int, ...],
+    *,
+    cp_size: int,
+) -> tuple[int, ...]:
+    counts = [0 for _ in range(cp_size)]
+    previous = -1
+    for owner in owners:
+        if owner != previous:
+            counts[int(owner)] += 1
+        previous = int(owner)
+    return tuple(counts)
+
+
+def _rounded(value: int, multiple: int) -> int:
+    return ((int(value) + int(multiple) - 1) // int(multiple)) * int(multiple)
+
+
+def _stage_indexer_tile_pairs(
+    pieces: list[ProfiledChunkPiece],
+    *,
+    profile: ContextParallelWorkloadProfile,
+) -> int:
+    queries: dict[tuple[int, int], int] = {}
+    for (
+        _q_index,
+        _k_index,
+        q_start,
+        q_end,
+        k_start,
+        k_end,
+        _mask_kind,
+        _family,
+    ) in pieces:
+        query = (q_start, q_end)
+        queries[query] = queries.get(query, 0) + k_end - k_start
+
+    tile_pairs = 0
+    for (q_start, q_end), k_tokens in queries.items():
+        if k_tokens <= 0:
+            continue
+        k_chunk = min(k_tokens, int(profile.indexer_max_k_tokens))
+        q_chunk = max(1, int(profile.indexer_score_workspace_elements) // k_chunk)
+        rounded_q = sum(
+            _rounded(
+                min(q_chunk, q_end - start),
+                int(profile.query_tile_size),
+            )
+            for start in range(q_start, q_end, q_chunk)
+        )
+        rounded_k = sum(
+            _rounded(
+                min(k_chunk, k_tokens - start),
+                int(profile.key_tile_size),
+            )
+            for start in range(0, k_tokens, k_chunk)
+        )
+        tile_pairs += rounded_q * rounded_k
+    return tile_pairs
+
+
+def _intervals_size(intervals: list[tuple[int, int]]) -> int:
+    if not intervals:
+        return 0
+    ordered = sorted(set(intervals))
+    total = 0
+    current_start, current_end = ordered[0]
+    for start, end in ordered[1:]:
+        if start <= current_end:
+            current_end = max(current_end, end)
+        else:
+            total += current_end - current_start
+            current_start, current_end = start, end
+    return total + current_end - current_start
+
+
+def _profiled_chunk_pieces(
+    row_spec: PackedRowAttentionSpec,
+    *,
+    chunk_ranges: tuple[TokenRange, ...],
+) -> tuple[ProfiledChunkPiece, ...]:
+    pieces = []
+    chunk_starts = tuple(int(range_.start) for range_ in chunk_ranges)
+    chunk_ends = tuple(int(range_.end) for range_ in chunk_ranges)
+    for slice_ in row_spec.slices:
+        q_parts = _indexed_intersections(
+            slice_.q_range,
+            chunk_ranges,
+            candidate_starts=chunk_starts,
+            candidate_ends=chunk_ends,
+        )
+        k_parts = _indexed_intersections(
+            slice_.k_range,
+            chunk_ranges,
+            candidate_starts=chunk_starts,
+            candidate_ends=chunk_ends,
+        )
+        for q_index, q_piece in q_parts:
+            for k_index, k_piece in k_parts:
+                mask_kind = _resolve_stage_mask_kind(
+                    mask_kind=slice_.mask_kind,
+                    q_piece=q_piece,
+                    k_piece=k_piece,
+                )
+                if mask_kind is not None:
+                    pieces.append(
+                        (
+                            q_index,
+                            k_index,
+                            int(q_piece.start),
+                            int(q_piece.end),
+                            int(k_piece.start),
+                            int(k_piece.end),
+                            mask_kind.value,
+                            slice_.family_index,
+                        )
+                    )
+    return tuple(dict.fromkeys(pieces))
+
+
+def _profiled_rank_statistics(
+    *,
+    chunk_pieces: tuple[ProfiledChunkPiece, ...],
+    chunk_ranges: tuple[TokenRange, ...],
+    owners: tuple[int, ...],
+    wave_assignment: tuple[int, ...],
+    cp_size: int,
+    profile: ContextParallelWorkloadProfile,
+) -> list[dict[str, int]]:
+    wave_count = max(wave_assignment, default=0) + 1 if wave_assignment else 0
+    stages: list[list[list[ProfiledChunkPiece]]] = [
+        [[] for _ in range(wave_count + 1)] for _ in range(cp_size)
+    ]
+    recv_ranges: list[list[list[list[tuple[int, int]]]]] = [
+        [[[] for _ in range(cp_size)] for _ in range(wave_count)]
+        for _ in range(cp_size)
+    ]
+    send_ranges: list[list[list[list[tuple[int, int]]]]] = [
+        [[[] for _ in range(cp_size)] for _ in range(wave_count)]
+        for _ in range(cp_size)
+    ]
+    for piece in chunk_pieces:
+        q_index, k_index, _q_start, _q_end, k_start, k_end, _mask, _family = piece
+        destination = int(owners[q_index])
+        source = int(owners[k_index])
+        if source == destination:
+            stages[destination][0].append(piece)
+            continue
+        wave = int(wave_assignment[k_index])
+        stages[destination][wave + 1].append(piece)
+        interval = (k_start, k_end)
+        recv_ranges[destination][wave][source].append(interval)
+        send_ranges[source][wave][destination].append(interval)
+
+    query_tokens = [0 for _ in range(cp_size)]
+    for range_, owner in zip(chunk_ranges, owners, strict=True):
+        query_tokens[int(owner)] += int(range_.size())
+    statistics = []
+    for rank in range(cp_size):
+        combined_k_tokens = _intervals_size(
+            [(piece[4], piece[5]) for piece in stages[rank][0]]
+        )
+        recv_tokens = 0
+        send_tokens = 0
+        remote_peers: set[int] = set()
+        for wave_recv, wave_send in zip(
+            recv_ranges[rank],
+            send_ranges[rank],
+            strict=True,
+        ):
+            for peer, (peer_recv, peer_send) in enumerate(
+                zip(wave_recv, wave_send, strict=True)
+            ):
+                recv_size = _intervals_size(peer_recv)
+                send_size = _intervals_size(peer_send)
+                recv_tokens += recv_size
+                send_tokens += send_size
+                combined_k_tokens += recv_size
+                if peer != rank and (recv_size or send_size):
+                    remote_peers.add(peer)
+        statistics.append(
+            {
+                "query_tokens": query_tokens[rank],
+                "tile_pairs": sum(
+                    _stage_indexer_tile_pairs(pieces, profile=profile)
+                    for pieces in stages[rank]
+                ),
+                "combined_k_tokens": combined_k_tokens,
+                "fetch_send_tokens": send_tokens,
+                "fetch_recv_tokens": recv_tokens,
+                "remote_peers": len(remote_peers),
+            }
+        )
+    return statistics
+
+
+def _evaluate_profiled_assignment(
+    *,
+    chunk_pieces: tuple[ProfiledChunkPiece, ...],
+    chunk_ranges: tuple[TokenRange, ...],
+    owners: tuple[int, ...],
+    wave_assignment: tuple[int, ...],
+    cp_size: int,
+    profile: ContextParallelWorkloadProfile,
+) -> dict[str, Any]:
+    rank_stats = _profiled_rank_statistics(
+        chunk_pieces=chunk_pieces,
+        chunk_ranges=chunk_ranges,
+        owners=owners,
+        wave_assignment=wave_assignment,
+        cp_size=cp_size,
+        profile=profile,
+    )
+    query_flops = 0
+    indexer_flops = 0
+    hbm_k_bytes = 0
+    peak_memory_bytes = 0
+    fetch_send_bytes = 0
+    fetch_recv_bytes = 0
+    dkv_send_bytes = 0
+    dkv_recv_bytes = 0
+    for rank in rank_stats:
+        for stage in profile.stages:
+            query_flops = max(
+                query_flops,
+                rank["query_tokens"] * int(stage.query_flops_per_token),
+            )
+            indexer_flops = max(
+                indexer_flops,
+                rank["tile_pairs"] * int(stage.tile_pair_flops),
+            )
+            hbm_k_bytes = max(
+                hbm_k_bytes,
+                rank["combined_k_tokens"] * int(stage.k_hbm_bytes_per_token),
+            )
+            peak_memory_bytes = max(
+                peak_memory_bytes,
+                rank["query_tokens"] * int(stage.query_memory_bytes_per_token)
+                + rank["combined_k_tokens"] * int(stage.k_memory_bytes_per_token),
+            )
+            fetch_send_bytes = max(
+                fetch_send_bytes,
+                rank["fetch_send_tokens"] * int(stage.k_fetch_bytes_per_token),
+            )
+            fetch_recv_bytes = max(
+                fetch_recv_bytes,
+                rank["fetch_recv_tokens"] * int(stage.k_fetch_bytes_per_token),
+            )
+            dkv_send_bytes = max(
+                dkv_send_bytes,
+                rank["fetch_recv_tokens"] * int(stage.dkv_reduce_bytes_per_token),
+            )
+            dkv_recv_bytes = max(
+                dkv_recv_bytes,
+                rank["fetch_send_tokens"] * int(stage.dkv_reduce_bytes_per_token),
+            )
+
+    range_counts = _ownership_range_counts(owners, cp_size=cp_size)
+    max_network_bytes = max(
+        fetch_send_bytes,
+        fetch_recv_bytes,
+        dkv_send_bytes,
+        dkv_recv_bytes,
+    )
+    return {
+        "score": query_flops,
+        "query_flops": query_flops,
+        "indexer_flops": indexer_flops,
+        "hbm_k_bytes": hbm_k_bytes,
+        "peak_memory_bytes": peak_memory_bytes,
+        "max_network_bytes": max_network_bytes,
+        "fetch_send_bytes": fetch_send_bytes,
+        "fetch_recv_bytes": fetch_recv_bytes,
+        "dkv_send_bytes": dkv_send_bytes,
+        "dkv_recv_bytes": dkv_recv_bytes,
+        "max_remote_peers": max(
+            (rank["remote_peers"] for rank in rank_stats),
+            default=0,
+        ),
+        "max_ownership_ranges": max(range_counts, default=0),
+        "rank_query_tokens": tuple(rank["query_tokens"] for rank in rank_stats),
+        "rank_tile_pairs": tuple(rank["tile_pairs"] for rank in rank_stats),
+        "rank_combined_k_tokens": tuple(
+            rank["combined_k_tokens"] for rank in rank_stats
+        ),
+        "rank_fetch_send_tokens": tuple(
+            rank["fetch_send_tokens"] for rank in rank_stats
+        ),
+        "rank_fetch_recv_tokens": tuple(
+            rank["fetch_recv_tokens"] for rank in rank_stats
+        ),
+        "rank_remote_peers": tuple(rank["remote_peers"] for rank in rank_stats),
+        "ownership_range_counts": range_counts,
+    }
+
+
+def _profiled_assignment_key(
+    evaluation: dict[str, Any],
+    owners: tuple[int, ...],
+) -> tuple[Any, ...]:
+    # These terms retain their own physical units; they are never added together.
+    return (
+        int(evaluation["query_flops"]),
+        int(evaluation["max_network_bytes"]),
+        int(evaluation["hbm_k_bytes"]),
+        int(evaluation["indexer_flops"]),
+        int(evaluation["max_remote_peers"]),
+        int(evaluation["max_ownership_ranges"]),
+        owners,
+    )
+
+
+def _search_chunk_assignment(
+    *,
+    row_spec: PackedRowAttentionSpec | None = None,
+    chunk_ranges: tuple[TokenRange, ...],
+    pair_matrix: list[list[int]] | torch.Tensor,
+    q_weights: list[float],
+    cp_size: int,
+    config: ContextParallelConfig,
+) -> tuple[tuple[int, ...], tuple[int, ...], dict[str, Any]]:
+    generic = _search_generic_chunk_assignment(
+        chunk_ranges=chunk_ranges,
+        pair_matrix=pair_matrix,
+        q_weights=q_weights,
+        cp_size=cp_size,
+        config=config,
+    )
+    profile = config.workload_profile
+    if profile is None:
+        return generic
+    if row_spec is None:
+        raise RuntimeError("Profile-aware CP planning requires the packed row spec.")
+
+    chunk_pieces = _profiled_chunk_pieces(row_spec, chunk_ranges=chunk_ranges)
+    lengths = [float(range_.size()) for range_ in chunk_ranges]
+    candidates = [
+        generic[0],
+        _contiguous_chunk_assignment(q_weights=lengths, cp_size=cp_size),
+        _folded_chunk_assignment(weights=lengths, cp_size=cp_size),
+    ]
+    baseline = _evaluate_profiled_assignment(
+        chunk_pieces=chunk_pieces,
+        chunk_ranges=chunk_ranges,
+        owners=generic[0],
+        wave_assignment=generic[1],
+        cp_size=cp_size,
+        profile=profile,
+    )
+    best_owners = generic[0]
+    best_eval = baseline
+    seen = {generic[0]}
+    for owners in candidates[1:]:
+        if not owners or owners in seen:
+            continue
+        seen.add(owners)
+        if len(set(owners)) != cp_size:
+            continue
+        range_counts = _ownership_range_counts(owners, cp_size=cp_size)
+        if max(range_counts, default=0) > int(profile.max_ownership_ranges_per_rank):
+            continue
+        evaluation = _evaluate_profiled_assignment(
+            chunk_pieces=chunk_pieces,
+            chunk_ranges=chunk_ranges,
+            owners=owners,
+            wave_assignment=generic[1],
+            cp_size=cp_size,
+            profile=profile,
+        )
+        if int(evaluation["peak_memory_bytes"]) > int(
+            baseline["peak_memory_bytes"]
+        ) or _profiled_assignment_key(evaluation, owners) >= _profiled_assignment_key(
+            baseline, generic[0]
+        ):
+            continue
+        if _profiled_assignment_key(evaluation, owners) < _profiled_assignment_key(
+            best_eval, best_owners
+        ):
+            best_owners = owners
+            best_eval = evaluation
+    return best_owners, generic[1], best_eval
+
+
 def _flatten_ranges_by_peer(
     ranges_by_peer: tuple[tuple[TokenRange, ...], ...],
 ) -> tuple[TokenRange, ...]:
@@ -1707,6 +2192,9 @@ def prepare_cp_micro(
     block_mask_variants: tuple[CpBlockMaskVariant, ...] = (),
     target_device: torch.device | None = None,
     ref_logprobs: torch.Tensor | None = None,
+    model_support_handler: Any | None = None,
+    attention_head_dim: int | None = None,
+    attention_value_head_dim: int | None = None,
 ) -> PreparedMegatronBatch:
     """Prepare one CP microbatch with a CPU-only planning phase.
 
@@ -1715,6 +2203,10 @@ def prepare_cp_micro(
     `target_device`. Passing CUDA `group_ids` or `parent_ids` still works for
     older direct callers, but it reintroduces D2H syncs and invalidates the
     host-ahead/device-behind lookahead assumption.
+
+    Model-owned state is built exactly once here, after rank-local dispatch.
+    Its handler must only enqueue device work: scalar CUDA reads would expose
+    planning on the host and invalidate lookahead overlap.
     """
     state, rank_plan, spec, pad_multiple = prepare_megatron_context_parallel_state(
         micro=micro,
@@ -1727,7 +2219,7 @@ def prepare_cp_micro(
         block_mask_variants=block_mask_variants,
         target_device=target_device,
     )
-    tensors = dispatch_megatron_context_parallel_training_tensors(
+    tensors, workload = dispatch_megatron_context_parallel_training_tensors(
         micro=micro,
         rank_plan=rank_plan,
         spec=spec,
@@ -1737,6 +2229,23 @@ def prepare_cp_micro(
         cp_group=cp_group,
         ref_logprobs=ref_logprobs,
     )
+    if model_support_handler is not None:
+        from art.megatron.model_support.spec import PrefixTreeModelStateContext
+
+        state.model_state = dict(
+            model_support_handler.build_prefix_tree_model_state(
+                PrefixTreeModelStateContext(
+                    input_pos=tensors.input_pos,
+                    group_ids=micro["group_ids"],
+                    parent_ids=micro["parent_ids"],
+                    device=tensors.tokens.device,
+                    attention_token_layout_index=rank_plan.token_layout_index,
+                    attention_head_dim=attention_head_dim,
+                    attention_value_head_dim=attention_value_head_dim,
+                    context_parallel_state=state,
+                )
+            )
+        )
     if tensors.token_uids is not None:
         state = replace(state, trace_token_uids=tensors.token_uids)
     if prepare_execution_state:
@@ -1750,11 +2259,52 @@ def prepare_cp_micro(
         tensors=tensors,
         packed_seq_params=None,
         attention_state=state,
+        workload=workload,
         rank_plan=rank_plan,
         pad_multiple=pad_multiple,
     )
 
 
+def preplan_megatron_context_parallel_state(
+    *,
+    group_ids: torch.Tensor,
+    parent_ids: torch.Tensor,
+    original_seq_len: int,
+    topology: ParallelTopology,
+    config: ContextParallelConfig,
+    cp_rank: int,
+    build_gdn_execution_spec: bool = False,
+    gdn_planner_config: Any | None = None,
+) -> str:
+    """Warm one rank's immutable CPU plans without touching CUDA or collectives."""
+    if int(topology.cp) <= 1:
+        raise RuntimeError("context-parallel preplanning requires CP > 1")
+    planning_key, bundle, _group_ids, _parent_ids = _get_or_build_planning_bundle(
+        group_ids=group_ids,
+        parent_ids=parent_ids,
+        topology=topology,
+        config=config,
+        original_seq_len=original_seq_len,
+        build_gdn_execution_spec=build_gdn_execution_spec,
+    )
+    _get_or_build_bundle_rank_plan(
+        planning_key=planning_key,
+        bundle=bundle,
+        original_seq_len=original_seq_len,
+        target_rank=cp_rank,
+        block_size=int(config.block_size),
+    )
+    if build_gdn_execution_spec:
+        _plan_gdn_rank_execution(
+            planning_key=planning_key,
+            bundle=bundle,
+            topology=topology,
+            cp_rank=cp_rank,
+            gdn_planner_config=gdn_planner_config,
+        )
+    return planning_key
+
+
 def prepare_megatron_context_parallel_state(
     *,
     micro: PackedTensors,
@@ -1797,7 +2347,13 @@ def prepare_megatron_context_parallel_state(
         original_seq_len=int(micro["tokens"].shape[1]),
         build_gdn_execution_spec=build_gdn_execution_spec,
     )
-    rank_plan = bundle.rank_plans[int(cp_rank)]
+    rank_plan = _get_or_build_bundle_rank_plan(
+        planning_key=planning_key,
+        bundle=bundle,
+        original_seq_len=int(micro["tokens"].shape[1]),
+        target_rank=cp_rank,
+        block_size=int(config.block_size),
+    )
     gdn_execution_plan = None
     if build_gdn_execution_spec:
         _plan_gdn_rank_execution(
@@ -1842,7 +2398,7 @@ def dispatch_megatron_context_parallel_training_tensors(
     target_device: torch.device | None = None,
     cp_group: Any | None = None,
     ref_logprobs: torch.Tensor | None = None,
-) -> DispatchedPackedTensors:
+) -> tuple[DispatchedPackedTensors, TrainingMicrobatchWorkload]:
     """Gather this rank's training tensors and optionally move them to device.
 
     Dispatch may enqueue H2D copies when `target_device` is CUDA, but it must
@@ -1896,12 +2452,17 @@ def maybe_dispatch(
     ) -> torch.Tensor | None:
         return None if tensor is None else dispatch(tensor, pad_value)
 
+    local_labels = dispatch(labels, -100, move_to_target=False)
+    lm_head_selection = LmHeadTokenSelection.from_labels(
+        local_labels,
+        target_device=target_device,
+    )
     local_token_uids = (
         None if token_uids is None else dispatch(token_uids, -1, move_to_target=False)
     )
-    return DispatchedPackedTensors(
+    tensors = DispatchedPackedTensors(
         tokens=dispatch(micro["tokens"], 0),
-        labels=dispatch(labels, -100),
+        labels=_to_target_device(local_labels, target_device),
         input_pos=dispatch(micro["input_pos"], 0),
         assistant_mask=dispatch(assistant_mask, False).to(dtype=torch.bool),
         group_ids=dispatch(shifted_group_ids, 0),
@@ -1909,11 +2470,19 @@ def maybe_dispatch(
         advantages=dispatch(advantages, 0.0),
         weights=dispatch(weights, 0.0),
         valid_lengths=rank_plan.local_valid_lengths,
+        lm_head_selection=lm_head_selection,
         original_logprobs=maybe_dispatch(original_logprobs, 0.0),
         ref_logprobs=maybe_dispatch(ref_logprobs, float("nan")),
         loss_all_reduce_group=cp_group,
         token_uids=None if local_token_uids is None else local_token_uids.contiguous(),
     )
+    workload = TrainingMicrobatchWorkload(
+        logical_nonpadding_tokens=sum(rank_plan.local_valid_lengths),
+        loss_bearing_tokens=int((local_labels != -100).sum().item()),
+        executed_token_equivalents=int(local_labels.numel()),
+        nominal_schedule_capacity_tokens=rank_plan.original_seq_len,
+    )
+    return tensors, workload
 
 
 def get_or_build_runtime_plan(
@@ -1978,6 +2547,7 @@ def _runtime_plan_assignment(
         chunk_ranges=chunk_ranges,
     )
     owners, wave_assignment, _planner_eval = _search_chunk_assignment(
+        row_spec=row_spec,
         chunk_ranges=chunk_ranges,
         pair_matrix=pair_matrix,
         q_weights=q_weights,
@@ -2068,7 +2638,10 @@ def _runtime_plan_cache_key(
 
 
 def _dataclass_payload(value: Any) -> dict[str, Any]:
-    return dict(value.__dict__)
+    return {
+        key: (item.model_dump(mode="json") if isinstance(item, BaseModel) else item)
+        for key, item in value.__dict__.items()
+    }
 
 
 def _attn_slice_payload(slice_: AttnSlice) -> dict[str, Any]:
diff --git a/src/art/megatron/context_parallel/types.py b/src/art/megatron/context_parallel/types.py
index 0673101be..d55087759 100644
--- a/src/art/megatron/context_parallel/types.py
+++ b/src/art/megatron/context_parallel/types.py
@@ -5,9 +5,11 @@
 from typing import Any
 
 from megatron.core.packed_seq_params import PackedSeqParams
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, ConfigDict, Field
 import torch
 
+from art.megatron.selective_lm_head import LmHeadTokenSelection
+
 from .layout_index import TokenLayoutIndex
 from .loss_inputs import ContextParallelLossInputs
 
@@ -50,6 +52,34 @@ class PackedBatchAttentionSpec:
     rows: tuple[PackedRowAttentionSpec, ...]
 
 
+class ContextParallelStageWorkProfile(BaseModel):
+    """Architecture work assigned to one physical pipeline rank."""
+
+    model_config = ConfigDict(frozen=True)
+
+    physical_pipeline_rank: int = Field(ge=0)
+    query_flops_per_token: int = Field(ge=0)
+    tile_pair_flops: int = Field(ge=0)
+    k_hbm_bytes_per_token: int = Field(ge=0)
+    k_fetch_bytes_per_token: int = Field(ge=0)
+    dkv_reduce_bytes_per_token: int = Field(ge=0)
+    query_memory_bytes_per_token: int = Field(ge=0)
+    k_memory_bytes_per_token: int = Field(ge=0)
+
+
+class ContextParallelWorkloadProfile(BaseModel):
+    """Model-specific facts used to compare low-fragmentation CP layouts."""
+
+    model_config = ConfigDict(frozen=True)
+
+    stages: tuple[ContextParallelStageWorkProfile, ...] = Field(min_length=1)
+    query_tile_size: int = Field(gt=0)
+    key_tile_size: int = Field(gt=0)
+    indexer_score_workspace_elements: int = Field(gt=0)
+    indexer_max_k_tokens: int = Field(gt=0)
+    max_ownership_ranges_per_rank: int = Field(default=2, gt=0)
+
+
 @dataclass(frozen=True)
 class ContextParallelConfig:
     block_size: int = 128
@@ -73,6 +103,7 @@ class ContextParallelConfig:
     planner_remote_stage_token_floor: int = 4096
     planner_remote_stage_pair_floor: int = 4_000_000
     planner_remote_stage_underfill_ms: float = 0.287151
+    workload_profile: ContextParallelWorkloadProfile | None = None
 
 
 @dataclass(frozen=True)
@@ -142,12 +173,35 @@ class DispatchedPackedTensors(ContextParallelLossInputs):
     advantages: torch.Tensor
     weights: torch.Tensor
     valid_lengths: tuple[int, ...]
+    lm_head_selection: LmHeadTokenSelection
     original_logprobs: torch.Tensor | None = None
     ref_logprobs: torch.Tensor | None = None
     loss_all_reduce_group: Any | None = None
     token_uids: torch.Tensor | None = None
 
 
+class TrainingMicrobatchWorkload(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    logical_nonpadding_tokens: int = Field(ge=0)
+    loss_bearing_tokens: int = Field(ge=0)
+    executed_token_equivalents: int = Field(ge=0)
+    nominal_schedule_capacity_tokens: int = Field(ge=0)
+
+
+class TrainingStepWorkload(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    logical_nonpadding_tokens: int = Field(ge=0)
+    loss_bearing_tokens: int = Field(ge=0)
+    executed_token_equivalents: int = Field(ge=0)
+    nominal_schedule_capacity_tokens: int = Field(ge=0)
+    dummy_executed_token_equivalents: int = Field(ge=0)
+    dummy_schedule_capacity_tokens: int = Field(ge=0)
+    real_microbatches: int = Field(ge=0)
+    dummy_microbatches: int = Field(ge=0)
+
+
 @dataclass
 class ContextParallelExecutionCache:
     block_mask_context: Any | None = None
@@ -181,6 +235,7 @@ class ArtContextParallelState:
     group_ids: torch.Tensor
     parent_ids: torch.Tensor
     input_pos: torch.Tensor
+    model_state: dict[str, Any] = field(default_factory=dict)
     block_mask_variants: tuple[CpBlockMaskVariant, ...] = ()
     gdn_execution_spec: Any | None = None
     gdn_execution_plan: Any | None = None
@@ -203,6 +258,7 @@ class ArtContextParallelState:
 class PreparedMegatronBatch:
     tensors: DispatchedPackedTensors
     attention_state: Any
+    workload: TrainingMicrobatchWorkload
     packed_seq_params: PackedSeqParams | None = None
     rank_plan: RankRuntimePlan | None = None
     pad_multiple: int = 1
diff --git a/src/art/megatron/distributed_service.py b/src/art/megatron/distributed_service.py
new file mode 100644
index 000000000..07b8a3fd4
--- /dev/null
+++ b/src/art/megatron/distributed_service.py
@@ -0,0 +1,2754 @@
+from __future__ import annotations
+
+import asyncio
+from collections.abc import AsyncIterator, Awaitable, Callable
+from contextlib import asynccontextmanager, nullcontext
+import hashlib
+from itertools import groupby
+import json
+import logging
+import os
+from pathlib import Path
+import shutil
+import time
+from typing import Any, Literal, TypedDict, cast
+import uuid
+
+import httpx
+
+from art import dev, types
+from art.adapter_leases import in_flight_lora_name
+from art.dev.get_model_config import default_target_modules
+from art.distributed.art_runtime import ArtRuntime, DistributedPackedBatch
+from art.distributed.specs import ModelServiceSpec, NixlTransportSpec, TrainerMeshSpec
+from art.distributed.vllm_replica import (
+    ReplicaFailure,
+    ReplicaLaunchTemplate,
+    ReplicaUpdateReport,
+)
+from art.serving_capabilities import (
+    ServingCapabilities,
+    discover_serving_capabilities,
+)
+from art.utils.lifecycle import (
+    complete_task,
+    complete_to_thread,
+    consume_future_exception,
+)
+from art.utils.output_dirs import get_step_checkpoint_dir
+from art.vllm_runtime import (
+    get_external_vllm_runtime_config,
+    map_checkpoint_path_for_vllm,
+    normalize_vllm_server_url,
+    wait_for_vllm_http_runtime,
+)
+
+from .identity_lora import create_identity_lora
+from .lora_config import LORA_ALPHA, default_lora_rank_for_handler
+from .migrations import optimizer_state_path
+from .model_support import (
+    get_model_support_handler,
+    get_model_support_handler_for_spec,
+    get_model_support_spec,
+    model_uses_expert_parallel,
+)
+from .optimizer_state import (
+    CheckpointFile,
+    OptimizerAdapter,
+    adapter_generation_lease,
+    commit_optimizer_policy_advance,
+    format_megatron_resume_message,
+    new_optimizer_generation,
+    optimizer_adapter,
+    prepare_megatron_resume_state,
+    publish_adapter_checkpoint,
+    read_adapter_publication,
+    read_committed_optimizer_pointer,
+    resolve_committed_optimizer_policy,
+)
+from .runtime.data_plane import SFTBatchData
+from .runtime.publication import (
+    DurableTrainerPublication,
+    TrainerRankPublication,
+    commit_trainer_publication,
+)
+from .runtime.specs import (
+    AdapterReady,
+    CurrentSFTConfig,
+    CurrentTrainConfig,
+    DurableTrainOutput,
+    ExperimentalTrainConfig,
+    HybridEpRuntimeSpec,
+    ResidentLoraInspectionResult,
+    ResidentLoraInspectionSpec,
+    ResidentScoreJobSpec,
+    ResidentScoreResult,
+    SFTJobSpec,
+    TrainAccepted,
+    TrainCancelled,
+    TrainCompleted,
+    TrainerGeneration,
+    TrainerJobSpec,
+    TrainerRuntimeSpec,
+    TrainFailed,
+    TrainingRunSpec,
+    TrainJobSpec,
+    TrainProgress,
+)
+from .runtime_config import get_megatron_runtime_config
+
+logger = logging.getLogger(__name__)
+_POLICY_TIMING_HISTORY = 64
+
+
+class _TrainerJobFields(TypedDict):
+    job_id: str
+    run_id: str
+    training_session_id: str
+    expected_learner_version: int
+    learner_version: int
+    source: TrainerGeneration
+    output: DurableTrainOutput
+    publication_targets: tuple[Any, ...]
+
+
+async def _post_vllm(
+    url: str,
+    *,
+    api_key: str | None,
+    timeout_s: float = 30.0,
+    **kwargs: Any,
+) -> httpx.Response:
+    async with httpx.AsyncClient(timeout=timeout_s) as client:
+        return await client.post(url, headers=_headers(api_key), **kwargs)
+
+
+def _retire_completed(
+    records: dict[int, Any], step: int, completed: asyncio.Future[Any]
+) -> None:
+    if records.get(step) is completed:
+        records.pop(step)
+
+
+def _hybrid_ep_runtime_spec(
+    mesh: TrainerMeshSpec,
+    *,
+    run_id: str,
+    transport: NixlTransportSpec | None,
+) -> HybridEpRuntimeSpec | None:
+    if mesh.topology.ep <= 1:
+        return None
+    group_size = mesh.topology.etp * mesh.topology.ep
+    domain_sizes: set[int] = set()
+    multinode = False
+    for offset in range(0, len(mesh.ranks), group_size):
+        group = mesh.ranks[offset : offset + group_size]
+        domains = [
+            (host_id, tuple(ranks))
+            for host_id, ranks in groupby(group, key=lambda rank: rank.host_id)
+        ]
+        if len({host_id for host_id, _ in domains}) != len(domains):
+            raise ValueError("HybridEP ranks for each host must be contiguous")
+        domain_sizes.update(len(ranks) for _, ranks in domains)
+        multinode |= len(domains) > 1
+    if len(domain_sizes) != 1:
+        raise ValueError(
+            "HybridEP TP x EP groups require equal ranks per NVLink domain"
+        )
+    if multinode and transport is None:
+        raise ValueError("cross-host expert parallelism requires NIXL transport")
+    return HybridEpRuntimeSpec(
+        ranks_per_nvlink_domain=domain_sizes.pop(),
+        run_id=run_id,
+        nixl_transport=transport if multinode else None,
+    )
+
+
+class DistributedMegatronService:
+    """One model's durable checkpoints and run-scoped distributed runtimes."""
+
+    propagate_close_errors = True
+    close_timeout_s = 300.0
+
+    def __init__(
+        self,
+        *,
+        model_name: str,
+        base_model: str,
+        config: dev.BackendModelConfig,
+        output_dir: str,
+        runtime: ArtRuntime,
+        enable_expert_replay: bool,
+    ) -> None:
+        self.model_name = model_name
+        self.base_model = base_model
+        self.config = config
+        self.output_dir = output_dir
+        self.runtime = runtime
+        self.enable_expert_replay = enable_expert_replay
+        self._latest_step = 0
+        self._serving_step = 0
+        self._durable_step = 0
+        self._durable_optimizer_step = 0
+        self._resume_prepared = False
+        self._training_session_id = uuid.uuid4().hex
+        self._learner_generation: TrainerGeneration | None = None
+        self._trainer_resident_generation: TrainerGeneration | None = None
+        self._trainer: Any = None
+        self._trainer_preparation_task: asyncio.Task[None] | None = None
+        self._trainer_preparation_step: asyncio.Future[int] | None = None
+        self._trainer_preparation_s = 0.0
+        # Nested acquisitions must follow train -> serving -> mutation.
+        self._train_lock = asyncio.Lock()
+        self._mutation_lock = asyncio.Lock()
+        self._serving_lock = asyncio.Lock()
+        self._durability_lock = asyncio.Lock()
+        self._pipeline_train_dispatch: asyncio.Event | None = None
+        self._managed_service_name: str | None = None
+        self._base_url: str | None = None
+        self._serving_capabilities: ServingCapabilities | None = None
+        self._api_key_value: str | None = None
+        self._current_lora_name: str | None = None
+        self._vllm_sleeping = False
+        self._published_adapters: dict[int, OptimizerAdapter] = {}
+        self._loaded_adapter_steps: set[int] = set()
+        self._loaded_exact_adapter_steps: set[int] = set()
+        self._exact_adapter_refcounts: dict[int, int] = {}
+        self._recovery_tasks: set[asyncio.Task[None]] = set()
+        self._publication_tasks: dict[int, asyncio.Task[None]] = {}
+        self._durability_tasks: set[asyncio.Task[Any]] = set()
+        self._prepared_adapter_transfers: dict[str, Any] = {}
+        self._loaded_adapter_transfers: dict[int, tuple[Any, str]] = {}
+        self._next_publication_preparation: (
+            tuple[
+                Any,
+                TrainerGeneration,
+                asyncio.Task[tuple[Any, ...]],
+            ]
+            | None
+        ) = None
+        self._serving_futures: dict[int, asyncio.Future[None]] = {}
+        self._publication_failure: BaseException | None = None
+        self._publication_metrics: dict[int, dict[str, float]] = {}
+        self._emitted_publication_metrics: dict[int, set[str]] = {}
+        self._trainer_completion_times: dict[int, float] = {}
+        self._serving_activation_times: dict[int, float] = {}
+        self._close_task: asyncio.Task[None] | None = None
+        self._closed = False
+
+    @property
+    def openai_server_port(self) -> int:
+        return self._model_service_spec().leader_endpoint.port
+
+    def arm_pipeline_train_dispatch(self, event: asyncio.Event) -> None:
+        if self._pipeline_train_dispatch is not None:
+            raise RuntimeError("pipeline trainer dispatch fence is already armed")
+        self._pipeline_train_dispatch = event
+
+    def cancel_pipeline_train_dispatch(self, event: asyncio.Event) -> None:
+        if self._pipeline_train_dispatch is event:
+            self._pipeline_train_dispatch = None
+
+    def _take_pipeline_train_dispatch(self) -> asyncio.Event | None:
+        event = self._pipeline_train_dispatch
+        self._pipeline_train_dispatch = None
+        return event
+
+    @property
+    def active_learner_step(self) -> int:
+        return self._latest_step
+
+    @property
+    def serving_step(self) -> int:
+        return self._serving_step
+
+    @property
+    def durable_step(self) -> int:
+        return self._durable_step
+
+    @property
+    def durable_optimizer_step(self) -> int:
+        return self._durable_optimizer_step
+
+    def drain_publication_metrics(self) -> dict[str, float]:
+        metrics = {
+            "publication/active_learner_serving_lag_steps": float(
+                self._latest_step - self._serving_step
+            ),
+            "publication/durable_optimizer_lag_steps": float(
+                self._latest_step - self._durable_optimizer_step
+            ),
+            "publication/queue_depth": float(
+                sum(not task.done() for task in self._publication_tasks.values())
+            ),
+        }
+        for step in sorted(self._publication_metrics):
+            values = self._publication_metrics[step]
+            emitted = self._emitted_publication_metrics.setdefault(step, set())
+            for name, value in values.items():
+                if name not in emitted:
+                    metrics[f"publication/{name}"] = value
+            emitted.update(values)
+            task = self._publication_tasks.get(step)
+            if task is None or task.done():
+                self._publication_metrics.pop(step, None)
+                self._emitted_publication_metrics.pop(step, None)
+        return metrics
+
+    async def finalize_publication_metrics(self, step: int) -> dict[str, float]:
+        async with self._mutation_lock:
+            self._require_open()
+            if step != self._latest_step:
+                raise ValueError(
+                    f"final publication step {step} != learner step {self._latest_step}"
+                )
+            publication = self._publication_tasks.get(step)
+        if publication is not None:
+            await asyncio.shield(publication)
+        self._raise_publication_failure()
+        return self.drain_publication_metrics()
+
+    async def wait_for_serving(self, step: int) -> None:
+        async with self._mutation_lock:
+            self._require_open()
+            self._raise_publication_failure()
+            if step < 0 or step > self._latest_step:
+                raise ValueError(
+                    f"serving step {step} is outside learner lineage 0..{self._latest_step}"
+                )
+            serving = self._serving_futures.get(step)
+        async with self._serving_lock:
+            if step <= self._serving_step:
+                return
+            if serving is None:
+                raise RuntimeError(f"learner step {step} has no serving publication")
+        await asyncio.shield(serving)
+        self._raise_publication_failure()
+
+    def policy_activation_timing(self, step: int) -> tuple[float, float]:
+        try:
+            return (
+                self._trainer_completion_times[step],
+                self._serving_activation_times[step],
+            )
+        except KeyError as error:
+            raise RuntimeError(
+                f"policy {step} lacks an authoritative trainer/serving timestamp"
+            ) from error
+
+    @staticmethod
+    def _record_policy_timestamp(history: dict[int, float], step: int) -> None:
+        history[step] = time.monotonic()
+        while len(history) > _POLICY_TIMING_HISTORY:
+            history.pop(next(iter(history)))
+
+    def _record_serving_activation(self, step: int) -> None:
+        if (
+            step in self._trainer_completion_times
+            and step not in self._serving_activation_times
+        ):
+            self._record_policy_timestamp(self._serving_activation_times, step)
+
+    def checkpoint_materialization(self, step: int) -> asyncio.Task[None]:
+        self._require_open()
+        self._raise_publication_failure()
+        generation = self._learner_generation
+        if generation is None or generation.policy_step != step:
+            raise RuntimeError(
+                f"learner generation {step} is unavailable for materialization"
+            )
+
+        async def wait() -> None:
+            publication = self._publication_tasks.get(step)
+            if publication is not None:
+                await asyncio.shield(publication)
+            if step not in self._published_adapters:
+                raise RuntimeError(f"learner generation {step} is not materialized")
+
+        task = asyncio.create_task(wait())
+        task.add_done_callback(consume_future_exception)
+        return task
+
+    @property
+    def rollout_weight_update_mode(self) -> str:
+        return self.config.get("rollout_weight_update_mode", "step_lora")
+
+    @property
+    def _temporal_gpu_sharing(self) -> bool:
+        return (
+            get_external_vllm_runtime_config(self.config) is None
+            and self._model_service_spec().temporal_gpu_sharing
+        )
+
+    def _serving_lora_name(self, step: int) -> str:
+        if self.rollout_weight_update_mode == "in_flight_lora":
+            return in_flight_lora_name(self.model_name)
+        return f"{self.model_name}@{step}"
+
+    @property
+    def _allow_unvalidated_arch(self) -> bool:
+        return bool(self.config.get("allow_unvalidated_arch", False))
+
+    def _require_open(self) -> None:
+        if self._closed:
+            raise RuntimeError("distributed model service is closed")
+
+    def _trainer_is_current(self) -> bool:
+        return (
+            self._trainer is not None
+            and self._trainer.valid
+            and self._trainer.learner_version == self._latest_step
+        )
+
+    def _resident_trainer_for_generation(
+        self, generation: TrainerGeneration
+    ) -> Any | None:
+        if (
+            self._trainer_is_current()
+            and self._trainer_resident_generation == generation
+        ):
+            return self._trainer
+        return None
+
+    @property
+    def _optimizer_state_path(self) -> str:
+        path = optimizer_state_path(self.output_dir)
+        os.makedirs(path, exist_ok=True)
+        return path
+
+    def _lora_config(self) -> dev.LoRAConfig:
+        return cast(dev.LoRAConfig, self.config.get("lora_config") or {})
+
+    def _random_state(self) -> int | None:
+        for key in ("lora_config", "init_args"):
+            value = self.config.get(key, {}).get("random_state")
+            if value is not None:
+                return int(value)
+        return None
+
+    @property
+    def _model_identifier(self) -> str:
+        value = self.config.get("init_args", {}).get("model_name", self.base_model)
+        if not isinstance(value, str) or not value:
+            raise ValueError("init_args.model_name must be a non-empty string")
+        return value
+
+    def _resolve_current_lora_path(self) -> str:
+        if self._trainer_is_current():
+            if self._learner_generation is None:
+                raise RuntimeError("resident trainer has no learner generation")
+            self._resume_prepared = True
+            return self._learner_generation.adapter_path
+        resume = prepare_megatron_resume_state(
+            output_dir=self.output_dir,
+            optimizer_state_path=self._optimizer_state_path,
+        )
+        print(format_megatron_resume_message(resume))
+        self._latest_step = resume.step
+        self._published_adapters = {
+            step: adapter
+            for step, adapter in self._published_adapters.items()
+            if step <= resume.step
+        }
+        path = get_step_checkpoint_dir(self.output_dir, self._latest_step)
+        if not (Path(path) / "adapter_model.safetensors").is_file():
+            if self._latest_step != 0:
+                raise RuntimeError(
+                    f"committed adapter is missing for step {self._latest_step}"
+                )
+            lora = self._lora_config()
+            handler = get_model_support_handler(
+                self.base_model,
+                allow_unvalidated_arch=self._allow_unvalidated_arch,
+            )
+            create_identity_lora(
+                self._model_identifier,
+                path,
+                rank=lora.get("rank"),
+                target_modules=lora.get("target_modules"),
+                random_state=self._random_state(),
+                allow_unvalidated_arch=self._allow_unvalidated_arch,
+                handler=handler,
+            )
+        if self._latest_step == 0:
+            adapter = optimizer_adapter(
+                path,
+                0,
+                training_session_id=self._training_session_id,
+            )
+        else:
+            policy = resolve_committed_optimizer_policy(
+                self._optimizer_state_path,
+                initial_adapter_path=get_step_checkpoint_dir(self.output_dir, 0),
+            )
+            adapter = policy.policy_adapter
+            if adapter.step != self._latest_step:
+                raise RuntimeError("resume policy and checkpoint step disagree")
+            self._training_session_id = adapter.training_session_id
+        self._published_adapters[self._latest_step] = adapter
+        self._learner_generation = TrainerGeneration(
+            training_session_id=adapter.training_session_id,
+            policy_step=adapter.step,
+            generation_id=adapter.generation_id,
+            adapter_path=adapter.identity,
+        )
+        self._durable_step = resume.step
+        self._durable_optimizer_step = resume.optimizer_step or 0
+        self._resume_prepared = True
+        return adapter.identity
+
+    def _runtime_spec(self) -> TrainerRuntimeSpec:
+        mesh = self.runtime.topology.trainer
+        if mesh is None:
+            raise RuntimeError("ART runtime has no trainer mesh")
+        runtime_config = get_megatron_runtime_config()
+        if runtime_config.topology != mesh.topology:
+            raise ValueError(
+                "Megatron runtime topology does not match the ART trainer mesh"
+            )
+        lora = self._lora_config()
+        support_spec = get_model_support_spec(
+            self.base_model,
+            allow_unvalidated_arch=self._allow_unvalidated_arch,
+        )
+        handler = get_model_support_handler_for_spec(support_spec)
+        targets = lora.get("target_modules") or default_target_modules(self.base_model)
+        revision = str(self.config.get("init_args", {}).get("revision") or "default")
+        compile_enabled = os.environ.get(
+            "ART_DISABLE_MEGATRON_COMPILE", "0"
+        ).lower() not in {"1", "true", "yes", "on"}
+        hybrid_ep = _hybrid_ep_runtime_spec(
+            mesh,
+            run_id=self.runtime.runtime_id,
+            transport=self.runtime.nixl_transport,
+        )
+        identity = {
+            "art": _art_source_revision(),
+            "model": self._model_identifier,
+            "support_model": self.base_model,
+            "revision": revision,
+            "handler": handler.key,
+            "mesh": mesh.model_dump(mode="json"),
+            "model_initialization": self.config.get(
+                "megatron_model_initialization", "pretrained"
+            ),
+        }
+        return TrainerRuntimeSpec(
+            art_revision=identity["art"],
+            model_identifier=self._model_identifier,
+            model_revision=revision,
+            model_initialization=identity["model_initialization"],
+            cache_root=self.runtime.topology.cluster.cache_root,
+            model_support_key=support_spec.key,
+            handler_name=handler.key,
+            lora_rank=int(lora.get("rank") or default_lora_rank_for_handler(handler)),
+            lora_alpha=float(lora.get("alpha", LORA_ALPHA)),
+            lora_target_modules=tuple(targets),
+            dtype=_trainer_dtype(self.config),
+            trainer_mesh=mesh,
+            packed_sequence_length=runtime_config.packed_sequence_length,
+            snapshot_pool_capacity=runtime_config.snapshot_pool_capacity,
+            compile_enabled=compile_enabled,
+            compile_cache=runtime_config.compile_cache and compile_enabled,
+            compile_fingerprint=_digest({**identity, "compile": compile_enabled}),
+            optimizer_layout_fingerprint=_digest(
+                {"mesh": mesh.model_dump(mode="json")}
+            ),
+            allow_unvalidated_arch=self._allow_unvalidated_arch,
+            enable_moe_routing_replay=self.enable_expert_replay
+            and model_uses_expert_parallel(
+                self.base_model,
+                allow_unvalidated_arch=self._allow_unvalidated_arch,
+            ),
+            streaming_weight_offload=runtime_config.streaming_weight_offload,
+            offload_between_jobs=self._temporal_gpu_sharing,
+            random_state=self._random_state(),
+            hybrid_ep=hybrid_ep,
+        )
+
+    async def _ensure_trainer_locked(self) -> tuple[Any, tuple[int, str] | None]:
+        if self._trainer_is_current():
+            return self._trainer, None
+        current, reconcile_step = await self._prepare_for_packing_locked()
+        assert self._trainer is None
+        self._trainer = await self._launch_trainer(current)
+        self._trainer_resident_generation = None
+        reconcile = None if reconcile_step is None else (reconcile_step, current)
+        return self._trainer, reconcile
+
+    async def _launch_trainer(self, current: str) -> Any:
+        runtime_spec = self._runtime_spec()
+        run_spec = TrainingRunSpec(
+            run_id=uuid.uuid4().hex,
+            runtime_fingerprint=runtime_spec.fingerprint,
+            training_session_id=self._training_session_id,
+            initial_learner_version=self._latest_step,
+            initial_adapter_path=current,
+            optimizer_state_path=self._optimizer_state_path,
+            initial_event_timeout_s=self.runtime.topology.cluster.startup_timeout_s,
+        )
+        return await self.runtime.start_trainer(runtime_spec, run_spec)
+
+    def prefetch_trainer(self) -> None:
+        if (
+            self._trainer_is_current()
+            or self._trainer_preparation_task is not None
+            or self._temporal_gpu_sharing
+        ):
+            return
+        self._require_open()
+        source_step = asyncio.get_running_loop().create_future()
+        source_step.add_done_callback(consume_future_exception)
+        task = asyncio.create_task(self._prepare_trainer(source_step))
+        task.add_done_callback(consume_future_exception)
+        self._trainer_preparation_step = source_step
+        self._trainer_preparation_task = task
+
+    async def _prepare_trainer(self, source_step: asyncio.Future[int]) -> None:
+        started = time.perf_counter()
+        trainer: Any = None
+        assigned = False
+        try:
+            async with self._mutation_lock:
+                self._raise_publication_failure()
+                current, reconcile_step = await self._prepare_for_packing_locked()
+                step = self._latest_step
+                source_step.set_result(step)
+            trainer = await self._launch_trainer(current)
+            async with self._mutation_lock:
+                if self._trainer is not None:
+                    raise RuntimeError("trainer appeared during background preparation")
+                self._trainer = trainer
+                trainer = None
+                assigned = True
+                self._trainer_resident_generation = None
+                if self._latest_step != step:
+                    raise RuntimeError("learner changed during trainer preparation")
+            if reconcile_step is not None and self._base_url is not None:
+                async with self._serving_lock:
+                    await self._reconcile_serving_locked(reconcile_step, current)
+        except BaseException as error:
+            if not source_step.done():
+                source_step.set_exception(error)
+            if trainer is not None:
+                try:
+                    _, interrupted = await complete_task(
+                        asyncio.create_task(self.runtime.stop_trainer(trainer))
+                    )
+                except BaseException as cleanup_error:
+                    raise BaseExceptionGroup(
+                        "trainer preparation and cleanup failed",
+                        [error, cleanup_error],
+                    ) from None
+                if interrupted is not None:
+                    error.add_note("trainer preparation cleanup observed cancellation")
+            elif assigned:
+                await self._cleanup_failed_trainer_transaction(
+                    self._trainer, None, error
+                )
+            raise
+        finally:
+            self._trainer_preparation_s = time.perf_counter() - started
+
+    async def _await_trainer_preparation(self) -> None:
+        task = self._trainer_preparation_task
+        if task is None:
+            return
+        await asyncio.shield(task)
+        if self._trainer_preparation_task is task:
+            self._trainer_preparation_task = None
+            self._trainer_preparation_step = None
+
+    async def prepare_for_packing(self) -> int:
+        self._require_open()
+        self._raise_publication_failure()
+        if (source_step := self._trainer_preparation_step) is not None:
+            return await asyncio.shield(source_step)
+        async with self._train_lock:
+            async with self._mutation_lock:
+                _current, reconcile_step = await self._prepare_for_packing_locked()
+                step = self._latest_step
+            if reconcile_step is not None:
+                async with self._serving_lock:
+                    await self._reconcile_serving_locked(step, _current)
+            return step
+
+    async def prepare_cp_lookahead(
+        self,
+        batch: DistributedPackedBatch,
+        *,
+        global_grad_accumulation_sequences: int | None,
+    ) -> dict[str, float]:
+        mesh = self.runtime.topology.trainer
+        if mesh is None or mesh.topology.cp <= 1:
+            return {}
+        await self._await_trainer_preparation()
+        async with self._mutation_lock:
+            self._require_open()
+            self._raise_publication_failure()
+            trainer = self._trainer
+            if trainer is None or not self._trainer_is_current():
+                raise RuntimeError("CP lookahead requires the current resident trainer")
+        return await trainer.prepare_cp_lookahead(
+            batch.leases,
+            global_grad_accumulation_sequences=(global_grad_accumulation_sequences),
+        )
+
+    async def _prepare_for_packing_locked(self) -> tuple[str, int | None]:
+        if self._trainer_is_current():
+            return self._resolve_current_lora_path(), None
+        if self._trainer is not None:
+            _, cancelled = await complete_task(
+                asyncio.create_task(self.runtime.stop_trainer(self._trainer))
+            )
+            self._trainer = None
+            self._trainer_resident_generation = None
+            if cancelled is not None:
+                raise cancelled
+        previous_step = self._latest_step
+        current, cancelled = await complete_to_thread(self._resolve_current_lora_path)
+        if cancelled is not None:
+            raise cancelled
+        reconcile_step = (
+            self._latest_step if self._latest_step != previous_step else None
+        )
+        return current, reconcile_step
+
+    async def _reconcile_serving_locked(self, step: int, checkpoint: str) -> None:
+        if self._base_url is None:
+            self._serving_step = step
+            return
+        previous_name = self._current_lora_name
+        await self._register_lora_for_step_locked(step, checkpoint)
+        invalid_exact = {
+            step
+            for step in self._loaded_exact_adapter_steps
+            if step > self._serving_step
+        }
+        for step in sorted(invalid_exact):
+            name = (
+                f"{self.model_name}:eval@{step}"
+                if self.rollout_weight_update_mode == "in_flight_lora"
+                else f"{self.model_name}@{step}"
+            )
+            await self._unload_adapter(name)
+            self._loaded_exact_adapter_steps.discard(step)
+            self._exact_adapter_refcounts.pop(step, None)
+        for step in sorted(
+            step for step in self._loaded_adapter_steps if step > self._serving_step
+        ):
+            await self._unload_adapter(f"{self.model_name}@{step}")
+            await self._release_loaded_adapter_transfer(step)
+            self._loaded_adapter_steps.discard(step)
+        if previous_name == f"{self.model_name}:active" and previous_name != (
+            self._current_lora_name
+        ):
+            assert previous_name is not None
+            await self._unload_adapter(previous_name)
+
+    @asynccontextmanager
+    async def _trainer_transaction(
+        self,
+        trainer: Any,
+        job: TrainerJobSpec,
+        start: Callable[[], AsyncIterator[Any]],
+    ) -> AsyncIterator[AsyncIterator[Any]]:
+        cold = self._trainer_resident_generation != job.source
+        source = self._published_adapters.get(job.source.policy_step) if cold else None
+        if cold and (
+            source is None
+            or source.training_session_id != job.source.training_session_id
+            or source.generation_id != job.source.generation_id
+            or source.identity != str(Path(job.source.adapter_path).absolute())
+        ):
+            raise RuntimeError("cold trainer source generation is not registered")
+        with adapter_generation_lease(source) if source is not None else nullcontext():
+            events: AsyncIterator[Any] | None = None
+            try:
+                events = start()
+                yield events
+                close = getattr(events, "aclose", None)
+                if close is not None:
+                    await close()
+            except BaseException as error:
+                await self._cleanup_failed_trainer_transaction(trainer, events, error)
+                raise
+
+    async def _cleanup_failed_trainer_transaction(
+        self,
+        trainer: Any,
+        events: AsyncIterator[Any] | None,
+        primary: BaseException,
+    ) -> None:
+        async def cleanup() -> None:
+            failures: list[BaseException] = []
+            try:
+                await self._discard_next_publication_preparation()
+            except BaseException as error:
+                failures.append(error)
+            try:
+                await self._release_prepared_adapter_transfers()
+            except BaseException as error:
+                failures.append(error)
+            close = None if events is None else getattr(events, "aclose", None)
+            if close is not None:
+                try:
+                    await close()
+                except BaseException as error:
+                    failures.append(error)
+            try:
+                await self._invalidate_trainer_and_restore_serving(trainer)
+            except BaseException as error:
+                failures.append(error)
+            if failures:
+                raise BaseExceptionGroup("failed trainer job cleanup failed", failures)
+
+        try:
+            _, interrupted = await complete_task(asyncio.create_task(cleanup()))
+        except BaseException as cleanup_error:
+            raise BaseExceptionGroup(
+                "trainer job and cancellation-safe cleanup failed",
+                [primary, cleanup_error],
+            ) from None
+        if interrupted is not None:
+            primary.add_note("trainer cleanup completed after another cancellation")
+
+    async def _release_prepared_adapter_transfers(self) -> None:
+        prepared = tuple(self._prepared_adapter_transfers.items())
+        results = await asyncio.gather(
+            *(
+                manager.release_adapter_transfer(generation_id)
+                for generation_id, manager in prepared
+            ),
+            return_exceptions=True,
+        )
+        failures = []
+        for (generation_id, manager), result in zip(prepared, results, strict=True):
+            if isinstance(result, BaseException):
+                failures.append(result)
+            elif self._prepared_adapter_transfers.get(generation_id) is manager:
+                self._prepared_adapter_transfers.pop(generation_id)
+        if failures:
+            raise BaseExceptionGroup(
+                "prepared adapter transfer cleanup failed", failures
+            )
+
+    async def _discard_next_publication_preparation(self) -> None:
+        prepared, self._next_publication_preparation = (
+            self._next_publication_preparation,
+            None,
+        )
+        if prepared is None:
+            return
+        _, generation, task = prepared
+        task.cancel()
+        await asyncio.gather(task, return_exceptions=True)
+        manager = self._prepared_adapter_transfers.pop(generation.generation_id, None)
+        if manager is not None:
+            await manager.release_adapter_transfer(generation.generation_id)
+
+    async def _release_adapter_transfer(
+        self,
+        manager: Any,
+        generation_id: str,
+        primary: BaseException | None = None,
+    ) -> asyncio.CancelledError | None:
+        try:
+            _, interrupted = await complete_task(
+                asyncio.create_task(manager.release_adapter_transfer(generation_id))
+            )
+        except BaseException as cleanup_error:
+            if primary is not None:
+                raise BaseExceptionGroup(
+                    "adapter transfer and cleanup failed", [primary, cleanup_error]
+                ) from None
+            raise
+        return interrupted
+
+    async def _release_loaded_adapter_transfer(self, step: int) -> None:
+        transfer = self._loaded_adapter_transfers.get(step)
+        if transfer is None:
+            return
+        manager, generation_id = transfer
+        interrupted = await self._release_adapter_transfer(manager, generation_id)
+        if self._loaded_adapter_transfers.get(step) == transfer:
+            self._loaded_adapter_transfers.pop(step)
+        if interrupted is not None:
+            raise interrupted
+
+    async def _release_loaded_adapter_transfers(self) -> None:
+        results = await asyncio.gather(
+            *(
+                self._release_loaded_adapter_transfer(step)
+                for step in tuple(self._loaded_adapter_transfers)
+            ),
+            return_exceptions=True,
+        )
+        failures = [result for result in results if isinstance(result, BaseException)]
+        if failures:
+            raise BaseExceptionGroup("loaded adapter transfer cleanup failed", failures)
+
+    @asynccontextmanager
+    async def _trainer_failure_boundary(self) -> AsyncIterator[None]:
+        try:
+            yield
+        except BaseException as error:
+            await self._cleanup_failed_trainer_transaction(self._trainer, None, error)
+            raise
+
+    async def _invalidate_trainer_and_restore_serving(self, trainer: Any) -> None:
+        failures: list[BaseException] = []
+        async with self._mutation_lock:
+            owned = trainer is not None and self._trainer is trainer
+            if owned:
+                self._trainer = None
+                self._trainer_resident_generation = None
+        if owned:
+            try:
+                await self.runtime.stop_trainer(trainer)
+            except BaseException as error:
+                failures.append(error)
+        if self._temporal_gpu_sharing and self._vllm_sleeping:
+            async with self._serving_lock:
+                if self._vllm_sleeping:
+                    try:
+                        await self._wake_for_serving_locked()
+                    except BaseException as error:
+                        failures.append(error)
+                        service_name = self._managed_service_name
+                        if service_name is not None:
+                            failures.extend(
+                                await self._rollback_server_start_safely(service_name)
+                            )
+                        self._clear_serving_state()
+        if len(failures) == 1:
+            raise failures[0]
+        if failures:
+            raise BaseExceptionGroup("failed trainer invalidation failed", failures)
+
+    async def _trainer_metrics(
+        self,
+        job: TrainerJobSpec,
+        events: AsyncIterator[Any],
+    ) -> AsyncIterator[tuple[bool, dict[str, float]]]:
+        snapshot_prepared = False
+        completed = False
+        final_metrics: dict[str, float] | None = None
+        async for event in events:
+            if event.job_id != job.job_id or event.run_id != job.run_id:
+                raise RuntimeError("trainer returned an event for a different job")
+            if isinstance(event, TrainAccepted):
+                continue
+            if isinstance(event, TrainProgress):
+                if event.step_index + 1 == event.num_steps:
+                    final_metrics = dict(event.metrics)
+                    self._record_policy_timestamp(
+                        self._trainer_completion_times, job.learner_version
+                    )
+                else:
+                    yield False, dict(event.metrics)
+                continue
+            if isinstance(event, AdapterReady):
+                if snapshot_prepared:
+                    raise RuntimeError("trainer returned duplicate snapshot events")
+                if (
+                    event.learner_version != job.learner_version
+                    or event.adapter_path != job.output_adapter_path
+                ):
+                    raise RuntimeError("trainer prepared the wrong generation")
+                snapshot_prepared = True
+                continue
+            if isinstance(event, TrainCompleted):
+                if completed or not snapshot_prepared:
+                    raise RuntimeError("trainer completed without one snapshot")
+                if event.learner_version != job.learner_version:
+                    raise RuntimeError("trainer completed the wrong learner")
+                snapshot_metrics = {
+                    name: value
+                    for name, value in event.metrics.items()
+                    if name.startswith("snapshot_")
+                }
+                self._publication_metrics[job.learner_version] = snapshot_metrics
+                self._emitted_publication_metrics[job.learner_version] = set(
+                    snapshot_metrics
+                )
+                final_metrics = dict(event.metrics)
+                completed = True
+                continue
+            if isinstance(event, TrainFailed):
+                raise RuntimeError(
+                    f"distributed Megatron job failed ({event.error_type}): "
+                    f"{event.message}"
+                )
+            if isinstance(event, TrainCancelled):
+                raise asyncio.CancelledError(event.reason)
+        if not snapshot_prepared or not completed or final_metrics is None:
+            raise RuntimeError("trainer ended without preparing a generation")
+        yield True, final_metrics
+
+    async def _prepare_serving_publication(
+        self,
+        trainer: Any,
+        generation_id: str,
+    ) -> tuple[Any, ...]:
+        if self._managed_service_name is None:
+            return ()
+        manager = self.runtime.model_service(self._managed_service_name)
+        trainer_host = trainer.runtime_spec.trainer_mesh.ranks[0].host_id
+        inference_hosts = {member.host_id for member in manager.spec.members}
+        try:
+            targets = await manager.prepare_adapter_transfer(
+                generation_id,
+                get_step_checkpoint_dir(self.output_dir, 0),
+                transport="local" if inference_hosts == {trainer_host} else "nixl",
+            )
+            if not targets:
+                raise RuntimeError("model service returned no adapter transfer targets")
+        except BaseException as error:
+            interrupted = await self._release_adapter_transfer(
+                manager, generation_id, error
+            )
+            if interrupted is not None:
+                error.add_note("adapter transfer cleanup observed cancellation")
+            raise
+        self._prepared_adapter_transfers[generation_id] = manager
+        return targets
+
+    def _training_generation(self, step: int) -> TrainerGeneration:
+        return TrainerGeneration(
+            training_session_id=self._training_session_id,
+            policy_step=step,
+            generation_id=new_optimizer_generation(step),
+            adapter_path=get_step_checkpoint_dir(self.output_dir, step),
+        )
+
+    async def _take_publication_preparation(
+        self, trainer: Any, step: int
+    ) -> tuple[TrainerGeneration, tuple[Any, ...]]:
+        prepared, self._next_publication_preparation = (
+            self._next_publication_preparation,
+            None,
+        )
+        if prepared is not None:
+            prepared_trainer, generation, task = prepared
+            if prepared_trainer is trainer and generation.policy_step == step:
+                return generation, await task
+            self._next_publication_preparation = prepared
+            await self._discard_next_publication_preparation()
+        generation = self._training_generation(step)
+        targets = await self._prepare_serving_publication(
+            trainer, generation.generation_id
+        )
+        return generation, targets
+
+    def _prefetch_publication_preparation(self, trainer: Any, step: int) -> None:
+        if self._managed_service_name is None:
+            return
+        if self._next_publication_preparation is not None:
+            raise RuntimeError("next publication preparation already exists")
+        generation = self._training_generation(step)
+        previous_serving = self._serving_futures.get(step - 2)
+
+        async def prepare() -> tuple[Any, ...]:
+            if previous_serving is not None:
+                await asyncio.shield(previous_serving)
+            return await self._prepare_serving_publication(
+                trainer, generation.generation_id
+            )
+
+        task = asyncio.create_task(prepare())
+        task.add_done_callback(consume_future_exception)
+        self._next_publication_preparation = trainer, generation, task
+
+    async def _run_train_job(
+        self,
+        build_job: Callable[[_TrainerJobFields], TrainerJobSpec],
+        start_job: Callable[[Any, TrainerJobSpec], AsyncIterator[Any]],
+        *,
+        lineage_error: str,
+        wait_for_serving: bool = False,
+    ) -> AsyncIterator[dict[str, float]]:
+        trainer_prepare_started = time.perf_counter()
+        await self._await_trainer_preparation()
+        trainer_prepare_wait_s = time.perf_counter() - trainer_prepare_started
+        lock_started = time.perf_counter()
+        async with self._train_lock:
+            lock_wait_s = time.perf_counter() - lock_started
+            setup_started = time.perf_counter()
+            async with self._trainer_failure_boundary():
+                if self._temporal_gpu_sharing and self._base_url is not None:
+                    previous = self._serving_futures.get(self._latest_step)
+                    if previous is not None:
+                        await asyncio.shield(previous)
+                    async with self._serving_lock:
+                        await self._sleep_for_training_locked()
+                async with self._mutation_lock:
+                    self._require_open()
+                    self._raise_publication_failure()
+                    trainer, reconcile = await self._ensure_trainer_locked()
+                    source = self._learner_generation
+                    if source is None:
+                        raise RuntimeError("trainer has no source generation")
+                    next_step = self._latest_step + 1
+
+                preparation_started = time.perf_counter()
+                (
+                    output_generation,
+                    publication_targets,
+                ) = await self._take_publication_preparation(trainer, next_step)
+                preparation_wait_s = time.perf_counter() - preparation_started
+
+                async with self._mutation_lock:
+                    self._require_open()
+                    self._raise_publication_failure()
+                    if self._trainer is not trainer or not self._trainer_is_current():
+                        raise RuntimeError(
+                            "trainer changed while preparing serving publication"
+                        )
+                    if (
+                        self._learner_generation != source
+                        or self._latest_step != next_step - 1
+                    ):
+                        raise RuntimeError(lineage_error)
+                    output = DurableTrainOutput(
+                        generation=output_generation,
+                        staging_adapter_path=(
+                            f"{self.output_dir}/megatron_runtime/staging/"
+                            f"{output_generation.generation_id}"
+                        ),
+                        optimizer_state_path=self._optimizer_state_path,
+                    )
+                    job = build_job(
+                        _TrainerJobFields(
+                            job_id=uuid.uuid4().hex,
+                            run_id=trainer.run_spec.run_id,
+                            training_session_id=self._training_session_id,
+                            expected_learner_version=self._latest_step,
+                            learner_version=next_step,
+                            source=source,
+                            output=output,
+                            publication_targets=publication_targets,
+                        )
+                    )
+
+                if reconcile is not None:
+                    reconcile_step, checkpoint = reconcile
+                    async with self._serving_lock:
+                        await self._reconcile_serving_locked(reconcile_step, checkpoint)
+                self._prefetch_publication_preparation(trainer, next_step + 1)
+            setup_s = time.perf_counter() - setup_started
+
+            final_metrics: dict[str, float] | None = None
+            async with self._trainer_transaction(
+                trainer, job, lambda: start_job(trainer, job)
+            ) as events:
+                async for final, metrics in self._trainer_metrics(job, events):
+                    if final:
+                        final_metrics = metrics
+                    else:
+                        yield metrics
+            assert final_metrics is not None
+
+            commit_started = time.perf_counter()
+            async with self._trainer_failure_boundary():
+                async with self._mutation_lock:
+                    if self._latest_step != job.expected_learner_version:
+                        raise RuntimeError(lineage_error)
+                    self._latest_step = next_step
+                    self._learner_generation = output_generation
+                    self._trainer_resident_generation = output_generation
+                    self._schedule_publication(
+                        output_generation,
+                        trainer=trainer,
+                        publication_targets=job.publication_targets,
+                    )
+            commit_s = time.perf_counter() - commit_started
+            if wait_for_serving:
+                await self.wait_for_serving(next_step)
+            final_metrics.update(
+                {
+                    "time/step_service_lock_wait_s": lock_wait_s,
+                    "time/step_service_trainer_prepare_s": (
+                        self._trainer_preparation_s
+                    ),
+                    "time/step_service_trainer_prepare_wait_s": (
+                        trainer_prepare_wait_s
+                    ),
+                    "time/step_service_job_setup_s": setup_s,
+                    "time/step_service_publication_prepare_wait_s": (
+                        preparation_wait_s
+                    ),
+                    "time/step_service_generation_commit_s": commit_s,
+                }
+            )
+            yield final_metrics
+
+    async def train_packed(
+        self,
+        batch: DistributedPackedBatch,
+        config: types.TrainConfig,
+        experimental_config: dev.TrainConfig,
+    ) -> AsyncIterator[dict[str, float]]:
+        def build_job(fields: _TrainerJobFields) -> TrainerJobSpec:
+            values = {
+                key: value
+                for key, value in experimental_config.items()
+                if key in ExperimentalTrainConfig.model_fields and value is not None
+            }
+            return TrainJobSpec(
+                **fields,
+                batch=batch.leases.ref,
+                config=CurrentTrainConfig.model_validate(config.model_dump()),
+                experimental_config=ExperimentalTrainConfig.model_validate(values),
+            )
+
+        dispatch_event = self._take_pipeline_train_dispatch()
+        async for metrics in self._run_train_job(
+            build_job,
+            lambda trainer, job: trainer.train(
+                job,
+                batch.leases,
+                on_dispatched=dispatch_event.set
+                if dispatch_event is not None
+                else None,
+            ),
+            lineage_error="learner lineage changed during training",
+        ):
+            yield metrics
+
+    def _require_resident_score_locked(
+        self,
+        expected_learner_version: int,
+    ) -> tuple[Any, TrainerGeneration]:
+        self._require_open()
+        self._raise_publication_failure()
+        source = self._learner_generation
+        trainer = self._resident_trainer_for_generation(source) if source else None
+        if expected_learner_version != self._latest_step:
+            raise ValueError(
+                "resident diagnostic learner version mismatch: "
+                f"request={expected_learner_version}, current={self._latest_step}"
+            )
+        if (
+            source is None
+            or source.policy_step != expected_learner_version
+            or trainer is None
+        ):
+            raise RuntimeError(
+                "resident scoring requires the exact hydrated warm trainer generation"
+            )
+        return trainer, source
+
+    def _require_resident_inspection_locked(
+        self,
+        expected_learner_version: int,
+    ) -> tuple[Any, TrainerGeneration]:
+        self._require_open()
+        self._raise_publication_failure()
+        source = self._learner_generation
+        trainer = self._trainer
+        if expected_learner_version != self._latest_step:
+            raise ValueError(
+                "resident inspection learner version mismatch: "
+                f"request={expected_learner_version}, current={self._latest_step}"
+            )
+        if (
+            source is None
+            or source.policy_step != expected_learner_version
+            or trainer is None
+            or not self._trainer_is_current()
+            or trainer.run_spec.training_session_id != source.training_session_id
+            or self._trainer_resident_generation not in (None, source)
+        ):
+            raise RuntimeError(
+                "resident inspection requires the exact current warm trainer run"
+            )
+        return trainer, source
+
+    async def score_resident_packed(
+        self,
+        batch: DistributedPackedBatch,
+        *,
+        expected_learner_version: int,
+        global_grad_accumulation_sequences: int,
+        top_k: int = 20,
+    ) -> ResidentScoreResult:
+        await self._await_trainer_preparation()
+        async with self._train_lock:
+            async with self._mutation_lock:
+                trainer, source = self._require_resident_score_locked(
+                    expected_learner_version
+                )
+                job = ResidentScoreJobSpec(
+                    job_id=uuid.uuid4().hex,
+                    run_id=trainer.run_spec.run_id,
+                    learner=source,
+                    batch=batch.leases.ref,
+                    global_grad_accumulation_sequences=(
+                        global_grad_accumulation_sequences
+                    ),
+                    top_k=top_k,
+                )
+
+            try:
+                if self._temporal_gpu_sharing and self._base_url is not None:
+                    serving = self._serving_futures.get(expected_learner_version)
+                    if serving is not None:
+                        await asyncio.shield(serving)
+                    async with self._serving_lock:
+                        await self._sleep_for_training_locked()
+                async with self._trainer_failure_boundary():
+                    result = await trainer.score(job, batch.leases)
+                    async with self._mutation_lock:
+                        current_trainer, current_source = (
+                            self._require_resident_score_locked(
+                                expected_learner_version
+                            )
+                        )
+                        if current_trainer is not trainer or current_source != source:
+                            raise RuntimeError(
+                                "resident learner generation changed during scoring"
+                            )
+                    if result.learner != source:
+                        raise RuntimeError(
+                            "resident score returned a different learner generation"
+                        )
+                    if result.expected_score_count != batch.loss_bearing_tokens:
+                        raise RuntimeError(
+                            "resident score target coverage differs from packed data"
+                        )
+                    return result
+            finally:
+                if self._temporal_gpu_sharing and self._vllm_sleeping:
+                    async with self._serving_lock:
+                        await self._wake_for_serving_locked()
+
+    async def inspect_resident_lora(
+        self,
+        *,
+        expected_learner_version: int,
+    ) -> ResidentLoraInspectionResult:
+        await self._await_trainer_preparation()
+        async with self._train_lock:
+            trainer: Any = None
+            try:
+                if self._temporal_gpu_sharing and self._base_url is not None:
+                    serving = self._serving_futures.get(expected_learner_version)
+                    if serving is not None:
+                        await asyncio.shield(serving)
+                    async with self._serving_lock:
+                        await self._sleep_for_training_locked()
+                async with self._mutation_lock:
+                    trainer, reconcile = await self._ensure_trainer_locked()
+                    source_trainer, source = self._require_resident_inspection_locked(
+                        expected_learner_version
+                    )
+                    if source_trainer is not trainer:
+                        raise RuntimeError(
+                            "resident inspection selected another trainer"
+                        )
+                    request = ResidentLoraInspectionSpec(
+                        request_id=uuid.uuid4().hex,
+                        run_id=trainer.run_spec.run_id,
+                        learner=source,
+                        target_modules=trainer.runtime_spec.lora_target_modules,
+                    )
+                if reconcile is not None:
+                    reconcile_step, checkpoint = reconcile
+                    async with self._serving_lock:
+                        await self._reconcile_serving_locked(reconcile_step, checkpoint)
+                result = await trainer.inspect_resident_lora(request)
+                async with self._mutation_lock:
+                    current_trainer, current_source = (
+                        self._require_resident_inspection_locked(
+                            expected_learner_version
+                        )
+                    )
+                    if current_trainer is not trainer or current_source != source:
+                        raise RuntimeError(
+                            "resident learner generation changed during LoRA inspection"
+                        )
+                if result.learner != source:
+                    raise RuntimeError(
+                        "resident LoRA inspection returned another learner generation"
+                    )
+                return result
+            except BaseException as error:
+                if trainer is not None and not trainer.valid:
+                    await self._cleanup_failed_trainer_transaction(trainer, None, error)
+                raise
+            finally:
+                if self._temporal_gpu_sharing and self._vllm_sleeping:
+                    async with self._serving_lock:
+                        await self._wake_for_serving_locked()
+
+    def _schedule_publication(
+        self,
+        generation: TrainerGeneration,
+        *,
+        trainer: Any = None,
+        durable: DurableTrainerPublication | None = None,
+        publication_targets: tuple[Any, ...] = (),
+    ) -> None:
+        if (trainer is None) == (durable is None):
+            raise ValueError(
+                "publication requires exactly one trainer stream or durable result"
+            )
+        step = generation.policy_step
+        if step in self._publication_tasks:
+            raise RuntimeError(f"generation publication already exists for step {step}")
+        transfer_manager = self._prepared_adapter_transfers.get(
+            generation.generation_id
+        )
+        if publication_targets and transfer_manager is None:
+            raise RuntimeError("adapter transfer publication is not prepared")
+        publication_waiter = (
+            trainer.wait_for_publication(generation.generation_id)
+            if trainer is not None
+            else None
+        )
+        loop = asyncio.get_running_loop()
+        previous = self._serving_futures.get(step - 1)
+        if previous is None:
+            previous = loop.create_future()
+            previous.set_result(None)
+        serving = loop.create_future()
+        serving.add_done_callback(consume_future_exception)
+        self._serving_futures[step] = serving
+        serving.add_done_callback(
+            lambda done: _retire_completed(self._serving_futures, step, done)
+        )
+        previous_publication = self._publication_tasks.get(step - 1)
+        publication = (
+            self._publish_generation(
+                generation,
+                durable=durable,
+                publication_waiter=publication_waiter,
+                previous_publication=previous_publication,
+                publication_targets=publication_targets,
+                transfer_manager=transfer_manager,
+                previous_serving=previous,
+                serving=serving,
+            )
+            if publication_targets
+            else self._publish_generation(
+                generation,
+                durable=durable,
+                publication_waiter=publication_waiter,
+                previous_publication=previous_publication,
+                previous_serving=previous,
+                serving=serving,
+            )
+        )
+        task = asyncio.create_task(publication)
+        self._publication_tasks[step] = task
+        self._prepared_adapter_transfers.pop(generation.generation_id, None)
+        task.add_done_callback(consume_future_exception)
+        task.add_done_callback(
+            lambda done: _retire_completed(self._publication_tasks, step, done)
+        )
+
+    async def _resolve_durable_publication(
+        self,
+        generation: TrainerGeneration,
+        *,
+        durable: DurableTrainerPublication | None,
+        publication_waiter: Awaitable[tuple[TrainerRankPublication, ...]] | None,
+        previous_publication: asyncio.Task[None] | None,
+    ) -> tuple[DurableTrainerPublication, float]:
+        started = time.monotonic()
+        records = (
+            asyncio.ensure_future(publication_waiter)
+            if publication_waiter is not None
+            else None
+        )
+        if records is not None:
+            records.add_done_callback(consume_future_exception)
+        try:
+            if previous_publication is not None:
+                await asyncio.shield(previous_publication)
+            if records is not None:
+                rank_publications = await asyncio.shield(records)
+                async with self._durability_lock:
+                    durable = await asyncio.to_thread(
+                        commit_trainer_publication,
+                        self._optimizer_state_path,
+                        generation,
+                        rank_publications,
+                    )
+        finally:
+            if records is not None and not records.done():
+                records.cancel()
+        return cast(DurableTrainerPublication, durable), time.monotonic() - started
+
+    async def _publish_generation(
+        self,
+        generation: TrainerGeneration,
+        *,
+        durable: DurableTrainerPublication | None,
+        publication_waiter: Awaitable[tuple[TrainerRankPublication, ...]] | None,
+        previous_publication: asyncio.Task[None] | None,
+        publication_targets: tuple[Any, ...] = (),
+        transfer_manager: Any = None,
+        previous_serving: asyncio.Future[None],
+        serving: asyncio.Future[None],
+    ) -> None:
+        metrics = self._publication_metrics.setdefault(generation.policy_step, {})
+        manager = transfer_manager
+        transfer_owned = manager is not None
+        durable_task = asyncio.create_task(
+            self._resolve_durable_publication(
+                generation,
+                durable=durable,
+                publication_waiter=publication_waiter,
+                previous_publication=previous_publication,
+            )
+        )
+        self._durability_tasks.add(durable_task)
+        durable_task.add_done_callback(consume_future_exception)
+        durable_task.add_done_callback(self._durability_tasks.discard)
+        try:
+            materialization_started = time.monotonic()
+            if manager is None:
+                durable_result, _ = await asyncio.shield(durable_task)
+                adapter = durable_result.adapter
+                checkpoint = generation.adapter_path
+                metrics["adapter_materialization_s"] = (
+                    time.monotonic() - materialization_started
+                )
+            else:
+                received = await manager.wait_adapter_transfer(generation.generation_id)
+                if len(received) != len(publication_targets):
+                    raise RuntimeError("Not every inference host received the adapter")
+                paths = {result.path for result in received}
+                sizes = {
+                    (result.tensor_bytes, result.config_bytes) for result in received
+                }
+                if len(paths) != 1 or len(sizes) != 1:
+                    raise RuntimeError(
+                        "Inference hosts materialized different adapters"
+                    )
+                tensor_bytes, config_bytes = sizes.pop()
+                checkpoint = paths.pop()
+                adapter = OptimizerAdapter(
+                    identity=str(Path(generation.adapter_path).absolute()),
+                    training_session_id=generation.training_session_id,
+                    step=generation.policy_step,
+                    generation_id=generation.generation_id,
+                    files=(
+                        CheckpointFile(
+                            name="adapter_config.json", size_bytes=config_bytes
+                        ),
+                        CheckpointFile(
+                            name="adapter_model.safetensors", size_bytes=tensor_bytes
+                        ),
+                    ),
+                )
+                metrics["adapter_transport_wait_s"] = (
+                    time.monotonic() - materialization_started
+                )
+                metrics["adapter_transport_bytes"] = float(tensor_bytes * len(received))
+                metrics["adapter_materialization_s"] = max(
+                    result.materialization_s for result in received
+                )
+                metrics["adapter_transport_pool_wait_s"] = max(
+                    result.pool_wait_s for result in received
+                )
+                metrics["adapter_transport_prepare_s"] = max(
+                    result.prepare_s for result in received
+                )
+                metrics["adapter_transport_registration_s"] = max(
+                    result.registration_s for result in received
+                )
+                metrics["adapter_transport_sender_staging_s"] = max(
+                    result.sender_staging_s for result in received
+                )
+                metrics["adapter_transport_sender_registration_s"] = max(
+                    result.sender_registration_s for result in received
+                )
+                metrics["adapter_transport_capacity_bytes"] = float(
+                    sum(result.capacity_bytes for result in received)
+                )
+                metrics["adapter_transport_capacity_utilization"] = sum(
+                    result.used_bytes for result in received
+                ) / sum(result.capacity_bytes for result in received)
+            await previous_serving
+            self._raise_publication_failure()
+            activation_started = time.monotonic()
+            async with self._mutation_lock:
+                self._published_adapters[generation.policy_step] = adapter
+            async with self._serving_lock:
+                await self._register_lora_for_step_locked(
+                    generation.policy_step,
+                    checkpoint,
+                )
+                if (
+                    manager is not None
+                    and self.rollout_weight_update_mode != "in_flight_lora"
+                ):
+                    self._loaded_adapter_transfers[generation.policy_step] = (
+                        manager,
+                        generation.generation_id,
+                    )
+                    transfer_owned = False
+            metrics["serving_activation_s"] = time.monotonic() - activation_started
+            if manager is None and not serving.done():
+                serving.set_result(None)
+            if manager is not None and transfer_owned:
+                interrupted = await self._release_adapter_transfer(
+                    manager, generation.generation_id
+                )
+                transfer_owned = False
+                if interrupted is not None:
+                    raise interrupted
+            if manager is not None:
+                if not serving.done():
+                    serving.set_result(None)
+
+            durable_result, durable_s = await asyncio.shield(durable_task)
+            if durable_result.adapter != adapter:
+                raise RuntimeError("Durable and serving adapter manifests differ")
+            async with self._mutation_lock:
+                self._durable_step = max(self._durable_step, durable_result.resume_step)
+                self._durable_optimizer_step = max(
+                    self._durable_optimizer_step, durable_result.optimizer_step
+                )
+                metrics["durable_checkpoint_s"] = durable_s
+                metrics["durable_checkpoint_lag_steps"] = float(
+                    self._latest_step - self._durable_optimizer_step
+                )
+            logger.info(
+                "Published trainer generation session=%s step=%d generation=%s "
+                "launch=%.3fs activate=%.3fs durable=%.3fs durable_lag=%d",
+                generation.training_session_id,
+                generation.policy_step,
+                generation.generation_id,
+                metrics["snapshot_launch_s"],
+                metrics["serving_activation_s"],
+                metrics["durable_checkpoint_s"],
+                self._latest_step - self._durable_optimizer_step,
+            )
+        except BaseException as error:
+            if manager is not None and transfer_owned:
+                try:
+                    interrupted = await self._release_adapter_transfer(
+                        manager, generation.generation_id, error
+                    )
+                except BaseException as cleanup_error:
+                    error = cleanup_error
+                else:
+                    transfer_owned = False
+                    if interrupted is not None:
+                        error.add_note("adapter transfer cleanup observed cancellation")
+            if not serving.done():
+                serving.set_exception(error)
+            self._publication_failure = error
+            async with self._serving_lock:
+                cleanup = await self._rollback_server_start_safely(
+                    self._managed_service_name
+                )
+                self._clear_serving_state()
+            logger.exception(
+                "Trainer generation publication failed session=%s step=%d generation=%s",
+                generation.training_session_id,
+                generation.policy_step,
+                generation.generation_id,
+            )
+            if cleanup:
+                raise BaseExceptionGroup(
+                    "generation publication and serving teardown failed",
+                    [error, *cleanup],
+                ) from None
+            raise error
+
+    def _raise_publication_failure(self) -> None:
+        if self._publication_failure is not None:
+            raise RuntimeError("trainer generation publication failed") from (
+                self._publication_failure
+            )
+
+    async def resolve_global_grad_accumulation_sequences(
+        self, config: types.TrainConfig
+    ) -> int:
+        if config.grad_accumulation_sequences is not None:
+            return int(config.grad_accumulation_sequences)
+        mesh = self.runtime.topology.trainer
+        assert mesh is not None
+        topology = mesh.topology
+        return len(mesh.ranks) // (topology.tp * topology.cp * topology.pp)
+
+    async def start_openai_server(
+        self, config: dev.OpenAIServerConfig | None
+    ) -> tuple[str, int]:
+        async with self._train_lock:
+            self._require_open()
+            if serving := self._serving_futures.get(self._latest_step):
+                await serving
+            async with self._serving_lock:
+                if self._base_url:
+                    return _host_port(self._base_url)
+                if self._managed_service_name is not None:
+                    raise RuntimeError("managed model service is unavailable")
+            async with self._mutation_lock:
+                lora_path = await asyncio.to_thread(self._resolve_current_lora_path)
+                step = self._latest_step
+            async with self._serving_lock:
+                if self._base_url:
+                    return _host_port(self._base_url)
+                if self._managed_service_name is not None:
+                    raise RuntimeError("managed model service is unavailable")
+                return await self._start_openai_server_locked(
+                    config, lora_path=lora_path, step=step
+                )
+
+    async def _start_openai_server_locked(
+        self,
+        config: dev.OpenAIServerConfig | None,
+        *,
+        lora_path: str,
+        step: int,
+    ) -> tuple[str, int]:
+        api_key = self._api_key(config)
+        external = get_external_vllm_runtime_config(self.config)
+        if external is not None:
+            base_url = normalize_vllm_server_url(external.server_url)
+            headers = _headers(external.api_key)
+            await wait_for_vllm_http_runtime(
+                base_url=base_url,
+                timeout=external.health_timeout_s,
+                headers=headers,
+            )
+            capabilities = await discover_serving_capabilities(
+                base_url=base_url,
+                headers=headers,
+                allow_openai_compatible=True,
+            )
+            lora_name, _ = await self._load_adapter_at(
+                lora_path,
+                step,
+                base_url=base_url,
+                api_key=api_key,
+                active_step=step,
+            )
+            self._publish_serving_state(
+                managed_service_name=None,
+                base_url=base_url,
+                capabilities=capabilities,
+                api_key=api_key,
+                current_lora_name=lora_name,
+                serving_step=step,
+            )
+            return _host_port(base_url)
+
+        service = self._model_service_spec()
+        server_args = dict((config or {}).get("server_args", {}))
+        if "port" in server_args:
+            from .runtime.local import with_local_serving_port
+
+            self.runtime.topology = with_local_serving_port(
+                self.runtime.topology,
+                model_name=self.model_name,
+                port=cast(int, server_args["port"]),
+            )
+            service = self._model_service_spec()
+        template = ReplicaLaunchTemplate(
+            served_model_name=self._serving_lora_name(step),
+            lora_path=lora_path,
+            initial_policy_version=step,
+            engine_args=self._engine_args(config),
+            server_args=self._server_args(config),
+        )
+        await self.runtime.start_model_service(
+            service, template, on_failure=self._replica_failed
+        )
+        base_url = service.leader_endpoint.url
+        try:
+            capabilities = await discover_serving_capabilities(
+                base_url=base_url,
+                headers=_headers(api_key),
+                allow_openai_compatible=False,
+            )
+            generation_id = self._generation_id_for_step(step)
+            update_identity = uuid.uuid4().hex
+            manager = self.runtime.model_service(service.name)
+            state = manager.prepare_update(update_identity=update_identity)
+            report = ReplicaUpdateReport(
+                replica_id=service.name,
+                generation=state.generation,
+                generation_digest=state.generation_digest,
+                policy_version=str(step),
+                policy_digest=generation_id,
+                update_identity=update_identity,
+            )
+            if manager.verify_update(report).phase != "ready":
+                raise RuntimeError("model service rejected its initial policy")
+        except BaseException as error:
+            cleanup = await self._rollback_server_start_safely(service.name)
+            if cleanup:
+                raise BaseExceptionGroup(
+                    "vLLM startup validation and rollback failed", [error, *cleanup]
+                ) from None
+            raise
+        self._publish_serving_state(
+            managed_service_name=service.name,
+            base_url=base_url,
+            capabilities=capabilities,
+            api_key=api_key,
+            current_lora_name=template.served_model_name,
+            serving_step=step,
+        )
+        return _host_port(base_url)
+
+    def _publish_serving_state(
+        self,
+        *,
+        managed_service_name: str | None,
+        base_url: str,
+        capabilities: ServingCapabilities,
+        api_key: str | None,
+        current_lora_name: str,
+        serving_step: int,
+    ) -> None:
+        self._managed_service_name = managed_service_name
+        self._base_url = base_url
+        self._serving_capabilities = capabilities
+        self._api_key_value = api_key
+        self._current_lora_name = current_lora_name
+        self._serving_step = serving_step
+        self._loaded_adapter_steps.add(serving_step)
+
+    def _clear_serving_state(self) -> None:
+        self._managed_service_name = None
+        self._unpublish_serving_state()
+
+    def _unpublish_serving_state(self) -> None:
+        self._base_url = None
+        self._serving_capabilities = None
+        self._api_key_value = None
+        self._current_lora_name = None
+        self._loaded_adapter_steps.clear()
+        self._loaded_exact_adapter_steps.clear()
+        self._exact_adapter_refcounts.clear()
+        self._vllm_sleeping = False
+
+    async def _replica_failed(self, failure: ReplicaFailure) -> None:
+        if self._closed or failure.replica_id != self._managed_service_name:
+            return
+        task = asyncio.create_task(self._recover_failed_replica(failure))
+        self._recovery_tasks.add(task)
+        task.add_done_callback(self._recovery_tasks.discard)
+        task.add_done_callback(consume_future_exception)
+
+    async def _recover_failed_replica(self, failure: ReplicaFailure) -> None:
+        async with self._train_lock:
+            async with self._serving_lock:
+                try:
+                    if self._closed or failure.replica_id != self._managed_service_name:
+                        return
+                    manager = self.runtime.model_service(failure.replica_id)
+                    state = manager.state
+                    if (
+                        state.generation != failure.generation
+                        or state.generation_digest != failure.generation_digest
+                        or state.phase != "quarantined"
+                    ):
+                        return
+                    await self._recover_replica_locked(failure)
+                except asyncio.CancelledError:
+                    raise
+                except BaseException:
+                    self._unpublish_serving_state()
+                    logger.exception(
+                        "vLLM replica %s generation %d recovery failed",
+                        failure.replica_id,
+                        failure.generation,
+                    )
+
+    async def _recover_replica_locked(self, failure: ReplicaFailure) -> None:
+        service = self._model_service_spec()
+        manager = self.runtime.model_service(failure.replica_id)
+        serving_step = self._serving_step
+        serving_adapter = self._published_adapters.get(serving_step)
+        if serving_adapter is None:
+            raise RuntimeError(
+                f"serving generation {serving_step} is not registered for recovery"
+            )
+        checkpoint = serving_adapter.identity
+        generation_id = serving_adapter.generation_id
+        current_lora_name = self._current_lora_name or self._serving_lora_name(
+            serving_step
+        )
+        bootstrap_name = self._serving_lora_name(serving_step)
+        base_url = service.leader_endpoint.url
+        exact_steps = tuple(sorted(self._loaded_exact_adapter_steps))
+        try:
+            state = await manager.restart(
+                served_model_name=bootstrap_name,
+                lora_path=checkpoint,
+                initial_policy_version=serving_step,
+            )
+            self._vllm_sleeping = False
+            capability = await discover_serving_capabilities(
+                base_url=base_url,
+                headers=_headers(self._api_key()),
+                allow_openai_compatible=False,
+            )
+            if capability != self._serving_capabilities:
+                raise RuntimeError("restarted vLLM replica capabilities changed")
+            update_identity = uuid.uuid4().hex
+            manager.prepare_update(update_identity=update_identity)
+            lora_name = bootstrap_name
+            if current_lora_name != bootstrap_name:
+                lora_name, lora_path = await self._load_adapter_at(
+                    checkpoint,
+                    serving_step,
+                    base_url=base_url,
+                    api_key=self._api_key(),
+                    active_step=serving_step - 1,
+                )
+            report = ReplicaUpdateReport(
+                replica_id=failure.replica_id,
+                generation=state.generation,
+                generation_digest=state.generation_digest,
+                policy_version=str(serving_step),
+                policy_digest=generation_id,
+                update_identity=update_identity,
+            )
+            if manager.verify_update(report).phase != "ready":
+                raise RuntimeError("restarted vLLM replica rejected current policy")
+            if current_lora_name != bootstrap_name:
+                await self._unload_adapter_at(bootstrap_name, base_url)
+            for step in exact_steps:
+                if step == serving_step and self.rollout_weight_update_mode != (
+                    "in_flight_lora"
+                ):
+                    continue
+                await self._load_adapter_at(
+                    get_step_checkpoint_dir(self.output_dir, step),
+                    step,
+                    exact=True,
+                    base_url=base_url,
+                    api_key=self._api_key(),
+                    active_step=serving_step,
+                )
+            self._current_lora_name = lora_name
+            self._loaded_adapter_steps = {serving_step}
+            self._loaded_exact_adapter_steps = set(exact_steps)
+            await self._release_loaded_adapter_transfers()
+        except BaseException as error:
+            manager.quarantine(f"replica recovery failed: {error}")
+            try:
+                await manager.stop()
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "replica recovery and teardown failed", [error, cleanup_error]
+                ) from None
+            try:
+                await self._release_loaded_adapter_transfers()
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "replica recovery and transfer cleanup failed",
+                    [error, cleanup_error],
+                ) from None
+            raise
+
+    def _model_service_spec(self) -> ModelServiceSpec:
+        services = tuple(
+            service
+            for service in self.runtime.topology.model_services
+            if service.name == self.model_name
+        )
+        if len(services) != 1:
+            raise RuntimeError(
+                f"runtime topology has no unique service {self.model_name!r}"
+            )
+        return services[0]
+
+    async def _rollback_server_start(
+        self, service_name: str | None
+    ) -> list[BaseException]:
+        if service_name is None:
+            return []
+        try:
+            await self.runtime.stop_model_service(service_name)
+        except BaseException as error:
+            return [error]
+        try:
+            await self._release_loaded_adapter_transfers()
+        except BaseException as error:
+            return [error]
+        return []
+
+    async def _rollback_server_start_safely(
+        self, service_name: str | None
+    ) -> list[BaseException]:
+        failures, cancelled = await complete_task(
+            asyncio.create_task(self._rollback_server_start(service_name))
+        )
+        if cancelled is not None:
+            failures.append(cancelled)
+        return failures
+
+    def _engine_args(self, server: dev.OpenAIServerConfig | None) -> dict[str, object]:
+        handler = get_model_support_handler(
+            self.base_model,
+            allow_unvalidated_arch=self._allow_unvalidated_arch,
+        )
+        values = dict(self.config.get("engine_args", {}))
+        values.update(dict((server or {}).get("engine_args", {})))
+        for key, value in handler.vllm_engine_args().items():
+            values.setdefault(key, value)
+        values["enable_sleep_mode"] = self._temporal_gpu_sharing
+        values["enable_lora"] = True
+        values.setdefault("max_loras", 2)
+        values.setdefault("generation_config", "vllm")
+        for key in ("model", "served_model_name"):
+            values.pop(key, None)
+        return values
+
+    def _server_args(self, server: dev.OpenAIServerConfig | None) -> dict[str, object]:
+        handler = get_model_support_handler(
+            self.base_model,
+            allow_unvalidated_arch=self._allow_unvalidated_arch,
+        )
+        values: dict[str, object] = {
+            "return_tokens_as_token_ids": True,
+            "enable_auto_tool_choice": True,
+            "tool_call_parser": "hermes",
+            **handler.vllm_server_args(),
+            **dict((server or {}).get("server_args", {})),
+        }
+        for key in ("port", "host", "lora_modules"):
+            values.pop(key, None)
+        return values
+
+    def _api_key(self, server: dev.OpenAIServerConfig | None = None) -> str | None:
+        value = dict((server or {}).get("server_args", {})).get("api_key")
+        external = get_external_vllm_runtime_config(self.config)
+        if external is not None:
+            if value is not None and value != external.api_key:
+                raise ValueError(
+                    "OpenAI server api_key conflicts with external vLLM credentials"
+                )
+            return external.api_key
+        if value is not None:
+            return cast(str, value)
+        return self._api_key_value
+
+    async def _sleep_for_training_locked(self) -> None:
+        if not self._temporal_gpu_sharing or self._base_url is None:
+            return
+        if self._vllm_sleeping:
+            return
+        self._vllm_sleeping = True
+        await self._sleep_vllm_at(self._base_url, self._api_key())
+
+    async def _wake_for_serving_locked(self) -> None:
+        if not self._vllm_sleeping or self._base_url is None:
+            return
+        await self._wake_vllm_at(self._base_url, self._api_key())
+        self._vllm_sleeping = False
+
+    @staticmethod
+    async def _sleep_vllm_at(base_url: str, api_key: str | None) -> None:
+        response = await _post_vllm(
+            f"{base_url}/sleep",
+            api_key=api_key,
+            params={"level": 1, "mode": "wait"},
+            timeout_s=300.0,
+        )
+        response.raise_for_status()
+
+    @staticmethod
+    async def _wake_vllm_at(base_url: str, api_key: str | None) -> None:
+        response = await _post_vllm(
+            f"{base_url}/wake_up", api_key=api_key, timeout_s=300.0
+        )
+        response.raise_for_status()
+
+    async def _load_adapter(
+        self, checkpoint: str, step: int, *, exact: bool = False
+    ) -> tuple[str, str]:
+        if self._base_url is None:
+            raise RuntimeError("vLLM serving has not started")
+        return await self._load_adapter_at(
+            checkpoint,
+            step,
+            exact=exact,
+            base_url=self._base_url,
+            api_key=self._api_key(),
+            active_step=self._serving_step,
+        )
+
+    async def _load_adapter_at(
+        self,
+        checkpoint: str,
+        step: int,
+        *,
+        base_url: str,
+        api_key: str | None,
+        active_step: int,
+        exact: bool = False,
+    ) -> tuple[str, str]:
+        name = (
+            f"{self.model_name}:eval@{step}"
+            if exact and self.rollout_weight_update_mode == "in_flight_lora"
+            else self._serving_lora_name(step)
+        )
+        path = map_checkpoint_path_for_vllm(self.config, checkpoint)
+        in_flight = (
+            not exact
+            and self.rollout_weight_update_mode == "in_flight_lora"
+            and step != active_step
+        )
+        endpoint = (
+            "/art/in_flight_lora_update" if in_flight else "/v1/load_lora_adapter"
+        )
+        payload = (
+            {
+                "model_name": name,
+                "lora_slot": name,
+                "lora_path": path,
+                "policy_version": step,
+            }
+            if in_flight
+            else {"lora_name": name, "lora_path": path}
+        )
+        async with httpx.AsyncClient(timeout=60.0) as client:
+            response = await client.post(
+                f"{base_url}{endpoint}",
+                json=payload,
+                headers=_headers(api_key),
+            )
+        response.raise_for_status()
+        return str(payload.get("lora_slot", name)), path
+
+    async def register_lora_for_step(self, step: int, checkpoint: str) -> None:
+        await self._await_trainer_preparation()
+        async with self._train_lock:
+            self._require_open()
+            policy = await asyncio.to_thread(
+                resolve_committed_optimizer_policy,
+                self._optimizer_state_path,
+                initial_adapter_path=get_step_checkpoint_dir(self.output_dir, 0),
+            )
+            if policy.policy_adapter.step != step or policy.policy_adapter.identity != (
+                str(Path(checkpoint).absolute())
+            ):
+                raise RuntimeError(
+                    "distributed LoRA registration requires a committed policy step"
+                )
+            adapter = policy.policy_adapter
+            generation = TrainerGeneration(
+                training_session_id=adapter.training_session_id,
+                policy_step=adapter.step,
+                generation_id=adapter.generation_id,
+                adapter_path=adapter.identity,
+            )
+            async with self._mutation_lock:
+                self._published_adapters[step] = adapter
+                self._training_session_id = adapter.training_session_id
+                self._learner_generation = generation
+                self._latest_step = step
+                self._durable_step = step
+                self._durable_optimizer_step = (
+                    0
+                    if policy.optimizer_anchor is None
+                    else policy.optimizer_anchor.step
+                )
+            async with self._serving_lock:
+                await self._register_lora_for_step_locked(step, checkpoint)
+
+    async def advance_without_training(
+        self,
+        *,
+        expected_step: int,
+        learner_version: int,
+    ) -> dict[str, float]:
+        await self._await_trainer_preparation()
+        async with self._train_lock:
+            metrics = self.drain_publication_metrics()
+            async with self._mutation_lock:
+                self._require_open()
+                self._raise_publication_failure()
+                if expected_step != self._latest_step:
+                    raise ValueError(
+                        "no-op policy transition expected the wrong learner step"
+                    )
+                if learner_version != expected_step + 1:
+                    raise ValueError("a no-op policy transition must advance one step")
+                previous = self._publication_tasks.get(expected_step)
+                source = self._learner_generation
+                if source is None or source.policy_step != expected_step:
+                    raise RuntimeError("no-op transition has no immutable source")
+                trainer = self._resident_trainer_for_generation(source)
+            if previous is not None:
+                await asyncio.shield(previous)
+            source_adapter = self._published_adapters.get(expected_step)
+            if source_adapter is None:
+                raise RuntimeError("no-op source generation is not durably published")
+
+            async with self._mutation_lock:
+                self._published_adapters[expected_step] = source_adapter
+                generation = TrainerGeneration(
+                    training_session_id=self._training_session_id,
+                    policy_step=learner_version,
+                    generation_id=new_optimizer_generation(learner_version),
+                    adapter_path=get_step_checkpoint_dir(
+                        self.output_dir, learner_version
+                    ),
+                )
+            snapshot_metrics: dict[str, float] = {}
+            if trainer is not None:
+                try:
+                    snapshot_metrics.update(
+                        await trainer.advance_without_training(
+                            source=source,
+                            output=generation,
+                            optimizer_state_path=self._optimizer_state_path,
+                            adapter=None,
+                        )
+                    )
+                except BaseException as error:
+                    await self._cleanup_failed_trainer_transaction(trainer, None, error)
+                    raise
+
+            async def commit() -> None:
+                prepare_started = time.monotonic()
+                published = await asyncio.to_thread(
+                    _commit_adapter_alias,
+                    self._optimizer_state_path,
+                    self.output_dir,
+                    expected_step,
+                    source_adapter,
+                    generation,
+                    f"{self.output_dir}/megatron_runtime/staging/"
+                    f"{generation.generation_id}",
+                )
+                snapshot_metrics["snapshot_launch_s"] = (
+                    time.monotonic() - prepare_started
+                )
+                async with self._mutation_lock:
+                    if self._latest_step != expected_step:
+                        raise RuntimeError(
+                            "learner lineage changed during no-op commit"
+                        )
+                    self._published_adapters[learner_version] = published
+                    self._latest_step = learner_version
+                    self._learner_generation = generation
+                    self._trainer_resident_generation = (
+                        generation if trainer is not None else None
+                    )
+                    self._publication_metrics[learner_version] = snapshot_metrics
+                    pointer = read_committed_optimizer_pointer(
+                        self._optimizer_state_path
+                    )
+                    self._schedule_publication(
+                        generation,
+                        durable=DurableTrainerPublication(
+                            adapter=published,
+                            resume_step=learner_version,
+                            optimizer_step=0 if pointer is None else pointer.step,
+                        ),
+                    )
+
+            try:
+                _, cancelled = await complete_task(asyncio.create_task(commit()))
+            except BaseException as error:
+                if trainer is not None:
+                    await self._cleanup_failed_trainer_transaction(trainer, None, error)
+                raise
+            metrics.update(self.drain_publication_metrics())
+            if cancelled is not None:
+                raise cancelled
+            return metrics
+
+    async def _register_lora_for_step_locked(
+        self,
+        step: int,
+        checkpoint: str,
+    ) -> None:
+        if self._base_url is None:
+            self._serving_step = step
+            self._record_serving_activation(step)
+            return
+        await self._wake_for_serving_locked()
+        generation_id = self._generation_id_for_step(step)
+        update_identity = uuid.uuid4().hex
+        manager = (
+            self.runtime.model_service(self._managed_service_name)
+            if self._managed_service_name is not None
+            else None
+        )
+        try:
+            state = (
+                manager.prepare_update(update_identity=update_identity)
+                if manager is not None
+                else None
+            )
+            lora_name = self._serving_lora_name(step)
+            lora_name, _lora_path = await self._load_adapter(checkpoint, step)
+            if manager is not None and state is not None:
+                report = ReplicaUpdateReport(
+                    replica_id=manager.spec.name,
+                    generation=state.generation,
+                    generation_digest=state.generation_digest,
+                    policy_version=str(step),
+                    policy_digest=generation_id,
+                    update_identity=update_identity,
+                )
+                if manager.verify_update(report).phase != "ready":
+                    raise RuntimeError("model service rejected its policy update")
+        except BaseException as error:
+            if manager is not None:
+                manager.quarantine("partial or failed LoRA update")
+            try:
+                cleanup = await self._rollback_server_start_safely(
+                    self._managed_service_name
+                )
+            finally:
+                self._clear_serving_state()
+            if cleanup:
+                raise BaseExceptionGroup(
+                    "policy publication and serving rollback failed", [error, *cleanup]
+                ) from None
+            raise
+        if self.rollout_weight_update_mode != "in_flight_lora":
+            self._loaded_adapter_steps.add(step)
+        self._current_lora_name = lora_name
+        self._serving_step = step
+        self._record_serving_activation(step)
+
+    def _generation_id_for_step(self, step: int) -> str:
+        published = self._published_adapters.get(step)
+        if published is None:
+            raise RuntimeError(f"No immutable generation is registered for step {step}")
+        return published.generation_id
+
+    async def acquire_exact_adapter(self, step: int, checkpoint: str) -> str:
+        self._require_open()
+        async with self._mutation_lock:
+            published = step in self._published_adapters
+            generation = self._learner_generation
+            materialization = (
+                self.checkpoint_materialization(step)
+                if self.rollout_weight_update_mode == "in_flight_lora"
+                and generation is not None
+                and generation.policy_step == step
+                else None
+            )
+        if materialization is not None:
+            await asyncio.shield(materialization)
+        if not published:
+            adapter = await asyncio.to_thread(
+                read_adapter_publication,
+                checkpoint,
+                step=step,
+                verify_files=True,
+            )
+            if adapter is None:
+                if step != 0:
+                    raise RuntimeError("exact adapter is not an immutable generation")
+                adapter = optimizer_adapter(
+                    checkpoint,
+                    0,
+                    training_session_id=self._training_session_id,
+                )
+            async with self._mutation_lock:
+                self._require_open()
+                self._published_adapters.setdefault(step, adapter)
+        async with self._serving_lock:
+            self._require_open()
+            lora_name = (
+                f"{self.model_name}:eval@{step}"
+                if self.rollout_weight_update_mode == "in_flight_lora"
+                else f"{self.model_name}@{step}"
+            )
+            if step not in self._loaded_exact_adapter_steps:
+                if (
+                    self.rollout_weight_update_mode == "in_flight_lora"
+                    or step not in self._loaded_adapter_steps
+                ):
+                    lora_name, _lora_path = await self._load_adapter(
+                        checkpoint, step, exact=True
+                    )
+                self._loaded_exact_adapter_steps.add(step)
+                self._exact_adapter_refcounts[step] = 0
+            self._exact_adapter_refcounts[step] += 1
+        return (
+            f"{self.model_name}:eval@{step}"
+            if self.rollout_weight_update_mode == "in_flight_lora"
+            else f"{self.model_name}@{step}"
+        )
+
+    async def release_exact_adapter(self, step: int) -> None:
+        async with self._serving_lock:
+            self._require_open()
+            count = self._exact_adapter_refcounts.get(step, 0)
+            if count <= 1:
+                if self.rollout_weight_update_mode == "in_flight_lora":
+                    await self._unload_adapter(f"{self.model_name}:eval@{step}")
+                self._exact_adapter_refcounts.pop(step, None)
+                self._loaded_exact_adapter_steps.discard(step)
+            else:
+                self._exact_adapter_refcounts[step] = count - 1
+
+    async def prune_loaded_adapters(self, *, retain_steps: set[int]) -> None:
+        async with self._serving_lock:
+            self._require_open()
+            for step in sorted(self._loaded_exact_adapter_steps - retain_steps):
+                if self._exact_adapter_refcounts.get(step, 0) == 0:
+                    name = (
+                        f"{self.model_name}:eval@{step}"
+                        if self.rollout_weight_update_mode == "in_flight_lora"
+                        else f"{self.model_name}@{step}"
+                    )
+                    await self._unload_adapter(name)
+                    self._loaded_exact_adapter_steps.discard(step)
+            if self.rollout_weight_update_mode == "in_flight_lora":
+                return
+            for step in sorted(
+                self._loaded_adapter_steps - retain_steps - {self._serving_step}
+            ):
+                await self._unload_adapter(f"{self.model_name}@{step}")
+                await self._release_loaded_adapter_transfer(step)
+                self._loaded_adapter_steps.discard(step)
+
+    @asynccontextmanager
+    async def checkpoint_retention_lease(self) -> AsyncIterator[frozenset[int]]:
+        # Disk pruning keeps mutation, not serving, held after ordered acquisition.
+        async with self._serving_lock:
+            await self._mutation_lock.acquire()
+        try:
+            self._require_open()
+            protected = frozenset((self._latest_step, self._serving_step))
+            yield protected
+        finally:
+            self._mutation_lock.release()
+
+    async def _unload_adapter(self, name: str) -> None:
+        if self._base_url is None:
+            raise RuntimeError("vLLM serving has not started")
+        await self._unload_adapter_at(name, self._base_url)
+
+    async def _unload_adapter_at(self, name: str, base_url: str) -> None:
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            response = await client.post(
+                f"{base_url}/v1/unload_lora_adapter",
+                json={"lora_name": name},
+                headers=_headers(self._api_key()),
+            )
+        if response.status_code != 404:
+            response.raise_for_status()
+
+    async def get_serving_capabilities(self) -> ServingCapabilities:
+        if self._serving_capabilities is None:
+            raise RuntimeError("vLLM serving capabilities have not been discovered")
+        return self._serving_capabilities
+
+    async def vllm_engine_is_sleeping(self) -> bool:
+        return self._vllm_sleeping
+
+    async def train_sft(
+        self, batches: list[Any], config: Any, verbose: bool = False
+    ) -> AsyncIterator[dict[str, float]]:
+        del verbose
+        payload = tuple(
+            SFTBatchData(
+                trajectory_tensors=tuple(batch.trajectory_tensors),
+                learning_rate=float(batch.learning_rate),
+                num_trajectories=int(batch.num_trajectories),
+                num_tokens=int(batch.num_tokens),
+                num_trainable_tokens=int(batch.num_trainable_tokens),
+            )
+            for batch in batches
+        )
+        if not payload:
+            return
+
+        def build_job(fields: _TrainerJobFields) -> TrainerJobSpec:
+            return SFTJobSpec(
+                **fields,
+                batch_id=uuid.uuid4().hex,
+                num_batches=len(payload),
+                config=CurrentSFTConfig.model_validate(config.model_dump()),
+            )
+
+        async for metrics in self._run_train_job(
+            build_job,
+            lambda trainer, job: trainer.train_sft(job, payload),
+            lineage_error="learner lineage changed during SFT",
+            wait_for_serving=True,
+        ):
+            yield metrics
+
+    async def aclose(self) -> None:
+        if self._close_task is not None and self._close_task.done():
+            try:
+                self._close_task.result()
+            except BaseException:
+                self._close_task = None
+        if self._close_task is None:
+            self._closed = True
+            self._close_task = asyncio.create_task(self._close())
+            self._close_task.add_done_callback(consume_future_exception)
+        await asyncio.shield(self._close_task)
+
+    async def _close(self) -> None:
+        failures: list[BaseException] = []
+        preparation = self._trainer_preparation_task
+        if preparation is not None:
+            try:
+                _, interrupted = await complete_task(preparation)
+            except BaseException as error:
+                failures.append(error)
+            else:
+                if interrupted is not None:
+                    failures.append(interrupted)
+            self._trainer_preparation_task = None
+            self._trainer_preparation_step = None
+        async with self._train_lock:
+            publications = tuple(self._publication_tasks.values())
+            durability_tasks = tuple(self._durability_tasks)
+            recovery_tasks = tuple(self._recovery_tasks)
+            for task in recovery_tasks:
+                task.cancel()
+            if recovery_tasks:
+                await asyncio.gather(*recovery_tasks, return_exceptions=True)
+            self._recovery_tasks.clear()
+            async with self._mutation_lock:
+                trainer = self._trainer
+            shutdown = [*publications, *durability_tasks]
+            trainer_task = None
+            if trainer is not None:
+                trainer_task = asyncio.create_task(self.runtime.stop_trainer(trainer))
+                shutdown.append(trainer_task)
+            results = await asyncio.gather(*shutdown, return_exceptions=True)
+            if trainer_task is not None and not isinstance(results[-1], BaseException):
+                async with self._mutation_lock:
+                    if self._trainer is trainer:
+                        self._trainer = None
+            publication_failures = [
+                result
+                for result in results[: len(publications)]
+                if isinstance(result, BaseException)
+            ]
+            failures.extend(
+                result for result in results if isinstance(result, BaseException)
+            )
+            if self._publication_failure is not None and not publication_failures:
+                failures.append(self._publication_failure)
+            async with self._mutation_lock:
+                self._publication_tasks.clear()
+                self._durability_tasks.clear()
+                self._serving_futures.clear()
+                self._publication_metrics.clear()
+                self._emitted_publication_metrics.clear()
+                self._trainer_completion_times.clear()
+                self._serving_activation_times.clear()
+            try:
+                await self._discard_next_publication_preparation()
+            except BaseException as error:
+                failures.append(error)
+            try:
+                await self._release_prepared_adapter_transfers()
+            except BaseException as error:
+                failures.append(error)
+            async with self._serving_lock:
+                serving_stopped = False
+                if self._managed_service_name is not None:
+                    result = await asyncio.gather(
+                        self.runtime.stop_model_service(self._managed_service_name),
+                        return_exceptions=True,
+                    )
+                    serving_failures = [
+                        value for value in result if isinstance(value, BaseException)
+                    ]
+                    failures.extend(serving_failures)
+                    if not serving_failures:
+                        serving_stopped = True
+                        self._clear_serving_state()
+                elif (
+                    get_external_vllm_runtime_config(self.config) is not None
+                    and self._base_url is not None
+                ):
+                    names = {
+                        *(
+                            self._serving_lora_name(step)
+                            for step in self._loaded_adapter_steps
+                        ),
+                        *(
+                            f"{self.model_name}:eval@{step}"
+                            if self.rollout_weight_update_mode == "in_flight_lora"
+                            else f"{self.model_name}@{step}"
+                            for step in self._loaded_exact_adapter_steps
+                        ),
+                    }
+                    if self._current_lora_name is not None:
+                        names.add(self._current_lora_name)
+                    results = await asyncio.gather(
+                        *(
+                            self._unload_adapter_at(name, self._base_url)
+                            for name in sorted(names)
+                        ),
+                        return_exceptions=True,
+                    )
+                    serving_failures = [
+                        value for value in results if isinstance(value, BaseException)
+                    ]
+                    failures.extend(serving_failures)
+                    if not serving_failures:
+                        serving_stopped = True
+                        self._clear_serving_state()
+                else:
+                    serving_stopped = True
+                    self._clear_serving_state()
+                if serving_stopped:
+                    try:
+                        await self._release_loaded_adapter_transfers()
+                    except BaseException as error:
+                        failures.append(error)
+            _, cancelled = await complete_to_thread(
+                lambda: _remove_staging_root(self.output_dir)
+            )
+            if cancelled is not None:
+                failures.append(cancelled)
+            if failures:
+                raise BaseExceptionGroup(
+                    "distributed model service close failed", failures
+                )
+
+
+def _remove_staging_checkpoint(staging: str) -> None:
+    if os.path.exists(staging):
+        shutil.rmtree(staging)
+
+
+def _remove_staging_root(output_dir: str) -> None:
+    _remove_staging_checkpoint(f"{output_dir}/megatron_runtime/staging")
+
+
+def _publish_adapter_alias(
+    source: OptimizerAdapter,
+    generation: TrainerGeneration,
+    staging_path: str,
+) -> OptimizerAdapter:
+    staging = Path(staging_path)
+    if staging.exists() or Path(generation.adapter_path).exists():
+        raise RuntimeError("no-op adapter generation path already exists")
+    with adapter_generation_lease(source):
+        staging.mkdir(parents=True)
+        try:
+            for name in ("adapter_config.json", "adapter_model.safetensors"):
+                os.link(Path(source.identity) / name, staging / name)
+            return publish_adapter_checkpoint(
+                staging,
+                step=generation.policy_step,
+                training_session_id=generation.training_session_id,
+                generation_id=generation.generation_id,
+            )
+        except BaseException:
+            _remove_staging_checkpoint(str(staging))
+            raise
+
+
+def _commit_adapter_alias(
+    optimizer_state_path: str,
+    output_dir: str,
+    expected_step: int,
+    source: OptimizerAdapter,
+    generation: TrainerGeneration,
+    staging_path: str,
+) -> OptimizerAdapter:
+    try:
+        published = _publish_adapter_alias(source, generation, staging_path)
+        commit_optimizer_policy_advance(
+            optimizer_state_path,
+            initial_adapter_path=get_step_checkpoint_dir(output_dir, 0),
+            expected_step=expected_step,
+            adapter=published,
+        )
+        return published
+    except BaseException as error:
+        try:
+            policy = resolve_committed_optimizer_policy(
+                optimizer_state_path,
+                initial_adapter_path=get_step_checkpoint_dir(output_dir, 0),
+            )
+        except BaseException as state_error:
+            raise BaseExceptionGroup(
+                "no-op policy commit state is ambiguous", [error, state_error]
+            ) from None
+        if policy.policy_adapter.generation_id == generation.generation_id:
+            return policy.policy_adapter
+        failures: list[BaseException] = []
+        latest = Path(output_dir) / "megatron_runtime/latest-adapter.json"
+        try:
+            if latest.is_file():
+                adapter = OptimizerAdapter.model_validate_json(
+                    latest.read_text("utf-8")
+                )
+                if adapter.generation_id == generation.generation_id:
+                    latest.unlink()
+        except BaseException as cleanup_error:
+            failures.append(cleanup_error)
+        for path in (
+            staging_path,
+            generation.adapter_path,
+        ):
+            try:
+                _remove_staging_checkpoint(path)
+            except BaseException as cleanup_error:
+                failures.append(cleanup_error)
+        if failures:
+            raise BaseExceptionGroup(
+                "no-op policy commit and rollback failed", [error, *failures]
+            ) from None
+        raise
+
+
+def _trainer_dtype(
+    config: dev.BackendModelConfig,
+) -> Literal["bfloat16", "float16", "float32"]:
+    value = str(config.get("init_args", {}).get("dtype") or "bfloat16").lower()
+    value = {
+        "bf16": "bfloat16",
+        "fp16": "float16",
+        "fp32": "float32",
+        "torch.bfloat16": "bfloat16",
+        "torch.float16": "float16",
+        "torch.float32": "float32",
+    }.get(value, value)
+    if value not in {"bfloat16", "float16", "float32"}:
+        raise ValueError(f"unsupported Megatron trainer dtype {value!r}")
+    return cast(
+        Literal["bfloat16", "float16", "float32"],
+        value,
+    )
+
+
+def _digest(value: object) -> str:
+    return hashlib.sha256(
+        json.dumps(value, sort_keys=True, separators=(",", ":")).encode()
+    ).hexdigest()
+
+
+def _art_source_revision() -> str:
+    root = Path(__file__).resolve().parents[1]
+    digest = hashlib.sha256()
+    for path in sorted(root.rglob("*.py")):
+        digest.update(str(path.relative_to(root)).encode())
+        digest.update(path.read_bytes())
+    return digest.hexdigest()
+
+
+def _headers(api_key: str | None) -> dict[str, str] | None:
+    return {"Authorization": f"Bearer {api_key}"} if api_key else None
+
+
+def _host_port(base_url: str) -> tuple[str, int]:
+    from urllib.parse import urlparse
+
+    parsed = urlparse(base_url)
+    assert parsed.hostname is not None
+    return parsed.hostname, parsed.port or (443 if parsed.scheme == "https" else 80)
diff --git a/src/art/megatron/dsv4/bridge.py b/src/art/megatron/dsv4/bridge.py
index 8cf02a910..fc786b9d2 100644
--- a/src/art/megatron/dsv4/bridge.py
+++ b/src/art/megatron/dsv4/bridge.py
@@ -9,7 +9,6 @@
     GatedMLPMapping,
     ReplicatedMapping,
     RowParallelMapping,
-    extract_expert_number_from_param,
 )
 from megatron.bridge.models.deepseek.deepseek_v3_bridge import DeepSeekV3Bridge
 from megatron.bridge.models.mla_provider import MLAModelProvider
@@ -310,142 +309,6 @@ def _dsv4_source_export_name(name: str) -> str:
     return name
 
 
-def _dsv4_full_parallel_shape(task: WeightConversionTask) -> list[int]:
-    param_weight = task.param_weight
-    if param_weight is None:
-        raise RuntimeError(f"Missing DSV4 export param for {task.global_param_name}")
-    shape = list(param_weight.shape)
-    if not bool(getattr(param_weight, "tensor_model_parallel", False)):
-        tp_size = int(getattr(task.mapping, "tp_size", 1) or 1)
-        if task.global_param_name in {
-            "embedding.word_embeddings.weight",
-            "output_layer.weight",
-        } or task.global_param_name.endswith(".self_attention.attn_sink"):
-            shape[0] *= tp_size
-        elif task.global_param_name.endswith(
-            (
-                ".self_attention.wq_b.weight",
-                ".self_attention.wo_a.weight",
-                ".self_attention.indexer.linear_wq_b.weight",
-            )
-        ):
-            shape[0] *= tp_size
-        elif task.global_param_name.endswith(
-            (
-                ".self_attention.wo_b.weight",
-                ".mlp.shared_experts.linear_fc2.weight",
-            )
-        ):
-            shape[1] *= tp_size
-        return shape
-    partition_dim = int(getattr(param_weight, "partition_dim", 0) or 0)
-    shape[partition_dim] *= int(getattr(task.mapping, "tp_size", 1) or 1)
-    return shape
-
-
-def _dsv4_gated_shape(task: WeightConversionTask) -> list[int]:
-    param_weight = task.param_weight
-    if param_weight is None:
-        raise RuntimeError(f"Missing DSV4 export param for {task.global_param_name}")
-    shape = list(param_weight.shape)
-    shape[0] *= int(getattr(task.mapping, "tp_size", 1) or 1)
-    if shape[0] % 2 != 0:
-        raise ValueError(
-            f"Expected even DSV4 gated export dim for {task.global_param_name}: {shape}"
-        )
-    shape[0] //= 2
-    return shape
-
-
-def _dsv4_expert_down_shape(task: WeightConversionTask) -> list[int]:
-    param_weight = task.param_weight
-    if param_weight is None:
-        raise RuntimeError(f"Missing DSV4 export param for {task.global_param_name}")
-    shape = list(param_weight.shape)
-    if len(shape) > 1:
-        shape[1] *= int(getattr(task.mapping, "tp_size", 1) or 1)
-    return shape
-
-
-def _dsv4_expert_names(
-    *,
-    task: WeightConversionTask,
-    export_name: str,
-) -> list[str]:
-    config = getattr(task.megatron_module, "config", None)
-    num_experts = int(getattr(config, "num_moe_experts", 0) or 0)
-    ep_size = int(getattr(task.mapping, "ep_size", 1) or 1)
-    if num_experts <= 0 or num_experts % ep_size != 0:
-        raise ValueError(
-            f"Cannot infer DSV4 expert metadata for {task.global_param_name}: "
-            f"num_experts={num_experts}, ep_size={ep_size}."
-        )
-    experts_per_rank = num_experts // ep_size
-    local_expert = (
-        extract_expert_number_from_param(task.mapping.megatron_param) % experts_per_rank
-    )
-    return [
-        _set_dsv4_expert_id(export_name, local_expert + experts_per_rank * ep_rank)
-        for ep_rank in range(ep_size)
-    ]
-
-
-def _dsv4_quantized_expert_metadata(
-    name: str,
-    shape: list[int],
-) -> list[tuple[str, torch.dtype, list[int]]]:
-    if len(shape) != 2 or shape[1] % 32 != 0:
-        raise ValueError(f"Expected 2-D K%32 DSV4 expert weight for {name}: {shape}")
-    return [
-        (name, torch.uint8, [shape[0], shape[1] // 2]),
-        (
-            f"{name.removesuffix('.weight')}.scale",
-            torch.float8_e8m0fnu,
-            [shape[0], shape[1] // 32],
-        ),
-    ]
-
-
-def _dsv4_modified_metadata(
-    pending_fused: dict[str, dict[int, tuple[torch.dtype, list[int]]]],
-    name: str,
-    dtype: torch.dtype,
-    shape: list[int],
-) -> list[tuple[str, torch.dtype, list[int]]]:
-    fused_key = _dsv4_fused_export_key(name)
-    if fused_key is not None:
-        target, part_index = fused_key
-        parts = pending_fused.setdefault(target, {})
-        if part_index in parts:
-            raise ValueError(
-                f"Duplicate DSV4 fused metadata part {part_index}: {name}."
-            )
-        parts[part_index] = (dtype, shape)
-        if len(parts) < 2:
-            return []
-        pending_fused.pop(target)
-        first_dtype, first_shape = parts[0]
-        second_dtype, second_shape = parts[1]
-        if first_dtype != second_dtype or first_shape[1:] != second_shape[1:]:
-            raise ValueError(
-                f"Cannot fuse DSV4 metadata parts for {target}: "
-                f"{first_dtype}/{first_shape} vs {second_dtype}/{second_shape}."
-            )
-        return [
-            (target, first_dtype, [first_shape[0] + second_shape[0], *first_shape[1:]])
-        ]
-
-    source_expert = _dsv4_canonical_expert_source_name(name)
-    source_name = (
-        source_expert if source_expert is not None else _dsv4_source_export_name(name)
-    )
-    if _is_dsv4_routed_expert_weight(source_name):
-        return _dsv4_quantized_expert_metadata(source_name, shape)
-    if _is_dsv4_hash_router_table(source_name):
-        return [(source_name, torch.int32, shape)]
-    return [(source_name, dtype, shape)]
-
-
 def _load_dsv4_hf_tensor(
     hf_param: str, hf_state_dict: Mapping[str, torch.Tensor]
 ) -> torch.Tensor:
@@ -513,7 +376,9 @@ def has_glob(self, pattern: str) -> bool:
 
 
 def _install_dsv4_source_aliases(hf_pretrained: Any) -> None:
-    state = hf_pretrained.state
+    state = getattr(hf_pretrained, "state", None)
+    if state is None:
+        return
     source = getattr(state, "source", None)
     if source is None or isinstance(source, _Dsv4AliasStateSource):
         return
@@ -1196,61 +1061,6 @@ def maybe_modify_converted_hf_weight(
                     remapped[source_name] = weight
         return remapped
 
-    def iter_merged_vllm_weight_metadata(
-        self,
-        weight_export: Any,
-    ) -> Iterable[tuple[str, torch.dtype, list[int]]]:
-        pending_fused: dict[str, dict[int, tuple[torch.dtype, list[int]]]] = {}
-        for task in weight_export.conversion_tasks:
-            mapping_name = type(task.mapping).__name__
-            dtype = task.param_weight.dtype
-            if mapping_name == "_ArtDsv4ExpertGateUpMapping":
-                shape = _dsv4_gated_shape(task)
-                export = cast(dict[str, str], task.mapping.export_hf_param)
-                for gate_name, up_name in zip(
-                    _dsv4_expert_names(task=task, export_name=export["gate"]),
-                    _dsv4_expert_names(task=task, export_name=export["up"]),
-                    strict=True,
-                ):
-                    yield from _dsv4_modified_metadata(
-                        pending_fused, gate_name, dtype, shape
-                    )
-                    yield from _dsv4_modified_metadata(
-                        pending_fused, up_name, dtype, shape
-                    )
-                continue
-
-            if mapping_name == "_ArtDsv4ExpertDownMapping":
-                shape = _dsv4_expert_down_shape(task)
-                export_name = cast(str, task.mapping.export_hf_param)
-                for name in _dsv4_expert_names(task=task, export_name=export_name):
-                    yield from _dsv4_modified_metadata(
-                        pending_fused, name, dtype, shape
-                    )
-                continue
-
-            if isinstance(task.mapping.export_hf_param, dict):
-                shape = _dsv4_gated_shape(task)
-                export = cast(dict[str, str], task.mapping.export_hf_param)
-                yield from _dsv4_modified_metadata(
-                    pending_fused, export["gate"], dtype, shape
-                )
-                yield from _dsv4_modified_metadata(
-                    pending_fused, export["up"], dtype, shape
-                )
-                continue
-
-            yield from _dsv4_modified_metadata(
-                pending_fused,
-                cast(str, task.mapping.export_hf_param),
-                dtype,
-                _dsv4_full_parallel_shape(task),
-            )
-        if pending_fused:
-            raise ValueError(
-                f"Incomplete DSV4 fused metadata parts: {sorted(pending_fused)}"
-            )
-
 
 _DSV4_BRIDGE_REGISTERED = False
 
diff --git a/src/art/megatron/dsv4/compressor.py b/src/art/megatron/dsv4/compressor.py
index fda1b2bf6..295586764 100644
--- a/src/art/megatron/dsv4/compressor.py
+++ b/src/art/megatron/dsv4/compressor.py
@@ -17,7 +17,6 @@
 from art.megatron.dsv4.utils import rotate_activation
 from art.megatron.prefix_tree import (
     PrefixTreeRow,
-    PrefixTreeSegment,
     parse_prefix_tree,
 )
 
@@ -34,11 +33,11 @@ class Dsv4CompressionLayout(NamedTuple):
 
 class _Dsv4CompressionPlan(NamedTuple):
     row: PrefixTreeRow
-    position_to_index_by_group: dict[int, dict[int, int]]
-    positions_by_group: dict[int, list[int]]
+    position_bounds_by_group: dict[int, tuple[int, int]]
+    branch_mapping_by_group: dict[int, tuple[torch.Tensor, torch.Tensor]]
     group_pre_order: dict[int, int]
     group_post_order: dict[int, int]
-    query_group_pre_order: list[int]
+    query_group_pre_order: torch.Tensor
 
 
 class Dsv4PrefixTreeState(BaseModel):
@@ -78,52 +77,6 @@ def build_prefix_tree_compression_layouts(
     }
 
 
-def _segment_positions(
-    position_row: torch.Tensor,
-    segment: PrefixTreeSegment,
-) -> list[int]:
-    positions = [int(value) for value in position_row[segment.start : segment.end]]
-    if positions != list(range(positions[0], positions[-1] + 1)):
-        raise ValueError(
-            "DSV4 prefix-tree compression requires contiguous positions within "
-            f"group={segment.group_id}, got {positions[:4]}...{positions[-4:]}."
-        )
-    return positions
-
-
-def _segment_path(
-    row: PrefixTreeRow,
-    segment: PrefixTreeSegment,
-) -> tuple[PrefixTreeSegment, ...]:
-    by_group = {candidate.group_id: candidate for candidate in row.segments}
-    return tuple(
-        by_group[group_id] for group_id in (*segment.ancestors, segment.group_id)
-    )
-
-
-def _branch_position_map(
-    *,
-    row: PrefixTreeRow,
-    segment: PrefixTreeSegment,
-    positions_by_group: dict[int, list[int]],
-) -> dict[int, int]:
-    by_group = {candidate.group_id: candidate for candidate in row.segments}
-    mapping: dict[int, int] = {}
-    expected = 0
-    for path_segment in _segment_path(row, segment):
-        positions = positions_by_group[path_segment.group_id]
-        if positions[0] != expected:
-            raise ValueError(
-                "DSV4 prefix-tree compression requires contiguous branch "
-                f"positions; expected {expected}, got {positions[0]} for "
-                f"group={path_segment.group_id}."
-            )
-        for offset, logical_pos in enumerate(positions):
-            mapping[logical_pos] = by_group[path_segment.group_id].start + offset
-        expected = positions[-1] + 1
-    return mapping
-
-
 def _group_preorder_intervals(
     row: PrefixTreeRow,
 ) -> tuple[dict[int, int], dict[int, int]]:
@@ -180,54 +133,87 @@ def _build_prefix_tree_compression_plan(
     group_cpu = group_ids.detach().cpu()
     parent_cpu = parent_ids.detach().cpu()
     (row,) = parse_prefix_tree(group_ids=group_cpu, parent_ids=parent_cpu)
-    positions_by_group = {
-        segment.group_id: _segment_positions(position_cpu[0], segment)
+    position_row = position_cpu[0]
+    prior_end = max(row.valid_tokens - 1, 0)
+    same_group = group_cpu[0, 1 : row.valid_tokens] == group_cpu[0, :prior_end]
+    invalid = torch.nonzero(
+        same_group
+        & (position_row[1 : row.valid_tokens] != position_row[:prior_end] + 1),
+        as_tuple=False,
+    ).flatten()
+    if invalid.numel():
+        index = int(invalid[0]) + 1
+        segment = next(item for item in row.segments if item.start <= index < item.end)
+        positions = position_row[segment.start : segment.end].tolist()
+        raise ValueError(
+            "DSV4 prefix-tree compression requires contiguous positions within "
+            f"group={segment.group_id}, got {positions[:4]}...{positions[-4:]}."
+        )
+    position_bounds_by_group = {
+        segment.group_id: (
+            int(position_row[segment.start]),
+            int(position_row[segment.end - 1]),
+        )
         for segment in row.segments
     }
-    group_pre_order, group_post_order = _group_preorder_intervals(row)
-    query_group_pre_order = [-1] * int(position_ids.shape[1])
+    by_group = {segment.group_id: segment for segment in row.segments}
+    for segment in row.segments:
+        expected = (
+            0
+            if not segment.ancestors
+            else position_bounds_by_group[segment.ancestors[-1]][1] + 1
+        )
+        actual = position_bounds_by_group[segment.group_id][0]
+        if actual != expected:
+            raise ValueError(
+                "DSV4 prefix-tree compression requires contiguous branch "
+                f"positions; expected {expected}, got {actual} for "
+                f"group={segment.group_id}."
+            )
+    branch_mapping_by_group = {}
     for segment in row.segments:
-        pre_order = group_pre_order[segment.group_id]
-        for token_index in range(segment.start, segment.end):
-            query_group_pre_order[token_index] = pre_order
-    position_to_index_by_group = {
-        segment.group_id: _branch_position_map(
-            row=row,
-            segment=segment,
-            positions_by_group=positions_by_group,
+        path = tuple(
+            by_group[group_id] for group_id in (*segment.ancestors, segment.group_id)
         )
-        for segment in row.segments
-    }
+        branch_mapping_by_group[segment.group_id] = (
+            torch.tensor(
+                [position_bounds_by_group[item.group_id][1] + 1 for item in path]
+            ),
+            torch.tensor(
+                [
+                    item.start - position_bounds_by_group[item.group_id][0]
+                    for item in path
+                ]
+            ),
+        )
+    group_pre_order, group_post_order = _group_preorder_intervals(row)
+    query_group_pre_order = torch.full(
+        (int(position_ids.shape[1]),), -1, dtype=torch.int32
+    )
+    for segment in row.segments:
+        query_group_pre_order[segment.start : segment.end] = group_pre_order[
+            segment.group_id
+        ]
     return _Dsv4CompressionPlan(
         row=row,
-        position_to_index_by_group=position_to_index_by_group,
-        positions_by_group=positions_by_group,
+        position_bounds_by_group=position_bounds_by_group,
+        branch_mapping_by_group=branch_mapping_by_group,
         group_pre_order=group_pre_order,
         group_post_order=group_post_order,
         query_group_pre_order=query_group_pre_order,
     )
 
 
-def _logical_window_indices(
-    position_to_index: dict[int, int],
+def _map_branch_positions(
+    logical_positions: torch.Tensor,
     *,
-    logical_start: int,
-    ratio: int,
-    allow_negative_padding: bool,
-) -> list[int]:
-    indices: list[int] = []
-    for logical_pos in range(logical_start, logical_start + ratio):
-        index = position_to_index.get(logical_pos)
-        if index is None:
-            if logical_pos < 0 and allow_negative_padding:
-                indices.append(-1)
-                continue
-            raise ValueError(
-                "DSV4 prefix-tree compression window references missing "
-                f"logical position {logical_pos}."
-            )
-        indices.append(index)
-    return indices
+    path_ends: torch.Tensor,
+    path_offsets: torch.Tensor,
+) -> torch.Tensor:
+    valid = logical_positions >= 0
+    safe = logical_positions.clamp_min(0)
+    path = torch.searchsorted(path_ends, safe, right=True)
+    return torch.where(valid, safe + path_offsets[path], -1)
 
 
 def build_prefix_tree_compression_layout(
@@ -252,66 +238,74 @@ def _emit_prefix_tree_compression_layout(
     plan: _Dsv4CompressionPlan,
     ratio: int,
 ) -> Dsv4CompressionLayout:
-    current_rows: list[list[int]] = []
-    previous_rows: list[list[int]] = []
-    entry_pre_orders: list[int] = []
-    entry_post_orders: list[int] = []
-    start_rows: list[int] = []
-    end_rows: list[int] = []
+    current_rows: list[torch.Tensor] = []
+    previous_rows: list[torch.Tensor] = []
+    entry_pre_orders: list[torch.Tensor] = []
+    entry_post_orders: list[torch.Tensor] = []
+    start_rows: list[torch.Tensor] = []
+    end_rows: list[torch.Tensor] = []
+    offsets = torch.arange(ratio)
 
     for segment in plan.row.segments:
-        position_to_index = plan.position_to_index_by_group[segment.group_id]
-        segment_positions = plan.positions_by_group[segment.group_id]
-        segment_end_pos = segment_positions[-1]
+        segment_end_pos = plan.position_bounds_by_group[segment.group_id][1]
         parent_end_pos = (
             -1
             if not segment.ancestors
-            else plan.positions_by_group[segment.ancestors[-1]][-1]
+            else plan.position_bounds_by_group[segment.ancestors[-1]][1]
+        )
+        starts = torch.arange(
+            ((parent_end_pos + 1) // ratio) * ratio,
+            ((segment_end_pos + 1) // ratio) * ratio,
+            ratio,
+        )
+        if not starts.numel():
+            continue
+        path_ends, path_offsets = plan.branch_mapping_by_group[segment.group_id]
+        logical_positions = starts[:, None] + offsets
+        current_rows.append(
+            _map_branch_positions(
+                logical_positions,
+                path_ends=path_ends,
+                path_offsets=path_offsets,
+            )
+        )
+        previous_rows.append(
+            _map_branch_positions(
+                logical_positions - ratio,
+                path_ends=path_ends,
+                path_offsets=path_offsets,
+            )
         )
-        usable = ((segment_end_pos + 1) // ratio) * ratio
-        for logical_start in range(0, usable, ratio):
-            if logical_start + ratio - 1 <= parent_end_pos:
-                continue
-            current_rows.append(
-                _logical_window_indices(
-                    position_to_index,
-                    logical_start=logical_start,
-                    ratio=ratio,
-                    allow_negative_padding=False,
-                )
+        entry_pre_orders.append(
+            torch.full(
+                (starts.numel(),),
+                plan.group_pre_order[segment.group_id],
+                dtype=torch.int32,
             )
-            previous_rows.append(
-                _logical_window_indices(
-                    position_to_index,
-                    logical_start=logical_start - ratio,
-                    ratio=ratio,
-                    allow_negative_padding=True,
-                )
+        )
+        entry_post_orders.append(
+            torch.full(
+                (starts.numel(),),
+                plan.group_post_order[segment.group_id],
+                dtype=torch.int32,
             )
-            entry_pre_orders.append(plan.group_pre_order[segment.group_id])
-            entry_post_orders.append(plan.group_post_order[segment.group_id])
-            start_rows.append(logical_start)
-            end_rows.append(logical_start + ratio - 1)
-
-    entry_count = len(current_rows)
-    current = torch.empty((entry_count, ratio), dtype=torch.long)
-    previous = torch.empty_like(current)
-    if entry_count:
-        current[:] = torch.tensor(current_rows, dtype=torch.long)
-        previous[:] = torch.tensor(previous_rows, dtype=torch.long)
-    entry_pre_order = torch.tensor(entry_pre_orders, dtype=torch.int32)
-    entry_post_order = torch.tensor(entry_post_orders, dtype=torch.int32)
-    starts = torch.tensor(start_rows, dtype=torch.long)
-    ends = torch.tensor(end_rows, dtype=torch.long)
-    query_pre_order = torch.tensor(plan.query_group_pre_order, dtype=torch.int32)
+        )
+        start_rows.append(starts)
+        end_rows.append(starts + ratio - 1)
+
+    empty = torch.empty((0, ratio), dtype=torch.long)
     return Dsv4CompressionLayout(
-        current,
-        previous,
-        entry_pre_order,
-        entry_post_order,
-        starts,
-        ends,
-        query_pre_order,
+        torch.cat(current_rows) if current_rows else empty,
+        torch.cat(previous_rows) if previous_rows else empty.clone(),
+        torch.cat(entry_pre_orders)
+        if entry_pre_orders
+        else torch.empty(0, dtype=torch.int32),
+        torch.cat(entry_post_orders)
+        if entry_post_orders
+        else torch.empty(0, dtype=torch.int32),
+        torch.cat(start_rows) if start_rows else torch.empty(0, dtype=torch.long),
+        torch.cat(end_rows) if end_rows else torch.empty(0, dtype=torch.long),
+        plan.query_group_pre_order,
     )
 
 
@@ -486,6 +480,8 @@ def __init__(
 
         self._keep_fp32_parameters = ("ape",)
         setattr(self.ape, "_keep_fp32", True)
+        if config.perform_initialization:
+            nn.init.zeros_(self.ape)
 
         base = cfg.dsv4_compress_rope_theta
         assert rope_head_dim == 64
diff --git a/src/art/megatron/dsv4/deepseek_v4.py b/src/art/megatron/dsv4/deepseek_v4.py
index 1ade6a202..2905d12e0 100644
--- a/src/art/megatron/dsv4/deepseek_v4.py
+++ b/src/art/megatron/dsv4/deepseek_v4.py
@@ -355,6 +355,8 @@ def __init__(
         self._keep_fp32_buffers = ("attn_sink",)
         self.attn_sink = nn.Parameter(attn_sink)
         setattr(self.attn_sink, "_keep_fp32", True)
+        if config.perform_initialization:
+            nn.init.zeros_(self.attn_sink)
 
         self.wq_a = TELinear(
             self.dim,
diff --git a/src/art/megatron/dsv4/hf_config.py b/src/art/megatron/dsv4/hf_config.py
index 75f6a0b63..c2eb684b1 100644
--- a/src/art/megatron/dsv4/hf_config.py
+++ b/src/art/megatron/dsv4/hf_config.py
@@ -49,7 +49,11 @@ def _ensure_torchvision_nms_schema() -> None:
             "nms(Tensor dets, Tensor scores, float iou_threshold) -> Tensor"
         )
     except RuntimeError as exc:
-        if "Only a single TORCH_LIBRARY" not in str(exc) and "already" not in str(exc):
+        if (
+            "Only a single TORCH_LIBRARY" not in str(exc)
+            and "already" not in str(exc)
+            and "multiple times" not in str(exc)
+        ):
             raise
         _TORCHVISION_LIB = torch.library.Library("torchvision", "FRAGMENT")
         try:
@@ -57,7 +61,9 @@ def _ensure_torchvision_nms_schema() -> None:
                 "nms(Tensor dets, Tensor scores, float iou_threshold) -> Tensor"
             )
         except RuntimeError as define_exc:
-            if "already" not in str(define_exc):
+            if "already" not in str(define_exc) and "multiple times" not in str(
+                define_exc
+            ):
                 raise
 
 
diff --git a/src/art/megatron/dsv4/hf_oracle.py b/src/art/megatron/dsv4/hf_oracle.py
new file mode 100644
index 000000000..4f4564cb6
--- /dev/null
+++ b/src/art/megatron/dsv4/hf_oracle.py
@@ -0,0 +1,293 @@
+from types import MethodType
+from typing import Any
+
+import torch
+from torch import nn
+
+from art.megatron.dsv4.compressor import (
+    Dsv4CompressionLayout,
+    build_prefix_tree_compression_layouts,
+    compressed_layout_visibility,
+)
+from art.megatron.dsv4.kernel.precision_aligned_ops import linear_bf16_fp32
+
+_COMPRESSOR_TYPES = {"DeepseekV4CSACompressor", "DeepseekV4HCACompressor"}
+_RMS_NORM_TYPE = "DeepseekV4RMSNorm"
+
+
+def _aligned_linear_forward(module: nn.Linear, x: torch.Tensor) -> torch.Tensor:
+    return linear_bf16_fp32(x, module.weight)
+
+
+def _patch_aligned_linear(module: nn.Linear) -> None:
+    if module.bias is not None:
+        raise RuntimeError("DSV4 compressor oracle projections must be bias-free")
+    if getattr(module, "_art_dsv4_aligned", False):
+        return
+    module.forward = MethodType(_aligned_linear_forward, module)
+    module._art_dsv4_aligned = True
+
+
+def _cast_compressor_output(
+    _module: nn.Module,
+    inputs: tuple[Any, ...],
+    output: tuple[torch.Tensor, torch.Tensor | None],
+) -> tuple[torch.Tensor, torch.Tensor | None]:
+    compressed_kv, block_bias = output
+    return compressed_kv.to(inputs[0].dtype), block_bias
+
+
+def _cast_indexer_key(
+    _module: nn.Module,
+    inputs: tuple[Any, ...],
+) -> tuple[Any, ...]:
+    q, compressed_kv, *rest = inputs
+    return q, compressed_kv.to(q.dtype), *rest
+
+
+def _cast_norm_output(
+    _module: nn.Module,
+    inputs: tuple[Any, ...],
+    output: torch.Tensor,
+) -> torch.Tensor:
+    return output.to(inputs[0].dtype)
+
+
+def _gather_projected(tensor: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
+    if int(tensor.shape[0]) != 1:
+        raise ValueError("DSV4 HF prefix compression requires batch size one")
+    safe_indices = indices.clamp(0, max(int(tensor.shape[1]) - 1, 0))
+    gathered = tensor[0].index_select(0, safe_indices.reshape(-1))
+    return gathered.view(1, *indices.shape, tensor.shape[-1])
+
+
+def _compress_prefix_projected(
+    module: Any,
+    kv: torch.Tensor,
+    gate: torch.Tensor,
+    layout: Dsv4CompressionLayout,
+) -> torch.Tensor:
+    ratio = int(module.compress_rate)
+    current_valid = layout.current_indices >= 0
+    current_kv = _gather_projected(kv, layout.current_indices)
+    current_gate = _gather_projected(gate, layout.current_indices)
+    current_kv = torch.where(
+        current_valid.unsqueeze(-1), current_kv, torch.zeros_like(current_kv)
+    )
+    current_gate = torch.where(
+        current_valid.unsqueeze(-1),
+        current_gate,
+        torch.full_like(current_gate, float("-inf")),
+    )
+    position_bias = module.position_bias.view(1, 1, ratio, -1)
+    if ratio == 4:
+        head_dim = int(module.head_dim)
+        previous_valid = layout.previous_indices >= 0
+        previous_kv = _gather_projected(kv, layout.previous_indices)
+        previous_gate = _gather_projected(gate, layout.previous_indices)
+        previous_kv = torch.where(
+            previous_valid.unsqueeze(-1),
+            previous_kv,
+            torch.zeros_like(previous_kv),
+        )
+        previous_gate = torch.where(
+            previous_valid.unsqueeze(-1),
+            previous_gate,
+            torch.full_like(previous_gate, float("-inf")),
+        )
+        current_gate = current_gate + position_bias
+        previous_gate = previous_gate + position_bias
+        slots_kv = torch.cat(
+            [previous_kv[..., :head_dim], current_kv[..., head_dim:]], dim=2
+        )
+        slots_gate = torch.cat(
+            [previous_gate[..., :head_dim], current_gate[..., head_dim:]], dim=2
+        )
+    else:
+        slots_kv = current_kv
+        slots_gate = current_gate + position_bias
+    compressed = (
+        slots_kv * slots_gate.softmax(dim=2, dtype=torch.float32).to(slots_kv.dtype)
+    ).sum(dim=2)
+    compressed = module.kv_norm(compressed)
+    positions = layout.entry_start_positions.unsqueeze(0)
+    cos, sin = module.rotary_emb(
+        compressed, position_ids=positions, layer_type=module.rope_layer_type
+    )
+    from transformers.models.deepseek_v4.modeling_deepseek_v4 import (
+        apply_rotary_pos_emb,
+    )
+
+    return apply_rotary_pos_emb(compressed.unsqueeze(1), cos, sin).squeeze(1)
+
+
+def _require_fresh_compressor_cache(past_key_values: Any, layer_idx: int) -> None:
+    if past_key_values is None:
+        return
+    cache_layer = past_key_values.layers[layer_idx]
+    nonempty = []
+    for name in ("buffer_kv", "buffer_gate", "compressed_kv"):
+        values = getattr(cache_layer, name, {})
+        nonempty.extend(
+            f"{name}.{key}" for key, value in values.items() if value is not None
+        )
+    for name in ("overlap_kv", "overlap_gate"):
+        values = getattr(cache_layer, name, {})
+        nonempty.extend(
+            f"{name}.{key}" for key, value in values.items() if value is not None
+        )
+    nonempty.extend(
+        f"entry_count.{key}={value}"
+        for key, value in getattr(cache_layer, "entry_count", {}).items()
+        if value
+    )
+    if nonempty:
+        raise ValueError(
+            "DSV4 HF prefix oracle requires fresh compressor cache state, got "
+            + ", ".join(nonempty)
+        )
+
+
+def _prefix_indexer_forward(
+    module: Any,
+    hidden_states: torch.Tensor,
+    q_residual: torch.Tensor | None = None,
+    position_ids: torch.Tensor | None = None,
+    past_key_values: Any = None,
+    layer_idx: int = 0,
+) -> torch.Tensor:
+    layout = getattr(module, "_art_dsv4_prefix_layout", None)
+    if layout is None:
+        if q_residual is None and position_ids is None and past_key_values is None:
+            return module._art_dsv4_flat_forward(hidden_states)
+        return module._art_dsv4_flat_forward(
+            hidden_states, q_residual, position_ids, past_key_values, layer_idx
+        )
+    if q_residual is None or position_ids is None:
+        raise ValueError("DSV4 HF prefix indexer requires query and position inputs")
+    _require_fresh_compressor_cache(past_key_values, layer_idx)
+    batch, seq_len, _ = hidden_states.shape
+    kv = module.kv_proj(hidden_states)
+    gate = module.gate_proj(hidden_states)
+    compressed = _compress_prefix_projected(module, kv, gate, layout)
+    cos, sin = module.rotary_emb(
+        hidden_states,
+        position_ids=position_ids,
+        layer_type=module.rope_layer_type,
+    )
+    from transformers.models.deepseek_v4.modeling_deepseek_v4 import (
+        apply_rotary_pos_emb,
+    )
+
+    q = module.q_b_proj(q_residual).view(
+        batch, seq_len, module.num_heads, module.head_dim
+    )
+    q = apply_rotary_pos_emb(q.transpose(1, 2), cos, sin).transpose(1, 2)
+    scores = module.scorer(q, compressed, hidden_states)
+    visible = compressed_layout_visibility(layout, position_ids=position_ids)
+    scores = scores.masked_fill(~visible, float("-inf"))
+    top_k = min(int(module.index_topk), int(compressed.shape[1]))
+    indices = scores.topk(top_k, dim=-1).indices
+    valid = visible.gather(-1, indices)
+    return torch.where(valid, indices, torch.full_like(indices, -1))
+
+
+def _prefix_compressor_forward(
+    module: Any,
+    hidden_states: torch.Tensor,
+    q_residual: torch.Tensor | None = None,
+    position_ids: torch.Tensor | None = None,
+    past_key_values: Any = None,
+    layer_idx: int = 0,
+) -> tuple[torch.Tensor, torch.Tensor | None]:
+    layout = getattr(module, "_art_dsv4_prefix_layout", None)
+    if layout is None:
+        if q_residual is None and position_ids is None and past_key_values is None:
+            return module._art_dsv4_flat_forward(hidden_states)
+        return module._art_dsv4_flat_forward(
+            hidden_states, q_residual, position_ids, past_key_values, layer_idx
+        )
+    if q_residual is None or position_ids is None:
+        raise ValueError("DSV4 HF prefix compressor requires query and position inputs")
+    _require_fresh_compressor_cache(past_key_values, layer_idx)
+    kv = module.kv_proj(hidden_states)
+    gate = module.gate_proj(hidden_states)
+    compressed = _compress_prefix_projected(module, kv, gate, layout)
+    compressed_kv = compressed.unsqueeze(1)
+    if hasattr(module, "indexer"):
+        top_k_indices = module.indexer(
+            hidden_states, q_residual, position_ids, past_key_values, layer_idx
+        )
+        compressed_len = int(compressed.shape[1])
+        valid = top_k_indices >= 0
+        safe_indices = torch.where(
+            valid, top_k_indices, torch.full_like(top_k_indices, compressed_len)
+        )
+        block_bias = compressed.new_full(
+            (*safe_indices.shape[:2], 1, compressed_len + 1), float("-inf")
+        ).transpose(1, 2)
+        block_bias.scatter_(-1, safe_indices.unsqueeze(1), 0.0)
+        return compressed_kv, block_bias[..., :compressed_len]
+    visible = compressed_layout_visibility(layout, position_ids=position_ids).unsqueeze(
+        1
+    )
+    block_bias = compressed.new_zeros(visible.shape).masked_fill(
+        ~visible, float("-inf")
+    )
+    return compressed_kv, block_bias
+
+
+def _patch_prefix_forward(module: Any, forward: Any) -> None:
+    module._art_dsv4_flat_forward = module.forward
+    module.forward = MethodType(forward, module)
+
+
+def prepare_hf_reference_model(model: Any) -> Any:
+    """Align native HF compressor precision with the training/serving path."""
+    for module in model.modules():
+        if type(module).__name__ == _RMS_NORM_TYPE:
+            module.register_forward_hook(_cast_norm_output)
+    compressors = [
+        module
+        for module in model.modules()
+        if type(module).__name__ in _COMPRESSOR_TYPES
+    ]
+    if not compressors:
+        raise RuntimeError("Native DSV4 HF model has no recognized compressor")
+    for compressor in compressors:
+        _patch_aligned_linear(compressor.kv_proj)
+        _patch_aligned_linear(compressor.gate_proj)
+        _patch_prefix_forward(compressor, _prefix_compressor_forward)
+        compressor.register_forward_hook(_cast_compressor_output)
+        indexer = getattr(compressor, "indexer", None)
+        if indexer is None:
+            continue
+        _patch_aligned_linear(indexer.kv_proj)
+        _patch_aligned_linear(indexer.gate_proj)
+        _patch_prefix_forward(indexer, _prefix_indexer_forward)
+        indexer.scorer.register_forward_pre_hook(_cast_indexer_key)
+    return model
+
+
+def set_hf_reference_prefix_tree(
+    model: Any,
+    *,
+    position_ids: torch.Tensor,
+    group_ids: torch.Tensor,
+    parent_ids: torch.Tensor,
+) -> None:
+    device = next(model.parameters()).device
+    layouts = build_prefix_tree_compression_layouts(
+        position_ids=position_ids.unsqueeze(0),
+        group_ids=group_ids.unsqueeze(0),
+        parent_ids=parent_ids.unsqueeze(0),
+        device=device,
+    )
+    for module in model.modules():
+        if type(module).__name__ not in _COMPRESSOR_TYPES:
+            continue
+        layout = layouts[int(module.compress_rate)]
+        module._art_dsv4_prefix_layout = layout
+        indexer = getattr(module, "indexer", None)
+        if indexer is not None:
+            indexer._art_dsv4_prefix_layout = layout
diff --git a/src/art/megatron/dsv4/hyper_connection.py b/src/art/megatron/dsv4/hyper_connection.py
index abe37397f..c8d14f67a 100644
--- a/src/art/megatron/dsv4/hyper_connection.py
+++ b/src/art/megatron/dsv4/hyper_connection.py
@@ -32,6 +32,11 @@ def __init__(self, config: TransformerConfig):
         )
         for param in (self.hc_head_fn, self.hc_head_base, self.hc_head_scale):
             setattr(param, "_keep_fp32", True)
+        if config.perform_initialization:
+            assert config.init_method is not None
+            config.init_method(self.hc_head_fn)
+            torch.nn.init.zeros_(self.hc_head_base)
+            torch.nn.init.ones_(self.hc_head_scale)
 
     def forward(self):
         raise NotImplementedError
diff --git a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla.py b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla.py
index 20fefe44f..7a430502f 100644
--- a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla.py
+++ b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla.py
@@ -51,7 +51,7 @@ def forward(ctx, q, kv, attn_sink, topk_idxs, sm_scale=None, output_dtype=None):
             )
 
         output = o if output_dtype is None else o.to(output_dtype)
-        ctx.save_for_backward(q, kv, attn_sink, topk_idxs, output.clone(), lse)
+        ctx.save_for_backward(q, kv, attn_sink, topk_idxs, lse)
         ctx.sm_scale = sm_scale
 
         return output
@@ -59,7 +59,7 @@ def forward(ctx, q, kv, attn_sink, topk_idxs, sm_scale=None, output_dtype=None):
     @staticmethod
     def backward(ctx: Any, *grad_outputs: Any):
         do = grad_outputs[0]
-        q, kv, attn_sink, topk_idxs, output, lse = ctx.saved_tensors
+        q, kv, attn_sink, topk_idxs, lse = ctx.saved_tensors
         sm_scale = ctx.sm_scale
 
         with preserve_tilelang_env():
@@ -71,7 +71,6 @@ def backward(ctx: Any, *grad_outputs: Any):
                 q,
                 kv,
                 attn_sink,
-                output.to(q.dtype),
                 do.to(q.dtype),
                 topk_idxs,
                 lse,
diff --git a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_bwd.py b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_bwd.py
index b854f5063..778218534 100644
--- a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_bwd.py
+++ b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_bwd.py
@@ -23,52 +23,107 @@
 def preprocess(
     H,
     D,
-    block_ND=32,
-    num_stages=5,
+    topk,
+    sm_scale=None,
+    block_size=64,
+    num_stages=0,
+    threads=128,
+    indices_dtype=T.int32,
     dtype=T.bfloat16,
     accum_dtype=T.float32,
 ):
+    assert topk % block_size == 0
     assert dtype == T.bfloat16
     assert accum_dtype == T.float32
     B = T.dynamic("batch")
     S = T.dynamic("seq_len")
-    shape = [B, S, H, D]
+    S_kv = T.dynamic("seq_len_kv")
+    if sm_scale is None:
+        sm_scale = D ** (-0.5)
+
+    q_shape = [B, S, H, D]
+    kv_shape = [B, S_kv, D]
+    indices_shape = [B, S, topk]
+    padded_H = max(tilelang.math.next_power_of_2(H), 16)
+    block_H = min(64, padded_H)
+    assert padded_H % block_H == 0
+    NH = padded_H // block_H
+    BS = block_size
+    NS = tilelang.cdiv(topk, block_size)
 
     @T.prim_func
     def preprocess_kernel(
-        O: T.Tensor(shape, dtype),  # type: ignore
-        dO: T.Tensor(shape, dtype),  # type: ignore
+        Q: T.Tensor(q_shape, dtype),  # type: ignore
+        KV: T.Tensor(kv_shape, dtype),  # type: ignore
+        dO: T.Tensor(q_shape, dtype),  # type: ignore
+        Indices: T.Tensor(indices_shape, indices_dtype),  # type: ignore
+        Lse: T.Tensor([B, S, H], accum_dtype),  # type: ignore
         Delta: T.Tensor([B, S, H], accum_dtype),  # type: ignore
     ):
-        with T.Kernel(H, T.ceildiv(S, block_ND), B) as (bx, by, bz):
-            o = T.alloc_fragment([block_ND, block_ND], accum_dtype)
-            do = T.alloc_fragment([block_ND, block_ND], accum_dtype)
-            delta = T.alloc_fragment([block_ND], accum_dtype)
-            acc = T.alloc_fragment([block_ND, block_ND], accum_dtype)
-            T.clear(acc)
-            for k in T.Pipelined(T.ceildiv(D, block_ND), num_stages=num_stages):
-                T.copy(
-                    O[
-                        bz,
-                        by * block_ND : (by + 1) * block_ND,
-                        bx,
-                        k * block_ND : (k + 1) * block_ND,
-                    ],
-                    o,
+        with T.Kernel(S, B, NH, threads=threads) as (s_i, by, bz):
+            Q_shared = T.alloc_shared([block_H, D], dtype)
+            KV_shared = T.alloc_shared([BS, D], dtype)
+            dO_shared = T.alloc_shared([block_H, D], dtype)
+            P_shared_cast = T.alloc_shared([block_H, BS], dtype)
+            dP_shared_cast = T.alloc_shared([block_H, BS], dtype)
+            mask = T.alloc_fragment([BS], "bool")
+            safe_indices = T.alloc_fragment([BS], indices_dtype)
+            acc_p = T.alloc_fragment([block_H, BS], accum_dtype)
+            acc_dp = T.alloc_fragment([block_H, BS], accum_dtype)
+            delta = T.alloc_fragment([block_H], accum_dtype)
+            delta_i = T.alloc_fragment([block_H], accum_dtype)
+
+            T.copy(Q[by, s_i, bz * block_H : (bz + 1) * block_H, :], Q_shared)
+            T.copy(dO[by, s_i, bz * block_H : (bz + 1) * block_H, :], dO_shared)
+            T.clear(delta)
+
+            for i_i in T.Pipelined(NS, num_stages=num_stages):
+                for bi_i in T.Parallel(BS):
+                    mask[bi_i] = Indices[by, s_i, i_i * BS + bi_i] != -1
+                    safe_indices[bi_i] = T.if_then_else(
+                        mask[bi_i], Indices[by, s_i, i_i * BS + bi_i], 0
+                    )
+                for bi_i, d_i in T.Parallel(BS, D):
+                    KV_shared[bi_i, d_i] = KV[by, safe_indices[bi_i], d_i]
+
+                T.gemm(
+                    Q_shared,
+                    KV_shared,
+                    acc_p,
+                    transpose_B=True,
+                    policy=T.GemmWarpPolicy.FullCol,
+                    clear_accum=True,
                 )
-                T.copy(
-                    dO[
-                        bz,
-                        by * block_ND : (by + 1) * block_ND,
-                        bx,
-                        k * block_ND : (k + 1) * block_ND,
-                    ],
-                    do,
+                T.copy(acc_p, P_shared_cast)
+                for h_i, bi_i in T.Parallel(block_H, BS):
+                    acc_p[h_i, bi_i] = P_shared_cast[h_i, bi_i] * sm_scale
+                T.copy(acc_p, P_shared_cast)
+                for h_i, bi_i in T.Parallel(block_H, BS):
+                    acc_p[h_i, bi_i] = T.if_then_else(
+                        mask[bi_i],
+                        T.exp2(
+                            P_shared_cast[h_i, bi_i] * 1.44269504
+                            - Lse[by, s_i, bz * block_H + h_i]
+                        ),
+                        0,
+                    )
+
+                T.gemm(
+                    dO_shared,
+                    KV_shared,
+                    acc_dp,
+                    transpose_B=True,
+                    policy=T.GemmWarpPolicy.FullCol,
+                    clear_accum=True,
                 )
-                for i, j in T.Parallel(block_ND, block_ND):
-                    acc[i, j] += o[i, j] * do[i, j]
-            T.reduce_sum(acc, delta, 1)
-            T.copy(delta, Delta[bz, by * block_ND : (by + 1) * block_ND, bx])
+                T.copy(acc_dp, dP_shared_cast)
+                for h_i, bi_i in T.Parallel(block_H, BS):
+                    acc_dp[h_i, bi_i] = acc_p[h_i, bi_i] * dP_shared_cast[h_i, bi_i]
+                T.reduce_sum(acc_dp, delta_i, dim=1)
+                for h_i in T.Parallel(block_H):
+                    delta[h_i] += delta_i[h_i]
+
+            T.copy(delta, Delta[by, s_i, bz * block_H : (bz + 1) * block_H])
 
     return preprocess_kernel
 
@@ -132,7 +187,6 @@ def bwd(
 
     if sm_scale is None:
         sm_scale = D ** (-0.5)
-    sm_scale_mul_reciprocal_log2 = sm_scale * 1.44269504  # log2(e)
 
     q_shape = [B, S, H, D]
     kv_shape = [B, S_kv, D]
@@ -206,21 +260,25 @@ def sparse_mqa_bwd_kernel(
                     transpose_B=True,
                     policy=T.GemmWarpPolicy.FullCol,
                 )
+                T.copy(acc_p, P_shared_cast)
+                for h_i, bi_i in T.Parallel(block_H, BS):
+                    acc_p[h_i, bi_i] = P_shared_cast[h_i, bi_i] * sm_scale
+                T.copy(acc_p, P_shared_cast)
                 for h_i, bi_i in T.Parallel(block_H, BS):
                     acc_p[h_i, bi_i] = T.if_then_else(
-                        mask[bi_i], acc_p[h_i, bi_i], -T.infinity(acc_p.dtype)
+                        mask[bi_i], P_shared_cast[h_i, bi_i], -T.infinity(acc_p.dtype)
                     )
 
                 # P = exp2(scores * sm_scale_log2e - LSE)
                 for h_i, bi_i in T.Parallel(block_H, BS):
                     acc_p[h_i, bi_i] = T.exp2(
-                        acc_p[h_i, bi_i] * sm_scale_mul_reciprocal_log2
-                        - Lse[by, s_i, bz * block_H + h_i]
+                        acc_p[h_i, bi_i] * 1.44269504 - Lse[by, s_i, bz * block_H + h_i]
                     )
 
                 T.copy(acc_p, P_shared_cast)
 
-                # dP = P * (dO @ KV^T - Delta)
+                # BF16 matmul in the canonical path rounds dO @ KV before the
+                # FP32 softmax derivative.
                 T.gemm(
                     dO_shared,
                     KV_shared,
@@ -230,14 +288,17 @@ def sparse_mqa_bwd_kernel(
                     clear_accum=True,
                 )
 
+                T.copy(acc_dp, dP_shared_cast)
+
                 for h_i, bi_i in T.Parallel(block_H, BS):
-                    acc_dp[h_i, bi_i] = (
-                        acc_p[h_i, bi_i]
-                        * (acc_dp[h_i, bi_i] - Delta[by, s_i, bz * block_H + h_i])
-                        * sm_scale
+                    acc_dp[h_i, bi_i] = acc_p[h_i, bi_i] * (
+                        dP_shared_cast[h_i, bi_i] - Delta[by, s_i, bz * block_H + h_i]
                     )
 
                 T.copy(acc_dp, dP_shared_cast)
+                for h_i, bi_i in T.Parallel(block_H, BS):
+                    acc_dp[h_i, bi_i] = dP_shared_cast[h_i, bi_i] * sm_scale
+                T.copy(acc_dp, dP_shared_cast)
 
                 # dQ += dP @ KV
                 T.gemm(
@@ -314,14 +375,13 @@ def _tilelang_input_dtype(torch_dtype):
     raise TypeError(f"DSV4 sparse MLA TileLang launch requires bf16, got {torch_dtype}")
 
 
-def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=None):
+def sparse_mqa_bwd_interface(q, kv, attn_sink, do, topk_idxs, lse, sm_scale=None):
     """Backward interface for V4 sparse MQA attention.
 
     Args:
         q:         [B, S, H, D] bf16
         kv:        [B, S_kv, D] bf16
         attn_sink: [H] fp32
-        o:         [B, S, H, D] bf16 (forward output)
         do:        [B, S, H, D] bf16 (grad of output)
         topk_idxs: [B, S, topk] int32
         lse:       [B, S, H] fp32 (log-sum-exp from forward)
@@ -338,7 +398,7 @@ def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=N
     _, S_kv, _ = kv.shape
     topk = topk_idxs.shape[-1]
     dtype = _tilelang_input_dtype(q.dtype)
-    assert kv.dtype == q.dtype and o.dtype == q.dtype and do.dtype == q.dtype
+    assert kv.dtype == q.dtype and do.dtype == q.dtype
 
     # Pad topk to next multiple of block_size (kernel requires divisibility)
     block_size = 64
@@ -356,9 +416,9 @@ def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=N
     with preserve_tilelang_env():
         # Keep sequence lengths dynamic so changing packed workloads reuse the
         # same generated kernels.  Model/tile dimensions remain static.
-        preprocess_kernel = preprocess(H, D, dtype=dtype)
+        preprocess_kernel = preprocess(H, D, topk, sm_scale, dtype=dtype)
         postprocess_kernel = postprocess(D, dtype=dtype)
-        delta = preprocess_kernel(o, do)
+        delta = preprocess_kernel(q, kv, do, topk_idxs, lse)
     dkv = torch.zeros_like(kv, dtype=torch.float32)
     d_attn_sink = torch.zeros_like(attn_sink)
     if topk <= block_size:
@@ -372,7 +432,15 @@ def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=N
                 dtype=dtype,
             )
             dq = bwd_kernel(
-                q, kv, do, attn_sink, topk_idxs, lse, delta, dkv, d_attn_sink
+                q,
+                kv,
+                do,
+                attn_sink,
+                topk_idxs,
+                lse,
+                delta,
+                dkv,
+                d_attn_sink,
             )
     else:
         dq_accum = torch.zeros_like(q, dtype=torch.float32)
@@ -389,7 +457,15 @@ def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=N
             for start in range(0, topk, block_size):
                 chunk = topk_idxs[:, :, start : start + block_size].contiguous()
                 dq_i = bwd_kernel(
-                    q, kv, do, attn_sink, chunk, lse, delta, dkv, d_attn_sink
+                    q,
+                    kv,
+                    do,
+                    attn_sink,
+                    chunk,
+                    lse,
+                    delta,
+                    dkv,
+                    d_attn_sink,
                 )
                 dq_accum.add_(dq_i.float())
         dq = dq_accum.to(q.dtype)
diff --git a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_fwd.py b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_fwd.py
index 860b27c06..aee26598e 100644
--- a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_fwd.py
+++ b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_fwd.py
@@ -43,9 +43,7 @@ def sparse_mqa_fwd(
         f"topk ({topk}) must be divisible by block_I ({block_I})"
     )
     if sm_scale is None:
-        sm_scale = (1.0 / dim) ** 0.5 * 1.44269504  # log2(e)
-    else:
-        sm_scale = sm_scale * 1.44269504  # log2(e)
+        sm_scale = (1.0 / dim) ** 0.5
 
     batch = T.dynamic("batch")
     seq_len = T.dynamic("seq_len")
@@ -101,8 +99,6 @@ def main(
             m_i_prev = T.alloc_fragment([H_per_block], accum_dtype)
 
             T.fill(acc_o, 0)
-            T.fill(sumexp, 0)
-            T.fill(m_i, -(2**30))
 
             b_i = by
             s_i = bx if REPLICATE_H == 1 else (bx // REPLICATE_H)
@@ -110,6 +106,10 @@ def main(
             H0 = 0 if REPLICATE_H == 1 else (bx % REPLICATE_H) * 64
             H1 = H0 + H_per_block
 
+            for h_i in T.Parallel(H_per_block):
+                sumexp[h_i] = 1
+                m_i[h_i] = AttnSink[H0 + h_i]
+
             T.copy(Q[b_i, s_i, H0:H1, :D], Q_shared)
 
             for i_i in T.Pipelined(NI, num_stages=num_stages):
@@ -135,42 +135,40 @@ def main(
                     transpose_B=True,
                     policy=T.GemmWarpPolicy.FullRow,
                 )
+                T.copy(acc_s, S_shared)
+                for h_i, bi_i in T.Parallel(H_per_block, BI):
+                    acc_s[h_i, bi_i] = S_shared[h_i, bi_i] * sm_scale
+                T.copy(acc_s, S_shared)
                 for h_i, bi_i in T.Parallel(H_per_block, BI):
                     acc_s[h_i, bi_i] = T.if_then_else(
-                        mask[bi_i], acc_s[h_i, bi_i], -T.infinity(acc_s.dtype)
+                        mask[bi_i], S_shared[h_i, bi_i], -T.infinity(acc_s.dtype)
                     )
                 T.copy(m_i, m_i_prev)
                 T.reduce_max(acc_s, m_i, dim=1, clear=False)
                 for h_i in T.Parallel(H_per_block):
                     m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i])
                 for h_i in T.Parallel(H_per_block):
-                    alpha[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale)
+                    alpha[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * 1.44269504)
                 for h_i, bi_i in T.Parallel(H_per_block, BI):
                     acc_s[h_i, bi_i] = T.exp2(
-                        acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale
+                        (acc_s[h_i, bi_i] - m_i[h_i]) * 1.44269504
                     )
                 T.reduce_sum(acc_s, sumexp_i, dim=1)
                 for h_i in T.Parallel(H_per_block):
-                    sumexp[h_i] = sumexp[h_i] * alpha[h_i] + sumexp_i[h_i]
+                    sumexp_i[h_i] = sumexp[h_i] * alpha[h_i] + sumexp_i[h_i]
+                    alpha[h_i] = sumexp[h_i] * alpha[h_i] / sumexp_i[h_i]
+                    sumexp[h_i] = sumexp_i[h_i]
                 for h_i, d_i in T.Parallel(H_per_block, D):
                     acc_o[h_i, d_i] = acc_o[h_i, d_i] * alpha[h_i]
 
+                for h_i, bi_i in T.Parallel(H_per_block, BI):
+                    acc_s[h_i, bi_i] /= sumexp[h_i]
                 T.copy(acc_s, S_shared)
                 T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
 
-            # attn_sink: add exp(attn_sink[h] - max_scaled) to softmax denominator
-            # attn_sink is a pre-scaled logit (same space as scores*sm_scale), so only convert to log2 base
-            for h_i in T.Parallel(H_per_block):
-                sumexp[h_i] += T.exp2(
-                    AttnSink[H0 + h_i] * 1.44269504 - m_i[h_i] * sm_scale
-                )
-
-            # Rescale output
-            for h_i, d_i in T.Parallel(H_per_block, D):
-                acc_o[h_i, d_i] /= sumexp[h_i]
             # LSE = log2(sumexp) + m_i * sm_scale (in log2 space)
             for h_i in T.Parallel(H_per_block):
-                sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale
+                sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * 1.44269504
 
             T.copy(acc_o, Output[b_i, s_i, H0:H1, :])
             T.copy(sumexp, Lse[b_i, s_i, H0:H1])
diff --git a/src/art/megatron/dsv4/layer.py b/src/art/megatron/dsv4/layer.py
index f7a42c0a7..a19f635de 100644
--- a/src/art/megatron/dsv4/layer.py
+++ b/src/art/megatron/dsv4/layer.py
@@ -260,6 +260,14 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
             self.hc_ffn_scale,
         ):
             setattr(param, "_keep_fp32", True)
+        if self.config.perform_initialization:
+            assert self.config.init_method is not None
+            self.config.init_method(self.hc_attn_fn)
+            self.config.init_method(self.hc_ffn_fn)
+            for param in (self.hc_attn_base, self.hc_ffn_base):
+                torch.nn.init.zeros_(param)
+            for param in (self.hc_attn_scale, self.hc_ffn_scale):
+                torch.nn.init.ones_(param)
         self.hc_util = DeepSeekV4HyperConnectionUtil(self.config)
 
     def forward(
diff --git a/src/art/megatron/expert_parallel.py b/src/art/megatron/expert_parallel.py
new file mode 100644
index 000000000..0fcc68748
--- /dev/null
+++ b/src/art/megatron/expert_parallel.py
@@ -0,0 +1,222 @@
+from __future__ import annotations
+
+import copy
+import functools
+import math
+import os
+from typing import Any, cast
+
+from megatron.bridge.models.conversion.param_mapping import AutoMapping
+from megatron.core.transformer.moe.router import TopKRouter
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+import torch
+
+
+class ExpertParallelLayout(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    logical_experts: int = Field(gt=0)
+    ep_size: int = Field(gt=0)
+    physical_to_logical: tuple[int | None, ...]
+
+    @model_validator(mode="after")
+    def _validate_layout(self) -> ExpertParallelLayout:
+        if len(self.physical_to_logical) % self.ep_size:
+            raise ValueError("physical expert slots must divide evenly across EP ranks")
+        logical = tuple(
+            expert for expert in self.physical_to_logical if expert is not None
+        )
+        if logical != tuple(range(self.logical_experts)):
+            raise ValueError(
+                "physical expert slots must contain every logical expert once"
+            )
+        for ep_rank in range(self.ep_size):
+            local = self.local_logical_experts(ep_rank)
+            real_count = sum(expert is not None for expert in local)
+            if any(expert is None for expert in local[:real_count]):
+                raise ValueError("masked expert slots must be local-rank suffixes")
+        return self
+
+    @classmethod
+    def build(
+        cls,
+        logical_experts: int,
+        ep_size: int,
+        *,
+        slots_per_rank_multiple: int = 1,
+    ) -> ExpertParallelLayout:
+        if slots_per_rank_multiple <= 0:
+            raise ValueError("slots_per_rank_multiple must be positive")
+        logical_slots_per_rank = math.ceil(logical_experts / ep_size)
+        slots_per_rank = (
+            math.ceil(logical_slots_per_rank / slots_per_rank_multiple)
+            * slots_per_rank_multiple
+        )
+        short_rank_count = logical_slots_per_rank * ep_size - logical_experts
+        short_ranks = (
+            {
+                math.floor((index + 0.5) * ep_size / short_rank_count)
+                for index in range(short_rank_count)
+            }
+            if short_rank_count
+            else set()
+        )
+        next_expert = 0
+        physical_to_logical: list[int | None] = []
+        for ep_rank in range(ep_size):
+            local_count = logical_slots_per_rank - (ep_rank in short_ranks)
+            physical_to_logical.extend(range(next_expert, next_expert + local_count))
+            physical_to_logical.extend([None] * (slots_per_rank - local_count))
+            next_expert += local_count
+        return cls(
+            logical_experts=logical_experts,
+            ep_size=ep_size,
+            physical_to_logical=tuple(physical_to_logical),
+        )
+
+    @property
+    def physical_experts(self) -> int:
+        return len(self.physical_to_logical)
+
+    @property
+    def slots_per_rank(self) -> int:
+        return self.physical_experts // self.ep_size
+
+    @property
+    def logical_to_physical(self) -> tuple[int, ...]:
+        result = [0] * self.logical_experts
+        for physical, logical in enumerate(self.physical_to_logical):
+            if logical is not None:
+                result[logical] = physical
+        return tuple(result)
+
+    def local_logical_experts(self, ep_rank: int) -> tuple[int | None, ...]:
+        if not 0 <= ep_rank < self.ep_size:
+            raise ValueError(f"invalid EP rank {ep_rank} for EP={self.ep_size}")
+        start = ep_rank * self.slots_per_rank
+        return self.physical_to_logical[start : start + self.slots_per_rank]
+
+    def logical_expert(self, physical_expert: int) -> int | None:
+        if not 0 <= physical_expert < self.physical_experts:
+            raise ValueError(
+                f"invalid physical expert {physical_expert}; "
+                f"expected [0, {self.physical_experts})"
+            )
+        return self.physical_to_logical[physical_expert]
+
+
+def configure_expert_parallel_layout(config: Any) -> ExpertParallelLayout | None:
+    logical_experts = int(getattr(config, "num_moe_experts", 0) or 0)
+    ep_size = int(getattr(config, "expert_model_parallel_size", 1) or 1)
+    if logical_experts == 0:
+        return None
+    raw_ranks_per_domain = os.environ.get("NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN")
+    ranks_per_domain = int(raw_ranks_per_domain) if raw_ranks_per_domain else None
+    if ranks_per_domain is not None and ranks_per_domain <= 0:
+        raise ValueError("HybridEP ranks per NVLink domain must be positive")
+    layout = ExpertParallelLayout.build(
+        logical_experts,
+        ep_size,
+        slots_per_rank_multiple=(
+            1 if ranks_per_domain is None else 4 // math.gcd(4, ranks_per_domain)
+        ),
+    )
+    if layout.physical_experts == logical_experts:
+        return None
+    config.art_expert_parallel_layout = layout
+    return layout
+
+
+def activate_expert_parallel_layout(config: Any) -> ExpertParallelLayout | None:
+    layout = get_expert_parallel_layout(config)
+    if layout is not None:
+        config.num_moe_experts = layout.physical_experts
+    return layout
+
+
+def get_expert_parallel_layout(config: Any) -> ExpertParallelLayout | None:
+    layout = getattr(config, "art_expert_parallel_layout", None)
+    if layout is None:
+        return None
+    if not isinstance(layout, ExpertParallelLayout):
+        raise TypeError(f"invalid ART expert parallel layout: {type(layout).__name__}")
+    return layout
+
+
+class _LogicalRouterMixin:
+    def __init__(
+        self,
+        config: Any,
+        pg_collection: Any = None,
+        is_mtp_layer: bool = False,
+    ) -> None:
+        layout = get_expert_parallel_layout(config)
+        if layout is None:
+            raise RuntimeError("logical router requires a non-uniform expert layout")
+        logical_config = copy.copy(config)
+        logical_config.num_moe_experts = layout.logical_experts
+        parent = cast(Any, super())
+        parent.__init__(
+            config=logical_config,
+            pg_collection=pg_collection,
+            is_mtp_layer=is_mtp_layer,
+        )
+        physical_to_logical = [
+            layout.logical_experts if expert is None else expert
+            for expert in layout.physical_to_logical
+        ]
+        cast(Any, self).register_buffer(
+            "_physical_to_logical",
+            torch.tensor(physical_to_logical, dtype=torch.int64),
+            persistent=False,
+        )
+
+    def forward(
+        self,
+        input: torch.Tensor,
+        padding_mask: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        probabilities, routing_map = cast(Any, super()).forward(input, padding_mask)
+        physical_to_logical = cast(torch.Tensor, getattr(self, "_physical_to_logical"))
+        return (
+            _expand_logical_experts(probabilities, physical_to_logical),
+            _expand_logical_experts(routing_map, physical_to_logical),
+        )
+
+
+@functools.cache
+def logical_router_type(router_type: type) -> type:
+    if issubclass(router_type, _LogicalRouterMixin):
+        return router_type
+    logical_type = type(
+        f"ArtLogical{router_type.__name__}",
+        (_LogicalRouterMixin, router_type),
+        {"__module__": __name__},
+    )
+    AutoMapping.register_module_type(logical_type.__name__, "replicated")
+    return logical_type
+
+
+LogicalTopKRouter = logical_router_type(TopKRouter)
+
+
+def _expand_logical_experts(
+    tensor: torch.Tensor, physical_to_logical: torch.Tensor
+) -> torch.Tensor:
+    tensor = torch.cat(
+        (tensor, tensor.new_zeros(*tensor.shape[:-1], 1)),
+        dim=-1,
+    )
+    return tensor.index_select(-1, physical_to_logical)
+
+
+def patch_moe_routers(block_spec: Any) -> int:
+    patched = 0
+    for layer_spec in getattr(block_spec, "layer_specs", ()) or ():
+        layer_submodules = getattr(layer_spec, "submodules", None)
+        mlp_spec = getattr(layer_submodules, "mlp", None)
+        moe_submodules = getattr(mlp_spec, "submodules", None)
+        if moe_submodules is not None and hasattr(moe_submodules, "router"):
+            moe_submodules.router = logical_router_type(moe_submodules.router)
+            patched += 1
+    return patched
diff --git a/src/art/megatron/flex_attn/attention.py b/src/art/megatron/flex_attn/attention.py
index b284813f6..ab93a4bac 100644
--- a/src/art/megatron/flex_attn/attention.py
+++ b/src/art/megatron/flex_attn/attention.py
@@ -61,12 +61,14 @@ def forward(
         backend = flex_backend_for_head_dims(
             head_dim=int(q.shape[-1]),
             head_dim_v=int(v.shape[-1]),
+            device=q.device,
         )
         result = get_dense_compiled_flex_attention(
             backend=backend,
             head_dim=int(q.shape[-1]),
             head_dim_v=int(v.shape[-1]),
             triton_num_stages_2_head_dims=self.triton_num_stages_2_head_dims,
+            device=q.device,
         )(
             q,
             k,
diff --git a/src/art/megatron/flex_attn/compiled.py b/src/art/megatron/flex_attn/compiled.py
index 00b220d96..bc38248e6 100644
--- a/src/art/megatron/flex_attn/compiled.py
+++ b/src/art/megatron/flex_attn/compiled.py
@@ -27,9 +27,18 @@
 SparseBlockSize: TypeAlias = int | tuple[int, int]
 
 
-def flex_backend_for_head_dims(*, head_dim: int, head_dim_v: int) -> FlexBackend:
+def flex_backend_for_head_dims(
+    *,
+    head_dim: int,
+    head_dim_v: int,
+    device: torch.device | None = None,
+) -> FlexBackend:
     if _FORCED_FLEX_BACKEND != "FLASH":
         return "TRITON"
+    if device is not None and device.type == "cuda":
+        major, _minor = torch.cuda.get_device_capability(device)
+        if major in {10, 11}:
+            return "TRITON"
     if int(head_dim) > 256 or int(head_dim_v) > 256:
         return "TRITON"
     return "FLASH"
@@ -51,6 +60,14 @@ def normalize_flex_lse(
     FlexKernelOptions,
     {"BACKEND": "TRITON", "num_stages": 2},
 )
+_BLACKWELL_WIDE_HEAD_FLEX_KERNEL_OPTIONS = cast(
+    FlexKernelOptions,
+    {
+        "BACKEND": "TRITON",
+        "num_stages": 2,
+        "BLOCK_M": 32,
+    },
+)
 _FORCED_FLEX_KERNEL_OPTIONS = cast(
     FlexKernelOptions,
     {"BACKEND": _FORCED_FLEX_BACKEND},
@@ -72,7 +89,12 @@ def flash_sparse_block_size_for_head_dim(
     head_dim_v: int,
     device: torch.device,
 ) -> tuple[int, int]:
-    if flex_backend_for_head_dims(head_dim=head_dim, head_dim_v=head_dim_v) != "FLASH":
+    if (
+        flex_backend_for_head_dims(
+            head_dim=head_dim, head_dim_v=head_dim_v, device=device
+        )
+        != "FLASH"
+    ):
         return (128, 128)
     if device.type != "cuda":
         return (128, 128)
@@ -240,13 +262,41 @@ def _needs_triton_num_stages_2(
     )
 
 
+def _needs_blackwell_wide_head_tile(
+    *,
+    backend: FlexBackend,
+    head_dim: int,
+    head_dim_v: int,
+    triton_num_stages_2_head_dims: tuple[int, ...],
+    device: torch.device | None,
+) -> bool:
+    if device is None or device.type != "cuda":
+        return False
+    major, _minor = torch.cuda.get_device_capability(device)
+    return major == 10 and _needs_triton_num_stages_2(
+        backend=backend,
+        head_dim=head_dim,
+        head_dim_v=head_dim_v,
+        triton_num_stages_2_head_dims=triton_num_stages_2_head_dims,
+    )
+
+
 def get_dense_compiled_flex_attention(
     *,
     backend: FlexBackend,
     head_dim: int,
     head_dim_v: int,
     triton_num_stages_2_head_dims: tuple[int, ...] = (),
+    device: torch.device | None = None,
 ) -> Any:
+    if _needs_blackwell_wide_head_tile(
+        backend=backend,
+        head_dim=head_dim,
+        head_dim_v=head_dim_v,
+        triton_num_stages_2_head_dims=triton_num_stages_2_head_dims,
+        device=device,
+    ):
+        return blackwell_wide_head_dense_compiled_flex_attention
     if _needs_triton_num_stages_2(
         backend=backend,
         head_dim=head_dim,
@@ -268,8 +318,17 @@ def get_sparse_compiled_flex_attention(
     head_dim: int,
     head_dim_v: int,
     triton_num_stages_2_head_dims: tuple[int, ...] = (),
+    device: torch.device | None = None,
 ) -> Any:
     del family_key
+    if _needs_blackwell_wide_head_tile(
+        backend=backend,
+        head_dim=head_dim,
+        head_dim_v=head_dim_v,
+        triton_num_stages_2_head_dims=triton_num_stages_2_head_dims,
+        device=device,
+    ):
+        return blackwell_wide_head_sparse_compiled_flex_attention
     if _needs_triton_num_stages_2(
         backend=backend,
         head_dim=head_dim,
@@ -296,6 +355,9 @@ def get_sparse_compiled_flex_attention(
 triton_num_stages_2_dense_compiled_flex_attention = torch.compile(
     _flex_attention_with_options(_TRITON_NUM_STAGES_2_FLEX_KERNEL_OPTIONS),
 )
+blackwell_wide_head_dense_compiled_flex_attention = torch.compile(
+    _flex_attention_with_options(_BLACKWELL_WIDE_HEAD_FLEX_KERNEL_OPTIONS),
+)
 
 sparse_compiled_flex_attention = torch.compile(
     _sparse_flex_attention_with_options(_FORCED_FLEX_KERNEL_OPTIONS),
@@ -309,3 +371,6 @@ def get_sparse_compiled_flex_attention(
 triton_num_stages_2_sparse_compiled_flex_attention = torch.compile(
     _sparse_flex_attention_with_options(_TRITON_NUM_STAGES_2_FLEX_KERNEL_OPTIONS),
 )
+blackwell_wide_head_sparse_compiled_flex_attention = torch.compile(
+    _sparse_flex_attention_with_options(_BLACKWELL_WIDE_HEAD_FLEX_KERNEL_OPTIONS),
+)
diff --git a/src/art/megatron/gdn/__init__.py b/src/art/megatron/gdn/__init__.py
index a62769edb..a6fd2eb30 100644
--- a/src/art/megatron/gdn/__init__.py
+++ b/src/art/megatron/gdn/__init__.py
@@ -2,12 +2,15 @@
 
 from .fla_cp import chunk_gated_delta_rule_native_cp
 from .gdn_prefix_tree import (
+    GdnGlobalExecutionDecision,
     GdnPackedExecutionSpec,
     GdnPlannerConfig,
     GdnRankExecutionPlan,
     GdnSegmentBucketPlan,
     GdnSegmentSpec,
+    build_gdn_global_execution_decision,
     build_gdn_rank_execution_plan,
+    materialize_gdn_rank_execution_plan,
     move_gdn_rank_execution_plan_to_device,
     parse_gdn_prefix_tree_segments,
 )
@@ -16,13 +19,16 @@
 
 __all__ = [
     "chunk_gated_delta_rule_native_cp",
+    "GdnGlobalExecutionDecision",
     "GdnPackedExecutionSpec",
     "GdnPlannerConfig",
     "GdnRankExecutionPlan",
     "GdnSegmentSpec",
     "GdnSegmentBucketPlan",
+    "build_gdn_global_execution_decision",
     "build_gdn_rank_execution_plan",
     "exchange_rank_tensor_all_to_all",
+    "materialize_gdn_rank_execution_plan",
     "move_gdn_rank_execution_plan_to_device",
     "parse_gdn_prefix_tree_segments",
     "run_gdn_layer",
diff --git a/src/art/megatron/gdn/gdn_prefix_tree.py b/src/art/megatron/gdn/gdn_prefix_tree.py
index eeb7a1a1c..e122a09e0 100644
--- a/src/art/megatron/gdn/gdn_prefix_tree.py
+++ b/src/art/megatron/gdn/gdn_prefix_tree.py
@@ -4,6 +4,7 @@
 from dataclasses import dataclass, replace
 from typing import Any, Literal, NamedTuple, cast
 
+from pydantic import BaseModel, ConfigDict
 import torch
 
 from art.megatron.context_parallel.layout_index import TokenLayoutIndex
@@ -303,6 +304,25 @@ def gdn_token_indices(self) -> tuple[int, ...]:
         return _tokens_from_rank_ranges(self.gdn_token_ranges)
 
 
+class GdnGlobalExecutionDecision(BaseModel):
+    """All-rank GDN decisions without rank-local planner tensors."""
+
+    model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)
+
+    cp_size: int
+    source_layout: TokenLayoutIndex
+    depth_count: int
+    gdn_token_counts_by_rank: tuple[int, ...]
+    owner_by_node: tuple[int, ...]
+    chained_nodes: tuple[bool, ...]
+    tree_has_children: tuple[bool, ...]
+    gdn_ranges_by_rank_by_position: tuple[tuple[tuple[int, int, int], ...], ...]
+    gdn_ranges_by_rank_by_source: tuple[tuple[tuple[int, int, int], ...], ...]
+    segments_by_rank_depth: tuple[tuple[tuple[GdnSegmentSpec, ...], ...], ...]
+    chain_segments_by_depth: tuple[tuple[GdnSegmentSpec, ...], ...]
+    cross_rank_token_count: int
+
+
 @dataclass(frozen=True)
 class _AttentionLayoutIndex:
     """Counting index for CP attention token ownership."""
@@ -366,41 +386,34 @@ def build_gdn_rank_execution_plan(
     fork buckets for short work where CP collectives would be inefficient.
     """
 
-    planner_config = planner_config or GdnPlannerConfig()
-    target_device = torch.device(device)
-    if target_device.type != "cpu":
-        cpu_plan = build_gdn_rank_execution_plan(
-            spec,
-            device="cpu",
-            cp_rank=cp_rank,
-            cp_size=cp_size,
-            attention_token_layout_index=attention_token_layout_index,
-            planner_config=planner_config,
-        )
-        return move_gdn_rank_execution_plan_to_device(cpu_plan, target_device)
-    return _build_tree_rank_execution_plan(
+    resolved_config = planner_config or GdnPlannerConfig()
+    decision = build_gdn_global_execution_decision(
         spec,
-        device=device,
-        cp_rank=cp_rank,
         cp_size=cp_size,
         attention_token_layout_index=attention_token_layout_index,
-        planner_config=planner_config,
+        planner_config=resolved_config,
+    )
+    return materialize_gdn_rank_execution_plan(
+        spec,
+        decision,
+        device=device,
+        cp_rank=cp_rank,
+        planner_config=resolved_config,
     )
 
 
-def _build_tree_rank_execution_plan(
+def build_gdn_global_execution_decision(
     spec: GdnPackedExecutionSpec,
     *,
-    device: torch.device | str,
-    cp_rank: int,
-    cp_size: int,
-    attention_token_layout_index: TokenLayoutIndex | None,
-    planner_config: GdnPlannerConfig,
-) -> GdnRankExecutionPlan:
+    cp_size: int = 1,
+    attention_token_layout_index: TokenLayoutIndex | None = None,
+    planner_config: GdnPlannerConfig | None = None,
+) -> GdnGlobalExecutionDecision:
+    """Select one deterministic all-rank GDN assignment without tensors."""
+
+    planner_config = planner_config or GdnPlannerConfig()
     if cp_size < 1:
         raise ValueError(f"cp_size must be >= 1, got {cp_size}")
-    if cp_rank < 0 or cp_rank >= cp_size:
-        raise ValueError(f"cp_rank must be in [0, {cp_size}), got {cp_rank}")
     if not spec.tree_segments:
         raise ValueError("tree GDN planning requires tree segments")
     if len(spec.tree_parent_indices) != len(spec.tree_segments):
@@ -408,11 +421,6 @@ def _build_tree_rank_execution_plan(
     if len(spec.tree_depths) != len(spec.tree_segments):
         raise ValueError("tree depth metadata length must match tree segments")
 
-    from art.megatron.gdn.layout import (
-        _reverse_exchange_plan,
-        build_local_rank_cp_exchange_plan_from_dest_ranges,
-    )
-
     source_layout = _attention_source_layout(
         spec,
         cp_size=cp_size,
@@ -549,27 +557,85 @@ def assign_tree(node_index: int) -> None:
         tuple(sorted(ranges)) for ranges in gdn_ranges_by_rank
     )
 
+    return GdnGlobalExecutionDecision(
+        cp_size=cp_size,
+        source_layout=source_layout,
+        depth_count=depth_count,
+        gdn_token_counts_by_rank=tuple(rank_loads),
+        owner_by_node=tuple(owner_by_node),
+        chained_nodes=tuple(chained_nodes),
+        tree_has_children=tuple(tree_has_children),
+        gdn_ranges_by_rank_by_position=gdn_ranges_by_rank_by_position,
+        gdn_ranges_by_rank_by_source=gdn_ranges_by_rank_by_source,
+        segments_by_rank_depth=tuple(
+            tuple(tuple(segments) for segments in rank_depths)
+            for rank_depths in segments_by_rank_depth
+        ),
+        chain_segments_by_depth=tuple(
+            tuple(segments) for segments in chain_segments_by_depth
+        ),
+        cross_rank_token_count=cross_rank_token_count,
+    )
+
+
+def materialize_gdn_rank_execution_plan(
+    spec: GdnPackedExecutionSpec,
+    decision: GdnGlobalExecutionDecision,
+    *,
+    device: torch.device | str,
+    cp_rank: int = 0,
+    planner_config: GdnPlannerConfig | None = None,
+) -> GdnRankExecutionPlan:
+    """Build only one rank's tensor metadata from a global decision."""
+
+    cp_size = int(decision.cp_size)
+    if cp_rank < 0 or cp_rank >= cp_size:
+        raise ValueError(f"cp_rank must be in [0, {cp_size}), got {cp_rank}")
+    target_device = torch.device(device)
+    if target_device.type != "cpu":
+        cpu_plan = materialize_gdn_rank_execution_plan(
+            spec,
+            decision,
+            device="cpu",
+            cp_rank=cp_rank,
+            planner_config=planner_config,
+        )
+        return move_gdn_rank_execution_plan_to_device(cpu_plan, target_device)
+
+    from art.megatron.gdn.layout import (
+        _reverse_exchange_plan,
+        build_local_rank_cp_exchange_plan_from_dest_ranges,
+    )
+
+    planner_config = planner_config or GdnPlannerConfig()
+    device = target_device
+    source_layout = decision.source_layout
+    depth_count = int(decision.depth_count)
+    rank_loads = decision.gdn_token_counts_by_rank
+    gdn_ranges_by_rank_by_position = decision.gdn_ranges_by_rank_by_position
+    gdn_ranges_by_rank_by_source = decision.gdn_ranges_by_rank_by_source
+
     attention_to_gdn = build_local_rank_cp_exchange_plan_from_dest_ranges(
         source_layout=source_layout,
         device=device,
         local_rank=cp_rank,
         dest_ranges_by_rank=gdn_ranges_by_rank_by_position,
-        cross_rank_token_count=cross_rank_token_count,
+        cross_rank_token_count=decision.cross_rank_token_count,
     )
     local_token_ranges = gdn_ranges_by_rank_by_source[cp_rank]
     if cp_size == 1:
         tree_segment_buckets_by_depth = _build_chunk_aligned_cp1_tree_buckets(
             spec,
-            tuple(tree_has_children),
+            decision.tree_has_children,
             device=device,
             planner_config=planner_config,
         )
     else:
         tree_segment_buckets_by_depth = tuple(
             _build_tree_bucket_plans(
-                tuple(segments_by_rank_depth[cp_rank][depth]),
+                decision.segments_by_rank_depth[cp_rank][depth],
                 spec.tree_parent_indices,
-                tuple(tree_has_children),
+                decision.tree_has_children,
                 local_token_ranges=local_token_ranges,
                 sequence_length=spec.sequence_length,
                 device=device,
@@ -579,9 +645,9 @@ def assign_tree(node_index: int) -> None:
     tree_chain_buckets_by_depth = (
         tuple(
             _build_tree_bucket_plans(
-                tuple(chain_segments_by_depth[depth]),
+                decision.chain_segments_by_depth[depth],
                 spec.tree_parent_indices,
-                tuple(tree_has_children),
+                decision.tree_has_children,
                 local_token_ranges=local_token_ranges,
                 sequence_length=spec.sequence_length,
                 device=device,
@@ -597,8 +663,8 @@ def assign_tree(node_index: int) -> None:
     )
     tree_state_exchanges_by_depth = _build_tree_state_exchanges_by_depth(
         spec,
-        owner_by_node=tuple(owner_by_node),
-        chained_nodes=tuple(chained_nodes),
+        owner_by_node=decision.owner_by_node,
+        chained_nodes=decision.chained_nodes,
         cp_rank=cp_rank,
         cp_size=cp_size,
         depth_count=depth_count,
@@ -1161,47 +1227,6 @@ def assign_tree(node_index: int) -> None:
     )
 
 
-def _add_local_search_load(
-    rank_loads: list[int],
-    owner: int,
-    segment: GdnSegmentSpec,
-    *,
-    segment_attention_counts: dict[tuple[int, int, int], tuple[int, ...]],
-) -> int:
-    rank_loads[owner] += segment.length
-    return segment.length - segment_attention_counts[_segment_key(segment)][owner]
-
-
-def _estimate_local_rank_kernel_work(
-    local_segments_by_rank_depth: tuple[tuple[tuple[GdnSegmentSpec, ...], ...], ...],
-) -> tuple[tuple[int, ...], int, int]:
-    estimate = _estimate_local_runtime_from_lengths(
-        tuple(
-            tuple(
-                tuple(segment.length for segment in segments)
-                for segments in rank_segments
-            )
-            for rank_segments in local_segments_by_rank_depth
-        )
-    )
-    return (
-        estimate.rank_work,
-        max(estimate.rank_bucket_counts, default=0),
-        max(estimate.rank_segment_counts, default=0),
-    )
-
-
-def _estimate_local_rank_kernel_work_from_lengths(
-    local_lengths_by_rank_depth: tuple[tuple[tuple[int, ...], ...], ...],
-) -> tuple[tuple[int, ...], int, int]:
-    estimate = _estimate_local_runtime_from_lengths(local_lengths_by_rank_depth)
-    return (
-        estimate.rank_work,
-        max(estimate.rank_bucket_counts, default=0),
-        max(estimate.rank_segment_counts, default=0),
-    )
-
-
 def _estimate_local_runtime_from_lengths(
     local_lengths_by_rank_depth: tuple[tuple[tuple[int, ...], ...], ...],
 ) -> _GdnLocalRuntimeEstimate:
@@ -1227,36 +1252,6 @@ def _estimate_local_runtime_from_lengths(
     )
 
 
-def _estimate_chain_rank_kernel_work(
-    chain_segments_by_depth: tuple[tuple[GdnSegmentSpec, ...], ...],
-    *,
-    chain_rank_counts_by_key: dict[GdnSegmentDecisionKey, tuple[int, ...]],
-    cp_size: int,
-) -> tuple[tuple[int, ...], int]:
-    estimate = _estimate_chain_runtime_from_counts(
-        tuple(
-            tuple(
-                chain_rank_counts_by_key[_segment_key(segment)] for segment in segments
-            )
-            for segments in chain_segments_by_depth
-        ),
-        cp_size=cp_size,
-    )
-    return estimate.rank_work, estimate.bucket_count
-
-
-def _estimate_chain_rank_kernel_work_from_counts(
-    chain_rank_counts_by_depth: tuple[tuple[tuple[int, ...], ...], ...],
-    *,
-    cp_size: int,
-) -> tuple[tuple[int, ...], int]:
-    estimate = _estimate_chain_runtime_from_counts(
-        chain_rank_counts_by_depth,
-        cp_size=cp_size,
-    )
-    return estimate.rank_work, estimate.bucket_count
-
-
 def _estimate_chain_runtime_from_counts(
     chain_rank_counts_by_depth: tuple[tuple[tuple[int, ...], ...], ...],
     *,
diff --git a/src/art/megatron/glm52/__init__.py b/src/art/megatron/glm52/__init__.py
new file mode 100644
index 000000000..2d9f1b8d7
--- /dev/null
+++ b/src/art/megatron/glm52/__init__.py
@@ -0,0 +1 @@
+"""GLM-5.2 model and prefix-tree sparse-attention support."""
diff --git a/src/art/megatron/glm52/attention.py b/src/art/megatron/glm52/attention.py
new file mode 100644
index 000000000..33ae6787a
--- /dev/null
+++ b/src/art/megatron/glm52/attention.py
@@ -0,0 +1,421 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from functools import partial
+from typing import Any
+
+from megatron.core.process_groups_config import ProcessGroupCollection
+from megatron.core.tensor_parallel.mappings import (
+    copy_to_tensor_model_parallel_region,
+    gather_from_sequence_parallel_region,
+)
+from megatron.core.transformer.attention import Attention
+from megatron.core.transformer.enums import AttnMaskType
+from megatron.core.transformer.spec_utils import build_module
+from megatron.core.transformer.transformer_config import MLATransformerConfig
+from megatron.core.typed_torch import apply_module, not_none
+from megatron.core.utils import get_pg_size
+import torch
+
+from art.megatron.glm52.cp_attention import context_parallel_sparse_mla
+from art.megatron.glm52.indexer import (
+    Glm52RoutedTopk,
+    context_parallel_tree_topk,
+    indexer_rope,
+    streaming_tree_topk,
+)
+from art.megatron.glm52.sparse_mla import sparse_mla
+from art.megatron.glm52.state import Glm52PrefixTreeState, require_glm52_state
+
+
+def _tensor(value: Any) -> torch.Tensor:
+    return value[0] if isinstance(value, tuple) else value
+
+
+def _latent_rms_norm(x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
+    x_float = x.float()
+    normalized = x_float * torch.rsqrt(x_float.square().mean(-1, keepdim=True) + 1e-6)
+    return weight * normalized.to(x.dtype)
+
+
+def _interleaved_rope(
+    x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+) -> torch.Tensor:
+    even, odd = x[..., 0::2], x[..., 1::2]
+    return torch.cat((even * cos - odd * sin, odd * cos + even * sin), dim=-1)
+
+
+class Glm52Indexer(torch.nn.Module):
+    def __init__(
+        self,
+        config: MLATransformerConfig,
+        *,
+        linear_builder: Any,
+        norm_builder: Any,
+        tp_group: Any,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.tp_group = tp_group
+        self.heads = int(not_none(config.dsa_indexer_n_heads))
+        self.head_dim = int(not_none(config.dsa_indexer_head_dim))
+        self.topk = int(not_none(config.dsa_indexer_topk))
+        linear_kwargs = {
+            "config": config,
+            "init_method": config.init_method,
+            "bias": False,
+            "skip_bias_add": False,
+            "skip_weight_param_allocation": False,
+            "parallel_mode": "duplicated",
+        }
+        self.linear_wq_b = build_module(
+            linear_builder,
+            config.q_lora_rank,
+            self.heads * self.head_dim,
+            tp_comm_buffer_name="glm52_index_q",
+            **linear_kwargs,
+        )
+        self.linear_wk = build_module(
+            linear_builder,
+            config.hidden_size,
+            self.head_dim,
+            tp_comm_buffer_name="glm52_index_k",
+            **linear_kwargs,
+        )
+        norm_config = deepcopy(config)
+        norm_config.normalization = "LayerNorm"
+        self.k_norm = build_module(
+            norm_builder,
+            config=norm_config,
+            hidden_size=self.head_dim,
+            eps=1e-6,
+        )
+        self.linear_weights_proj = build_module(
+            linear_builder,
+            config.hidden_size,
+            self.heads,
+            tp_comm_buffer_name="glm52_index_weights",
+            **linear_kwargs,
+        )
+        self.requires_grad_(False)
+
+    def _gather_sequence(self, tensor: torch.Tensor) -> torch.Tensor:
+        if not self.config.sequence_parallel or get_pg_size(self.tp_group) == 1:
+            return tensor
+        return gather_from_sequence_parallel_region(
+            tensor,
+            tensor_parallel_output_grad=False,
+            group=self.tp_group,
+        )
+
+    @torch.no_grad()
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        q_residual: torch.Tensor,
+        state: Glm52PrefixTreeState,
+    ) -> torch.Tensor | Glm52RoutedTopk:
+        q = _tensor(self.linear_wq_b(q_residual)).view(
+            q_residual.shape[0], q_residual.shape[1], self.heads, self.head_dim
+        )
+        k = _tensor(self.linear_wk(hidden_states))
+        k = _tensor(apply_module(self.k_norm)(k)).to(q.dtype)
+        weights = _tensor(self.linear_weights_proj(hidden_states)).float()
+        q = self._gather_sequence(q)
+        k = self._gather_sequence(k)
+        weights = self._gather_sequence(weights)
+        expected = (q.shape[1], q.shape[0])
+        if state.position_ids.shape != expected:
+            raise RuntimeError(
+                "GLM-5.2 indexer state/token shape mismatch: "
+                f"state={tuple(state.position_ids.shape)} tokens={expected}."
+            )
+        q = q.permute(1, 0, 2, 3).contiguous()
+        k = k.permute(1, 0, 2).contiguous()
+        q, k = indexer_rope(q, k, state.rope_cos, state.rope_sin)
+        weights = weights.permute(1, 0, 2).contiguous()
+        weights *= (self.heads * self.head_dim) ** -0.5
+        if state.context_parallel_state is not None:
+            return context_parallel_tree_topk(q, k, weights, state, topk=self.topk)
+        return streaming_tree_topk(
+            q.contiguous(),
+            k.contiguous(),
+            weights,
+            state.indexer_rows,
+            topk=self.topk,
+        )
+
+
+class Glm52SparseCore(torch.nn.Module):
+    def __init__(
+        self,
+        *,
+        config: MLATransformerConfig,
+        layer_number: int,
+        pg_collection: ProcessGroupCollection,
+        linear_builder: Any,
+        norm_builder: Any,
+        **_: Any,
+    ) -> None:
+        super().__init__()
+        pattern = tuple(getattr(config, "glm52_indexer_types"))
+        layer_index = int(layer_number) - 1
+        if not 0 <= layer_index < len(pattern):
+            raise ValueError(
+                f"GLM-5.2 layer index {layer_index} is outside its index pattern."
+            )
+        full_layers = [
+            index for index in range(layer_index + 1) if pattern[index] == "full"
+        ]
+        if not full_layers:
+            raise ValueError(
+                f"GLM-5.2 shared index layer {layer_index} has no preceding full layer."
+            )
+        self.full_layer_index = full_layers[-1]
+        self.indexer = (
+            Glm52Indexer(
+                config,
+                linear_builder=linear_builder,
+                norm_builder=norm_builder,
+                tp_group=pg_collection.tp,
+            )
+            if pattern[layer_index] == "full"
+            else None
+        )
+
+    def topk(
+        self,
+        hidden_states: torch.Tensor,
+        q_residual: torch.Tensor,
+        state: Glm52PrefixTreeState,
+    ) -> torch.Tensor | Glm52RoutedTopk:
+        if self.indexer is not None:
+            indices = self.indexer(hidden_states.detach(), q_residual.detach(), state)
+            state.topk_by_full_layer[self.full_layer_index] = indices
+            return indices
+        indices = state.topk_by_full_layer.get(self.full_layer_index)
+        if indices is None:
+            raise RuntimeError(
+                "GLM-5.2 shared index layer ran before its full index layer "
+                f"{self.full_layer_index}."
+            )
+        return indices
+
+
+def glm52_core_builder(linear_builder: Any, norm_builder: Any):
+    return partial(
+        Glm52SparseCore,
+        linear_builder=linear_builder,
+        norm_builder=norm_builder,
+    )
+
+
+class Glm52SelfAttention(Attention):
+    def __init__(
+        self,
+        config: MLATransformerConfig,
+        submodules: Any,
+        layer_number: int,
+        attn_mask_type: AttnMaskType,
+        attention_type: str = "self",
+        cp_comm_type: str | None = None,
+        pg_collection: ProcessGroupCollection | None = None,
+        **kwargs: Any,
+    ) -> None:
+        del kwargs
+        super().__init__(
+            config,
+            submodules,
+            layer_number,
+            attn_mask_type,
+            attention_type,
+            cp_comm_type=cp_comm_type,
+            pg_collection=pg_collection,
+        )
+        self.config: MLATransformerConfig
+        q_down_kwargs = {
+            "parallel_mode": "duplicated",
+            "skip_weight_param_allocation": False,
+        }
+        self.linear_q_down_proj = build_module(
+            submodules.linear_q_down_proj,
+            config.hidden_size,
+            config.q_lora_rank,
+            config=config,
+            init_method=config.init_method,
+            bias=False,
+            skip_bias_add=False,
+            tp_comm_buffer_name="q_down_proj",
+            **q_down_kwargs,
+        )
+        self.linear_q_up_proj = build_module(
+            submodules.linear_q_up_proj,
+            config.q_lora_rank,
+            config.num_attention_heads
+            * (config.qk_head_dim + config.qk_pos_emb_head_dim),
+            config=config,
+            init_method=config.init_method,
+            gather_output=False,
+            bias=False,
+            skip_bias_add=False,
+            is_expert=False,
+            tp_comm_buffer_name="q_up_proj",
+            tp_group=self.tp_group,
+        )
+        self.linear_kv_down_proj = build_module(
+            submodules.linear_kv_down_proj,
+            config.hidden_size,
+            config.kv_lora_rank + config.qk_pos_emb_head_dim,
+            config=config,
+            init_method=config.init_method,
+            bias=False,
+            skip_bias_add=False,
+            tp_comm_buffer_name="kv_down_proj",
+            parallel_mode="duplicated",
+            skip_weight_param_allocation=False,
+        )
+        self.linear_kv_up_proj = build_module(
+            submodules.linear_kv_up_proj,
+            config.kv_lora_rank,
+            config.num_attention_heads * (config.qk_head_dim + config.v_head_dim),
+            config=config,
+            init_method=config.init_method,
+            gather_output=False,
+            bias=False,
+            skip_bias_add=False,
+            is_expert=False,
+            tp_comm_buffer_name="kv_up_proj",
+            tp_group=self.tp_group,
+        )
+        self.q_layernorm = build_module(
+            submodules.q_layernorm,
+            config=config,
+            hidden_size=config.q_lora_rank,
+            eps=1e-6,
+        )
+        self.kv_layernorm = build_module(
+            submodules.kv_layernorm,
+            config=config,
+            hidden_size=config.kv_lora_rank,
+            eps=1e-6,
+        )
+        self.softmax_scale = (config.qk_head_dim + config.qk_pos_emb_head_dim) ** -0.5
+        self.q_a_lora: Any = None
+        self.q_b_lora: Any = None
+        self.kv_a_lora: Any = None
+
+    def get_query_key_value_tensors(self, *args: Any, **kwargs: Any):
+        del args, kwargs
+        raise RuntimeError("GLM-5.2 uses its absorbed sparse-MLA forward path.")
+
+    def _gather_replicated_sequence(self, tensor: torch.Tensor) -> torch.Tensor:
+        if not self.config.sequence_parallel or get_pg_size(self.tp_group) == 1:
+            return tensor
+        return gather_from_sequence_parallel_region(
+            tensor,
+            tensor_parallel_output_grad=False,
+            group=self.tp_group,
+        )
+
+    def _column_lora_input(self, tensor: torch.Tensor) -> torch.Tensor:
+        if get_pg_size(self.tp_group) == 1:
+            return tensor
+        if self.config.sequence_parallel:
+            return gather_from_sequence_parallel_region(tensor, group=self.tp_group)
+        return copy_to_tensor_model_parallel_region(tensor, group=self.tp_group)
+
+    @torch.compiler.disable
+    def forward(  # ty: ignore[invalid-method-override]
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        attention_bias: Any = None,
+        **_: Any,
+    ) -> tuple[torch.Tensor, None]:
+        del attention_mask
+        state = require_glm52_state(attention_bias)
+        q_compressed = _tensor(self.linear_q_down_proj(hidden_states))
+        if self.q_a_lora is not None:
+            q_compressed = q_compressed + self.q_a_lora(hidden_states)
+        q_residual = _latent_rms_norm(q_compressed, self.q_layernorm.weight)
+        q = _tensor(self.linear_q_up_proj(q_residual))
+        if self.q_b_lora is not None:
+            q = q + self.q_b_lora(self._column_lora_input(q_residual))
+        kv_combined = _tensor(self.linear_kv_down_proj(hidden_states))
+        if self.kv_a_lora is not None:
+            kv_combined = kv_combined + self.kv_a_lora(hidden_states)
+        kv_compressed, k_rope = kv_combined.split(
+            (self.config.kv_lora_rank, self.config.qk_pos_emb_head_dim), dim=-1
+        )
+        kv_compressed = _latent_rms_norm(kv_compressed, self.kv_layernorm.weight)
+        kv_compressed = self._gather_replicated_sequence(kv_compressed)
+        k_rope = self._gather_replicated_sequence(k_rope)
+        seq_len, batch = kv_compressed.shape[:2]
+        heads = self.num_attention_heads_per_partition
+        q = q.view(
+            seq_len,
+            batch,
+            heads,
+            self.config.qk_head_dim + self.config.qk_pos_emb_head_dim,
+        )
+        q_nope, q_rope = q.split(
+            (self.config.qk_head_dim, self.config.qk_pos_emb_head_dim), dim=-1
+        )
+        if state.rope_cos.shape[:2] != (batch, seq_len):
+            raise RuntimeError(
+                "GLM-5.2 RoPE state does not match the attention tokens: "
+                f"layer={self.layer_number}, rope={tuple(state.rope_cos.shape[:2])}, "
+                f"tokens={(batch, seq_len)}, hidden={tuple(hidden_states.shape)}"
+            )
+        cos = state.rope_cos.permute(1, 0, 2).unsqueeze(2).to(q.dtype)
+        sin = state.rope_sin.permute(1, 0, 2).unsqueeze(2).to(q.dtype)
+        q_rope = _interleaved_rope(q_rope, cos, sin)
+        k_rope = _interleaved_rope(k_rope.unsqueeze(2), cos, sin).squeeze(2)
+
+        kv_weight = self.linear_kv_up_proj.weight.view(
+            heads,
+            self.config.qk_head_dim + self.config.v_head_dim,
+            self.config.kv_lora_rank,
+        )
+        key_weight, value_weight = kv_weight.split(
+            (self.config.qk_head_dim, self.config.v_head_dim), dim=1
+        )
+        q_absorbed = torch.einsum("sbhd,hdm->sbhm", q_nope, key_weight)
+        q_absorbed = torch.cat((q_absorbed, q_rope), dim=-1)
+        kv_absorbed = torch.cat((kv_compressed, k_rope), dim=-1)
+        core = self.core_attention
+        if not isinstance(core, Glm52SparseCore):
+            raise TypeError(f"Expected Glm52SparseCore, got {type(core).__name__}.")
+        topk = core.topk(hidden_states, q_residual, state)
+        q_absorbed = q_absorbed.permute(1, 0, 2, 3).contiguous()
+        kv_absorbed = kv_absorbed.permute(1, 0, 2).contiguous()
+        latent_out = (
+            context_parallel_sparse_mla(
+                q_absorbed,
+                kv_absorbed,
+                topk,
+                state,
+                scale=self.softmax_scale,
+                tp_group=self.tp_group if get_pg_size(self.tp_group) > 1 else None,
+            )
+            if isinstance(topk, Glm52RoutedTopk)
+            else sparse_mla(
+                q_absorbed,
+                kv_absorbed,
+                topk,
+                scale=self.softmax_scale,
+                tp_group=self.tp_group if get_pg_size(self.tp_group) > 1 else None,
+            )
+        )
+        value_out = torch.einsum("bshm,hdm->bshd", latent_out, value_weight)
+        value_out = value_out.permute(1, 0, 2, 3).reshape(
+            seq_len, batch, heads * self.config.v_head_dim
+        )
+        output, _bias = self.linear_proj(value_out)
+        return output, None
+
+    def backward_dw(self) -> None:
+        self.linear_q_down_proj.backward_dw()
+        self.linear_q_up_proj.backward_dw()
+        self.linear_kv_down_proj.backward_dw()
+        self.linear_proj.backward_dw()
diff --git a/src/art/megatron/glm52/cp_attention.py b/src/art/megatron/glm52/cp_attention.py
new file mode 100644
index 000000000..498fa53ef
--- /dev/null
+++ b/src/art/megatron/glm52/cp_attention.py
@@ -0,0 +1,177 @@
+from __future__ import annotations
+
+from typing import Any, cast
+
+import torch
+
+from art.megatron.context_parallel.types import ArtContextParallelState
+from art.megatron.glm52.cp_stage import (
+    drain_stage_fetches,
+    launch_remote_stage_fetches,
+    launch_remote_stage_reduce,
+    reduce_local_stage_rows_,
+    stage_kv_rows,
+)
+from art.megatron.glm52.indexer import Glm52RoutedTopk
+from art.megatron.glm52.sparse_mla import (
+    reduce_tensor_parallel_dkv,
+    sparse_mla_backward,
+    sparse_mla_forward,
+)
+from art.megatron.glm52.state import Glm52PrefixTreeState
+
+_LATENT_DIM = 512
+
+
+def _combined_stage_kv(
+    kv: torch.Tensor,
+    cp_state: ArtContextParallelState,
+) -> tuple[torch.Tensor, tuple[int, ...]]:
+    fetches = launch_remote_stage_fetches(kv, cp_state)
+    parts = tuple(
+        stage_kv_rows(kv, stage, cp_state, fetches)
+        for stage in cp_state.rank_plan.stage_plans
+    )
+    drain_stage_fetches(fetches)
+    if not parts:
+        raise RuntimeError("GLM-5.2 CP plan has no KV stages.")
+    return (
+        parts[0] if len(parts) == 1 else torch.cat(parts),
+        tuple(int(part.shape[0]) for part in parts),
+    )
+
+
+def _forward(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+    state: Glm52PrefixTreeState,
+    scale: float,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    cp_state = cast(ArtContextParallelState, state.context_parallel_state)
+    valid = int(sum(cp_state.rank_plan.local_valid_lengths))
+    kv_flat = kv[0, :valid].contiguous()
+    combined_kv, _ = _combined_stage_kv(kv_flat, cp_state)
+    combined_out, lse = sparse_mla_forward(
+        q[:, :valid].contiguous(),
+        combined_kv.unsqueeze(0),
+        indices[:, :valid].contiguous(),
+        scale=scale,
+    )
+    if valid == q.shape[1]:
+        return combined_out, combined_out[0], lse[0]
+    output = q.new_zeros((q.shape[0], q.shape[1], q.shape[2], _LATENT_DIM))
+    output[:, :valid].copy_(combined_out)
+    return output, combined_out[0], lse[0]
+
+
+def _backward(
+    grad_output: torch.Tensor,
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+    global_out: torch.Tensor,
+    global_lse: torch.Tensor,
+    state: Glm52PrefixTreeState,
+    scale: float,
+    tp_group: Any | None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    cp_state = cast(ArtContextParallelState, state.context_parallel_state)
+    valid = int(sum(cp_state.rank_plan.local_valid_lengths))
+    kv_flat = kv[0, :valid].contiguous()
+    dkv = torch.zeros_like(kv_flat)
+    combined_kv, stage_sizes = _combined_stage_kv(kv_flat, cp_state)
+    dq, combined_dkv = sparse_mla_backward(
+        q[:, :valid].contiguous(),
+        combined_kv.unsqueeze(0),
+        indices[:, :valid].contiguous(),
+        global_out.unsqueeze(0),
+        global_lse.unsqueeze(0),
+        grad_output[:, :valid].contiguous(),
+        scale=scale,
+    )
+    combined_dkv = reduce_tensor_parallel_dkv(
+        combined_dkv, tp_group=tp_group, dtype=kv.dtype
+    )
+    stage_starts = [0]
+    for size in stage_sizes:
+        stage_starts.append(stage_starts[-1] + size)
+    reductions = []
+    for stage_index in cp_state.rank_plan.backward_stage_indices:
+        stage_plan = cp_state.rank_plan.stage_plans[int(stage_index)]
+        start, end = stage_starts[int(stage_index) : int(stage_index) + 2]
+        dkv_stage = combined_dkv[0, start:end]
+        if stage_plan.is_local_stage:
+            reduce_local_stage_rows_(dkv, dkv_stage, stage_plan, cp_state)
+        else:
+            reductions.append(
+                launch_remote_stage_reduce(dkv_stage, stage_plan, cp_state, dkv)
+            )
+    for reduction in reductions:
+        reduction.wait_post_process()
+    if valid == q.shape[1]:
+        return dq, dkv.unsqueeze(0)
+    dq_padded, dkv_padded = torch.zeros_like(q), torch.zeros_like(kv)
+    dq_padded[:, :valid].copy_(dq)
+    dkv_padded[0, :valid].copy_(dkv)
+    return dq_padded, dkv_padded
+
+
+class _ContextParallelSparseMla(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx: Any,
+        q: torch.Tensor,
+        kv: torch.Tensor,
+        indices: torch.Tensor,
+        state: Glm52PrefixTreeState,
+        scale: float,
+        tp_group: Any | None,
+    ) -> torch.Tensor:
+        output, global_out, global_lse = _forward(q, kv, indices, state, scale)
+        ctx.save_for_backward(q, kv, indices, global_out, global_lse)
+        ctx.state = state
+        ctx.scale = float(scale)
+        ctx.tp_group = tp_group
+        return output
+
+    @staticmethod
+    def backward(ctx: Any, *grad_outputs: Any):
+        (grad_output,) = cast(tuple[torch.Tensor], grad_outputs)
+        q, kv, indices, global_out, global_lse = ctx.saved_tensors
+        dq, dkv = _backward(
+            grad_output,
+            q,
+            kv,
+            indices,
+            global_out,
+            global_lse,
+            ctx.state,
+            ctx.scale,
+            ctx.tp_group,
+        )
+        return dq, dkv, None, None, None, None
+
+
+def context_parallel_sparse_mla(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    topk: Glm52RoutedTopk,
+    state: Glm52PrefixTreeState,
+    *,
+    scale: float,
+    tp_group: Any | None = None,
+) -> torch.Tensor:
+    """Run sparse MLA once over the union of ART-planned KV stages."""
+    if q.ndim != 4 or kv.ndim != 3 or q.shape[:2] != kv.shape[:2]:
+        raise ValueError("GLM-5.2 CP sparse MLA expects q[B,S,H,576], kv[B,S,576].")
+    if q.shape[0] != 1:
+        raise ValueError("GLM-5.2 context parallel supports one packed row.")
+    return _ContextParallelSparseMla.apply(
+        q.contiguous(),
+        kv.contiguous(),
+        topk.indices.contiguous(),
+        state,
+        float(scale),
+        tp_group,
+    )
diff --git a/src/art/megatron/glm52/cp_stage.py b/src/art/megatron/glm52/cp_stage.py
new file mode 100644
index 000000000..c2b47fd9e
--- /dev/null
+++ b/src/art/megatron/glm52/cp_stage.py
@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+from typing import Any, cast
+
+import torch
+
+from art.megatron.context_parallel.comm import A2AVCommunicator
+from art.megatron.context_parallel.range_ops import range_gather, range_reduce_sum_
+from art.megatron.context_parallel.types import (
+    ArtContextParallelState,
+    DkvReducePlan,
+    StagePlan,
+)
+
+_COMMUNICATOR = A2AVCommunicator()
+
+
+def stage_query_rows(
+    tensor: torch.Tensor,
+    stage: StagePlan,
+    state: ArtContextParallelState,
+) -> torch.Tensor:
+    ranges = stage.owner_local_q_ranges
+    if len(ranges) == 1 and ranges[0].start == 0 and ranges[0].end == tensor.shape[0]:
+        return tensor
+    return range_gather(
+        tensor,
+        ranges,
+        range_meta_cache=state.execution_cache.range_meta,
+    )
+
+
+def stage_local_kv_rows(
+    tensor: torch.Tensor,
+    stage: StagePlan,
+    state: ArtContextParallelState,
+) -> torch.Tensor:
+    ranges = stage.owner_local_k_ranges
+    if len(ranges) == 1 and ranges[0].start == 0 and ranges[0].end == tensor.shape[0]:
+        return tensor
+    return range_gather(
+        tensor,
+        ranges,
+        range_meta_cache=state.execution_cache.range_meta,
+    )
+
+
+def launch_remote_stage_fetches(
+    tensor: torch.Tensor,
+    state: ArtContextParallelState,
+) -> dict[int, Any]:
+    return {
+        int(stage.stage_index): _COMMUNICATOR.launch_tensor_fetch(
+            tensor_local=tensor,
+            plan=cast(Any, stage.kv_fetch_plan),
+            group=state.cp_group,
+            async_op=True,
+            range_meta_cache=state.execution_cache.range_meta,
+        )
+        for stage in state.rank_plan.stage_plans
+        if not stage.is_local_stage
+    }
+
+
+def stage_kv_rows(
+    tensor: torch.Tensor,
+    stage: StagePlan,
+    state: ArtContextParallelState,
+    fetches: dict[int, Any],
+) -> torch.Tensor:
+    return (
+        stage_local_kv_rows(tensor, stage, state)
+        if stage.is_local_stage
+        else fetches.pop(int(stage.stage_index)).wait_post_process()
+    )
+
+
+def drain_stage_fetches(fetches: dict[int, Any]) -> None:
+    for work in fetches.values():
+        work.wait_post_process()
+    fetches.clear()
+
+
+def reduce_local_stage_rows_(
+    target: torch.Tensor,
+    stage_grad: torch.Tensor,
+    stage: StagePlan,
+    state: ArtContextParallelState,
+) -> None:
+    range_reduce_sum_(
+        stage_grad,
+        output_tensor=target,
+        ranges=stage.owner_local_k_ranges,
+        range_meta_cache=state.execution_cache.range_meta,
+    )
+
+
+def launch_remote_stage_reduce(
+    stage_grad: torch.Tensor,
+    stage: StagePlan,
+    state: ArtContextParallelState,
+    output: torch.Tensor,
+) -> Any:
+    return _COMMUNICATOR.launch_tensor_reduce(
+        remote=stage_grad.contiguous(),
+        plan=cast(DkvReducePlan, stage.dkv_reduce_plan),
+        group=state.cp_group,
+        async_op=True,
+        output=output,
+        range_meta_cache=state.execution_cache.range_meta,
+    )
diff --git a/src/art/megatron/glm52/indexer.py b/src/art/megatron/glm52/indexer.py
new file mode 100644
index 000000000..aff496a0e
--- /dev/null
+++ b/src/art/megatron/glm52/indexer.py
@@ -0,0 +1,568 @@
+from __future__ import annotations
+
+from typing import cast
+
+from pydantic import BaseModel, ConfigDict
+import torch
+import triton
+import triton.language as tl
+
+from art.megatron.context_parallel.types import ArtContextParallelState
+from art.megatron.glm52.cp_stage import (
+    drain_stage_fetches,
+    launch_remote_stage_fetches,
+    stage_kv_rows,
+    stage_query_rows,
+)
+from art.megatron.glm52.state import (
+    Glm52IndexerRowPlan,
+    Glm52PrefixTreeState,
+    Glm52StageState,
+)
+
+_MAX_SCORE_WORKSPACE_BYTES = 256 * 1024 * 1024
+_MAX_K_CHUNK = 32 * 1024
+
+
+class Glm52RoutedTopk(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+    indices: torch.Tensor
+
+
+@triton.jit
+def _canonicalize_topk_kernel(
+    ids_ptr,
+    topk: tl.constexpr,
+    block: tl.constexpr,
+):
+    row = tl.program_id(0)
+    columns = tl.arange(0, block)
+    ids = tl.load(
+        ids_ptr + row * topk + columns,
+        mask=columns < topk,
+        other=0x7FFF_FFFF,
+    )
+    ids = tl.where(ids >= 0, ids, 0x7FFF_FFFF)
+    ids = tl.sort(ids)
+    tl.store(
+        ids_ptr + row * topk + columns,
+        tl.where(ids == 0x7FFF_FFFF, -1, ids),
+        mask=columns < topk,
+    )
+
+
+def _canonicalize_topk_(ids: torch.Tensor) -> None:
+    topk = int(ids.shape[-1])
+    block = triton.next_power_of_2(topk)
+    _canonicalize_topk_kernel[(ids.numel() // topk,)](
+        ids,
+        topk=topk,  # ty: ignore[invalid-argument-type]
+        block=block,  # ty: ignore[invalid-argument-type]
+        num_warps=4,  # ty: ignore[unknown-argument]
+    )
+
+
+@triton.jit
+def _round_bf16(value):
+    bits = value.to(tl.int32, bitcast=True)
+    rounded = bits + 0x7FFF + ((bits >> 16) & 1)
+    return (rounded & -0x10000).to(tl.float32, bitcast=True)
+
+
+@triton.jit
+def _index_rope_kernel(
+    q_ptr,
+    k_ptr,
+    cos_ptr,
+    sin_ptr,
+    q_out_ptr,
+    k_out_ptr,
+    tokens,
+    stride_qb,
+    stride_qs,
+    stride_qh,
+    stride_qd,
+    stride_kb,
+    stride_ks,
+    stride_kd,
+    stride_rb,
+    stride_rs,
+    stride_rd,
+    heads: tl.constexpr,
+):
+    row = tl.program_id(0)
+    head = tl.program_id(1)
+    batch = row // tokens
+    token = row - batch * tokens
+    half = tl.arange(0, 32)
+    passthrough = tl.arange(0, 64)
+    rope_base = batch * stride_rb + token * stride_rs
+    cos = tl.load(cos_ptr + rope_base + half * stride_rd)
+    sin = tl.load(sin_ptr + rope_base + half * stride_rd)
+
+    q_base = batch * stride_qb + token * stride_qs + head * stride_qh
+    q_first = tl.load(q_ptr + q_base + half * stride_qd)
+    q_second = tl.load(q_ptr + q_base + (32 + half) * stride_qd)
+    q_ac = _round_bf16(q_first.to(tl.float32) * cos.to(tl.float32))
+    q_bs = _round_bf16(q_second.to(tl.float32) * sin.to(tl.float32))
+    q_bc = _round_bf16(q_second.to(tl.float32) * cos.to(tl.float32))
+    q_as = _round_bf16(q_first.to(tl.float32) * sin.to(tl.float32))
+    tl.store(
+        q_out_ptr + q_base + half * stride_qd,
+        _round_bf16(q_ac - q_bs),
+    )
+    tl.store(
+        q_out_ptr + q_base + (32 + half) * stride_qd,
+        _round_bf16(q_bc + q_as),
+    )
+    tl.store(
+        q_out_ptr + q_base + (64 + passthrough) * stride_qd,
+        tl.load(q_ptr + q_base + (64 + passthrough) * stride_qd),
+    )
+
+    k_base = batch * stride_kb + token * stride_ks
+    k_mask = head == 0
+    k_first = tl.load(k_ptr + k_base + half * stride_kd, mask=k_mask, other=0.0)
+    k_second = tl.load(k_ptr + k_base + (32 + half) * stride_kd, mask=k_mask, other=0.0)
+    k_ac = _round_bf16(k_first.to(tl.float32) * cos.to(tl.float32))
+    k_bs = _round_bf16(k_second.to(tl.float32) * sin.to(tl.float32))
+    k_bc = _round_bf16(k_second.to(tl.float32) * cos.to(tl.float32))
+    k_as = _round_bf16(k_first.to(tl.float32) * sin.to(tl.float32))
+    tl.store(
+        k_out_ptr + k_base + half * stride_kd,
+        _round_bf16(k_ac - k_bs),
+        mask=k_mask,
+    )
+    tl.store(
+        k_out_ptr + k_base + (32 + half) * stride_kd,
+        _round_bf16(k_bc + k_as),
+        mask=k_mask,
+    )
+    tl.store(
+        k_out_ptr + k_base + (64 + passthrough) * stride_kd,
+        tl.load(
+            k_ptr + k_base + (64 + passthrough) * stride_kd,
+            mask=k_mask,
+            other=0.0,
+        ),
+        mask=k_mask,
+    )
+
+
+def indexer_rope(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Apply half-split RoPE with the indexer's eager-BF16 rounding contract."""
+    if q.ndim != 4 or k.ndim != 3 or q.shape[:2] != k.shape[:2]:
+        raise ValueError("GLM-5.2 indexer RoPE expects q[B,S,H,128], k[B,S,128].")
+    if q.shape[-1] != 128 or k.shape[-1] != 128:
+        raise ValueError("GLM-5.2 indexer RoPE requires head_dim=128.")
+    if q.dtype is not torch.bfloat16 or k.dtype is not torch.bfloat16:
+        raise TypeError("GLM-5.2 indexer RoPE requires BF16 q/k.")
+    q = q.contiguous()
+    k = k.contiguous()
+    cos = cos.contiguous()
+    sin = sin.contiguous()
+    q_out = torch.empty_like(q)
+    k_out = torch.empty_like(k)
+    batch, tokens, heads, _ = q.shape
+    _index_rope_kernel[(batch * tokens, heads)](
+        q,
+        k,
+        cos,
+        sin,
+        q_out,
+        k_out,
+        tokens,
+        *q.stride(),
+        *k.stride(),
+        *cos.stride(),
+        heads=heads,  # ty: ignore[invalid-argument-type]
+        num_warps=1,  # ty: ignore[unknown-argument]
+    )
+    return q_out, k_out
+
+
+@triton.jit
+def _index_scores_kernel(
+    q_ptr,
+    k_ptr,
+    weights_ptr,
+    q_ids_ptr,
+    k_ids_ptr,
+    scores_ptr,
+    q_len,
+    k_len,
+    stride_qt,
+    stride_qh,
+    stride_qd,
+    stride_kt,
+    stride_kd,
+    stride_wt,
+    stride_wh,
+    stride_st,
+    stride_sk,
+    q_position_offset,
+    k_position_offset,
+    heads: tl.constexpr,
+    head_dim: tl.constexpr,
+    block_q: tl.constexpr,
+    block_k: tl.constexpr,
+    causal: tl.constexpr,
+    explicit_ids: tl.constexpr,
+    ranking_keys: tl.constexpr,
+):
+    q_block = tl.program_id(0)
+    k_block = tl.program_id(1)
+    q_offsets = q_block * block_q + tl.arange(0, block_q)
+    h_offsets = tl.arange(0, heads)
+    d_offsets = tl.arange(0, head_dim)
+    k_offsets = k_block * block_k + tl.arange(0, block_k)
+
+    qh_offsets = q_offsets[:, None] * heads + h_offsets[None, :]
+    qh_offsets = qh_offsets.reshape((block_q * heads,))
+    q = tl.load(
+        q_ptr
+        + (qh_offsets // heads)[:, None] * stride_qt
+        + (qh_offsets % heads)[:, None] * stride_qh
+        + d_offsets[None, :] * stride_qd,
+        mask=(qh_offsets[:, None] // heads < q_len),
+        other=0.0,
+    )
+    k = tl.load(
+        k_ptr + k_offsets[None, :] * stride_kt + d_offsets[:, None] * stride_kd,
+        mask=k_offsets[None, :] < k_len,
+        other=0.0,
+    )
+    dots = tl.dot(q, k).reshape((block_q, heads, block_k))
+    weights = tl.load(
+        weights_ptr + q_offsets[:, None] * stride_wt + h_offsets[None, :] * stride_wh,
+        mask=q_offsets[:, None] < q_len,
+        other=0.0,
+    )
+    scores = tl.sum(tl.maximum(dots, 0.0) * weights[:, :, None], axis=1)
+    valid = (q_offsets[:, None] < q_len) & (k_offsets[None, :] < k_len)
+    if explicit_ids:
+        q_positions = tl.load(q_ids_ptr + q_offsets, mask=q_offsets < q_len, other=-1)
+        k_positions = tl.load(
+            k_ids_ptr + k_offsets, mask=k_offsets < k_len, other=0x7FFF_FFFF
+        )
+    else:
+        q_positions = q_position_offset + q_offsets
+        k_positions = k_position_offset + k_offsets
+    if causal:
+        valid &= k_positions[None, :] <= q_positions[:, None]
+    scores = tl.where(valid, scores, float("-inf"))
+    output_offsets = (
+        scores_ptr + q_offsets[:, None] * stride_st + k_offsets[None, :] * stride_sk
+    )
+    output_mask = (q_offsets[:, None] < q_len) & (k_offsets[None, :] < k_len)
+    if ranking_keys:
+        canonical_scores = tl.where(scores == 0.0, 0.0, scores)
+        bits = canonical_scores.to(tl.int32, bitcast=True).to(tl.int64) & 0xFFFF_FFFF
+        ordered = tl.where(
+            (bits >> 31) != 0,
+            (~bits) & 0xFFFF_FFFF,
+            bits ^ 0x8000_0000,
+        )
+        primary = ordered - 0x8000_0000
+        global_ids = k_positions[None, :].to(tl.int64)
+        keys = (primary << 32) | (0xFFFF_FFFF - global_ids)
+        keys = tl.where(valid, keys, -0x8000_0000_0000_0000)
+        tl.store(output_offsets, keys, mask=output_mask)
+    else:
+        tl.store(output_offsets, scores, mask=output_mask)
+
+
+def _index_scores(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    weights: torch.Tensor,
+    *,
+    q_position_offset: int,
+    k_position_offset: int,
+    causal: bool,
+) -> torch.Tensor:
+    if q.dtype is not torch.bfloat16 or k.dtype is not torch.bfloat16:
+        raise TypeError(f"GLM-5.2 index q/k must be bf16, got {q.dtype}/{k.dtype}.")
+    if weights.dtype is not torch.float32:
+        raise TypeError(f"GLM-5.2 index weights must be fp32, got {weights.dtype}.")
+    if q.ndim != 3 or k.ndim != 2 or weights.shape != q.shape[:2]:
+        raise ValueError(
+            "GLM-5.2 index score shapes must be q[Q,H,D], k[K,D], w[Q,H], "
+            f"got {tuple(q.shape)}, {tuple(k.shape)}, {tuple(weights.shape)}."
+        )
+    q_len, heads, head_dim = q.shape
+    k_len = int(k.shape[0])
+    if int(k.shape[1]) != head_dim or 128 % heads:
+        raise ValueError(
+            f"Unsupported GLM-5.2 index shape heads={heads}, head_dim={head_dim}."
+        )
+    block_q = 128 // heads
+    block_k = 64
+    scores = torch.empty((q_len, k_len), device=q.device, dtype=torch.float32)
+    _index_scores_kernel[(triton.cdiv(q_len, block_q), triton.cdiv(k_len, block_k))](
+        q,
+        k,
+        weights,
+        q,
+        k,
+        scores,
+        q_len,
+        k_len,
+        *q.stride(),
+        *k.stride(),
+        *weights.stride(),
+        *scores.stride(),
+        q_position_offset=q_position_offset,  # ty: ignore[invalid-argument-type]
+        k_position_offset=k_position_offset,  # ty: ignore[invalid-argument-type]
+        heads=heads,  # ty: ignore[invalid-argument-type]
+        head_dim=head_dim,  # ty: ignore[invalid-argument-type]
+        block_q=block_q,  # ty: ignore[invalid-argument-type]
+        block_k=block_k,  # ty: ignore[invalid-argument-type]
+        causal=causal,  # ty: ignore[invalid-argument-type]
+        explicit_ids=False,  # ty: ignore[invalid-argument-type]
+        ranking_keys=False,  # ty: ignore[invalid-argument-type]
+        num_warps=8,  # ty: ignore[unknown-argument]
+        num_stages=3,  # ty: ignore[unknown-argument]
+    )
+    return scores
+
+
+def _index_score_keys(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    weights: torch.Tensor,
+    q_ids: torch.Tensor,
+    k_ids: torch.Tensor,
+) -> torch.Tensor:
+    q_len, heads, head_dim = q.shape
+    k_len = int(k.shape[0])
+    if q_ids.shape != (q_len,) or k_ids.shape != (k_len,):
+        raise ValueError("GLM-5.2 CP index ids must match query and key rows.")
+    block_q = 128 // heads
+    block_k = 64
+    keys = torch.empty((q_len, k_len), device=q.device, dtype=torch.int64)
+    _index_scores_kernel[(triton.cdiv(q_len, block_q), triton.cdiv(k_len, block_k))](
+        q,
+        k,
+        weights,
+        q_ids,
+        k_ids,
+        keys,
+        q_len,
+        k_len,
+        *q.stride(),
+        *k.stride(),
+        *weights.stride(),
+        *keys.stride(),
+        q_position_offset=0,  # ty: ignore[invalid-argument-type]
+        k_position_offset=0,  # ty: ignore[invalid-argument-type]
+        heads=heads,  # ty: ignore[invalid-argument-type]
+        head_dim=head_dim,  # ty: ignore[invalid-argument-type]
+        block_q=block_q,  # ty: ignore[invalid-argument-type]
+        block_k=block_k,  # ty: ignore[invalid-argument-type]
+        causal=True,  # ty: ignore[invalid-argument-type]
+        explicit_ids=True,  # ty: ignore[invalid-argument-type]
+        ranking_keys=True,  # ty: ignore[invalid-argument-type]
+        num_warps=8,  # ty: ignore[unknown-argument]
+        num_stages=3,  # ty: ignore[unknown-argument]
+    )
+    return keys
+
+
+def _merge_topk(
+    scores: torch.Tensor,
+    ids: torch.Tensor,
+    candidate_scores: torch.Tensor,
+    candidate_ids: torch.Tensor,
+    *,
+    topk: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    all_scores = torch.cat((scores, candidate_scores), dim=1)
+    all_ids = torch.cat((ids, candidate_ids), dim=1)
+    keep = min(topk, int(all_scores.shape[1]))
+    scores, positions = torch.topk(all_scores, keep, dim=1, sorted=False)
+    return scores, torch.gather(all_ids, 1, positions)
+
+
+def _gather_ranges(
+    tensor: torch.Tensor, ranges: tuple[tuple[int, int], ...]
+) -> torch.Tensor:
+    if len(ranges) == 1:
+        start, end = ranges[0]
+        return tensor[start:end]
+    return torch.cat(tuple(tensor[start:end] for start, end in ranges))
+
+
+def _stage_topk_update(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    weights: torch.Tensor,
+    stage: Glm52StageState,
+    best_keys: torch.Tensor,
+    *,
+    topk: int,
+) -> None:
+    max_score_elements = _MAX_SCORE_WORKSPACE_BYTES // torch.int64.itemsize
+    for query in stage.queries:
+        candidate_k = _gather_ranges(k, query.k_ranges).contiguous()
+        candidate_global_ids = _gather_ranges(
+            stage.global_k_ids, query.k_ranges
+        ).contiguous()
+        max_k_len = int(candidate_k.shape[0])
+        k_chunk_size = min(max_k_len, _MAX_K_CHUNK)
+        q_chunk_size = max(1, max_score_elements // max(k_chunk_size, 1))
+        for q_start in range(query.q_start, query.q_end, q_chunk_size):
+            q_end = min(q_start + q_chunk_size, query.q_end)
+            owner_rows = stage.owner_q_rows[q_start:q_end]
+            keys = best_keys.index_select(0, owner_rows)
+            q_ids = stage.global_q_ids[q_start:q_end]
+            for k_start in range(0, max_k_len, k_chunk_size):
+                k_end = min(k_start + k_chunk_size, max_k_len)
+                candidate_keys = _index_score_keys(
+                    q[q_start:q_end].contiguous(),
+                    candidate_k[k_start:k_end].contiguous(),
+                    weights[q_start:q_end].contiguous(),
+                    q_ids,
+                    candidate_global_ids[k_start:k_end],
+                )
+                keys = torch.topk(
+                    torch.cat((keys, candidate_keys), dim=1),
+                    topk,
+                    dim=1,
+                    sorted=False,
+                ).values
+            best_keys.index_copy_(0, owner_rows, keys)
+        del candidate_k, candidate_global_ids
+
+
+@torch.no_grad()
+def context_parallel_tree_topk(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    weights: torch.Tensor,
+    state: Glm52PrefixTreeState,
+    *,
+    topk: int,
+) -> Glm52RoutedTopk:
+    """Accumulate exact GLM indexer top-k on query owners across ART stages."""
+    cp_state = cast(ArtContextParallelState, state.context_parallel_state)
+    valid_tokens = int(sum(cp_state.rank_plan.local_valid_lengths))
+    q = q[:, :valid_tokens].reshape(valid_tokens, *q.shape[2:]).contiguous()
+    k = k[:, :valid_tokens].reshape(valid_tokens, k.shape[-1]).contiguous()
+    weights = weights[:, :valid_tokens].reshape(valid_tokens, weights.shape[-1])
+    invalid_key = torch.iinfo(torch.int64).min
+    best_keys = torch.full(
+        (valid_tokens, topk), invalid_key, device=q.device, dtype=torch.int64
+    )
+    works = launch_remote_stage_fetches(k, cp_state)
+    for stage_plan, stage in zip(
+        cp_state.rank_plan.stage_plans, state.stages, strict=True
+    ):
+        if not stage.queries:
+            continue
+        q_stage = stage_query_rows(q, stage_plan, cp_state)
+        weights_stage = stage_query_rows(weights, stage_plan, cp_state)
+        k_stage = stage_kv_rows(k, stage_plan, cp_state, works)
+        _stage_topk_update(
+            q_stage,
+            k_stage,
+            weights_stage,
+            stage,
+            best_keys,
+            topk=topk,
+        )
+        del q_stage, weights_stage, k_stage
+    drain_stage_fetches(works)
+    invalid = best_keys == invalid_key
+    best_ids = (0xFFFF_FFFF - (best_keys & 0xFFFF_FFFF)).to(torch.int32)
+    best_ids.masked_fill_(invalid, -1)
+    del best_keys
+    _canonicalize_topk_(best_ids)
+    route_map = state.route_by_global_id
+    if route_map is None:
+        raise RuntimeError("GLM-5.2 CP route map is missing.")
+    indices = torch.where(
+        best_ids >= 0,
+        route_map[best_ids.clamp_min(0).to(torch.int64)],
+        torch.full_like(best_ids, state.combined_k_rows),
+    ).view(1, valid_tokens, topk)
+    del best_ids
+    return Glm52RoutedTopk(indices=indices)
+
+
+@torch.compiler.disable
+def streaming_tree_topk(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    weights: torch.Tensor,
+    rows: tuple[Glm52IndexerRowPlan, ...],
+    *,
+    topk: int,
+) -> torch.Tensor:
+    """Exact tree-aware topk with bounded score workspace and no square logits."""
+    if not q.is_cuda or q.device != k.device or q.device != weights.device:
+        raise RuntimeError("GLM-5.2 indexer requires colocated CUDA tensors.")
+    if q.ndim != 4 or k.ndim != 3 or weights.ndim != 3:
+        raise ValueError("GLM-5.2 indexer expects q[B,S,H,D], k[B,S,D], w[B,S,H].")
+    batch, seq_len, _, _ = q.shape
+    if len(rows) != batch or k.shape[:2] != (batch, seq_len):
+        raise ValueError("GLM-5.2 index plan does not match the packed tensor shape.")
+    result = torch.full(
+        (batch, seq_len, topk),
+        -1,
+        device=q.device,
+        dtype=torch.int32,
+    )
+    max_score_elements = _MAX_SCORE_WORKSPACE_BYTES // torch.float32.itemsize
+    for row in rows:
+        for query in row.queries:
+            max_k_len = max(slice_.k_end - slice_.k_start for slice_ in query.slices)
+            k_chunk_size = min(max_k_len, _MAX_K_CHUNK)
+            q_chunk_size = max(1, max_score_elements // max(k_chunk_size, 1))
+            for q_start in range(query.q_start, query.q_end, q_chunk_size):
+                q_end = min(q_start + q_chunk_size, query.q_end)
+                q_chunk = q[row.row_index, q_start:q_end].contiguous()
+                w_chunk = weights[row.row_index, q_start:q_end].contiguous()
+                best_scores = torch.empty(
+                    (q_end - q_start, 0), device=q.device, dtype=torch.float32
+                )
+                best_ids = torch.empty(
+                    (q_end - q_start, 0), device=q.device, dtype=torch.int32
+                )
+                for slice_ in query.slices:
+                    for k_start in range(slice_.k_start, slice_.k_end, k_chunk_size):
+                        k_end = min(k_start + k_chunk_size, slice_.k_end)
+                        score_chunk = _index_scores(
+                            q_chunk,
+                            k[row.row_index, k_start:k_end].contiguous(),
+                            w_chunk,
+                            q_position_offset=q_start,
+                            k_position_offset=k_start,
+                            causal=slice_.causal,
+                        )
+                        keep = min(topk, k_end - k_start)
+                        candidate_scores, candidate_ids = torch.topk(
+                            score_chunk,
+                            keep,
+                            dim=1,
+                            sorted=False,
+                        )
+                        candidate_ids = (candidate_ids + k_start).to(torch.int32)
+                        candidate_ids.masked_fill_(torch.isneginf(candidate_scores), -1)
+                        best_scores, best_ids = _merge_topk(
+                            best_scores,
+                            best_ids,
+                            candidate_scores,
+                            candidate_ids,
+                            topk=topk,
+                        )
+                result[row.row_index, q_start:q_end, : best_ids.shape[1]] = best_ids
+    _canonicalize_topk_(result)
+    result.masked_fill_(result < 0, seq_len)
+    return result
diff --git a/src/art/megatron/glm52/lora.py b/src/art/megatron/glm52/lora.py
new file mode 100644
index 000000000..bb9ed990f
--- /dev/null
+++ b/src/art/megatron/glm52/lora.py
@@ -0,0 +1,223 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+from typing import Any, cast
+
+from megatron.core.extensions.transformer_engine import (
+    TEColumnParallelGroupedLinear,
+    TERowParallelGroupedLinear,
+)
+import torch
+
+from art.megatron.glm52.lora_projection import glm52_lora_a
+from art.megatron.kernels.cute_grouped_lora_quack import quack_grouped_lora_residual
+from art.megatron.lora import (
+    GRAD_SYNC_OP_SUM,
+    LORA_ALPHA,
+    TP_DEFAULT_GRAD_SYNC_DOMAIN,
+    LoRA,
+    LoRAParallelSpec,
+    MLPExpertsLinearFC1LoRA,
+    MLPExpertsLinearFC2LoRA,
+    SelfAttentionLinearProjLoRA,
+    _bind_expert_lora_layout,
+    _parallel_lora,
+    _targets_include,
+    _unwrap_attr,
+)
+
+
+class Glm52LoRA(LoRA):
+    def forward(
+        self, x: torch.Tensor, tokens_per_expert: list[int] | torch.Tensor | None = None
+    ) -> torch.Tensor:
+        if tokens_per_expert is not None:
+            raise ValueError("Glm52LoRA is only for non-expert projections.")
+        active = self.active_lora_tensors()
+        if active is None:
+            return x.new_zeros((*x.shape[:-1], self.out_features))
+        a_t, b_t, scale = active
+        out = glm52_lora_a(x, a_t) @ b_t
+        return out if scale == 1.0 else out * scale
+
+
+def _replicated_lora(
+    linear: Any,
+    *,
+    adapter_model_prefix: str,
+    rank: int,
+    alpha: int,
+) -> LoRA:
+    weight = linear.weight
+    parallel = LoRAParallelSpec(
+        grad_sync_domain=TP_DEFAULT_GRAD_SYNC_DOMAIN,
+        grad_sync_op=GRAD_SYNC_OP_SUM,
+    )
+    return Glm52LoRA(
+        adapter_model_prefix=adapter_model_prefix,
+        in_features=weight.shape[1],
+        out_features=weight.shape[0],
+        rank=rank,
+        alpha=alpha,
+        dtype=weight.dtype,
+        device=weight.device,
+        a_parallel_spec=parallel,
+        b_parallel_spec=parallel,
+        allreduce=True,
+    )
+
+
+def apply_glm52_attention_lora(
+    attention: Any,
+    *,
+    adapter_model_prefix: str,
+    provider: Any,
+    target_modules: set[str],
+    rank: int,
+    alpha: int = LORA_ALPHA,
+) -> None:
+    prefix = f"{adapter_model_prefix}.self_attn"
+    for target, attr, linear_attr in (
+        ("q_a_proj", "q_a_lora", "linear_q_down_proj"),
+        ("kv_a_proj_with_mqa", "kv_a_lora", "linear_kv_down_proj"),
+    ):
+        if _targets_include(target_modules, target):
+            setattr(
+                attention,
+                attr,
+                _replicated_lora(
+                    getattr(attention, linear_attr),
+                    adapter_model_prefix=f"{prefix}.{target}",
+                    rank=rank,
+                    alpha=alpha,
+                ),
+            )
+    if _targets_include(target_modules, "q_b_proj"):
+        linear = attention.linear_q_up_proj
+        attention.q_b_lora = _parallel_lora(
+            adapter_model_prefix=f"{prefix}.q_b_proj",
+            linear=linear,
+            out_features=linear.weight.shape[0],
+            rank=rank,
+            alpha=alpha,
+            layout="column",
+            lora_cls=Glm52LoRA,
+        )
+    if _targets_include(target_modules, "o_proj"):
+        attention.linear_proj = SelfAttentionLinearProjLoRA(
+            adapter_model_prefix=f"{prefix}.o_proj",
+            linear_proj=attention.linear_proj,
+            rank=rank,
+            alpha=alpha,
+            provider=provider,
+            lora_cls=Glm52LoRA,
+        )
+
+
+def _expert_lora_residual(
+    base: torch.Tensor,
+    x: torch.Tensor,
+    lora: LoRA,
+    tokens_per_expert: list[int] | torch.Tensor,
+) -> torch.Tensor:
+    active = lora.active_lora_tensors()
+    if active is None or x.shape[0] == 0:
+        return base
+    a_t, b_t, scale = active
+    return quack_grouped_lora_residual(
+        base, x, a_t, b_t, tokens_per_expert, scale=scale
+    )
+
+
+def _grouped_linear(
+    linear: Any, x: torch.Tensor, tokens_per_expert: list[int] | torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor | None]:
+    return cast(Callable[..., tuple[torch.Tensor, torch.Tensor | None]], linear)(
+        x, tokens_per_expert
+    )
+
+
+class Glm52MLPExpertsLinearFC1LoRA(MLPExpertsLinearFC1LoRA):
+    def forward(
+        self, x: torch.Tensor, tokens_per_expert: list[int] | torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        base, bias = _grouped_linear(self.linear_fc1, x, tokens_per_expert)
+        return _expert_lora_residual(base, x, self.lora, tokens_per_expert), bias
+
+
+class Glm52MLPExpertsLinearFC2LoRA(MLPExpertsLinearFC2LoRA):
+    def forward(
+        self, x: torch.Tensor, tokens_per_expert: list[int] | torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        base, bias = _grouped_linear(self.linear_fc2, x, tokens_per_expert)
+        return _expert_lora_residual(base, x, self.lora, tokens_per_expert), bias
+
+
+def wrap_glm52_grouped_moe_experts_3d(
+    experts: Any,
+    *,
+    adapter_model_prefix: str,
+    target_modules: set[str],
+    rank: int,
+    alpha: int,
+) -> None:
+    if not _targets_include(target_modules, "experts"):
+        return
+    if TEColumnParallelGroupedLinear is None or TERowParallelGroupedLinear is None:
+        raise RuntimeError("GLM-5.2 expert LoRA requires Transformer Engine")
+    linear_fc1 = Glm52MLPExpertsLinearFC1LoRA(
+        adapter_model_prefix=f"{adapter_model_prefix}.mlp.experts",
+        linear_fc1=_unwrap_attr(
+            experts.linear_fc1,
+            "linear_fc1",
+            TEColumnParallelGroupedLinear,
+        ),
+        rank=rank,
+        alpha=alpha,
+        num_local_experts=experts.num_local_experts,
+        fused_gate_up=True,
+    )
+    linear_fc2 = Glm52MLPExpertsLinearFC2LoRA(
+        adapter_model_prefix=f"{adapter_model_prefix}.mlp.experts",
+        linear_fc2=_unwrap_attr(
+            experts.linear_fc2,
+            "linear_fc2",
+            TERowParallelGroupedLinear,
+        ),
+        rank=rank,
+        alpha=alpha,
+        num_local_experts=experts.num_local_experts,
+    )
+    experts.linear_fc1 = linear_fc1
+    experts.linear_fc2 = linear_fc2
+    _bind_expert_lora_layout(experts, linear_fc1.lora, linear_fc2.lora)
+
+
+def add_glm52_attention_adapter_weights(
+    adapter_weights_by_base: dict[str, list[Any]],
+    *,
+    layer_prefix: str,
+    attention: Any,
+) -> None:
+    from art.megatron.weights.adapter_export import (
+        _simple_adapter_weight,
+        add_self_attention_adapter_weights,
+    )
+
+    add_self_attention_adapter_weights(
+        adapter_weights_by_base,
+        layer_prefix=layer_prefix,
+        self_attention=attention,
+    )
+    prefix = f"{layer_prefix}.self_attention"
+    for attr, base_name in (
+        ("q_a_lora", "linear_q_down_proj"),
+        ("q_b_lora", "linear_q_up_proj"),
+        ("kv_a_lora", "linear_kv_down_proj"),
+    ):
+        lora = getattr(attention, attr)
+        if lora is not None:
+            base_prefix = f"{prefix}.{base_name}"
+            adapter_weights_by_base[f"{base_prefix}.weight"] = [
+                _simple_adapter_weight(base_prefix, lora)
+            ]
diff --git a/src/art/megatron/glm52/lora_projection.py b/src/art/megatron/glm52/lora_projection.py
new file mode 100644
index 000000000..c3fcb9a2a
--- /dev/null
+++ b/src/art/megatron/glm52/lora_projection.py
@@ -0,0 +1,145 @@
+from typing import Any, cast
+
+import torch
+import triton
+import triton.language as tl
+
+_MAX_RANK = 512
+
+
+@triton.jit
+def _rank_one_kernel(
+    x,
+    a,
+    out,
+    m,
+    k: tl.constexpr,
+    block_m: tl.constexpr,
+    block_k: tl.constexpr,
+):
+    rows = tl.program_id(0) * block_m + tl.arange(0, block_m)
+    acc = tl.zeros((block_m,), tl.float32)
+    for k_start in range(0, k, block_k):
+        inner = k_start + tl.arange(0, block_k)
+        x_tile = tl.load(
+            x + rows[:, None] * k + inner[None, :],
+            mask=(rows[:, None] < m) & (inner[None, :] < k),
+            other=0.0,
+        ).to(tl.float32)
+        a_tile = tl.load(a + inner, mask=inner < k, other=0.0).to(tl.float32)
+        acc += tl.sum(x_tile * a_tile[None, :], axis=1)
+    tl.store(out + rows, acc, mask=rows < m)
+
+
+@triton.jit
+def _matrix_kernel(
+    x,
+    a,
+    out,
+    m,
+    k: tl.constexpr,
+    n: tl.constexpr,
+    block_m: tl.constexpr,
+    block_k: tl.constexpr,
+    block_n: tl.constexpr,
+):
+    rows = tl.program_id(0) * block_m + tl.arange(0, block_m)
+    cols = tl.program_id(1) * block_n + tl.arange(0, block_n)
+    acc = tl.zeros((block_m, block_n), tl.float32)
+    for k_start in range(0, k, block_k):
+        inner = k_start + tl.arange(0, block_k)
+        x_tile = tl.load(
+            x + rows[:, None] * k + inner[None, :],
+            mask=(rows[:, None] < m) & (inner[None, :] < k),
+            other=0.0,
+        ).to(tl.float32)
+        a_tile = tl.load(
+            a + inner[:, None] * n + cols[None, :],
+            mask=(inner[:, None] < k) & (cols[None, :] < n),
+            other=0.0,
+        ).to(tl.float32)
+        acc = tl.dot(x_tile, a_tile, acc, input_precision="tf32x3")
+    tl.store(
+        out + rows[:, None] * n + cols[None, :],
+        acc,
+        mask=(rows[:, None] < m) & (cols[None, :] < n),
+    )
+
+
+def _validate(x: torch.Tensor, a: torch.Tensor) -> None:
+    if not x.is_cuda or not a.is_cuda or x.device != a.device:
+        raise ValueError("GLM-5.2 LoRA projection requires tensors on one CUDA device.")
+    if x.dtype != torch.bfloat16 or a.dtype != torch.bfloat16:
+        raise ValueError("GLM-5.2 LoRA projection requires BF16 tensors.")
+    if x.ndim < 2 or a.ndim != 2 or x.shape[-1] != a.shape[0]:
+        raise ValueError(
+            f"GLM-5.2 LoRA projection shape mismatch: x={tuple(x.shape)}, "
+            f"A_T={tuple(a.shape)}."
+        )
+    if not x.is_contiguous() or not a.is_contiguous():
+        raise ValueError("GLM-5.2 LoRA projection requires contiguous tensors.")
+    if not 1 <= a.shape[1] <= _MAX_RANK:
+        raise ValueError(
+            f"GLM-5.2 LoRA rank must be in [1, {_MAX_RANK}], got {a.shape[1]}."
+        )
+
+
+def _forward(x: torch.Tensor, a: torch.Tensor) -> torch.Tensor:
+    _validate(x, a)
+    x_2d = x.view(-1, x.shape[-1])
+    m, k = x_2d.shape
+    n = a.shape[1]
+    out = torch.empty((m, n), dtype=x.dtype, device=x.device)
+    if m == 0:
+        return out.view(*x.shape[:-1], n)
+    if n == 1:
+        _rank_one_kernel[(triton.cdiv(m, 8),)](
+            x_2d,
+            a,
+            out,
+            m,
+            k=k,  # ty: ignore[invalid-argument-type]
+            block_m=8,  # ty: ignore[invalid-argument-type]
+            block_k=512,  # ty: ignore[invalid-argument-type]
+            num_warps=4,  # ty: ignore[unknown-argument]
+            num_stages=1,  # ty: ignore[unknown-argument]
+        )
+    else:
+        block_n = 16 if n <= 16 else 32
+        _matrix_kernel[(triton.cdiv(m, 64), triton.cdiv(n, block_n))](
+            x_2d,
+            a,
+            out,
+            m,
+            k=k,  # ty: ignore[invalid-argument-type]
+            n=n,  # ty: ignore[invalid-argument-type]
+            block_m=64,  # ty: ignore[invalid-argument-type]
+            block_k=64,  # ty: ignore[invalid-argument-type]
+            block_n=block_n,  # ty: ignore[invalid-argument-type]
+            num_warps=4,  # ty: ignore[unknown-argument]
+            num_stages=3,  # ty: ignore[unknown-argument]
+        )
+    return out.view(*x.shape[:-1], n)
+
+
+class _Glm52LoraA(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx: Any, x: torch.Tensor, a: torch.Tensor) -> torch.Tensor:
+        ctx.save_for_backward(x, a)
+        return _forward(x, a)
+
+    @staticmethod
+    def backward(ctx: Any, *grad_outputs: Any):
+        x, a = ctx.saved_tensors
+        grad_out = cast(torch.Tensor, grad_outputs[0])
+        grad_2d = grad_out.reshape(-1, grad_out.shape[-1])
+        grad_x = grad_a = None
+        if ctx.needs_input_grad[0]:
+            grad_x = (grad_2d @ a.T).view_as(x)
+        if ctx.needs_input_grad[1]:
+            grad_a = x.view(-1, x.shape[-1]).T @ grad_2d
+        return grad_x, grad_a
+
+
+def glm52_lora_a(x: torch.Tensor, a: torch.Tensor) -> torch.Tensor:
+    return _Glm52LoraA.apply(x, a)
diff --git a/src/art/megatron/glm52/sparse_mla.py b/src/art/megatron/glm52/sparse_mla.py
new file mode 100644
index 000000000..775fc089e
--- /dev/null
+++ b/src/art/megatron/glm52/sparse_mla.py
@@ -0,0 +1,147 @@
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+
+from art.megatron.glm52 import tilelang_sparse_mla
+
+_LATENT_DIM = 512
+_ROPE_DIM = 64
+_TOPK_BLOCK = 64
+
+
+def sparse_mla_forward(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+    *,
+    scale: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    _validate_inputs(q, kv, indices)
+    return tilelang_sparse_mla.forward(
+        q.contiguous(), kv.contiguous(), indices.contiguous(), float(scale)
+    )
+
+
+def sparse_mla_backward(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+    out: torch.Tensor,
+    lse: torch.Tensor,
+    grad_out: torch.Tensor,
+    *,
+    scale: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    _validate_inputs(q, kv, indices)
+    expected_out = (*q.shape[:-1], _LATENT_DIM)
+    if out.shape != expected_out or grad_out.shape != expected_out:
+        raise ValueError(
+            f"GLM-5.2 sparse MLA output and gradient must have shape {expected_out}."
+        )
+    if lse.shape != q.shape[:-1] or lse.dtype is not torch.float32:
+        raise ValueError("GLM-5.2 sparse MLA LSE must be fp32 with shape [B,S,H].")
+    return tilelang_sparse_mla.backward(
+        q.contiguous(),
+        kv.contiguous(),
+        indices.contiguous(),
+        out.contiguous(),
+        lse.contiguous(),
+        grad_out.contiguous(),
+        float(scale),
+    )
+
+
+def reduce_tensor_parallel_dkv(
+    grad_kv: torch.Tensor,
+    *,
+    tp_group: Any | None,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    if tp_group is not None:
+        torch.distributed.all_reduce(  # ty: ignore[possibly-missing-attribute]
+            grad_kv, group=tp_group
+        )
+    return grad_kv.to(dtype)
+
+
+class _SparseMla(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx: Any,
+        q: torch.Tensor,
+        kv: torch.Tensor,
+        indices: torch.Tensor,
+        scale: float,
+        tp_group: Any | None,
+    ) -> torch.Tensor:
+        out, lse = sparse_mla_forward(q, kv, indices, scale=scale)
+        ctx.save_for_backward(q, kv, indices, out, lse)
+        ctx.scale = float(scale)
+        ctx.tp_group = tp_group
+        return out
+
+    @staticmethod
+    def backward(ctx: Any, *grad_outputs: Any):
+        q, kv, indices, out, lse = ctx.saved_tensors
+        grad_q, grad_kv = sparse_mla_backward(
+            q,
+            kv,
+            indices,
+            out,
+            lse,
+            grad_outputs[0],
+            scale=ctx.scale,
+        )
+        grad_kv = reduce_tensor_parallel_dkv(
+            grad_kv, tp_group=ctx.tp_group, dtype=kv.dtype
+        )
+        return grad_q, grad_kv, None, None, None
+
+
+def sparse_mla(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+    *,
+    scale: float,
+    tp_group: Any | None = None,
+) -> torch.Tensor:
+    """Run GLM-5.2 list-sparse absorbed MLA."""
+    return _SparseMla.apply(
+        q.contiguous(),
+        kv.contiguous(),
+        indices.contiguous(),
+        float(scale),
+        tp_group,
+    )
+
+
+def _validate_inputs(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+) -> None:
+    if q.dtype is not torch.bfloat16 or kv.dtype is not torch.bfloat16:
+        raise TypeError(f"GLM-5.2 sparse MLA requires bf16, got {q.dtype}/{kv.dtype}.")
+    if indices.dtype is not torch.int32:
+        raise TypeError(
+            f"GLM-5.2 sparse MLA indices must be int32, got {indices.dtype}."
+        )
+    if not q.is_cuda or q.device != kv.device or q.device != indices.device:
+        raise RuntimeError("GLM-5.2 sparse MLA requires colocated CUDA tensors.")
+    if q.ndim != 4 or kv.ndim != 3 or indices.ndim != 3:
+        raise ValueError(
+            "GLM-5.2 sparse MLA expects q[B,S,H,576], kv[B,K,576], ids[B,S,T]."
+        )
+    if not 0 < q.shape[2] <= 64 or q.shape[3] != _LATENT_DIM + _ROPE_DIM:
+        raise ValueError("GLM-5.2 sparse MLA requires positive 576-dimensional heads.")
+    if kv.shape[-1] != q.shape[-1] or q.shape[:2] != indices.shape[:2]:
+        raise ValueError("GLM-5.2 sparse MLA tensor shapes do not match.")
+    if q.shape[0] != kv.shape[0]:
+        raise ValueError("GLM-5.2 sparse MLA batch dimensions do not match.")
+    if indices.shape[-1] % _TOPK_BLOCK:
+        raise ValueError(
+            f"GLM-5.2 sparse MLA top-k must be divisible by {_TOPK_BLOCK}."
+        )
diff --git a/src/art/megatron/glm52/spec.py b/src/art/megatron/glm52/spec.py
new file mode 100644
index 000000000..469069637
--- /dev/null
+++ b/src/art/megatron/glm52/spec.py
@@ -0,0 +1,299 @@
+from __future__ import annotations
+
+from copy import deepcopy
+from itertools import combinations
+from typing import Any, cast
+
+from megatron.core.extensions.transformer_engine_spec_provider import TESpecProvider
+from megatron.core.models.gpt.gpt_layer_specs import get_gpt_decoder_block_spec
+from megatron.core.transformer.enums import AttnMaskType, LayerType
+from megatron.core.transformer.multi_latent_attention import MLASelfAttentionSubmodules
+from megatron.core.transformer.pipeline_parallel_layer_layout import (
+    PipelineParallelLayerLayout,
+)
+from megatron.core.transformer.spec_utils import ModuleSpec
+
+from art.megatron.context_parallel.types import (
+    ContextParallelStageWorkProfile,
+    ContextParallelWorkloadProfile,
+)
+from art.megatron.glm52.attention import (
+    Glm52SelfAttention,
+    glm52_core_builder,
+)
+
+
+def build_glm52_pipeline_layout(
+    indexer_types: tuple[str, ...], pp_size: int, vp_size: int
+) -> list[list[str]]:
+    """Balance complete IndexShare groups across virtual and physical stages."""
+    starts = [index for index, mode in enumerate(indexer_types) if mode == "full"]
+    stages = pp_size * vp_size
+    if not indexer_types or not starts or starts[0] != 0:
+        raise ValueError("GLM-5.2 indexer_types must start with a full layer.")
+    if stages > len(starts):
+        raise ValueError(
+            f"GLM-5.2 has {len(starts)} complete IndexShare groups but {stages} "
+            "PP/VPP stages were requested."
+        )
+
+    def score(boundaries: tuple[int, ...]) -> tuple[Any, ...]:
+        chunks = [
+            end - start
+            for start, end in zip(boundaries[:-1], boundaries[1:], strict=True)
+        ]
+        physical = [sum(chunks[pp_rank::pp_size]) for pp_rank in range(pp_size)]
+        return (
+            max(chunks),
+            max(physical),
+            max(physical) - min(physical),
+            max(chunks) - min(chunks),
+            boundaries,
+        )
+
+    boundaries = min(
+        (
+            (0, *selected, len(indexer_types))
+            for selected in combinations(starts[1:], stages - 1)
+        ),
+        key=score,
+    )
+    layout = [
+        ["decoder"] * (end - start)
+        for start, end in zip(boundaries[:-1], boundaries[1:], strict=True)
+    ]
+    layout[0].insert(0, "embedding")
+    layout[-1].append("loss")
+    return layout
+
+
+def _glm52_pipeline_stage_ranges(config: Any) -> tuple[tuple[int, int, int], ...]:
+    """Return physical PP rank and layer ranges in VPP-major execution order."""
+    indexer_types = tuple(config.glm52_indexer_types)
+    layout = config.pipeline_model_parallel_layout
+    stages = int(config.pipeline_model_parallel_size or 1) * int(
+        config.virtual_pipeline_model_parallel_size or 1
+    )
+    if stages == 1 and layout is None:
+        return ((0, 0, len(indexer_types)),)
+    if not isinstance(layout, PipelineParallelLayerLayout):
+        raise RuntimeError("GLM-5.2 PP/VPP requires a finalized flexible layout.")
+    full_groups = indexer_types.count("full")
+    if stages > full_groups:
+        raise ValueError(
+            f"GLM-5.2 has {full_groups} complete IndexShare groups but {stages} "
+            "PP/VPP stages were configured."
+        )
+    offset = 0
+    ranges = []
+    for vp_rank in range(layout.virtual_pipeline_model_parallel_size):
+        for pp_rank in range(layout.pipeline_model_parallel_size):
+            count = layout.layout[pp_rank][vp_rank].count(LayerType.decoder)
+            if count:
+                if indexer_types[offset] != "full":
+                    raise ValueError(
+                        "GLM-5.2 pipeline chunk starts at shared index layer "
+                        f"{offset} (PP={pp_rank}, VPP={vp_rank}); split only at "
+                        "full IndexShare layers."
+                    )
+            ranges.append((pp_rank, offset, offset + count))
+            offset += count
+    if offset != len(indexer_types):
+        raise ValueError(
+            f"GLM-5.2 pipeline layout covers {offset} decoder layers, expected "
+            f"{len(indexer_types)}."
+        )
+    return tuple(ranges)
+
+
+def _validate_glm52_pipeline_layout(config: Any) -> None:
+    """Reject a finalized layout that makes shared layers cross process chunks."""
+    _glm52_pipeline_stage_ranges(config)
+
+
+def _train_matmul_flops(
+    in_features: int,
+    out_features: int,
+    *,
+    forward_executions: int,
+) -> int:
+    # Frozen base weights still execute one input-gradient matmul.
+    return 2 * int(in_features) * int(out_features) * (forward_executions + 1)
+
+
+def build_glm52_context_parallel_profile(
+    config: Any,
+) -> ContextParallelWorkloadProfile:
+    """Describe the GLM work that changes with CP token ownership."""
+    indexer_types = tuple(config.glm52_indexer_types)
+    moe_layers = tuple(bool(value) for value in config.moe_layer_freq)
+    if len(moe_layers) != len(indexer_types):
+        raise ValueError(
+            "GLM-5.2 MLP and indexer layer patterns must have equal length."
+        )
+
+    forward_executions = (
+        2 if getattr(config, "recompute_granularity", None) == "full" else 1
+    )
+    hidden = int(config.hidden_size)
+    heads = int(config.num_attention_heads)
+    q_rank = int(config.q_lora_rank)
+    kv_rank = int(config.kv_lora_rank)
+    qk_nope = int(config.qk_head_dim)
+    rope = int(config.qk_pos_emb_head_dim)
+    value = int(config.v_head_dim)
+    combined_dim = kv_rank + rope
+    topk = int(config.dsa_indexer_topk)
+    index_heads = int(config.dsa_indexer_n_heads)
+    index_dim = int(config.dsa_indexer_head_dim)
+    dense_intermediate = int(config.ffn_hidden_size)
+    shared_intermediate = int(config.moe_shared_expert_intermediate_size or 0)
+    experts = int(config.num_moe_experts)
+
+    attention_projection = sum(
+        (
+            _train_matmul_flops(hidden, q_rank, forward_executions=forward_executions),
+            _train_matmul_flops(
+                q_rank,
+                heads * (qk_nope + rope),
+                forward_executions=forward_executions,
+            ),
+            _train_matmul_flops(
+                hidden, combined_dim, forward_executions=forward_executions
+            ),
+            heads
+            * _train_matmul_flops(
+                qk_nope, kv_rank, forward_executions=forward_executions
+            ),
+            heads
+            * _train_matmul_flops(
+                kv_rank, value, forward_executions=forward_executions
+            ),
+            _train_matmul_flops(
+                heads * value, hidden, forward_executions=forward_executions
+            ),
+        )
+    )
+    sparse_attention = (
+        2 * (forward_executions + 2) * heads * topk * (combined_dim + kv_rank)
+    )
+    dense_mlp = _train_matmul_flops(
+        hidden, 2 * dense_intermediate, forward_executions=forward_executions
+    ) + _train_matmul_flops(
+        dense_intermediate, hidden, forward_executions=forward_executions
+    )
+    local_sparse_mlp = _train_matmul_flops(
+        hidden, experts, forward_executions=forward_executions
+    )
+    if shared_intermediate:
+        local_sparse_mlp += _train_matmul_flops(
+            hidden,
+            2 * shared_intermediate,
+            forward_executions=forward_executions,
+        ) + _train_matmul_flops(
+            shared_intermediate,
+            hidden,
+            forward_executions=forward_executions,
+        )
+    indexer_projection = (
+        2
+        * forward_executions
+        * (q_rank * index_heads * index_dim + hidden * index_dim + hidden * index_heads)
+    )
+    indexer_pair = forward_executions * index_heads * (2 * index_dim + 3)
+
+    pp_size = int(config.pipeline_model_parallel_size or 1)
+    stage_layers = [0 for _ in range(pp_size)]
+    stage_indexers = [0 for _ in range(pp_size)]
+    stage_query_flops = [0 for _ in range(pp_size)]
+    for pp_rank, start, end in _glm52_pipeline_stage_ranges(config):
+        full_indexers = indexer_types[start:end].count("full")
+        layer_count = end - start
+        query_flops = layer_count * (attention_projection + sparse_attention)
+        query_flops += sum(
+            local_sparse_mlp if moe_layers[layer] else dense_mlp
+            for layer in range(start, end)
+        )
+        query_flops += full_indexers * indexer_projection
+        stage_layers[pp_rank] += layer_count
+        stage_indexers[pp_rank] += full_indexers
+        stage_query_flops[pp_rank] += query_flops
+
+    stages = []
+    sparse_fetches = forward_executions + 1
+    for pp_rank, (layer_count, full_indexers, query_flops) in enumerate(
+        zip(stage_layers, stage_indexers, stage_query_flops, strict=True)
+    ):
+        k_fetch_bytes = layer_count * sparse_fetches * combined_dim * 2
+        k_fetch_bytes += full_indexers * forward_executions * index_dim * 2
+        dkv_reduce_bytes = layer_count * combined_dim * 2
+        # Each fetch concatenates CP stages and adds TileLang's sentinel row.
+        # Backward also zeroes four FP32 dKV splits, reduces, and casts to BF16.
+        k_hbm_bytes = layer_count * combined_dim * (8 * sparse_fetches + 42)
+        checkpoint_bytes = layer_count * hidden * 2
+        persistent_topk_bytes = full_indexers * topk * 4
+        sparse_query_workspace = (
+            2 * heads * combined_dim * 2
+            + 2 * heads * kv_rank * 2
+            + 2 * heads * 4
+            + topk * 4
+        )
+        # Backward holds original and padded BF16 KV, four FP32 dKV splits,
+        # the FP32 reduction result, and the returned BF16 dKV.
+        k_memory = combined_dim * (2 + 2 + 4 * 4 + 4 + 2)
+        stages.append(
+            ContextParallelStageWorkProfile(
+                physical_pipeline_rank=pp_rank,
+                query_flops_per_token=query_flops,
+                tile_pair_flops=full_indexers * indexer_pair,
+                k_hbm_bytes_per_token=k_hbm_bytes,
+                k_fetch_bytes_per_token=k_fetch_bytes,
+                dkv_reduce_bytes_per_token=dkv_reduce_bytes,
+                query_memory_bytes_per_token=(
+                    checkpoint_bytes + persistent_topk_bytes + sparse_query_workspace
+                ),
+                k_memory_bytes_per_token=k_memory,
+            )
+        )
+    return ContextParallelWorkloadProfile(
+        stages=tuple(stages),
+        query_tile_size=128 // index_heads,
+        key_tile_size=64,
+        indexer_score_workspace_elements=(256 * 1024 * 1024) // 8,
+        indexer_max_k_tokens=32 * 1024,
+    )
+
+
+def get_glm52_decoder_block_spec(config: Any, vp_stage: int | None = None) -> Any:
+    """Build GLM-5.2 layers without entering MCore's incomplete DSA path."""
+    _validate_glm52_pipeline_layout(config)
+    block_spec = deepcopy(
+        get_gpt_decoder_block_spec(
+            config,
+            use_transformer_engine=True,
+            normalization="RMSNorm",
+            vp_stage=vp_stage,
+        )
+    )
+    backend = TESpecProvider()
+    attention = ModuleSpec(
+        module=Glm52SelfAttention,
+        params={"attn_mask_type": AttnMaskType.causal},
+        submodules=MLASelfAttentionSubmodules(
+            linear_q_down_proj=backend.linear(),
+            linear_q_up_proj=backend.column_parallel_linear(),
+            linear_kv_down_proj=backend.linear(),
+            linear_kv_up_proj=backend.column_parallel_linear(),
+            core_attention=glm52_core_builder(
+                backend.linear(),
+                backend.layer_norm(rms_norm=False, for_qk=True),
+            ),
+            linear_proj=backend.row_parallel_linear(),
+            q_layernorm=backend.layer_norm(rms_norm=True, for_qk=True),
+            kv_layernorm=backend.layer_norm(rms_norm=True, for_qk=True),
+        ),
+        metainfo={"fuse_input_layernorm": False},
+    )
+    for layer_spec in block_spec.layer_specs or ():
+        cast(Any, layer_spec.submodules).self_attention = attention
+    return block_spec
diff --git a/src/art/megatron/glm52/state.py b/src/art/megatron/glm52/state.py
new file mode 100644
index 000000000..48e0ce3d7
--- /dev/null
+++ b/src/art/megatron/glm52/state.py
@@ -0,0 +1,285 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field
+import torch
+
+from art.megatron.context_parallel.builder import build_prefix_tree_attention_spec
+from art.megatron.context_parallel.types import AttnMaskKind
+
+# Preserve the CUDA float32 `pow` rounding used by the reference GLM RoPE.
+_ROPE_INV_FREQ_BITS = (
+    1065353216,
+    1058785356,
+    1052612689,
+    1046920992,
+    1041001025,
+    1034609764,
+    1028652027,
+    1023221913,
+    1016727752,
+    1010530219,
+    1004808260,
+    998954723,
+    992541049,
+    986556035,
+    981092721,
+    974671434,
+    968449313,
+    962697431,
+    956909580,
+    950473744,
+    944461757,
+    938965617,
+    932616387,
+    926369956,
+    920588484,
+    914865582,
+    908407833,
+    902369178,
+    896840579,
+    890562597,
+    884292128,
+    878481401,
+)
+
+
+class Glm52IndexerSlice(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    k_start: int
+    k_end: int
+    causal: bool
+
+
+class Glm52StageQueryPlan(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    q_start: int
+    q_end: int
+    k_ranges: tuple[tuple[int, int], ...]
+
+
+class Glm52IndexerQueryPlan(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    q_start: int
+    q_end: int
+    slices: tuple[Glm52IndexerSlice, ...]
+
+
+class Glm52IndexerRowPlan(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    row_index: int
+    valid_tokens: int
+    queries: tuple[Glm52IndexerQueryPlan, ...]
+
+
+class Glm52StageState(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+    stage_index: int
+    global_q_ids: torch.Tensor
+    global_k_ids: torch.Tensor
+    owner_q_rows: torch.Tensor
+    queries: tuple[Glm52StageQueryPlan, ...]
+
+
+class Glm52PrefixTreeState(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    position_ids: torch.Tensor
+    rope_cos: torch.Tensor
+    rope_sin: torch.Tensor
+    indexer_rows: tuple[Glm52IndexerRowPlan, ...] = ()
+    stages: tuple[Glm52StageState, ...] = ()
+    route_by_global_id: torch.Tensor | None = None
+    combined_k_rows: int = 0
+    context_parallel_state: Any | None = None
+    topk_by_full_layer: dict[int, Any] = Field(default_factory=dict)
+
+
+def _rope_state(
+    position_ids: torch.Tensor,
+    *,
+    device: torch.device,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    position_ids_device = position_ids.to(
+        device=device,
+        dtype=torch.int64,
+        non_blocking=True,
+    ).contiguous()
+    inv_freq = torch.tensor(_ROPE_INV_FREQ_BITS, device=device, dtype=torch.int32).view(
+        torch.float32
+    )
+    frequencies = position_ids_device.float().unsqueeze(-1) * inv_freq
+    return (
+        position_ids_device,
+        frequencies.cos().to(torch.bfloat16),
+        frequencies.sin().to(torch.bfloat16),
+    )
+
+
+def build_glm52_prefix_tree_state(
+    *,
+    position_ids: torch.Tensor,
+    group_ids: torch.Tensor,
+    parent_ids: torch.Tensor,
+    device: torch.device,
+) -> Glm52PrefixTreeState:
+    """Precompute immutable tree rectangles once for every GLM-5.2 layer."""
+    if position_ids.ndim != 2:
+        raise ValueError(
+            f"GLM-5.2 position_ids must be 2D, got {tuple(position_ids.shape)}."
+        )
+    batch_spec = build_prefix_tree_attention_spec(
+        group_ids=group_ids,
+        parent_ids=parent_ids,
+    )
+    rows: list[Glm52IndexerRowPlan] = []
+    for row in batch_spec.rows:
+        slices_by_query: dict[tuple[int, int], list[Glm52IndexerSlice]] = defaultdict(
+            list
+        )
+        for slice_ in row.slices:
+            slices_by_query[(slice_.q_range.start, slice_.q_range.end)].append(
+                Glm52IndexerSlice(
+                    k_start=slice_.k_range.start,
+                    k_end=slice_.k_range.end,
+                    causal=slice_.mask_kind is AttnMaskKind.CAUSAL,
+                )
+            )
+        queries = tuple(
+            Glm52IndexerQueryPlan(
+                q_start=q_start,
+                q_end=q_end,
+                slices=tuple(slices),
+            )
+            for (q_start, q_end), slices in sorted(slices_by_query.items())
+        )
+        rows.append(
+            Glm52IndexerRowPlan(
+                row_index=row.row_index,
+                valid_tokens=row.valid_tokens,
+                queries=queries,
+            )
+        )
+    position_ids_device, rope_cos, rope_sin = _rope_state(
+        position_ids,
+        device=device,
+    )
+    return Glm52PrefixTreeState(
+        position_ids=position_ids_device,
+        rope_cos=rope_cos,
+        rope_sin=rope_sin,
+        indexer_rows=tuple(rows),
+    )
+
+
+def build_glm52_context_parallel_state(
+    *,
+    position_ids: torch.Tensor,
+    context_parallel_state: Any,
+    device: torch.device,
+) -> Glm52PrefixTreeState:
+    """Materialize GLM stage ids once without reading CUDA data on the host."""
+    rank_plan = context_parallel_state.rank_plan
+    stages = []
+    route_by_global_id = torch.full(
+        (int(rank_plan.original_seq_len),), -1, dtype=torch.int32
+    )
+    combined_k_start = 0
+    for stage in rank_plan.stage_plans:
+        q_len = sum(range_.size() for range_ in stage.owner_local_q_ranges)
+        k_len = sum(range_.size() for range_ in stage.owner_local_k_ranges)
+        if combined_k_start + k_len > torch.iinfo(torch.int32).max:
+            raise RuntimeError(
+                "GLM-5.2 combined CP KV rows exceed int32 index capacity."
+            )
+        metadata = stage.mask_metadata
+        if metadata is None and (q_len or k_len):
+            raise RuntimeError(
+                f"GLM-5.2 stage {stage.stage_index} is missing exact token ids."
+            )
+        if metadata is None:
+            q_ids = k_ids = torch.empty(0, dtype=torch.int32, device=device)
+        else:
+            k_ids_cpu = metadata.k_token_indices[:k_len].to(torch.int64)
+            routes_cpu = torch.arange(
+                combined_k_start,
+                combined_k_start + k_len,
+                dtype=torch.int32,
+            )
+            existing = route_by_global_id[k_ids_cpu]
+            if bool(((existing >= 0) & (existing != routes_cpu)).any()):
+                raise RuntimeError(
+                    "GLM-5.2 CP stages assign one global KV id to multiple routes."
+                )
+            route_by_global_id[k_ids_cpu] = routes_cpu
+            q_ids = metadata.q_token_indices[:q_len].to(
+                device=device, dtype=torch.int32, non_blocking=True
+            )
+            k_ids = metadata.k_token_indices[:k_len].to(
+                device=device, dtype=torch.int32, non_blocking=True
+            )
+        owner_q_parts = tuple(
+            torch.arange(range_.start, range_.end, dtype=torch.int64)
+            for range_ in stage.owner_local_q_ranges
+            if range_.size() > 0
+        )
+        owner_q_rows = (
+            torch.cat(owner_q_parts)
+            if owner_q_parts
+            else torch.empty(0, dtype=torch.int64)
+        )
+        k_ranges_by_query: dict[tuple[int, int], list[tuple[int, int]]] = defaultdict(
+            list
+        )
+        for slice_ in stage.slices:
+            k_ranges_by_query[
+                (int(slice_.q_range.start), int(slice_.q_range.end))
+            ].append((int(slice_.k_range.start), int(slice_.k_range.end)))
+        stages.append(
+            Glm52StageState(
+                stage_index=int(stage.stage_index),
+                global_q_ids=q_ids.contiguous(),
+                global_k_ids=k_ids.contiguous(),
+                owner_q_rows=owner_q_rows.to(device=device, non_blocking=True),
+                queries=tuple(
+                    Glm52StageQueryPlan(
+                        q_start=q_start,
+                        q_end=q_end,
+                        k_ranges=tuple(k_ranges),
+                    )
+                    for (q_start, q_end), k_ranges in sorted(k_ranges_by_query.items())
+                ),
+            )
+        )
+        combined_k_start += k_len
+    position_ids_device, rope_cos, rope_sin = _rope_state(
+        position_ids,
+        device=device,
+    )
+    return Glm52PrefixTreeState(
+        position_ids=position_ids_device,
+        rope_cos=rope_cos,
+        rope_sin=rope_sin,
+        stages=tuple(stages),
+        route_by_global_id=route_by_global_id.to(device=device, non_blocking=True),
+        combined_k_rows=combined_k_start,
+        context_parallel_state=context_parallel_state,
+    )
+
+
+def require_glm52_state(attention_bias: Any) -> Glm52PrefixTreeState:
+    model_state = getattr(attention_bias, "model_state", None)
+    state = model_state.get("glm52") if isinstance(model_state, dict) else None
+    if not isinstance(state, Glm52PrefixTreeState):
+        raise RuntimeError(
+            "GLM-5.2 prefix-tree state is missing; build it once per packed "
+            "sequence through the model-support handler."
+        )
+    return state
diff --git a/src/art/megatron/glm52/tilelang_sparse_mla.py b/src/art/megatron/glm52/tilelang_sparse_mla.py
new file mode 100644
index 000000000..8ecc28031
--- /dev/null
+++ b/src/art/megatron/glm52/tilelang_sparse_mla.py
@@ -0,0 +1,651 @@
+# ruff: noqa
+# Adapted from Miles GLM and tile-ai/tilelang DeepSeek-V3.2 sparse MLA kernels.
+
+from collections.abc import Iterator
+from contextlib import contextmanager
+import importlib
+import os
+from typing import Any
+
+import torch
+
+_ENV_KEYS = (
+    "PYTHONPATH",
+    "TVM_IMPORT_PYTHON_PATH",
+    "TVM_LIBRARY_PATH",
+    "TL_CUTLASS_PATH",
+    "TL_TEMPLATE_PATH",
+    "TL_COMPOSABLE_KERNEL_PATH",
+)
+_PATH_MARKERS = ("/site-packages/tilelang/", "\\site-packages\\tilelang\\")
+
+
+def _clean(value: str | None) -> str | None:
+    if value is None:
+        return None
+    kept = [
+        part
+        for part in value.split(os.pathsep)
+        if not any(marker in part for marker in _PATH_MARKERS)
+    ]
+    return os.pathsep.join(kept) if kept else None
+
+
+def _restore(saved: dict[str, str | None]) -> None:
+    for key, value in saved.items():
+        if value is None:
+            os.environ.pop(key, None)
+        else:
+            os.environ[key] = value
+    for key in _ENV_KEYS:
+        value = _clean(os.environ.get(key))
+        if value is None:
+            os.environ.pop(key, None)
+        else:
+            os.environ[key] = value
+
+
+@contextmanager
+def _preserve_env() -> Iterator[None]:
+    saved = {key: os.environ.get(key) for key in _ENV_KEYS}
+    try:
+        yield
+    finally:
+        _restore(saved)
+
+
+with _preserve_env():
+    tilelang: Any = importlib.import_module("tilelang")
+    T: Any = importlib.import_module("tilelang.language")
+
+_LATENT = 512
+_ROPE = 64
+_DIM = _LATENT + _ROPE
+_HEAD_BLOCK = 16
+_DKV_SPLITS = 4
+_LOG2_E = 1.4426950408889634
+_LN_2 = 0.6931471805599453
+
+
+@tilelang.jit(
+    out_idx=[-2, -1],
+    pass_configs={
+        tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
+        tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
+    },
+)
+def _forward(heads, topk, scale, block_i=64, num_stages=2, threads=256):
+    assert topk % block_i == 0
+    batch = T.dynamic("batch")
+    q_tokens = T.dynamic("q_tokens")
+    kv_tokens = T.dynamic("kv_tokens")
+    q_shape = [batch, q_tokens, heads, _DIM]
+    kv_shape = [batch, kv_tokens, _DIM]
+    indices_shape = [batch, q_tokens, topk]
+    out_shape = [batch, q_tokens, heads, _LATENT]
+    lse_shape = [batch, q_tokens, heads]
+    blocks = topk // block_i
+    scale_log2 = scale * _LOG2_E
+
+    @T.prim_func
+    def main(
+        Q: T.Tensor(q_shape, T.bfloat16),  # type: ignore
+        KV: T.Tensor(kv_shape, T.bfloat16),  # type: ignore
+        Indices: T.Tensor(indices_shape, T.int32),  # type: ignore
+        Output: T.Tensor(out_shape, T.bfloat16),  # type: ignore
+        Lse: T.Tensor(lse_shape, T.float32),  # type: ignore
+    ):
+        with T.Kernel(q_tokens, batch, threads=threads) as (q_i, b_i):
+            q_shared = T.alloc_shared([heads, _LATENT], T.bfloat16)
+            q_rope_shared = T.alloc_shared([heads, _ROPE], T.bfloat16)
+            kv_shared = T.alloc_shared([block_i, _LATENT], T.bfloat16)
+            kv_rope_shared = T.alloc_shared([block_i, _ROPE], T.bfloat16)
+            scores_shared = T.alloc_shared([heads, block_i], T.bfloat16)
+            out_shared = T.alloc_shared([heads, _LATENT], T.bfloat16)
+            valid = T.alloc_fragment([block_i], "bool")
+            scores = T.alloc_fragment([heads, block_i], T.float32)
+            output = T.alloc_fragment([heads, _LATENT], T.float32)
+            row_sum = T.alloc_fragment([heads], T.float32)
+            block_sum = T.alloc_fragment([heads], T.float32)
+            row_max = T.alloc_fragment([heads], T.float32)
+            previous_max = T.alloc_fragment([heads], T.float32)
+            alpha = T.alloc_fragment([heads], T.float32)
+
+            T.copy(Q[b_i, q_i, :, :_LATENT], q_shared)
+            T.copy(Q[b_i, q_i, :, _LATENT:], q_rope_shared)
+            T.fill(output, 0)
+            T.fill(row_sum, 0)
+            T.fill(row_max, -(2**30))
+
+            for block in T.Pipelined(blocks, num_stages=num_stages):
+                for i in T.Parallel(block_i):
+                    index = Indices[b_i, q_i, block * block_i + i]
+                    valid[i] = (index >= 0) & (index < kv_tokens - 1)
+                for i, d in T.Parallel(block_i, _LATENT):
+                    kv_shared[i, d] = KV[b_i, Indices[b_i, q_i, block * block_i + i], d]
+                for i, d in T.Parallel(block_i, _ROPE):
+                    kv_rope_shared[i, d] = KV[
+                        b_i, Indices[b_i, q_i, block * block_i + i], _LATENT + d
+                    ]
+                for h, i in T.Parallel(heads, block_i):
+                    scores[h, i] = T.if_then_else(valid[i], 0, -T.infinity(T.float32))
+                T.gemm(
+                    q_shared,
+                    kv_shared,
+                    scores,
+                    transpose_B=True,
+                    policy=T.GemmWarpPolicy.FullRow,
+                )
+                T.gemm(
+                    q_rope_shared,
+                    kv_rope_shared,
+                    scores,
+                    transpose_B=True,
+                    policy=T.GemmWarpPolicy.FullRow,
+                )
+                T.copy(row_max, previous_max)
+                T.reduce_max(scores, row_max, dim=1, clear=False)
+                for h in T.Parallel(heads):
+                    row_max[h] = T.max(row_max[h], previous_max[h])
+                    alpha[h] = T.exp2((previous_max[h] - row_max[h]) * scale_log2)
+                for h, i in T.Parallel(heads, block_i):
+                    scores[h, i] = T.exp2((scores[h, i] - row_max[h]) * scale_log2)
+                T.reduce_sum(scores, block_sum, dim=1)
+                for h in T.Parallel(heads):
+                    row_sum[h] = row_sum[h] * alpha[h] + block_sum[h]
+                for h, d in T.Parallel(heads, _LATENT):
+                    output[h, d] *= alpha[h]
+                T.copy(scores, scores_shared)
+                T.gemm(
+                    scores_shared, kv_shared, output, policy=T.GemmWarpPolicy.FullRow
+                )
+
+            for h, d in T.Parallel(heads, _LATENT):
+                output[h, d] /= T.max(row_sum[h], 1e-20)
+            for h in T.Parallel(heads):
+                row_sum[h] = T.if_then_else(
+                    row_sum[h] > 0,
+                    (T.log2(row_sum[h]) + row_max[h] * scale_log2) * _LN_2,
+                    -T.infinity(T.float32),
+                )
+            T.copy(output, out_shared)
+            T.copy(out_shared, Output[b_i, q_i, :, :])
+            T.copy(row_sum, Lse[b_i, q_i, :])
+
+    return main
+
+
+@tilelang.jit(out_idx=[-1])
+def _delta(heads, block=32, num_stages=5):
+    batch = T.dynamic("batch")
+    tokens = T.dynamic("tokens")
+    shape = [batch, tokens, heads, _LATENT]
+
+    @T.prim_func
+    def main(
+        Output: T.Tensor(shape, T.bfloat16),  # type: ignore
+        GradOutput: T.Tensor(shape, T.bfloat16),  # type: ignore
+        Delta: T.Tensor([batch, tokens, heads], T.float32),  # type: ignore
+    ):
+        with T.Kernel(heads, T.ceildiv(tokens, block), batch) as (h_i, t_i, b_i):
+            output = T.alloc_fragment([block, block], T.float32)
+            grad = T.alloc_fragment([block, block], T.float32)
+            product = T.alloc_fragment([block, block], T.float32)
+            result = T.alloc_fragment([block], T.float32)
+            T.clear(product)
+            for d_i in T.Pipelined(T.ceildiv(_LATENT, block), num_stages=num_stages):
+                T.copy(
+                    Output[
+                        b_i,
+                        t_i * block : (t_i + 1) * block,
+                        h_i,
+                        d_i * block : (d_i + 1) * block,
+                    ],
+                    output,
+                )
+                T.copy(
+                    GradOutput[
+                        b_i,
+                        t_i * block : (t_i + 1) * block,
+                        h_i,
+                        d_i * block : (d_i + 1) * block,
+                    ],
+                    grad,
+                )
+                for i, d in T.Parallel(block, block):
+                    product[i, d] += output[i, d] * grad[i, d]
+            T.reduce_sum(product, result, dim=1)
+            T.copy(result, Delta[b_i, t_i * block : (t_i + 1) * block, h_i])
+
+    return main
+
+
+@tilelang.jit(
+    out_idx=[-2],
+    pass_configs={
+        tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
+        tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
+    },
+)
+def _backward(
+    heads,
+    topk,
+    scale,
+    dkv_splits=_DKV_SPLITS,
+    block_i=32,
+    num_stages=0,
+    threads=256,
+    use_tcgen_dq=False,
+):
+    batch = T.dynamic("batch")
+    q_tokens = T.dynamic("q_tokens")
+    kv_tokens = T.dynamic("kv_tokens")
+    assert topk % block_i == 0
+    assert not use_tcgen_dq or (
+        block_i in (32, 64) and threads == 256 and heads % 32 == 0
+    )
+    tcgen_group = 2 * block_i
+    q_shape = [batch, q_tokens, heads, _DIM]
+    kv_shape = [batch, kv_tokens, 1, _DIM]
+    grad_kv_shape = [batch, dkv_splits, kv_tokens, 1, _DIM]
+    out_shape = [batch, q_tokens, heads, _LATENT]
+    indices_shape = [batch, q_tokens, 1, topk]
+    row_shape = [batch, q_tokens, heads]
+    blocks = topk // block_i
+    scale_log2 = scale * _LOG2_E
+    split_store = 2
+
+    @T.macro
+    def prefetch_kv(KV, Indices, shared, b_i, q_i, offset, width, dim_offset):
+        for i, d in T.Parallel(
+            block_i,
+            width,
+            prefer_async=True,
+            annotations={"parallel_async_without_async_commit_wait": True},
+        ):
+            shared[i, d] = KV[b_i, Indices[b_i, q_i, 0, offset + i], 0, dim_offset + d]
+        T.ptx_commit_group()
+
+    @T.prim_func
+    def main(
+        Q: T.Tensor(q_shape, T.bfloat16),  # type: ignore
+        KV: T.Tensor(kv_shape, T.bfloat16),  # type: ignore
+        GradOutput: T.Tensor(out_shape, T.bfloat16),  # type: ignore
+        Indices: T.Tensor(indices_shape, T.int32),  # type: ignore
+        Lse: T.Tensor(row_shape, T.float32),  # type: ignore
+        Delta: T.Tensor(row_shape, T.float32),  # type: ignore
+        GradQ: T.Tensor(q_shape, T.bfloat16),  # type: ignore
+        GradKV: T.Tensor(grad_kv_shape, T.float32),  # type: ignore
+    ):
+        with T.Kernel(q_tokens, batch, threads=threads) as (q_i, b_i):
+            q_shared = T.alloc_shared([heads, _LATENT], T.bfloat16)
+            q_rope_shared = T.alloc_shared([heads, _ROPE], T.bfloat16)
+            kv_shared = T.alloc_shared([block_i, _LATENT], T.bfloat16)
+            kv_rope_shared = T.alloc_shared([block_i, _ROPE], T.bfloat16)
+            grad_out_shared = T.alloc_shared([heads, _LATENT], T.bfloat16)
+            probabilities_shared = T.alloc_shared([heads, block_i], T.bfloat16)
+            grad_scores_shared = T.alloc_shared([heads, block_i], T.bfloat16)
+            grad_q_shared = T.alloc_shared([heads, _LATENT], T.bfloat16)
+            grad_q_rope_shared = T.alloc_shared([heads, _ROPE], T.bfloat16)
+            if not use_tcgen_dq:
+                grad_kv_shared = T.alloc_shared(
+                    [block_i // split_store, _LATENT], T.float32
+                )
+                grad_kv_rope_shared = T.alloc_shared(
+                    [block_i // split_store, _ROPE], T.float32
+                )
+            if use_tcgen_dq:
+                grad_q_tmem = T.alloc_tmem([heads, _LATENT], T.float32)
+                grad_q_barrier = T.alloc_barrier(1)
+            valid = T.alloc_fragment([block_i], "bool")
+            probabilities = T.alloc_fragment([heads, block_i], T.float32)
+            grad_probabilities = T.alloc_fragment([heads, block_i], T.float32)
+            grad_q = T.alloc_fragment([heads, _LATENT], T.float32)
+            grad_q_rope = T.alloc_fragment([heads, _ROPE], T.float32)
+            grad_kv = T.alloc_fragment([block_i, _LATENT], T.float32)
+            if use_tcgen_dq:
+                grad_kv_tmem = T.alloc_tmem([block_i, _LATENT], T.float32)
+                grad_kv_barrier = T.alloc_barrier(1)
+                grad_kv_add_barrier = T.alloc_barrier(1)
+                T.annotate_layout(
+                    {
+                        grad_kv_tmem: T.Layout(
+                            [block_i, _LATENT],
+                            lambda i, j: [
+                                (j % 256) // tcgen_group * block_i + i,
+                                (j // 256) * tcgen_group + j % tcgen_group,
+                            ],
+                        ),
+                        grad_kv: T.Fragment(
+                            [block_i, _LATENT],
+                            forward_fn=lambda i, j: (
+                                (j // tcgen_group) * block_i + i,
+                                j % tcgen_group,
+                            ),
+                        ),
+                    }
+                )
+            grad_kv_rope = T.alloc_fragment([block_i, _ROPE], T.float32)
+
+            T.copy(Q[b_i, q_i, :, :_LATENT], q_shared)
+            T.copy(Q[b_i, q_i, :, _LATENT:], q_rope_shared)
+            T.copy(GradOutput[b_i, q_i, :, :], grad_out_shared)
+            if not use_tcgen_dq:
+                T.clear(grad_q)
+            T.clear(grad_q_rope)
+
+            if use_tcgen_dq:
+                prefetch_kv(KV, Indices, kv_shared, b_i, q_i, 0, _LATENT, 0)
+                prefetch_kv(KV, Indices, kv_rope_shared, b_i, q_i, 0, _ROPE, _LATENT)
+            for block in (
+                T.serial(blocks)
+                if use_tcgen_dq
+                else T.Pipelined(blocks, num_stages=num_stages)
+            ):
+                for i in T.Parallel(block_i):
+                    index = Indices[b_i, q_i, 0, block * block_i + i]
+                    valid[i] = (index >= 0) & (index < kv_tokens - 1)
+                for h, i in T.Parallel(heads, block_i):
+                    probabilities[h, i] = T.if_then_else(
+                        valid[i], 0, -T.infinity(T.float32)
+                    )
+                if use_tcgen_dq:
+                    T.ptx_wait_group(0)
+                    T.sync_threads()
+                if not use_tcgen_dq:
+                    for i, d in T.Parallel(block_i, _LATENT):
+                        kv_shared[i, d] = KV[
+                            b_i,
+                            Indices[b_i, q_i, 0, block * block_i + i],
+                            0,
+                            d,
+                        ]
+                    for i, d in T.Parallel(block_i, _ROPE):
+                        kv_rope_shared[i, d] = KV[
+                            b_i,
+                            Indices[b_i, q_i, 0, block * block_i + i],
+                            0,
+                            _LATENT + d,
+                        ]
+                T.gemm(
+                    q_shared,
+                    kv_shared,
+                    probabilities,
+                    transpose_B=True,
+                    policy=T.GemmWarpPolicy.FullCol,
+                )
+                T.gemm(
+                    q_rope_shared,
+                    kv_rope_shared,
+                    probabilities,
+                    transpose_B=True,
+                    policy=T.GemmWarpPolicy.FullCol,
+                )
+                for h, i in T.Parallel(heads, block_i):
+                    probabilities[h, i] = T.if_then_else(
+                        valid[i] & (Lse[b_i, q_i, h] > -1e30),
+                        T.exp2(
+                            (probabilities[h, i] * scale - Lse[b_i, q_i, h]) * _LOG2_E
+                        ),
+                        0,
+                    )
+                T.copy(probabilities, probabilities_shared)
+                if use_tcgen_dq:
+                    T.tcgen05_gemm(
+                        probabilities_shared,
+                        grad_out_shared,
+                        grad_kv_tmem,
+                        transpose_A=True,
+                        clear_accum=True,
+                        mbar=grad_kv_barrier,
+                    )
+                T.gemm(
+                    grad_out_shared,
+                    kv_shared,
+                    grad_probabilities,
+                    transpose_B=True,
+                    policy=T.GemmWarpPolicy.FullCol,
+                    clear_accum=True,
+                )
+                for h, i in T.Parallel(heads, block_i):
+                    grad_probabilities[h, i] = (
+                        probabilities[h, i]
+                        * (grad_probabilities[h, i] - Delta[b_i, q_i, h])
+                        * scale
+                    )
+                if use_tcgen_dq:
+                    T.mbarrier_wait_parity(grad_kv_barrier, block % 2)
+                    T.copy(grad_probabilities, probabilities_shared)
+                    T.tcgen05_gemm(
+                        probabilities_shared,
+                        kv_shared,
+                        grad_q_tmem,
+                        mbar=grad_q_barrier,
+                        clear_accum=block == 0,
+                    )
+                    # The next prefetch reuses kv_shared, so wait until TCGEN
+                    # has finished reading the current block from it.
+                    T.mbarrier_wait_parity(grad_q_barrier, block % 2)
+                    if block + 1 < blocks:
+                        prefetch_kv(
+                            KV,
+                            Indices,
+                            kv_shared,
+                            b_i,
+                            q_i,
+                            (block + 1) * block_i,
+                            _LATENT,
+                            0,
+                        )
+                    T.gemm(
+                        probabilities_shared,
+                        kv_rope_shared,
+                        grad_q_rope,
+                        policy=T.GemmWarpPolicy.FullCol,
+                    )
+                    if block + 1 < blocks:
+                        prefetch_kv(
+                            KV,
+                            Indices,
+                            kv_rope_shared,
+                            b_i,
+                            q_i,
+                            (block + 1) * block_i,
+                            _ROPE,
+                            _LATENT,
+                        )
+                else:
+                    T.copy(grad_probabilities, grad_scores_shared)
+                    T.gemm(
+                        grad_scores_shared,
+                        kv_shared,
+                        grad_q,
+                        policy=T.GemmWarpPolicy.FullCol,
+                    )
+                    T.gemm(
+                        grad_scores_shared,
+                        kv_rope_shared,
+                        grad_q_rope,
+                        policy=T.GemmWarpPolicy.FullCol,
+                    )
+                if use_tcgen_dq:
+                    T.tcgen05_gemm(
+                        probabilities_shared,
+                        q_shared,
+                        grad_kv_tmem,
+                        transpose_A=True,
+                        mbar=grad_kv_add_barrier,
+                    )
+                    T.clear(grad_kv_rope)
+                    T.gemm(
+                        probabilities_shared,
+                        q_rope_shared,
+                        grad_kv_rope,
+                        transpose_A=True,
+                        policy=T.GemmWarpPolicy.FullCol,
+                    )
+                    T.mbarrier_wait_parity(grad_kv_add_barrier, block % 2)
+                    T.copy(grad_kv_tmem, grad_kv)
+                    for i, d in T.Parallel(block_i, _LATENT):
+                        index = Indices[b_i, q_i, 0, block * block_i + i]
+                        if (index >= 0) & (index < kv_tokens - 1):
+                            T.atomic_add(
+                                GradKV[b_i, q_i % dkv_splits, index, 0, d],
+                                grad_kv[i, d],
+                            )
+                    for i, d in T.Parallel(block_i, _ROPE):
+                        index = Indices[b_i, q_i, 0, block * block_i + i]
+                        if (index >= 0) & (index < kv_tokens - 1):
+                            T.atomic_add(
+                                GradKV[
+                                    b_i,
+                                    q_i % dkv_splits,
+                                    index,
+                                    0,
+                                    _LATENT + d,
+                                ],
+                                grad_kv_rope[i, d],
+                            )
+                else:
+                    T.gemm(
+                        grad_scores_shared,
+                        q_shared,
+                        grad_kv,
+                        transpose_A=True,
+                        policy=T.GemmWarpPolicy.FullCol,
+                        clear_accum=True,
+                    )
+                    T.gemm(
+                        probabilities_shared,
+                        grad_out_shared,
+                        grad_kv,
+                        transpose_A=True,
+                        policy=T.GemmWarpPolicy.FullCol,
+                    )
+                    T.clear(grad_kv_rope)
+                    T.gemm(
+                        grad_scores_shared,
+                        q_rope_shared,
+                        grad_kv_rope,
+                        transpose_A=True,
+                        policy=T.GemmWarpPolicy.FullCol,
+                    )
+
+                    for split in range(split_store):
+                        for i, d in T.Parallel(block_i, _LATENT):
+                            if i < block_i // split_store:
+                                grad_kv_shared[i, d] = grad_kv[
+                                    i + split * (block_i // split_store), d
+                                ]
+                        for i, d in T.Parallel(block_i, _ROPE):
+                            if i < block_i // split_store:
+                                grad_kv_rope_shared[i, d] = grad_kv_rope[
+                                    i + split * (block_i // split_store), d
+                                ]
+                        for i, d in T.Parallel(block_i // split_store, _LATENT):
+                            source = i + split * (block_i // split_store)
+                            T.atomic_add(
+                                GradKV[
+                                    b_i,
+                                    q_i % dkv_splits,
+                                    Indices[b_i, q_i, 0, block * block_i + source],
+                                    0,
+                                    d,
+                                ],
+                                grad_kv_shared[i, d],
+                            )
+                        for i, d in T.Parallel(block_i // split_store, _ROPE):
+                            source = i + split * (block_i // split_store)
+                            T.atomic_add(
+                                GradKV[
+                                    b_i,
+                                    q_i % dkv_splits,
+                                    Indices[b_i, q_i, 0, block * block_i + source],
+                                    0,
+                                    _LATENT + d,
+                                ],
+                                grad_kv_rope_shared[i, d],
+                            )
+
+            if use_tcgen_dq:
+                T.copy(grad_q_tmem, grad_q)
+            T.copy(grad_q, grad_q_shared)
+            T.copy(grad_q_rope, grad_q_rope_shared)
+            T.copy(grad_q_shared, GradQ[b_i, q_i, :, :_LATENT])
+            T.copy(grad_q_rope_shared, GradQ[b_i, q_i, :, _LATENT:])
+            if use_tcgen_dq:
+                if T.get_thread_binding() // 32 == 0:
+                    T.deallocate_tmem(grad_kv_tmem)
+                    T.deallocate_tmem(grad_q_tmem)
+
+    return main
+
+
+def forward(
+    q: torch.Tensor, kv: torch.Tensor, indices: torch.Tensor, scale: float
+) -> tuple[torch.Tensor, torch.Tensor]:
+    heads = q.shape[2]
+    kernel_heads = (heads + _HEAD_BLOCK - 1) // _HEAD_BLOCK * _HEAD_BLOCK
+    if kernel_heads != heads:
+        q = torch.cat(
+            (q, q.new_zeros((*q.shape[:2], kernel_heads - heads, q.shape[3]))), dim=2
+        )
+    kv = torch.cat((kv, kv.new_zeros((kv.shape[0], 1, kv.shape[2]))), dim=1)
+    sm_major = torch.cuda.get_device_capability(q.device)[0]
+    threads = 128 if sm_major == 10 and kernel_heads == 32 else 256
+    with _preserve_env():
+        output, lse = _forward(
+            int(kernel_heads),
+            int(indices.shape[-1]),
+            float(scale),
+            threads=threads,
+        )(q, kv, indices)
+    return output[:, :, :heads], lse[:, :, :heads]
+
+
+def backward(
+    q: torch.Tensor,
+    kv: torch.Tensor,
+    indices: torch.Tensor,
+    output: torch.Tensor,
+    lse: torch.Tensor,
+    grad_output: torch.Tensor,
+    scale: float,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    heads = q.shape[2]
+    kernel_heads = (heads + _HEAD_BLOCK - 1) // _HEAD_BLOCK * _HEAD_BLOCK
+    if kernel_heads != heads:
+        pad_shape = (*q.shape[:2], kernel_heads - heads)
+        q = torch.cat((q, q.new_zeros((*pad_shape, q.shape[3]))), dim=2)
+        output = torch.cat(
+            (output, output.new_zeros((*pad_shape, output.shape[3]))), dim=2
+        )
+        grad_output = torch.cat(
+            (grad_output, grad_output.new_zeros((*pad_shape, grad_output.shape[3]))),
+            dim=2,
+        )
+        lse = torch.cat((lse, lse.new_zeros(pad_shape)), dim=2)
+    kv = torch.cat((kv, kv.new_zeros((kv.shape[0], 1, kv.shape[2]))), dim=1)
+    with _preserve_env():
+        delta = _delta(int(kernel_heads))(output, grad_output)
+        kv_grouped = kv.unsqueeze(2)
+        indices_grouped = indices.unsqueeze(2)
+        grad_kv = torch.zeros(
+            (kv.shape[0], _DKV_SPLITS, kv.shape[1], 1, kv.shape[2]),
+            device=kv.device,
+            dtype=torch.float32,
+        )
+        sm_major = torch.cuda.get_device_capability(q.device)[0]
+        use_tcgen = sm_major == 10 and kernel_heads % 32 == 0
+        grad_q = _backward(
+            int(kernel_heads),
+            int(indices.shape[-1]),
+            float(scale),
+            block_i=64 if use_tcgen else 32,
+            threads=256 if use_tcgen else min(256, int(kernel_heads) * 8),
+            use_tcgen_dq=use_tcgen,
+        )(q, kv_grouped, grad_output, indices_grouped, lse, delta, grad_kv)
+    return (
+        grad_q[:, :, :heads],
+        grad_kv.sum(dim=1)[:, :-1].squeeze(2),
+    )
diff --git a/src/art/megatron/hybrid_ep_setup.py b/src/art/megatron/hybrid_ep_setup.py
index f8413c7b4..83e246511 100644
--- a/src/art/megatron/hybrid_ep_setup.py
+++ b/src/art/megatron/hybrid_ep_setup.py
@@ -3,18 +3,22 @@
 import fcntl
 from hashlib import sha256
 from importlib.metadata import PackageNotFoundError, version
+import json
 import os
 from pathlib import Path
 import platform
 import shutil
 import subprocess
 import sys
+import tarfile
 import tempfile
+from urllib.request import Request, urlopen
 
 import torch
 
 PACKAGE = "art-deep-ep"
 SOURCE = Path(__file__).with_name("_hybrid_ep")
+NATIVE_ASSETS = Path(__file__).parent / "runtime" / "native_assets.json"
 
 
 def _output(command: list[str]) -> str:
@@ -43,47 +47,202 @@ def _arch_list() -> str:
                 "HybridEP requires exactly one TORCH_CUDA_ARCH_LIST value"
             )
         return architectures.pop()
-    if not torch.cuda.is_available():
-        raise RuntimeError(
-            "HybridEP setup requires a visible GPU or TORCH_CUDA_ARCH_LIST"
-        )
+    nvidia_smi = shutil.which("nvidia-smi")
+    if nvidia_smi is None:
+        raise RuntimeError("HybridEP setup requires nvidia-smi")
     capabilities = {
-        torch.cuda.get_device_capability(device)
-        for device in range(torch.cuda.device_count())
+        value.strip()
+        for value in _output(
+            [
+                nvidia_smi,
+                "--query-gpu=compute_cap",
+                "--format=csv,noheader,nounits",
+            ]
+        ).splitlines()
+        if value.strip()
     }
     if len(capabilities) != 1:
-        raise RuntimeError("HybridEP requires visible GPUs with one compute capability")
-    major, minor = capabilities.pop()
-    return f"{major}.{minor}"
+        raise RuntimeError("HybridEP requires host GPUs with one compute capability")
+    return capabilities.pop()
 
 
 def _source_hash() -> str:
     digest = sha256()
-    for path in sorted(path for path in SOURCE.rglob("*") if path.is_file()):
+    for path in sorted(
+        path
+        for path in SOURCE.rglob("*")
+        if path.is_file() and "__pycache__" not in path.parts
+    ):
         digest.update(str(path.relative_to(SOURCE)).encode())
         digest.update(path.read_bytes())
     return digest.hexdigest()
 
 
-def _build_identity() -> tuple[str, str]:
+def _native_archives() -> dict[str, tuple[Path, str]]:
+    from art.megatron.runtime.managed import _bundled_runtime_dir, _load_manifest
+
+    bundle = _bundled_runtime_dir()
+    if (bundle / "manifest.json").is_file():
+        manifest = _load_manifest(bundle)
+        return {
+            asset.filename: (bundle / asset.filename, asset.sha256)
+            for asset in manifest.source_archives
+        }
+
+    assets = json.loads(NATIVE_ASSETS.read_text())
+    root = _cache_root() / "native_archives"
+    root.mkdir(parents=True, exist_ok=True)
+    with (root / ".download.lock").open("w") as lock:
+        fcntl.flock(lock, fcntl.LOCK_EX)
+        for filename, asset in assets.items():
+            path = root / filename
+            if path.is_file() and _file_hash(path) == asset["sha256"]:
+                continue
+            partial = path.with_suffix(path.suffix + ".partial")
+            try:
+                with (
+                    urlopen(
+                        Request(asset["url"], headers={"User-Agent": "openpipe-art"})
+                    ) as response,
+                    partial.open("wb") as output,
+                ):
+                    shutil.copyfileobj(response, output)
+                if _file_hash(partial) != asset["sha256"]:
+                    raise RuntimeError(f"Native asset checksum mismatch: {filename}")
+                partial.replace(path)
+            finally:
+                partial.unlink(missing_ok=True)
+    return {
+        filename: (root / filename, asset["sha256"])
+        for filename, asset in assets.items()
+    }
+
+
+def _file_hash(path: Path) -> str:
+    digest = sha256()
+    with path.open("rb") as file:
+        for chunk in iter(lambda: file.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _prepare_nixl_build_environment() -> str:
+    from art.distributed.nixl_runtime import configure_nixl_environment
+
+    paths = configure_nixl_environment()
+    runtime_identity = f"{paths.module}=={version(paths.module.replace('_', '-'))}"
+    headers = ("NIXL_INCLUDE_DIR", "NIXL_GPU_INCLUDE_DIR", "UCX_INCLUDE_DIR")
+    if all(name in os.environ and Path(os.environ[name]).is_dir() for name in headers):
+        return (
+            runtime_identity
+            + ":"
+            + sha256(
+                "\0".join(os.environ[name] for name in headers).encode()
+            ).hexdigest()
+        )
+    nixl_home = Path(os.environ.get("NIXL_HOME", ""))
+    ucx_home = Path(os.environ.get("UCX_HOME", ""))
+    if (nixl_home / "include" / "nixl.h").is_file() and (
+        ucx_home / "include" / "ucp" / "api" / "device" / "ucp_device_impl.h"
+    ).is_file():
+        os.environ.update(
+            NIXL_INCLUDE_DIR=str(nixl_home / "include"),
+            NIXL_GPU_INCLUDE_DIR=str(nixl_home / "include" / "gpu" / "ucx"),
+            UCX_INCLUDE_DIR=str(ucx_home / "include"),
+        )
+        return (
+            runtime_identity
+            + ":"
+            + sha256(f"{nixl_home}\0{ucx_home}".encode()).hexdigest()
+        )
+
+    archives = _native_archives()
+    required = {"nixl-de8115ca.tar.gz", "ucx-1.21.0.tar.gz"}
+    if archives.keys() != required:
+        raise RuntimeError(
+            f"Megatron runtime has the wrong source archives: {archives}"
+        )
+    identity = sha256(
+        "".join(archives[name][1] for name in sorted(archives)).encode()
+    ).hexdigest()
+    destination = _cache_root() / "native_sources" / identity
+    lock_path = destination.with_suffix(".lock")
+    lock_path.parent.mkdir(parents=True, exist_ok=True)
+    with lock_path.open("w") as lock:
+        fcntl.flock(lock, fcntl.LOCK_EX)
+        if not destination.is_dir():
+            stage = Path(
+                tempfile.mkdtemp(prefix=f".{identity}.tmp-", dir=lock_path.parent)
+            )
+            try:
+                for name in sorted(required):
+                    with tarfile.open(archives[name][0]) as archive:
+                        archive.extractall(stage, filter="data")
+                stage.rename(destination)
+            finally:
+                if stage.exists():
+                    shutil.rmtree(stage)
+    nixl_source = next(destination.glob("nixl-*"), None)
+    ucx_source = next(destination.glob("ucx-*"), None)
+    if nixl_source is None or ucx_source is None:
+        raise RuntimeError(f"Native source archives are malformed: {destination}")
+    os.environ.update(
+        NIXL_INCLUDE_DIR=str(nixl_source / "src" / "api" / "cpp"),
+        NIXL_GPU_INCLUDE_DIR=str(nixl_source / "src" / "api" / "gpu" / "ucx"),
+        UCX_INCLUDE_DIR=str(ucx_source / "src"),
+        NIXL_LIBRARY_DIR=str(paths.library_dir),
+        NIXL_DEPENDENCY_LIBRARY_DIR=str(paths.dependency_library_dir),
+    )
+    return f"{runtime_identity}:{identity}"
+
+
+def _cuda_dependency_versions(cuda_home: Path) -> tuple[str, str]:
+    if torch.version.cuda and torch.version.cuda.startswith("12."):
+        return version("nvidia-cuda-cccl-cu12"), version("nvidia-nvtx-cu12")
+    if torch.version.cuda and torch.version.cuda.startswith("13."):
+        major, minor = torch.version.cuda.split(".")[:2]
+        cccl = _output(
+            ["dpkg-query", "-W", "-f=${Version}", f"cuda-cccl-{major}-{minor}"]
+        )
+        return cccl, version("nvidia-nvtx")
+    raise RuntimeError(f"HybridEP does not support torch CUDA {torch.version.cuda}")
+
+
+def _build_identity(
+    *, enable_multinode: bool | None = None, use_nixl: bool | None = None
+) -> tuple[str, str]:
     cuda_home = _cuda_home()
     arch_list = _arch_list()
     digest = sha256()
+    if enable_multinode is None:
+        enable_multinode = os.environ.get("HYBRID_EP_MULTINODE", "0") == "1"
+    if use_nixl is None:
+        use_nixl = os.environ.get("USE_NIXL", "0") == "1"
+    if use_nixl and not enable_multinode:
+        raise ValueError("NIXL HybridEP requires multi-node support")
+    nixl_runtime_identity = None
+    if use_nixl:
+        from art.distributed.nixl_runtime import validate_nixl_host
+
+        validate_nixl_host()
+        nixl_runtime_identity = _prepare_nixl_build_environment()
+    cccl_version, nvtx_version = _cuda_dependency_versions(cuda_home)
     values = [
         _source_hash(),
         sys.implementation.cache_tag,
         platform.machine(),
         torch.__version__,
         str(torch.version.cuda),
-        torch.__config__.show(),
-        version("nvidia-cuda-cccl-cu12"),
-        version("nvidia-nvtx-cu12"),
+        cccl_version,
+        nvtx_version,
         _output([str(cuda_home / "bin" / "nvcc"), "--version"]),
         _output([os.environ.get("CXX", "c++"), "--version"]),
         arch_list,
-        os.environ.get("HYBRID_EP_MULTINODE", "0"),
-        os.environ.get("USE_NIXL", "0"),
+        str(int(enable_multinode)),
+        str(int(use_nixl)),
     ]
+    if nixl_runtime_identity:
+        values.append(nixl_runtime_identity)
     for value in values:
         digest.update(value.encode())
         digest.update(b"\0")
@@ -99,6 +258,9 @@ def _installed_version() -> str | None:
 
 
 def _cache_root() -> Path:
+    root = os.environ.get("ART_MEGATRON_CACHE_ROOT")
+    if root:
+        return Path(root) / "hybrid_ep"
     return (
         Path(os.environ.get("XDG_CACHE_HOME", Path.home() / ".cache"))
         / "art"
@@ -107,8 +269,10 @@ def _cache_root() -> Path:
 
 
 def _uv() -> str:
-    if uv := shutil.which("uv"):
-        return uv
+    candidates = (Path(sys.executable).parent / "uv", shutil.which("uv"))
+    for candidate in candidates:
+        if candidate and Path(candidate).is_file():
+            return str(candidate)
     raise RuntimeError("HybridEP setup requires uv")
 
 
@@ -131,6 +295,7 @@ def _build_wheel(build_version: str, arch_list: str) -> Path:
             shutil.copytree(SOURCE, source)
             env = os.environ.copy()
             env["ART_HYBRID_EP_BUILD_VERSION"] = build_version
+            env["CUDA_HOME"] = str(_cuda_home())
             env["TORCH_CUDA_ARCH_LIST"] = arch_list
             subprocess.run(
                 [
@@ -182,12 +347,14 @@ def setup_hybrid_ep() -> str:
     return build_version
 
 
-def validate_hybrid_ep() -> None:
-    expected, _ = _build_identity()
-    if (installed := _installed_version()) != expected:
+def validate_hybrid_ep(*, require_multinode: bool = False) -> None:
+    candidates = [_build_identity(enable_multinode=True, use_nixl=True)[0]]
+    if not require_multinode:
+        candidates.append(_build_identity(enable_multinode=False, use_nixl=False)[0])
+    if (installed := _installed_version()) not in candidates:
         raise RuntimeError(
             "HybridEP is not built for this ART source and Megatron environment "
-            f"(expected {expected}, found {installed}). Run Megatron setup."
+            f"(expected one of {candidates}, found {installed}). Run Megatron setup."
         )
 
 
diff --git a/src/art/megatron/identity_lora.py b/src/art/megatron/identity_lora.py
new file mode 100644
index 000000000..fc0510da8
--- /dev/null
+++ b/src/art/megatron/identity_lora.py
@@ -0,0 +1,108 @@
+import os
+from typing import Any
+import warnings
+
+from peft.tuners.lora.config import LoraConfig
+import torch
+
+from art.dev.get_model_config import default_target_modules
+
+from .lora_config import LORA_ALPHA, default_lora_rank_for_handler
+from .model_support.lora_disk import normalize_lora_checkpoint_to_vllm
+from .model_support.spec import ModelSupportHandler
+
+
+def create_identity_lora(
+    base_model: str,
+    lora_path: str,
+    rank: int | None = None,
+    target_modules: list[str] | None = None,
+    lora_alpha: int = LORA_ALPHA,
+    random_state: int | None = None,
+    allow_unvalidated_arch: bool = False,
+    handler: ModelSupportHandler | None = None,
+) -> None:
+    """Create an identity LoRA adapter for a Megatron model."""
+    from unittest.mock import patch
+
+    from accelerate import init_empty_weights
+    from peft import get_peft_model
+    from transformers import AutoConfig, AutoModelForCausalLM
+
+    from .model_support import get_model_support_handler
+
+    if random_state is not None:
+        torch.manual_seed(random_state)
+    target_modules = target_modules or default_target_modules(base_model)
+    handler = handler or get_model_support_handler(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
+    if rank is None:
+        rank = default_lora_rank_for_handler(handler)
+    base_config = AutoConfig.from_pretrained(base_model, trust_remote_code=True)
+    model_config = handler.identity_lora_model_config(base_config)
+    with init_empty_weights():
+        model = AutoModelForCausalLM.from_config(
+            model_config, dtype=torch.bfloat16, trust_remote_code=True
+        )
+    model.name_or_path = base_model
+
+    lora_config = LoraConfig(
+        base_model_name_or_path=base_model,
+        r=rank,
+        lora_alpha=lora_alpha,
+        target_modules=[],
+        target_parameters=handler.identity_lora_target_parameters(
+            model,
+            target_modules=target_modules,
+        ),
+        bias="none",
+    )
+    meta = torch.device("meta")
+    orig_to = torch.nn.Module.to
+
+    def _skip_meta_to(
+        module: torch.nn.Module, *args: Any, **kwargs: Any
+    ) -> torch.nn.Module:
+        device = kwargs.get("device") or (args[0] if args else None)
+        if device == meta or str(device) == "meta":
+            dtype = kwargs.get("dtype")
+            return module if dtype is None else orig_to(module, dtype=dtype)
+        return orig_to(module, *args, **kwargs)
+
+    with warnings.catch_warnings():
+        if bool(getattr(handler, "is_moe", False)):
+            warnings.filterwarnings(
+                "ignore",
+                message=(
+                    r"Unsupported layer type '.*MoeExperts.*' encountered, "
+                    r"proceed at your own risk\."
+                ),
+                category=UserWarning,
+                module=r"peft\.tuners\.tuners_utils",
+            )
+        with patch.object(torch.nn.Module, "to", _skip_meta_to):
+            peft_model = get_peft_model(
+                model,
+                lora_config,
+                autocast_adapter_dtype=False,
+            )
+
+    os.makedirs(lora_path, exist_ok=True)
+    peft_model.save_pretrained(lora_path)
+    final_config = LoraConfig(
+        base_model_name_or_path=base_model,
+        r=rank,
+        lora_alpha=lora_alpha,
+        target_modules=target_modules,
+        bias="none",
+    ).to_dict()
+    normalize_lora_checkpoint_to_vllm(
+        lora_path,
+        handler=handler,
+        adapter_config=final_config,
+    )
+    del peft_model, model
+    if torch.cuda.is_initialized():
+        torch.cuda.synchronize()
+        torch.cuda.empty_cache()
diff --git a/src/art/megatron/kernels/cute_grouped_lora_quack.py b/src/art/megatron/kernels/cute_grouped_lora_quack.py
index c0c9a70d6..804b64e58 100644
--- a/src/art/megatron/kernels/cute_grouped_lora_quack.py
+++ b/src/art/megatron/kernels/cute_grouped_lora_quack.py
@@ -11,10 +11,62 @@
 
 from quack.gemm import gemm as quack_gemm
 import torch
+import triton
+import triton.language as tl
 
 _PADDED_LOW_RANK_TARGET = 8
 
 
+@triton.jit
+def _grouped_lora_wgrad_kernel(
+    big,
+    small,
+    expert_offsets,
+    out,
+    alpha,
+    BIG_D_STRIDE: tl.constexpr,
+    BIG_K_STRIDE: tl.constexpr,
+    SMALL_R_STRIDE: tl.constexpr,
+    SMALL_K_STRIDE: tl.constexpr,
+    D: tl.constexpr,
+    R: tl.constexpr,
+    TRANSPOSE_OUT: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+    BLOCK_R: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+) -> None:
+    expert = tl.program_id(2)
+    d = (tl.program_id(0) * BLOCK_D + tl.arange(0, BLOCK_D)).to(tl.int64)
+    r = (tl.program_id(1) * BLOCK_R + tl.arange(0, BLOCK_R)).to(tl.int64)
+    start = tl.load(expert_offsets + expert)
+    end = tl.load(expert_offsets + expert + 1)
+    acc = tl.zeros((BLOCK_D, BLOCK_R), tl.float32)
+    for k0 in tl.range(start, end, BLOCK_K, num_stages=3):
+        k = (k0 + tl.arange(0, BLOCK_K)).to(tl.int64)
+        big_tile = tl.load(
+            big + d[:, None] * BIG_D_STRIDE + k[None, :] * BIG_K_STRIDE,
+            mask=(d[:, None] < D) & (k[None, :] < end),
+            other=0.0,
+        )
+        small_tile = tl.load(
+            small + r[:, None] * SMALL_R_STRIDE + k[None, :] * SMALL_K_STRIDE,
+            mask=(r[:, None] < R) & (k[None, :] < end),
+            other=0.0,
+        )
+        acc += tl.dot(big_tile, tl.trans(small_tile))
+    base = expert * D * R
+    out_offsets = (
+        base + r[None, :] * D + d[:, None]
+        if TRANSPOSE_OUT
+        else base + d[:, None] * R + r[None, :]
+    )
+    tl.store(
+        out + out_offsets,
+        acc * alpha,
+        mask=(d[:, None] < D) & (r[None, :] < R),
+    )
+
+
 def _validate_rank(rank: int) -> None:
     if rank <= 0:
         raise ValueError(f"Grouped LoRA QuACK backend requires rank > 0, got {rank}")
@@ -264,7 +316,12 @@ def _varlen_quack_gemm(
     tile_n: int,
     alpha: float = 1.0,
     out: torch.Tensor | None = None,
+    residual: torch.Tensor | None = None,
 ) -> torch.Tensor:
+    if residual is not None:
+        if out is not None and out is not residual:
+            raise ValueError("Residual grouped GEMM requires aliased output")
+        out = residual
     if out is None:
         out = torch.empty(
             a.shape[0],
@@ -285,7 +342,7 @@ def _varlen_quack_gemm(
         a,
         b,
         out,
-        None,
+        residual,
         None,
         tile_M=tile_m,
         tile_N=tile_n,
@@ -310,6 +367,42 @@ def _varlen_quack_gemm_k(
     tile_n: int,
     alpha: float = 1.0,
 ) -> torch.Tensor:
+    # QuACK's SM100 varlen-K scheduler can fault under the integrated async launch
+    # sequence; keep its faster grouped GEMM path on Hopper and use exact ragged
+    # spans for the small-rank LoRA parameter gradients on Blackwell.
+    if torch.cuda.get_device_capability(a.device)[0] >= 10:
+        transpose_out = out_shape_m <= out_shape_n
+        big, small = (b, a) if transpose_out else (a, b)
+        d, rank = big.shape[0], small.shape[0]
+        out = torch.empty(
+            batch_count,
+            out_shape_m,
+            out_shape_n,
+            device=a.device,
+            dtype=a.dtype,
+        )
+        block_r = min(triton.next_power_of_2(rank), 32)
+        cast(Any, _grouped_lora_wgrad_kernel)[
+            (triton.cdiv(d, 64), triton.cdiv(rank, block_r), batch_count)
+        ](
+            big,
+            small,
+            expert_offsets,
+            out,
+            alpha,
+            BIG_D_STRIDE=big.stride(0),
+            BIG_K_STRIDE=big.stride(1),
+            SMALL_R_STRIDE=small.stride(0),
+            SMALL_K_STRIDE=small.stride(1),
+            D=d,
+            R=rank,
+            TRANSPOSE_OUT=transpose_out,
+            BLOCK_D=64,
+            BLOCK_R=block_r,
+            BLOCK_K=32,
+            num_warps=4,
+        )
+        return out
     out = torch.empty(
         batch_count,
         out_shape_m,
@@ -343,7 +436,20 @@ def forward(
         b_t: torch.Tensor,
         counts: torch.Tensor,
         scale: float,
+        residual: torch.Tensor | None,
     ) -> torch.Tensor:
+        has_residual = residual is not None
+        if residual is not None:
+            if not residual.is_contiguous():
+                raise ValueError("Residual grouped LoRA requires contiguous output")
+            residual = torch.empty(
+                0, device=residual.device, dtype=residual.dtype
+            ).set_(
+                residual.untyped_storage(),
+                residual.storage_offset(),
+                residual.size(),
+                residual.stride(),
+            )
         expert_offsets = _build_expert_offsets(counts, device=x.device)
         actual_rank = a_t.shape[-1]
         effective_rank = _effective_rank(actual_rank)
@@ -368,12 +474,13 @@ def forward(
             tile_m=64,
             tile_n=_matmul_tile_n(b_t.shape[-1]),
             alpha=scale,
+            residual=residual,
         )
-
         ctx.save_for_backward(x, a_t_eff, b_t_eff, tmp, expert_offsets)
         ctx.actual_rank = actual_rank
         ctx.effective_rank = effective_rank
         ctx.scale = scale
+        ctx.has_residual = has_residual
         return out
 
     @staticmethod
@@ -435,6 +542,7 @@ def backward(ctx, *grad_outputs: Any):
             grad_b_eff[:, :actual_rank, :].contiguous(),
             None,
             None,
+            grad_out if ctx.has_residual else None,
         )
 
 
@@ -651,7 +759,30 @@ def quack_grouped_lora(
     synchronization in the hot path.
     """
     counts_tensor = _validate_inputs(x, a_t, b_t, counts)
-    return _QuackGroupedLoraFn.apply(x, a_t, b_t, counts_tensor, scale)
+    return _QuackGroupedLoraFn.apply(x, a_t, b_t, counts_tensor, scale, None)
+
+
+@torch.compiler.disable
+def quack_grouped_lora_residual(
+    residual: torch.Tensor,
+    x: torch.Tensor,
+    a_t: torch.Tensor,
+    b_t: torch.Tensor,
+    counts: list[int] | torch.Tensor,
+    scale: float = 1.0,
+) -> torch.Tensor:
+    """Consume a base output and accumulate grouped LoRA into its storage."""
+    counts_tensor = _validate_inputs(x, a_t, b_t, counts)
+    expected = (x.shape[0], b_t.shape[-1])
+    if residual.shape != expected:
+        raise ValueError(
+            f"Expected residual shape {expected}, got {tuple(residual.shape)}"
+        )
+    if residual.device != x.device or residual.dtype != x.dtype:
+        raise ValueError("Residual must match grouped LoRA input device and dtype")
+    if x.shape[0] == 0:
+        return residual
+    return _QuackGroupedLoraFn.apply(x, a_t, b_t, counts_tensor, scale, residual)
 
 
 @torch.compiler.disable
diff --git a/src/art/megatron/lora.py b/src/art/megatron/lora.py
index 5bc10ed93..604560fe1 100644
--- a/src/art/megatron/lora.py
+++ b/src/art/megatron/lora.py
@@ -30,16 +30,18 @@
 from megatron.core.transformer.transformer_layer import TransformerLayer
 import torch
 
+from .expert_parallel import get_expert_parallel_layout
 from .kernels.cute_grouped_lora_quack import (
     quack_grouped_lora,
     quack_grouped_lora_dual,
 )
+from .lora_config import (
+    LORA_ALPHA,
+    MEGATRON_LORA_RANK_ENV,
+    MEGATRON_LORA_TARGET_MODULES_ENV,
+    default_lora_rank_for_handler,
+)
 
-MOE_LORA_RANK = 1
-DENSE_LORA_RANK = 8
-LORA_ALPHA = 32
-MEGATRON_LORA_RANK_ENV = "ART_MEGATRON_LORA_RANK"
-MEGATRON_LORA_TARGET_MODULES_ENV = "ART_MEGATRON_LORA_TARGET_MODULES"
 _LAYER_BLOCK_RE = re.compile(r"^(?P<block>.*\.layers\.\d+)\.")
 
 ShardDomain = Literal["tp", "expert_tp"]
@@ -189,6 +191,8 @@ class _LoraPublishTemplate(NamedTuple):
     shape: tuple[int, ...]
     dtype_name: str
     num_local_experts: int
+    expert_layout: tuple[int | None, ...]
+    is_expert: bool
     shard_domain: ShardDomain
     sharded: bool
     shard_world_size: int
@@ -197,6 +201,15 @@ class _LoraPublishTemplate(NamedTuple):
     component_sizes: tuple[int, ...]
 
 
+def _template_expert_ids(
+    template: _LoraPublishTemplate, ep_rank: int
+) -> tuple[int | None, ...]:
+    start = ep_rank * template.num_local_experts
+    if template.expert_layout:
+        return template.expert_layout[start : start + template.num_local_experts]
+    return tuple(range(start, start + template.num_local_experts))
+
+
 def _distributed_initialized() -> bool:
     is_initialized = getattr(torch.distributed, "is_initialized", None)
     return (
@@ -291,10 +304,6 @@ def _linear_disables_tensor_parallel_comm(linear: Any) -> bool:
     )
 
 
-def default_lora_rank_for_handler(handler: Any) -> int:
-    return MOE_LORA_RANK if bool(getattr(handler, "is_moe", False)) else DENSE_LORA_RANK
-
-
 def _configured_lora_rank(provider: Any, handler: Any) -> int:
     rank = getattr(provider, "_art_lora_rank", None)
     if rank is None:
@@ -315,6 +324,20 @@ def _configured_lora_target_modules(provider: Any, spec: Any) -> list[str]:
     return [str(target_module) for target_module in target_modules]
 
 
+def _compile_disabled_collective(function: _F) -> _F:
+    return cast(
+        _F,
+        torch.compiler.disable(
+            getattr(function, "_torchdynamo_orig_callable", function)
+        ),
+    )
+
+
+_gather_lora_sequence_parallel_region = _compile_disabled_collective(
+    gather_from_sequence_parallel_region
+)
+
+
 def _column_parallel_lora_input(x: torch.Tensor, linear: Any) -> torch.Tensor:
     if _linear_disables_tensor_parallel_comm(linear):
         return x
@@ -322,7 +345,8 @@ def _column_parallel_lora_input(x: torch.Tensor, linear: Any) -> torch.Tensor:
         bool(getattr(linear, "sequence_parallel", False))
         and int(getattr(linear, "tp_size", 1)) > 1
     ):
-        return gather_from_sequence_parallel_region(x)
+        # Torch 2.11 compiled autograd drops the gather's input-gradient edge.
+        return _gather_lora_sequence_parallel_region(x)
     return x
 
 
@@ -464,9 +488,12 @@ def __init__(
         allreduce: bool = True,
     ) -> None:
         super().__init__()
-        assert num_local_experts == 1 or "{expert}" in adapter_model_prefix, (
-            "adapter_model_prefix must contain the '{expert}' format placeholder if num_local_experts > 1"
-        )
+        is_expert = "{expert}" in adapter_model_prefix
+        if num_local_experts < 1 or (num_local_experts != 1 and not is_expert):
+            raise ValueError(
+                "num_local_experts must be positive and requires an '{expert}' "
+                "adapter_model_prefix when greater than one"
+            )
         self.adapter_model_prefix = adapter_model_prefix
         self.alpha = float(alpha)
         self.in_features = int(in_features)
@@ -474,16 +501,16 @@ def __init__(
         self.scale = alpha / rank
         self._slot_modules = torch.nn.ModuleDict()
         self._slot_keys: dict[LoRASlotRef, str] = {}
-        self.A_T = torch.nn.Parameter(
-            torch.zeros(
-                num_local_experts, in_features, rank, dtype=dtype, device=device
-            ).squeeze(0)
+        a_shape = (
+            (num_local_experts, in_features, rank) if is_expert else (in_features, rank)
         )
-        self.B_T = torch.nn.Parameter(
-            torch.zeros(
-                num_local_experts, rank, out_features, dtype=dtype, device=device
-            ).squeeze(0)
+        b_shape = (
+            (num_local_experts, rank, out_features)
+            if is_expert
+            else (rank, out_features)
         )
+        self.A_T = torch.nn.Parameter(torch.zeros(a_shape, dtype=dtype, device=device))
+        self.B_T = torch.nn.Parameter(torch.zeros(b_shape, dtype=dtype, device=device))
         _set_lora_parallel_metadata(
             self.A_T,
             parallel_spec=a_parallel_spec,
@@ -495,11 +522,39 @@ def __init__(
             allreduce=allreduce,
         )
         self._expert_offset = ps.get_expert_model_parallel_rank() * num_local_experts
+        self._expert_ids: tuple[int | None, ...] = tuple(
+            range(self._expert_offset, self._expert_offset + num_local_experts)
+        )
+        self._expert_layout: tuple[int | None, ...] = ()
         self.reset_lora_parameters()
 
     @property
     def num_local_experts(self) -> int:
-        return self.A_T.shape[0] if self.A_T.ndim == 3 else 1
+        return self.A_T.shape[0] if self.is_expert else 1
+
+    @property
+    def is_expert(self) -> bool:
+        return "{expert}" in self.adapter_model_prefix
+
+    @property
+    def expert_ids(self) -> tuple[int | None, ...]:
+        return self._expert_ids
+
+    def bind_expert_layout(
+        self,
+        expert_ids: tuple[int | None, ...],
+        physical_to_logical: tuple[int | None, ...],
+    ) -> None:
+        if not self.is_expert or len(expert_ids) != self.num_local_experts:
+            raise ValueError(
+                f"{self.adapter_model_prefix}: invalid local expert layout {expert_ids}"
+            )
+        self._expert_ids = expert_ids
+        self._expert_layout = physical_to_logical
+        for local_expert, logical_expert in enumerate(expert_ids):
+            if logical_expert is None:
+                self.A_T.data[local_expert].zero_()
+                self.B_T.data[local_expert].zero_()
 
     def _broadcast_if_replicated(self, param: torch.nn.Parameter) -> None:
         if not param.lora_tp_replicated:  # ty: ignore[unresolved-attribute]
@@ -528,9 +583,12 @@ def _broadcast_if_replicated(self, param: torch.nn.Parameter) -> None:
 
     def reset_lora_parameters(self) -> None:
         """Initialize LoRA weights (A=Kaiming, B=zeros) like PEFT defaults."""
-        if self.A_T.ndim == 3:
-            for expert in range(self.A_T.shape[0]):
-                torch.nn.init.kaiming_uniform_(self.A_T[expert].T, a=math.sqrt(5))
+        if self.is_expert:
+            for expert, logical_expert in enumerate(self.expert_ids):
+                if logical_expert is None:
+                    torch.nn.init.zeros_(self.A_T[expert])
+                else:
+                    torch.nn.init.kaiming_uniform_(self.A_T[expert].T, a=math.sqrt(5))
         else:
             torch.nn.init.kaiming_uniform_(self.A_T.T, a=math.sqrt(5))
         torch.nn.init.zeros_(self.B_T)
@@ -538,10 +596,11 @@ def reset_lora_parameters(self) -> None:
         self._broadcast_if_replicated(self.B_T)
 
     def _expected_weight_keys(self, suffix: str) -> list[str]:
-        if self.num_local_experts > 1:
+        if self.is_expert:
             return [
-                f"{self.adapter_model_prefix.format(expert=expert + self._expert_offset)}.{suffix}.weight"
-                for expert in range(self.num_local_experts)
+                f"{self.adapter_model_prefix.format(expert=expert)}.{suffix}.weight"
+                for expert in self.expert_ids
+                if expert is not None
             ]
         return [f"{self.adapter_model_prefix}.{suffix}.weight"]
 
@@ -617,6 +676,8 @@ def _adapter_weights(
             for suffix in ("lora_A", "lora_B")
             for key in self._expected_weight_keys(suffix)
         ]
+        if not all_keys:
+            return torch.zeros_like(self.A_T), torch.zeros_like(self.B_T)
         missing = [key for key in all_keys if key not in adapter_model]
         if len(missing) == len(all_keys) and not require:
             return None
@@ -638,8 +699,16 @@ def _adapter_weight(
         suffix: str,
     ) -> torch.Tensor:
         keys = self._expected_weight_keys(suffix)
-        if self.num_local_experts > 1:
-            return torch.stack([adapter_model[key].T for key in keys])
+        if self.is_expert:
+            loaded = [adapter_model[key].T for key in keys]
+            first = loaded[0]
+            real_weights = iter(loaded)
+            return torch.stack(
+                [
+                    torch.zeros_like(first) if expert is None else next(real_weights)
+                    for expert in self.expert_ids
+                ]
+            )
         return adapter_model[keys[0]].T
 
     def _localized_weight(
@@ -700,7 +769,7 @@ def _should_export_parameter(self, param: torch.nn.Parameter) -> bool:
         Determine if the given LoRA param should be exported in the sharded LoRA state dict
         (drop replicated ranks/params).
         """
-        if self.num_local_experts > 1:  # self is a MoE layer
+        if self.is_expert:
             if ps.get_expert_data_parallel_rank() != 0:
                 return False
         else:  # self is a non-MoE layer
@@ -761,10 +830,12 @@ def _export_items(
         for key, param in self._lora_params(ref):
             if not self._should_export_parameter(param):
                 continue
-            if self.num_local_experts > 1:
-                for expert in range(self.num_local_experts):
-                    full_key = f"{self.adapter_model_prefix.format(expert=expert + self._expert_offset)}.{key}"
-                    export_items.append((full_key, param, expert))
+            if self.is_expert:
+                for local_expert, logical_expert in enumerate(self.expert_ids):
+                    if logical_expert is None:
+                        continue
+                    full_key = f"{self.adapter_model_prefix.format(expert=logical_expert)}.{key}"
+                    export_items.append((full_key, param, local_expert))
             else:
                 export_items.append((f"{self.adapter_model_prefix}.{key}", param, None))
         return export_items
@@ -822,9 +893,7 @@ def forward(
             return x.new_zeros((*x.shape[:-1], self.out_features))
         a_t, b_t, scale = active
         if tokens_per_expert is not None:
-            assert self.num_local_experts > 1, (
-                "tokens_per_expert is only supported if num_local_experts > 1"
-            )
+            assert self.is_expert, "tokens_per_expert requires expert LoRA"
             bsz = tokens_per_expert
             if isinstance(bsz, list):
                 bsz = torch.tensor(bsz, dtype=torch.int64, device="cpu")
@@ -835,6 +904,16 @@ def forward(
         return out if scale == 1.0 else out * scale
 
 
+def _bind_expert_lora_layout(experts: Any, *loras: LoRA) -> None:
+    layout = get_expert_parallel_layout(getattr(experts, "config", None))
+    if layout is None:
+        return
+    ep_rank = int(experts.ep_group.rank())
+    expert_ids = layout.local_logical_experts(ep_rank)
+    for lora in loras:
+        lora.bind_expert_layout(expert_ids, layout.physical_to_logical)
+
+
 class LoRAPublishPlanner:
     def __init__(
         self,
@@ -887,6 +966,8 @@ def _collect_templates(
                             shape=_exported_param_shape(module, param),
                             dtype_name=_dtype_name(param.dtype),
                             num_local_experts=module.num_local_experts,
+                            expert_layout=module._expert_layout,
+                            is_expert=module.is_expert,
                             shard_domain=shard_domain,
                             sharded=sharded,
                             shard_world_size=(
@@ -918,7 +999,7 @@ def _metadata_for_template(
         adapter_dtypes: dict[str, torch.dtype],
     ) -> list[LoraShardMeta]:
         shard_ranks = range(template.shard_world_size) if template.sharded else (0,)
-        if template.num_local_experts <= 1:
+        if not template.is_expert:
             tp_ranks = (
                 _process_group_ranks(ps.get_tensor_model_parallel_group())
                 if _distributed_initialized()
@@ -943,8 +1024,8 @@ def _metadata_for_template(
                     shard_rank,
                 )
                 for ep_rank in range(ep_world_size)
-                for local_expert in range(template.num_local_experts)
-                for expert in [ep_rank * template.num_local_experts + local_expert]
+                for expert in _template_expert_ids(template, ep_rank)
+                if expert is not None
                 for shard_rank in shard_ranks
             ]
         return [
@@ -1033,7 +1114,7 @@ def _expert_owner_rank(ep_rank: int, shard_rank: int) -> int:
 
 
 def _exported_param_shape(module: LoRA, param: torch.nn.Parameter) -> tuple[int, ...]:
-    if module.num_local_experts > 1:
+    if module.is_expert:
         return tuple(int(dim) for dim in param[0].T.shape)
     return tuple(int(dim) for dim in param.T.shape)
 
@@ -1104,6 +1185,7 @@ def _parallel_lora(
     grad_sync_domain: GradSyncDomain = TP_DEFAULT_GRAD_SYNC_DOMAIN,
     allreduce: bool = True,
     num_local_experts: int = 1,
+    lora_cls: type[LoRA] = LoRA,
 ) -> LoRA:
     weight = getattr(linear, "weight0", None)
     if weight is None:
@@ -1124,7 +1206,7 @@ def _parallel_lora(
         grad_sync_domain=grad_sync_domain,
         grad_sync_op=GRAD_SYNC_OP_SUM if row_layout else GRAD_SYNC_OP_NONE,
     )
-    return LoRA(
+    return lora_cls(
         adapter_model_prefix=adapter_model_prefix,
         in_features=linear.in_features,
         out_features=out_features,
@@ -1149,8 +1231,9 @@ def _parallel_lora_pair(
     layout: Literal["column", "row"],
     suffixes: tuple[str, str],
     num_local_experts: int = 1,
+    lora_cls: type[LoRA] = LoRA,
 ) -> tuple[LoRA, LoRA]:
-    expert_parallel = num_local_experts > 1
+    expert_parallel = "{expert}" in adapter_model_prefix
     return cast(
         tuple[LoRA, LoRA],
         tuple(
@@ -1169,6 +1252,7 @@ def _parallel_lora_pair(
                 ),
                 allreduce=not expert_parallel,
                 num_local_experts=num_local_experts,
+                lora_cls=lora_cls,
             )
             for suffix in suffixes
         ),
@@ -1184,6 +1268,7 @@ def __init__(
         alpha: float,
         provider: GPTModelProvider,
         reduce_output: bool = True,
+        lora_cls: type[LoRA] = LoRA,
     ) -> None:
         super().__init__()
         self.provider = provider
@@ -1196,6 +1281,7 @@ def __init__(
             rank=rank,
             alpha=alpha,
             layout="row",
+            lora_cls=lora_cls,
         )
 
     def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:
@@ -1212,6 +1298,18 @@ def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:
         return base_output + lora_output, bias_output
 
 
+def _install_replicated_qkv_all_gather_compile_boundary() -> None:
+    from megatron.core.transformer import attention
+
+    # Torch 2.11 compiled autograd drops LoRA parameter edges through this gather.
+    gather = attention.all_gather_last_dim_from_tensor_parallel_region
+    if getattr(gather, "_art_replicated_qkv_compile_boundary", False):
+        return
+    gather = _compile_disabled_collective(gather)
+    setattr(gather, "_art_replicated_qkv_compile_boundary", True)
+    attention.all_gather_last_dim_from_tensor_parallel_region = gather
+
+
 class SelfAttentionLinearQKVLoRA(torch.nn.Module):
     def __init__(
         self,
@@ -1239,32 +1337,47 @@ def __init__(
         total_out_features_per_rank = int(weight.shape[0])
         kv_out_features = self.provider.kv_channels * self.provider.num_query_groups
         tp_world_size = ps.get_tensor_model_parallel_world_size()
-        assert kv_out_features % tp_world_size == 0, (
-            "kv_out_features must be divisible by tensor parallel size"
-        )
         q_out_features = self.provider.kv_channels * self.provider.num_attention_heads
-        assert q_out_features % tp_world_size == 0, (
-            "q_out_features must be divisible by tensor parallel size"
-        )
-        q_out_features_per_rank = q_out_features // tp_world_size
-        kv_out_features_per_rank = kv_out_features // tp_world_size
         self.attention_output_gate = bool(
             getattr(self.provider, "attention_output_gate", False)
         )
-        q_and_gate_out_features_per_rank = total_out_features_per_rank - (
-            2 * kv_out_features_per_rank
-        )
-        expected_q_out_features_per_rank = q_out_features_per_rank * (
-            2 if self.attention_output_gate else 1
-        )
-        assert q_and_gate_out_features_per_rank == expected_q_out_features_per_rank, (
-            "Unexpected per-rank QKV packing for this attention layout"
-        )
+        gate_multiplier = 2 if self.attention_output_gate else 1
+        self.replicated_qkv = self.provider.num_query_groups < tp_world_size
+        if self.replicated_qkv:
+            # Megatron forms global packed QKV, then gives each TP rank one slice.
+            _install_replicated_qkv_all_gather_compile_boundary()
+            q_and_gate_out_features_per_rank = q_out_features * gate_multiplier
+            kv_out_features_per_rank = kv_out_features
+            packed_width = q_and_gate_out_features_per_rank + 2 * kv_out_features
+            if packed_width != total_out_features_per_rank * tp_world_size:
+                raise ValueError(
+                    "Unexpected replicated-KV QKV packing: "
+                    f"global width {packed_width}, local width "
+                    f"{total_out_features_per_rank}, TP {tp_world_size}"
+                )
+            self.num_query_groups_per_partition = self.provider.num_query_groups
+        else:
+            assert kv_out_features % tp_world_size == 0, (
+                "kv_out_features must be divisible by tensor parallel size"
+            )
+            assert q_out_features % tp_world_size == 0, (
+                "q_out_features must be divisible by tensor parallel size"
+            )
+            q_out_features_per_rank = q_out_features // tp_world_size
+            kv_out_features_per_rank = kv_out_features // tp_world_size
+            q_and_gate_out_features_per_rank = total_out_features_per_rank - (
+                2 * kv_out_features_per_rank
+            )
+            expected_q_out_features_per_rank = q_out_features_per_rank * gate_multiplier
+            assert (
+                q_and_gate_out_features_per_rank == expected_q_out_features_per_rank
+            ), "Unexpected per-rank QKV packing for this attention layout"
+            self.num_query_groups_per_partition = (
+                self.provider.num_query_groups // tp_world_size
+            )
+        self.tp_rank = ps.get_tensor_model_parallel_rank()
         self.q_and_gate_out_features_per_rank = q_and_gate_out_features_per_rank
         self.kv_out_features_per_rank = kv_out_features_per_rank
-        self.num_query_groups_per_partition = (
-            self.provider.num_query_groups // tp_world_size
-        )
         self.num_attention_heads_per_group = (
             self.provider.num_attention_heads // self.provider.num_query_groups
         )
@@ -1276,6 +1389,7 @@ def __init__(
                 rank=rank,
                 alpha=alpha,
                 out_features=q_and_gate_out_features_per_rank,
+                replicated=self.replicated_qkv,
             )
             if _targets_include(target_modules, "q_proj")
             else None
@@ -1287,6 +1401,7 @@ def __init__(
                 rank=rank,
                 alpha=alpha,
                 out_features=kv_out_features_per_rank,
+                replicated=self.replicated_qkv,
             )
             if _targets_include(target_modules, "k_proj")
             else None
@@ -1298,6 +1413,7 @@ def __init__(
                 rank=rank,
                 alpha=alpha,
                 out_features=kv_out_features_per_rank,
+                replicated=self.replicated_qkv,
             )
             if _targets_include(target_modules, "v_proj")
             else None
@@ -1311,8 +1427,23 @@ def _build_qkv_lora(
         rank: int,
         alpha: float,
         out_features: int,
+        replicated: bool,
     ) -> LoRA:
         assert isinstance(linear_qkv.weight, torch.Tensor)
+        if replicated:
+            parallel_spec = LoRAParallelSpec(grad_sync_op=GRAD_SYNC_OP_SUM)
+            return LoRA(
+                adapter_model_prefix=adapter_model_prefix,
+                in_features=linear_qkv.in_features,
+                out_features=out_features,
+                rank=rank,
+                alpha=alpha,
+                dtype=linear_qkv.weight.dtype,
+                device=linear_qkv.weight.device,
+                a_parallel_spec=parallel_spec,
+                b_parallel_spec=parallel_spec,
+                allreduce=True,
+            )
         a_parallel_spec = LoRAParallelSpec(
             shard_domain="tp",
             sharded=False,
@@ -1378,29 +1509,32 @@ def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:
             self.kv_out_features_per_rank,
         )
         query_and_gate_5d = query_and_gate.reshape(
-            query_and_gate.shape[0],
-            query_and_gate.shape[1],
+            *query_and_gate.shape[:-1],
             self.num_query_groups_per_partition,
             self.num_attention_heads_per_group
             * (2 if self.attention_output_gate else 1),
             self.hidden_size_per_attention_head,
         )
         key_5d = key.reshape(
-            key.shape[0],
-            key.shape[1],
+            *key.shape[:-1],
             self.num_query_groups_per_partition,
             1,
             self.hidden_size_per_attention_head,
         )
         value_5d = value.reshape(
-            value.shape[0],
-            value.shape[1],
+            *value.shape[:-1],
             self.num_query_groups_per_partition,
             1,
             self.hidden_size_per_attention_head,
         )
-        qkv_5d = torch.cat([query_and_gate_5d, key_5d, value_5d], dim=3)
-        adapter_output = qkv_5d.reshape(qkv_5d.shape[0], qkv_5d.shape[1], -1)
+        adapter_output = torch.cat(
+            [query_and_gate_5d, key_5d, value_5d], dim=-2
+        ).flatten(-3)
+        if self.replicated_qkv:
+            local_width = linear_output.shape[-1]
+            adapter_output = adapter_output.narrow(
+                -1, self.tp_rank * local_width, local_width
+            )
 
         return linear_output + adapter_output, bias
 
@@ -1597,6 +1731,7 @@ def __init__(
         linear_fc1: TEColumnParallelLinear | TELayerNormColumnParallelLinear,
         rank: int,
         alpha: float,
+        lora_cls: type[LoRA] = LoRA,
     ) -> None:
         super().__init__()
         if isinstance(linear_fc1, TELayerNormColumnParallelLinear):
@@ -1611,6 +1746,7 @@ def __init__(
             alpha=alpha,
             layout="column",
             suffixes=("gate_proj", "up_proj"),
+            lora_cls=lora_cls,
         )
 
     def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:
@@ -1653,6 +1789,7 @@ def __init__(
         rank: int,
         alpha: float,
         provider: GPTModelProvider,
+        lora_cls: type[LoRA] = LoRA,
     ) -> None:
         super().__init__()
         self.row_parallel_lora = SelfAttentionLinearProjLoRA(
@@ -1662,6 +1799,7 @@ def __init__(
             alpha=alpha,
             provider=provider,
             reduce_output=not _linear_disables_tensor_parallel_comm(linear_fc2),
+            lora_cls=lora_cls,
         )
 
     def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:
@@ -1779,6 +1917,7 @@ def wrap_grouped_moe_experts(
     alpha: int,
     fused_gate_up: bool = False,
 ) -> None:
+    expert_loras: list[LoRA] = []
     wrap_fc1 = (
         _targets_include(target_modules, "experts")
         if fused_gate_up
@@ -1799,6 +1938,11 @@ def wrap_grouped_moe_experts(
             fused_gate_up=fused_gate_up,
         )
         setattr(experts, "linear_fc1", linear_fc1_lora)
+        expert_loras.extend(
+            (linear_fc1_lora.lora,)
+            if fused_gate_up
+            else (linear_fc1_lora.gate_lora, linear_fc1_lora.up_lora)
+        )
     wrap_fc2 = (
         wrap_fc1 if fused_gate_up else _targets_include(target_modules, "down_proj")
     )
@@ -1816,6 +1960,8 @@ def wrap_grouped_moe_experts(
             num_local_experts=experts.num_local_experts,
         )
         setattr(experts, "linear_fc2", linear_fc2_lora)
+        expert_loras.append(linear_fc2_lora.lora)
+    _bind_expert_lora_layout(experts, *expert_loras)
 
 
 def wrap_split_mlp_lora(
@@ -1826,6 +1972,7 @@ def wrap_split_mlp_lora(
     target_modules: set[str],
     rank: int,
     alpha: int,
+    lora_cls: type[LoRA] = LoRA,
 ) -> None:
     if _targets_include(target_modules, "gate_proj", "up_proj"):
         linear_fc1 = _unwrap_attr(
@@ -1838,6 +1985,7 @@ def wrap_split_mlp_lora(
             linear_fc1=linear_fc1,
             rank=rank,
             alpha=alpha,
+            lora_cls=lora_cls,
         )
     if _targets_include(target_modules, "down_proj"):
         linear_fc2 = _unwrap_attr(
@@ -1851,6 +1999,7 @@ def wrap_split_mlp_lora(
             rank=rank,
             alpha=alpha,
             provider=provider,
+            lora_cls=lora_cls,
         )
 
 
@@ -1880,6 +2029,7 @@ def wrap_dense_mlp(
     target_modules: set[str],
     rank: int,
     alpha: int,
+    lora_cls: type[LoRA] = LoRA,
 ) -> None:
     wrap_split_mlp_lora(
         mlp,
@@ -1888,6 +2038,7 @@ def wrap_dense_mlp(
         target_modules=target_modules,
         rank=rank,
         alpha=alpha,
+        lora_cls=lora_cls,
     )
 
 
@@ -1899,6 +2050,7 @@ def wrap_shared_experts_mlp(
     target_modules: set[str],
     rank: int,
     alpha: int,
+    lora_cls: type[LoRA] = LoRA,
 ) -> None:
     wrap_split_mlp_lora(
         shared_experts,
@@ -1907,6 +2059,7 @@ def wrap_shared_experts_mlp(
         target_modules=target_modules,
         rank=rank,
         alpha=alpha,
+        lora_cls=lora_cls,
     )
 
 
@@ -1967,26 +2120,3 @@ def iter_lora_slot_parameters(
                     continue
                 seen.add(param_id)
                 yield param
-
-
-def iter_lora_sites(
-    model: Sequence[torch.nn.Module],
-) -> Iterator[tuple[str, torch.nn.Parameter, torch.nn.Parameter]]:
-    """Yield every ambient and dynamic LoRA parameter pair exactly once."""
-    seen: set[int] = set()
-    for chunk in model:
-        for module in chunk.modules():
-            prefix = getattr(module, "adapter_model_prefix", None)
-            a_t = getattr(module, "A_T", None)
-            b_t = getattr(module, "B_T", None)
-            if (
-                not isinstance(prefix, str)
-                or not isinstance(a_t, torch.nn.Parameter)
-                or not isinstance(b_t, torch.nn.Parameter)
-                or id(module) in seen
-            ):
-                continue
-            seen.add(id(module))
-            yield prefix, a_t, b_t
-            for slot in getattr(module, "_slot_modules", {}).values():
-                yield prefix, slot.A_T, slot.B_T
diff --git a/src/art/megatron/lora_config.py b/src/art/megatron/lora_config.py
new file mode 100644
index 000000000..d84ddecdc
--- /dev/null
+++ b/src/art/megatron/lora_config.py
@@ -0,0 +1,11 @@
+from typing import Any
+
+MOE_LORA_RANK = 1
+DENSE_LORA_RANK = 8
+LORA_ALPHA = 32
+MEGATRON_LORA_RANK_ENV = "ART_MEGATRON_LORA_RANK"
+MEGATRON_LORA_TARGET_MODULES_ENV = "ART_MEGATRON_LORA_TARGET_MODULES"
+
+
+def default_lora_rank_for_handler(handler: Any) -> int:
+    return MOE_LORA_RANK if bool(getattr(handler, "is_moe", False)) else DENSE_LORA_RANK
diff --git a/src/art/megatron/migrations.py b/src/art/megatron/migrations.py
index abefe7bc0..793b287a3 100644
--- a/src/art/megatron/migrations.py
+++ b/src/art/megatron/migrations.py
@@ -2,97 +2,59 @@
 
 import os
 from pathlib import Path
-import re
 import warnings
 
-from ..utils.get_model_step import get_step_from_dir
-from .optimizer_state import (
-    commit_optimizer_generation,
-    optimizer_generation_files,
-    read_optimizer_commit,
-)
+from .optimizer_state import read_committed_optimizer_pointer
 
-_LEGACY_SHARD_RE = re.compile(r"^(?P<rank>\d+)-of-(?P<world>\d+)\.pt$")
+_IGNORED_ROOT_ENTRIES = {".writer.lock"}
 
 
 def optimizer_state_path(output_dir: str) -> str:
     return str(Path(output_dir) / "optimizer_states")
 
 
-def _legacy_shards(path: Path) -> tuple[Path, ...] | None:
-    if not path.exists():
-        return None
-    if not path.is_dir():
-        raise RuntimeError(f"Legacy optimizer path is not a directory: {path}")
-    entries = list(path.iterdir())
-    if not entries:
-        return None
-    matches = [
-        (item, match)
-        for item in entries
-        if item.is_file() and (match := _LEGACY_SHARD_RE.fullmatch(item.name))
-    ]
-    if len(matches) != len(entries):
-        unknown = sorted(
-            item.name for item in entries if item not in {m[0] for m in matches}
-        )
-        raise RuntimeError(
-            f"Legacy optimizer state at {path} contains unsupported entries: {unknown}"
-        )
-    worlds = {int(match.group("world")) for _, match in matches}
-    if len(worlds) != 1:
-        raise RuntimeError(f"Legacy optimizer shards at {path} mix world sizes")
-    world_size = worlds.pop()
-    by_rank = {int(match.group("rank")): item for item, match in matches}
-    if set(by_rank) != set(range(1, world_size + 1)):
-        raise RuntimeError(f"Legacy optimizer shards at {path} are incomplete")
-    return tuple(by_rank[rank] for rank in range(1, world_size + 1))
+def _contains_optimizer_state(path: Path) -> bool:
+    return path.is_dir() and any(
+        entry.name not in _IGNORED_ROOT_ENTRIES for entry in path.iterdir()
+    )
 
 
 def apply_megatron_migrations(output_dir: str) -> str:
-    """Apply all durable Megatron state migrations for one training run."""
-    # Keep future Megatron migrations centralized behind this call.
+    """Move one immutable split optimizer root to the unified run root."""
     destination = Path(optimizer_state_path(output_dir))
-    if read_optimizer_commit(str(destination)) is not None:
-        return str(destination)
-
-    candidates = {
-        mode: shards
+    split = tuple(
+        path
         for mode in ("rl", "sft")
-        if (shards := _legacy_shards(Path(output_dir) / f"optimizer_states_{mode}"))
-        is not None
-    }
-    if len(candidates) > 1:
+        if _contains_optimizer_state(
+            path := Path(output_dir) / f"optimizer_states_{mode}"
+        )
+    )
+    if destination.exists():
+        if split:
+            raise RuntimeError(
+                "Unified and split Megatron optimizer states both exist; ART "
+                "cannot infer which lineage to keep"
+            )
+        return str(destination)
+    if len(split) > 1:
         raise RuntimeError(
-            "Both legacy RL and SFT optimizer states exist. ART cannot infer which "
-            "state belongs to the latest checkpoint. Keep only the intended "
-            "optimizer_states_rl or optimizer_states_sft directory, or remove both "
-            "to explicitly reset the optimizer."
+            "Both legacy RL and SFT optimizer states exist. ART cannot infer "
+            "which lineage to keep"
         )
-    if not candidates:
+    if not split:
         return str(destination)
 
-    mode, shards = next(iter(candidates.items()))
-    step = get_step_from_dir(output_dir)
-    files = optimizer_generation_files(step, len(shards))
-    destination.mkdir(parents=True, exist_ok=True)
-    for source, name in zip(shards, files, strict=True):
-        target = destination / name
-        temporary = target.with_suffix(f"{target.suffix}.tmp")
-        if temporary.exists():
-            temporary.unlink()
-        os.link(source, temporary)
-        os.replace(temporary, target)
-    commit_optimizer_generation(
-        str(destination), step=step, world_size=len(shards), files=files
-    )
-    for source in shards:
-        source.unlink()
-    legacy_dir = Path(output_dir) / f"optimizer_states_{mode}"
-    legacy_dir.rmdir()
+    source = split[0]
+    # This validates the generation format and deliberately rejects loose shards.
+    read_committed_optimizer_pointer(str(source))
+    os.replace(source, destination)
+    directory_fd = os.open(destination.parent, os.O_RDONLY)
+    try:
+        os.fsync(directory_fd)
+    finally:
+        os.close(directory_fd)
     warnings.warn(
-        f"Migrated legacy {mode.upper()} optimizer state to the run-level optimizer "
-        f"commit at step {step}.",
+        f"Migrated split Megatron optimizer state {source.name} to {destination.name}.",
         stacklevel=2,
     )
     return str(destination)
diff --git a/src/art/megatron/model_support/__init__.py b/src/art/megatron/model_support/__init__.py
index 6a5735363..688c3dad0 100644
--- a/src/art/megatron/model_support/__init__.py
+++ b/src/art/megatron/model_support/__init__.py
@@ -23,9 +23,9 @@
     get_model_support_handler,
     get_model_support_handler_for_spec,
     get_model_support_spec,
+    get_model_support_spec_by_key,
     is_model_support_registered,
     list_model_support_specs,
-    model_requires_merged_rollout,
     model_supports_context_parallel,
     model_uses_expert_parallel,
     native_vllm_lora_status_for_model,
@@ -38,7 +38,6 @@
     ModelSupportHandler,
     ModelSupportSpec,
     NativeVllmLoraStatus,
-    RolloutWeightsMode,
 )
 
 _LAZY_EXPORT_MODULES = {
@@ -83,7 +82,6 @@ def __getattr__(name: str):
     "QWEN3_MOE_SPEC",
     "QWEN3_5_MOE_SPEC",
     "PROBE_ONLY_MODEL_SUPPORT_SPECS",
-    "RolloutWeightsMode",
     "UnsupportedModelArchitectureError",
     "VALIDATED_MODEL_SUPPORT_SPECS",
     "default_target_modules_for_model",
@@ -91,12 +89,12 @@ def __getattr__(name: str):
     "get_model_support_handler",
     "get_model_support_handler_for_spec",
     "get_model_support_spec",
+    "get_model_support_spec_by_key",
     "inspect_architecture",
     "is_model_support_registered",
     "list_model_support_specs",
     "model_uses_expert_parallel",
     "model_supports_context_parallel",
-    "model_requires_merged_rollout",
     "native_vllm_lora_status_for_model",
     "vllm_lora_config_for_model",
     "summarize_layer_families",
diff --git a/src/art/megatron/model_support/discovery.py b/src/art/megatron/model_support/discovery.py
index 6f27dd05d..cb957de9b 100644
--- a/src/art/megatron/model_support/discovery.py
+++ b/src/art/megatron/model_support/discovery.py
@@ -47,6 +47,7 @@ def inspect_architecture(
     provider_bundle = get_provider_bundle(
         base_model,
         torch_dtype=torch_dtype,
+        load_weights=False,
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
     discovered = provider_bundle.handler.collect_layer_families(
diff --git a/src/art/megatron/model_support/handlers/default_dense.py b/src/art/megatron/model_support/handlers/default_dense.py
index 96a9bab6d..fa63a2e5e 100644
--- a/src/art/megatron/model_support/handlers/default_dense.py
+++ b/src/art/megatron/model_support/handlers/default_dense.py
@@ -1,4 +1,5 @@
-from typing import Any, Literal, Sequence
+from contextlib import nullcontext
+from typing import Any, Callable, Literal, Sequence
 
 import torch
 
@@ -9,7 +10,6 @@
     HfWeightSource,
     LayerFamilyInstance,
     PrefixTreeModelStateContext,
-    RolloutWeightsMode,
     SharedExpertCompileState,
 )
 
@@ -106,6 +106,10 @@ def configure_provider_for_runtime(self, provider: Any) -> None:
         del provider
         return None
 
+    def context_parallel_workload_profile(self, provider: Any) -> Any | None:
+        del provider
+        return None
+
     def default_chat_template(self) -> str | None:
         return None
 
@@ -118,12 +122,7 @@ def configure_tokenizer(
         del internal_config
         return tokenizer
 
-    def vllm_engine_args(
-        self,
-        *,
-        rollout_weights_mode: RolloutWeightsMode,
-    ) -> dict[str, object]:
-        del rollout_weights_mode
+    def vllm_engine_args(self) -> dict[str, object]:
         return {}
 
     def vllm_server_args(self) -> dict[str, object]:
@@ -133,6 +132,20 @@ def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None:
         del model_chunks
         return None
 
+    def build_pipeline_microbatch_activator(
+        self,
+        model_chunks: Sequence[Any],
+    ) -> Callable[[Any, int], None] | None:
+        del model_chunks
+        return None
+
+    def preserve_pipeline_microbatch_activation(
+        self,
+        model_chunks: Sequence[Any],
+    ):
+        del model_chunks
+        return nullcontext()
+
     def build_prefix_tree_model_state(
         self,
         context: PrefixTreeModelStateContext,
@@ -177,6 +190,9 @@ def to_vllm_lora_tensors(
     def to_vllm_lora_config(self, adapter_config: dict[str, Any]) -> dict[str, Any]:
         return adapter_config
 
+    def vllm_lora_conversion_is_view_only(self) -> bool:
+        return False
+
     def from_vllm_lora_tensors(
         self,
         tensors: dict[str, torch.Tensor],
diff --git a/src/art/megatron/model_support/handlers/dsv4.py b/src/art/megatron/model_support/handlers/dsv4.py
index a920a4c5d..b0959a410 100644
--- a/src/art/megatron/model_support/handlers/dsv4.py
+++ b/src/art/megatron/model_support/handlers/dsv4.py
@@ -1,9 +1,10 @@
 from __future__ import annotations
 
+from contextlib import contextmanager
 import hashlib
 import os
 import re
-from typing import Any, Literal, Sequence, cast
+from typing import Any, Callable, Iterator, Literal, Sequence, cast
 
 import torch
 
@@ -29,6 +30,7 @@
 _ORACLE_INDEX_HEADS = 1
 _ORACLE_INDEX_TOPK = 1024
 _VALIDATION_NUM_LAYERS_ENV = "ART_DSV4_VALIDATION_NUM_LAYERS"
+_ORACLE_LAYER_RE = re.compile(r"(?P<prefix>(?:^|\.)decoder\.layers\.)(?P<layer>\d+)")
 _ORACLE_EXPERT_WEIGHT_RE = re.compile(r"\.mlp\.experts\..*\.weight(?P<expert>\d+)$")
 _DSV4_ART_MOE_EXPERT_KEY_RE = re.compile(
     r"^(?P<prefix>.*\.mlp\.experts)\.(?P<expert>\d+)\."
@@ -46,6 +48,34 @@
 _DSV4_MOE_COMPILE_WORKAROUND_FLAGS = ("te_triton_permute_with_mask_map",)
 
 
+def _dsv4_input_activator(
+    model: Any,
+) -> Callable[[torch.Tensor | None, torch.Tensor | None], None]:
+    from art.megatron.dsv4.deepseek_v4 import DeepSeekV4Attention
+    from art.megatron.dsv4.layer import Dsv4MoELayer
+
+    modules = tuple(model.modules())
+    input_setters = tuple(
+        child.set_input_ids for child in modules if isinstance(child, Dsv4MoELayer)
+    )
+    position_setters = tuple(
+        child.set_position_ids
+        for child in modules
+        if isinstance(child, DeepSeekV4Attention)
+    )
+
+    def activate(
+        input_ids: torch.Tensor | None,
+        position_ids: torch.Tensor | None,
+    ) -> None:
+        for setter in input_setters:
+            setter(input_ids)
+        for setter in position_setters:
+            setter(position_ids)
+
+    return activate
+
+
 class Dsv4Handler(DefaultMoeHandler):
     key = "dsv4"
     is_moe = True
@@ -69,6 +99,7 @@ def patch_provider(self, provider: Any, bridge: Any) -> None:
     def configure_provider_for_runtime(self, provider: Any) -> None:
         provider.mtp_num_layers = None
         provider.moe_shared_expert_overlap = False
+        provider.art_pipeline_activation_multiplier = provider.dsv4_hc_mult
         raw_num_layers = os.environ.get(_VALIDATION_NUM_LAYERS_ENV)
         if raw_num_layers is None:
             return
@@ -216,9 +247,6 @@ def include(name: str) -> bool:
     def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None:
         from megatron.core.models.gpt.gpt_model import GPTModel
 
-        from art.megatron.dsv4.deepseek_v4 import DeepSeekV4Attention
-        from art.megatron.dsv4.layer import Dsv4MoELayer
-
         for chunk in list(model_chunks):
             module: Any = chunk
             while hasattr(module, "module"):
@@ -229,26 +257,27 @@ def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None:
                 else cast(GPTModel, getattr(module, "language_model"))
             )
             preprocess = gpt_module._preprocess
+            activate = _dsv4_input_activator(gpt_module.decoder)
 
             def preprocess_hook(
-                *args: Any, _preprocess=preprocess, _gpt=gpt_module, **kwargs: Any
+                *args: Any,
+                _preprocess=preprocess,
+                _activate=activate,
+                **kwargs: Any,
             ):
                 input_ids = kwargs.get("input_ids")
                 position_ids = kwargs.get("position_ids")
-                for child in _gpt.decoder.modules():
-                    if isinstance(child, Dsv4MoELayer):
-                        child.set_input_ids(
-                            input_ids if isinstance(input_ids, torch.Tensor) else None
-                        )
-                    if isinstance(child, DeepSeekV4Attention):
-                        child.set_position_ids(
-                            position_ids
-                            if isinstance(position_ids, torch.Tensor)
-                            else None
-                        )
+                _activate(
+                    input_ids if isinstance(input_ids, torch.Tensor) else None,
+                    position_ids if isinstance(position_ids, torch.Tensor) else None,
+                )
                 preproc_output = list(_preprocess(*args, **kwargs))
-                decoder_input = cast(torch.Tensor, preproc_output[0])
-                if not decoder_input.requires_grad and decoder_input.is_leaf:
+                decoder_input = cast(torch.Tensor | None, preproc_output[0])
+                if (
+                    decoder_input is not None
+                    and not decoder_input.requires_grad
+                    and decoder_input.is_leaf
+                ):
                     decoder_input.requires_grad_(True)
                 table = preproc_output[1]
                 if isinstance(position_ids, torch.Tensor) and torch.is_tensor(table):
@@ -267,6 +296,40 @@ def preprocess_hook(
 
             setattr(gpt_module, "_preprocess", preprocess_hook)
 
+    def build_pipeline_microbatch_activator(
+        self,
+        model_chunks: Sequence[Any],
+    ) -> Callable[[Any, int], None]:
+        activators = tuple(_dsv4_input_activator(chunk) for chunk in model_chunks)
+
+        def activate(prepared: Any, chunk_index: int) -> None:
+            input_ids = getattr(prepared, "model_tokens", None)
+            position_ids = getattr(prepared, "model_input_pos", None)
+            if input_ids is None:
+                input_ids = prepared.input_ids
+                position_ids = prepared.position_ids
+            activators[chunk_index](input_ids, position_ids)
+
+        return activate
+
+    @contextmanager
+    def preserve_pipeline_microbatch_activation(
+        self,
+        model_chunks: Sequence[Any],
+    ) -> Iterator[None]:
+        states = [
+            (module, name, getattr(module, name))
+            for chunk in model_chunks
+            for module in chunk.modules()
+            for name in ("_dsv4_input_ids", "_dsv4_position_ids")
+            if hasattr(module, name)
+        ]
+        try:
+            yield
+        finally:
+            for module, name, value in states:
+                setattr(module, name, value)
+
     def collect_layer_families(self, provider: Any) -> list[LayerFamilyInstance]:
         ratios: list[int] = list(getattr(provider, "dsv4_compress_ratios", ()) or ())
 
@@ -385,16 +448,6 @@ def build_adapter_weights_by_base(
                     )
         return adapter_weights_by_base
 
-    def iter_merged_vllm_weight_metadata(
-        self,
-        weight_export: Any,
-    ) -> Any:
-        bridge = getattr(weight_export.bridge, "_model_bridge", None)
-        metadata_iter = getattr(bridge, "iter_merged_vllm_weight_metadata", None)
-        if metadata_iter is None:
-            return None
-        return metadata_iter(weight_export)
-
     def from_vllm_lora_tensors(
         self,
         tensors: dict[str, torch.Tensor],
@@ -478,6 +531,28 @@ def prepare_hf_reference_config(self, config: Any) -> None:
         config._experts_implementation = "eager"
         self._apply_oracle_shape_overrides(config)
 
+    def prepare_hf_reference_model(self, model: Any) -> Any:
+        from art.megatron.dsv4.hf_oracle import prepare_hf_reference_model
+
+        return prepare_hf_reference_model(model)
+
+    def prepare_hf_reference_forward(
+        self,
+        model: Any,
+        *,
+        position_ids: torch.Tensor,
+        group_ids: torch.Tensor,
+        parent_ids: torch.Tensor,
+    ) -> None:
+        from art.megatron.dsv4.hf_oracle import set_hf_reference_prefix_tree
+
+        set_hf_reference_prefix_tree(
+            model,
+            position_ids=position_ids,
+            group_ids=group_ids,
+            parent_ids=parent_ids,
+        )
+
     def hf_reference_from_pretrained_kwargs(
         self, *, config: Any, dtype: torch.dtype
     ) -> dict[str, Any]:
@@ -506,6 +581,30 @@ def normalize_hf_reference_state_for_hf_parity(
     ) -> None:
         _add_dsv4_hf_reference_source_aliases(state, config)
 
+    def hf_parity_gradient_group(self, param: str) -> str:
+        if param == "model.embed_tokens.weight":
+            return "embedding"
+        if (
+            param == "lm_head.weight"
+            or param == "model.norm.weight"
+            or param.startswith("model.hc_head.")
+        ):
+            return "final_envelope"
+        match = re.fullmatch(r"model\.layers\.(\d+)\.(.+)", param)
+        if match is None:
+            raise ValueError(f"Unmapped DSV4 HF-parity gradient: {param}")
+        layer, module = match.groups()
+        prefix = f"model.layers.{layer}"
+        if module.startswith(("attn_hc.", "self_attn.")):
+            return f"{prefix}.attention"
+        if module.startswith(("ffn_hc.", "mlp.")):
+            return f"{prefix}.ffn"
+        if module == "input_layernorm.weight":
+            return f"{prefix}.input_norm"
+        if module == "post_attention_layernorm.weight":
+            return f"{prefix}.post_attention_norm"
+        raise ValueError(f"Unmapped DSV4 HF-parity gradient: {param}")
+
     def configure_oracle_provider(self, provider: Any, *, case_config: Any) -> None:
         """Mirrors HF oracle reductions while keeping DSV4 hard kernel invariants."""
         hooks = list(getattr(provider, "_pre_wrap_hooks", []))
@@ -577,11 +676,12 @@ def _initialize_oracle_base_weights(
         ep_size = ps.get_expert_model_parallel_world_size()
         with torch.no_grad():
             for chunk in model_chunks:
+                global_layers = self._oracle_global_layer_indices(chunk)
                 for name, param in chunk.named_parameters():
                     if self._is_oracle_lora_tensor(name):
                         continue
                     init_name = self._oracle_base_tensor_name(
-                        name,
+                        self._oracle_global_layer_name(name, global_layers),
                         ep_rank=ep_rank,
                         ep_size=ep_size,
                     )
@@ -591,9 +691,37 @@ def _initialize_oracle_base_weights(
                         seed=seed,
                     )
                 for name, buffer in chunk.named_buffers():
-                    self._initialize_oracle_buffer(name, buffer, seed=seed)
+                    self._initialize_oracle_buffer(
+                        self._oracle_global_layer_name(name, global_layers),
+                        buffer,
+                        seed=seed,
+                    )
         return model_chunks
 
+    @staticmethod
+    def _oracle_global_layer_indices(chunk: Any) -> dict[int, int]:
+        from art.megatron.dsv4.layer import Dsv4TransformerLayer
+
+        indices: dict[int, int] = {}
+        for name, module in chunk.named_modules():
+            if not isinstance(module, Dsv4TransformerLayer):
+                continue
+            match = _ORACLE_LAYER_RE.search(name)
+            if match is None:
+                raise RuntimeError(f"Cannot locate DSV4 oracle layer in {name!r}")
+            indices[int(match.group("layer"))] = int(module.layer_number) - 1
+        return indices
+
+    @staticmethod
+    def _oracle_global_layer_name(name: str, indices: dict[int, int]) -> str:
+        match = _ORACLE_LAYER_RE.search(name)
+        if match is None:
+            return name
+        global_layer = indices[int(match.group("layer"))]
+        return (
+            f"{name[: match.start('layer')]}{global_layer}{name[match.end('layer') :]}"
+        )
+
     @staticmethod
     def _is_oracle_lora_tensor(name: str) -> bool:
         return "_lora." in name or ".lora." in name
@@ -1120,9 +1248,15 @@ def _dsv4_to_vllm_lora_tensors(
     canonical = _dsv4_from_vllm_lora_tensors(
         tensors,
         adapter_config=adapter_config,
+        split_experts=False,
     )
+    fused_prefixes: set[str] = set()
     grouped: dict[str, dict[int, dict[str, dict[str, torch.Tensor]]]] = {}
     for key, tensor in canonical.items():
+        fused_match = _DSV4_VLLM_MOE_KEY_RE.match(key)
+        if fused_match is not None:
+            fused_prefixes.add(fused_match.group("prefix"))
+            continue
         match = _DSV4_ART_MOE_EXPERT_KEY_RE.match(key)
         if match is not None:
             grouped.setdefault(match.group("prefix"), {}).setdefault(
@@ -1131,6 +1265,12 @@ def _dsv4_to_vllm_lora_tensors(
 
     transformed: dict[str, torch.Tensor] = {}
     used_keys: set[str] = set()
+    mixed_prefixes = fused_prefixes.intersection(grouped)
+    if mixed_prefixes:
+        raise RuntimeError(
+            f"Mixed fused and split DSV4 MoE LoRA block for {min(mixed_prefixes)}"
+        )
+
     for prefix, experts in grouped.items():
         vllm_prefix = _dsv4_to_vllm_lora_key(prefix)
         blocks = {
@@ -1182,6 +1322,7 @@ def _dsv4_from_vllm_lora_tensors(
     tensors: dict[str, torch.Tensor],
     *,
     adapter_config: dict[str, Any],
+    split_experts: bool = True,
 ) -> dict[str, torch.Tensor]:
     split_key = next(
         (key for key in tensors if _DSV4_SPLIT_MOE_EXPERT_KEY_RE.match(key)), None
@@ -1221,16 +1362,37 @@ def _dsv4_from_vllm_lora_tensors(
             raise RuntimeError(
                 f"Incomplete DSV4 vLLM MoE LoRA block for {prefix}"
             ) from exc
-        if gate_up_a.shape[0] % rank != 0:
+        non_2d = next(
+            (slot for slot, tensor in slots.items() if tensor.ndim != 2), None
+        )
+        if non_2d is not None:
             raise RuntimeError(
-                f"{prefix}: gate/up lora_A rows {gate_up_a.shape[0]} are not "
-                f"divisible by rank {rank}"
+                f"{prefix}: {non_2d} must be 2D, got {tuple(slots[non_2d].shape)}"
             )
-        if gate_up_b.shape[0] % 2 != 0:
+        if rank <= 0 or gate_up_a.shape[0] == 0 or gate_up_a.shape[0] % rank != 0:
             raise RuntimeError(
-                f"{prefix}: gate/up lora_B rows {gate_up_b.shape[0]} are not even"
+                f"{prefix}: gate/up lora_A rows {gate_up_a.shape[0]} are not "
+                f"divisible by rank {rank}"
             )
         num_experts = gate_up_a.shape[0] // rank
+        expected = {
+            "gate/up lora_B": (
+                tuple(gate_up_b.shape),
+                (2 * down_a.shape[1], gate_up_a.shape[0]),
+            ),
+            "down lora_A": (down_a.shape[0], gate_up_a.shape[0]),
+            "down lora_B": (
+                tuple(down_b.shape),
+                (gate_up_a.shape[1], gate_up_a.shape[0]),
+            ),
+        }
+        for slot, (actual, wanted) in expected.items():
+            if actual != wanted:
+                raise RuntimeError(
+                    f"{prefix}: {slot} shape {actual} does not match {wanted}"
+                )
+        if not split_experts:
+            continue
         gate_up_b_by_expert = _dsv4_unpack_vllm_3d_lora_b(
             gate_up_b,
             num_experts=num_experts,
@@ -1268,4 +1430,4 @@ def _dsv4_from_vllm_lora_tensors(
     for key, tensor in canonical.items():
         if key not in used_keys:
             transformed[key] = tensor
-    return transformed
+    return transformed if split_experts else canonical
diff --git a/src/art/megatron/model_support/handlers/gemma4.py b/src/art/megatron/model_support/handlers/gemma4.py
index b3e98c3e6..6cf1dd29d 100644
--- a/src/art/megatron/model_support/handlers/gemma4.py
+++ b/src/art/megatron/model_support/handlers/gemma4.py
@@ -67,14 +67,6 @@
     "moe_postprocess",
     "te_triton_permute_with_mask_map",
 )
-_GEMMA4_TRITON_NUM_STAGES_2_SIGNATURES = {
-    # google/gemma-4-31B-it: Triton flex attention raises "No valid triton
-    # configs" for global attention head_dim=512 with backend-only options.
-    ("dense", 60, 5376, 32, 256, 512, 4),
-    # google/gemma-4-26B-A4B-it hits the same Triton resource limit on global
-    # attention head_dim=512 with backend-only options.
-    ("moe", 30, 2816, 16, 256, 512, 2),
-}
 _ART_MOE_EXPERT_KEY_RE = re.compile(
     r"^(?P<prefix>.*\.mlp\.experts)\.(?P<expert>\d+)\."
     r"(?P<module>gate_up_proj|down_proj)\.(?P<lora>lora_[AB])\.weight$"
@@ -402,12 +394,13 @@ def _zero_gemma4_moe_lora_padding(
             logical, internal = _gemma4_moe_padding_sizes_from_provider(config)
             if logical == internal:
                 continue
-            for prefix, a_t, b_t in art_lora.iter_lora_sites([chunk]):
-                if ".mlp.experts." not in prefix:
+            for module in chunk.modules():
+                prefix = getattr(module, "adapter_model_prefix", None)
+                if not isinstance(prefix, str) or ".mlp.experts." not in prefix:
                     continue
-                if prefix.endswith(".gate_up_proj"):
+                if prefix.endswith(".gate_up_proj") and hasattr(module, "B_T"):
                     _zero_gemma4_moe_lora_padding_tensor_set(
-                        b_t,
+                        cast(torch.nn.Parameter, module.B_T),
                         dim=-1,
                         logical=logical,
                         internal=internal,
@@ -415,9 +408,9 @@ def _zero_gemma4_moe_lora_padding(
                         grads=grads,
                         params=params,
                     )
-                elif prefix.endswith(".down_proj"):
+                elif prefix.endswith(".down_proj") and hasattr(module, "A_T"):
                     _zero_gemma4_moe_lora_padding_tensor_set(
-                        a_t,
+                        cast(torch.nn.Parameter, module.A_T),
                         dim=-2,
                         logical=logical,
                         internal=internal,
@@ -518,7 +511,24 @@ def _canonicalize_gemma4_loaded_lora_state(
     }
 
 
-class Gemma4MoeHandler(DefaultMoeHandler):
+class _Gemma4TokenizerMixin:
+    def configure_tokenizer(
+        self,
+        tokenizer: Any,
+        *,
+        internal_config: Any,
+    ) -> Any:
+        if not any(
+            internal_config.get(key) is not None
+            for key in ("chat_template", "chat_template_path")
+        ):
+            from art.utils.chat_template import TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR
+
+            setattr(tokenizer, TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR, True)
+        return tokenizer
+
+
+class Gemma4MoeHandler(_Gemma4TokenizerMixin, DefaultMoeHandler):
     key = "gemma4_moe"
     is_moe = True
     native_vllm_lora_status = "validated"
@@ -790,7 +800,7 @@ def flex_attention_compile_crash_config(
 GEMMA4_MOE_HANDLER = Gemma4MoeHandler()
 
 
-class Gemma4DenseHandler(DefaultDenseHandler):
+class Gemma4DenseHandler(_Gemma4TokenizerMixin, DefaultDenseHandler):
     key = "gemma4_dense"
     native_vllm_lora_status = "validated"
 
@@ -1276,8 +1286,12 @@ def preprocess_hook(
                     setattr(gemma4_rotary, "cp_group", rotary_cp_group)
                     if local_rotary is not None:
                         setattr(local_rotary, "cp_group", local_rotary_cp_group)
-            decoder_input = cast(torch.Tensor, preproc_output[0])
-            if not decoder_input.requires_grad and decoder_input.is_leaf:
+            decoder_input = cast(torch.Tensor | None, preproc_output[0])
+            if (
+                decoder_input is not None
+                and not decoder_input.requires_grad
+                and decoder_input.is_leaf
+            ):
                 decoder_input.requires_grad_(True)
             rotary_pos_emb = preproc_output[1]
             if not isinstance(position_ids, torch.Tensor) or not isinstance(
@@ -1481,37 +1495,14 @@ def _gemma4_attention_pattern(provider: Any) -> tuple[int, int]:
 def _gemma4_flex_attention_compile_crash_config(
     provider: Any,
 ) -> FlexAttentionCompileCrashConfig:
-    signature = _gemma4_compile_crash_signature(provider)
     global_head_dim = int(getattr(provider, "global_head_dim", 0) or 0)
-    if signature in _GEMMA4_TRITON_NUM_STAGES_2_SIGNATURES or (
-        signature is None and global_head_dim >= 512
-    ):
+    if global_head_dim >= 512:
         return FlexAttentionCompileCrashConfig(
             triton_num_stages_2_head_dims=(global_head_dim,)
         )
     return FlexAttentionCompileCrashConfig()
 
 
-def _gemma4_compile_crash_signature(provider: Any) -> tuple[Any, ...] | None:
-    required_attrs = (
-        "num_layers",
-        "hidden_size",
-        "num_attention_heads",
-        "kv_channels",
-    )
-    if any(not hasattr(provider, attr) for attr in required_attrs):
-        return None
-    return (
-        "moe" if int(getattr(provider, "num_moe_experts", 0) or 0) > 0 else "dense",
-        int(provider.num_layers),
-        int(provider.hidden_size),
-        int(provider.num_attention_heads),
-        int(provider.kv_channels),
-        int(getattr(provider, "global_head_dim", 0) or 0),
-        int(getattr(provider, "num_global_key_value_heads", 0) or 0),
-    )
-
-
 def _is_gemma4_global_layer(layer_number: int, provider: Any) -> bool:
     layer_types = getattr(provider, "art_gemma4_layer_types", None)
     if layer_types is not None:
@@ -2213,6 +2204,7 @@ def _gemma4_text_only_mapping_registry(hf_config: Any | None = None) -> Any:
     from megatron.bridge.models.conversion.mapping_registry import (
         MegatronMappingRegistry,
     )
+    from megatron.bridge.models.conversion.param_mapping import AutoMapping
     from megatron.bridge.models.gemma.gemma4_bridge import _Gemma4QKVMapping
     from megatron.bridge.models.gemma_vl.gemma4_vl_bridge import Gemma4VLBridge
 
@@ -2271,7 +2263,11 @@ def megatron_to_hf(
 
     text_config = getattr(hf_config, "text_config", hf_config)
     is_moe = bool(getattr(text_config, "enable_moe_block", False))
-    language_mappings = []
+    language_mappings = (
+        []
+        if bool(getattr(text_config, "tie_word_embeddings", True))
+        else [AutoMapping("output_layer.weight", "lm_head.weight")]
+    )
     for mapping in upstream_registry.mappings:
         if not mapping.megatron_param.startswith("language_model."):
             continue
diff --git a/src/art/megatron/model_support/handlers/glm52.py b/src/art/megatron/model_support/handlers/glm52.py
new file mode 100644
index 000000000..ff4b4ab86
--- /dev/null
+++ b/src/art/megatron/model_support/handlers/glm52.py
@@ -0,0 +1,507 @@
+from __future__ import annotations
+
+from typing import Any, Literal, Sequence, cast
+
+import torch
+
+from art.megatron.model_support.handlers.default_dense import (
+    DefaultMoeHandler,
+    _compile_workaround_flags_for_provider,
+)
+from art.megatron.model_support.spec import (
+    CompileWorkaroundConfig,
+    ExpertPackedLoraGroup,
+    ExpertPackedLoraSlot,
+    LayerFamilyInstance,
+    PrefixTreeModelStateContext,
+)
+
+
+def _hf_config(bridge: Any) -> Any:
+    pretrained = bridge.hf_pretrained
+    return getattr(pretrained, "config", pretrained)
+
+
+def _from_vllm_expert_lora(
+    tensors: dict[str, torch.Tensor], adapter_config: dict[str, Any]
+) -> dict[str, torch.Tensor]:
+    slots = (
+        ("base_layer.lora_A.weight", "gate_up_proj", "lora_A", "rows"),
+        ("base_layer.lora_B.weight", "gate_up_proj", "lora_B", "cols"),
+        ("lora_A.weight", "down_proj", "lora_A", "rows"),
+        ("lora_B.weight", "down_proj", "lora_B", "cols"),
+    )
+    grouped: dict[str, dict[str, torch.Tensor]] = {}
+    used: set[str] = set()
+    for key, tensor in tensors.items():
+        for suffix, _projection, _lora, _layout in slots:
+            marker = f".{suffix}"
+            if key.endswith(marker) and key[: -len(marker)].endswith(".mlp.experts"):
+                grouped.setdefault(key[: -len(marker)], {})[suffix] = tensor
+                used.add(key)
+                break
+    if not grouped:
+        return tensors
+    try:
+        rank = int(adapter_config["r"])
+    except (KeyError, TypeError, ValueError) as exc:
+        raise RuntimeError(
+            "GLM-5.2 fused expert LoRA requires adapter rank r."
+        ) from exc
+    if rank <= 0:
+        raise RuntimeError(f"GLM-5.2 LoRA rank must be positive, got {rank}.")
+
+    result = {key: tensor for key, tensor in tensors.items() if key not in used}
+    for prefix, block in grouped.items():
+        missing = [suffix for suffix, *_ in slots if suffix not in block]
+        if missing:
+            raise RuntimeError(
+                f"Incomplete GLM-5.2 expert LoRA block {prefix}: {missing}"
+            )
+        gate_a = block[slots[0][0]]
+        if gate_a.ndim != 2 or gate_a.shape[0] % rank:
+            raise RuntimeError(
+                f"{prefix}: invalid fused expert A shape {tuple(gate_a.shape)} for rank {rank}."
+            )
+        experts = gate_a.shape[0] // rank
+        for suffix, projection, lora, layout in slots:
+            tensor = block[suffix]
+            packed = experts * rank
+            if tensor.ndim != 2 or tensor.shape[0 if layout == "rows" else 1] != packed:
+                raise RuntimeError(
+                    f"{prefix}.{suffix}: shape {tuple(tensor.shape)} does not encode "
+                    f"{experts} experts at rank {rank}."
+                )
+            unpacked = (
+                tensor.reshape(experts, rank, tensor.shape[1])
+                if layout == "rows"
+                else tensor.reshape(tensor.shape[0], rank, experts).permute(2, 0, 1)
+            )
+            for expert, expert_tensor in enumerate(unpacked):
+                key = f"{prefix}.{expert}.{projection}.{lora}.weight"
+                if key in result:
+                    raise RuntimeError(f"Duplicate GLM-5.2 expert LoRA tensor {key}.")
+                result[key] = expert_tensor.clone().contiguous()
+    return result
+
+
+class Glm52Handler(DefaultMoeHandler):
+    key = "glm52"
+    is_moe = True
+    cp_supported = True
+    native_vllm_lora_status = "validated"
+
+    def configure_tokenizer(
+        self,
+        tokenizer: Any,
+        *,
+        internal_config: Any,
+    ) -> Any:
+        if not any(
+            internal_config.get(key) is not None
+            for key in ("chat_template", "chat_template_path")
+        ):
+            from art.utils.chat_template import TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR
+
+            setattr(tokenizer, TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR, True)
+        return tokenizer
+
+    def compile_workaround_config(self, provider: Any) -> CompileWorkaroundConfig:
+        ep1_alltoall = (
+            int(getattr(provider, "expert_model_parallel_size", 1) or 1) == 1
+            and getattr(provider, "moe_token_dispatcher_type", None) == "alltoall"
+        )
+        flags = ("mlp_forward", "moe_forward")
+        if ep1_alltoall:
+            flags = (*flags, "moe_preprocess")
+        return CompileWorkaroundConfig(
+            flags=_compile_workaround_flags_for_provider(provider, flags),
+            shared_expert_state=self._shared_expert_compile_state(provider),
+        )
+
+    def patch_provider(self, provider: Any, bridge: Any) -> None:
+        from art.megatron.glm52.spec import (
+            build_glm52_pipeline_layout,
+            get_glm52_decoder_block_spec,
+        )
+
+        config = _hf_config(bridge)
+        required_dims = {
+            "kv_lora_rank": 512,
+            "qk_rope_head_dim": 64,
+            "v_head_dim": 256,
+            "index_head_dim": 128,
+        }
+        for name, expected in required_dims.items():
+            actual = int(getattr(config, name))
+            if actual != expected:
+                raise ValueError(f"GLM-5.2 requires {name}={expected}, got {actual}.")
+        topk = int(config.index_topk)
+        if topk % 32:
+            raise ValueError(f"GLM-5.2 index_topk must be divisible by 32, got {topk}.")
+        provider.transformer_layer_spec = get_glm52_decoder_block_spec
+        provider.experimental_attention_variant = None
+        provider.kv_channels = int(config.v_head_dim)
+        provider.num_moe_experts = int(config.n_routed_experts)
+        provider.num_query_groups = int(config.num_attention_heads)
+        provider.rotary_interleaved = False
+        provider.rope_type = "rope"
+        provider.position_embedding_type = "rope"
+        provider.rotary_base = float(config.rope_parameters["rope_theta"])
+        provider.rotary_scaling_factor = 1.0
+        provider.mscale = 1.0
+        provider.mscale_all_dim = 1.0
+        provider.mtp_num_layers = None
+        provider.dsa_indexer_n_heads = int(config.index_n_heads)
+        provider.dsa_indexer_head_dim = int(config.index_head_dim)
+        provider.dsa_indexer_topk = topk
+        provider.dsa_indexer_loss_coeff = 0.0
+        provider.dsa_indexer_use_sparse_loss = False
+        provider.glm52_indexer_types = tuple(config.indexer_types)
+        pp_size = int(provider.pipeline_model_parallel_size or 1)
+        vp_size = int(provider.virtual_pipeline_model_parallel_size or 1)
+        if pp_size * vp_size > 1 and provider.pipeline_model_parallel_layout is None:
+            provider.pipeline_model_parallel_layout = build_glm52_pipeline_layout(
+                provider.glm52_indexer_types,
+                pp_size,
+                vp_size,
+            )
+        provider.moe_layer_freq = [
+            0 if layer_type == "dense" else 1 for layer_type in config.mlp_layer_types
+        ]
+        provider.moe_shared_expert_intermediate_size = int(
+            config.moe_intermediate_size
+        ) * int(config.n_shared_experts)
+        provider.moe_router_bias_update_rate = 0.0
+        provider.moe_aux_loss_coeff = 0.0
+        provider.attention_softmax_in_fp32 = True
+
+    def configure_provider_for_runtime(self, provider: Any) -> None:
+        provider.mtp_num_layers = None
+        provider.mtp_loss_scaling_factor = None
+        provider.moe_shared_expert_overlap = False
+
+    def context_parallel_workload_profile(self, provider: Any) -> Any:
+        from art.megatron.glm52.spec import build_glm52_context_parallel_profile
+
+        profile = getattr(provider, "_art_context_parallel_workload_profile", None)
+        if profile is None:
+            profile = build_glm52_context_parallel_profile(provider)
+            provider._art_context_parallel_workload_profile = profile
+        return profile
+
+    def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None:
+        from megatron.core.models.gpt.gpt_model import GPTModel
+
+        for chunk in model_chunks:
+            module = chunk
+            while hasattr(module, "module"):
+                module = module.module
+            gpt = module if isinstance(module, GPTModel) else module.language_model
+            preprocess = gpt._preprocess
+
+            def preprocess_hook(*args: Any, _preprocess=preprocess, **kwargs: Any):
+                output = list(_preprocess(*args, **kwargs))
+                decoder_input = cast(torch.Tensor | None, output[0])
+                if (
+                    decoder_input is not None
+                    and decoder_input.is_leaf
+                    and not decoder_input.requires_grad
+                ):
+                    decoder_input.requires_grad_(True)
+                return tuple(output)
+
+            gpt._preprocess = preprocess_hook
+
+    def build_prefix_tree_model_state(
+        self, context: PrefixTreeModelStateContext
+    ) -> dict[str, Any]:
+        if context.input_pos is None:
+            raise RuntimeError("GLM-5.2 prefix-tree attention requires input_pos.")
+        from art.megatron.glm52.state import build_glm52_prefix_tree_state
+
+        if context.context_parallel_state is not None:
+            from art.megatron.glm52.state import build_glm52_context_parallel_state
+
+            return {
+                "glm52": build_glm52_context_parallel_state(
+                    position_ids=context.input_pos,
+                    context_parallel_state=context.context_parallel_state,
+                    device=context.device,
+                )
+            }
+
+        return {
+            "glm52": build_glm52_prefix_tree_state(
+                position_ids=context.input_pos,
+                group_ids=context.group_ids,
+                parent_ids=context.parent_ids,
+                device=context.device,
+            )
+        }
+
+    def correctness_precision(self) -> Literal["bf16", "fp32"]:
+        return "bf16"
+
+    def correctness_use_fp32_lora_reference(self) -> bool:
+        return False
+
+    def prepare_hf_reference_model(self, model: Any) -> Any:
+        for module in model.modules():
+            if type(module).__name__ == "GlmMoeDsaIndexer":
+                module.requires_grad_(False)
+        return model
+
+    def correctness_phase_pass_fns(self, oracle_harness: Any) -> dict[str, Any]:
+        nonzero = {"typical_abs_scale": 0.0, "candidate_abs_scale": 0.0}
+        forward = oracle_harness.MetricThresholdRule(
+            limits={"mean_abs_pct": 3.0}, minimums=nonzero
+        )
+        grad = oracle_harness.MetricThresholdRule(
+            limits={"mean_abs_pct": 5.0}, minimums=nonzero
+        )
+        return {
+            "forward": forward,
+            "outputs": forward,
+            "losses": oracle_harness.MetricThresholdRule(limits={"mean_abs_pct": 3.0}),
+            "grads": grad,
+            "deltas": grad,
+            "router_scores": forward,
+            "router_topk_ids": oracle_harness.MetricThresholdRule(
+                limits={"topk_mismatch_fraction": 0.0, "top1_mismatch_fraction": 0.0}
+            ),
+        }
+
+    def collect_layer_families(self, provider: Any) -> list[LayerFamilyInstance]:
+        pattern = tuple(provider.glm52_indexer_types)
+        full = [index for index, value in enumerate(pattern) if value == "full"]
+        complete_shared_groups = [
+            end - 1
+            for start, end in zip(full, full[1:], strict=False)
+            if end - start > 1
+        ]
+        shared = next(
+            (index for index, value in enumerate(pattern) if value == "shared"),
+            None,
+        )
+        sparse_mlp = next(
+            (index for index, value in enumerate(provider.moe_layer_freq) if value),
+            None,
+        )
+        families = [
+            LayerFamilyInstance(key="glm52_full_index_attention", layer_index=0),
+            LayerFamilyInstance(key="dense_mlp", layer_index=0),
+        ]
+        if shared is not None:
+            families.append(
+                LayerFamilyInstance(
+                    key="glm52_shared_index_attention", layer_index=shared
+                )
+            )
+        if len(complete_shared_groups) >= 2:
+            # Exercise shared-index reuse twice and retain four legal PP/VPP
+            # split points after the full-layer prelude.
+            families.append(
+                LayerFamilyInstance(
+                    key="glm52_repeated_index_share_groups",
+                    layer_index=complete_shared_groups[1],
+                )
+            )
+        if sparse_mlp is not None:
+            families.extend(
+                (
+                    LayerFamilyInstance(key="grouped_moe_mlp", layer_index=sparse_mlp),
+                    LayerFamilyInstance(
+                        key="shared_experts_mlp", layer_index=sparse_mlp
+                    ),
+                )
+            )
+        return families
+
+    def identity_lora_target_parameters(
+        self,
+        model: Any,
+        *,
+        target_modules: list[str],
+    ) -> list[str]:
+        targets = set(target_modules)
+        suffixes = tuple(f"{target}.weight" for target in targets - {"experts"})
+        return [
+            name
+            for name, _ in model.named_parameters()
+            if ".indexer." not in name
+            and (
+                name.endswith(suffixes)
+                or ("experts" in targets and ".experts." in name)
+            )
+        ]
+
+    def apply_lora_adapters(
+        self,
+        model_chunks: Sequence[Any],
+        provider: Any,
+        *,
+        target_modules: list[str],
+        rank: int,
+        alpha: int,
+    ) -> None:
+        from megatron.core.transformer.transformer_layer import TransformerLayer
+
+        from art.megatron.glm52.attention import Glm52SelfAttention
+        from art.megatron.glm52.lora import (
+            Glm52LoRA,
+            apply_glm52_attention_lora,
+            wrap_glm52_grouped_moe_experts_3d,
+        )
+        from art.megatron.lora import (
+            _adapter_model_prefix,
+            _is_language_transformer_layer_name,
+            wrap_dense_mlp,
+            wrap_shared_experts_mlp,
+        )
+
+        targets = set(target_modules)
+        if "kv_b_proj" in targets:
+            raise ValueError(
+                "GLM-5.2 does not support kv_b_proj LoRA because native vLLM "
+                "sparse MLA executes statically absorbed W_K/W_V weights."
+            )
+        for chunk in model_chunks:
+            for module_name, layer in chunk.named_modules():
+                if not isinstance(layer, TransformerLayer) or not (
+                    _is_language_transformer_layer_name(module_name)
+                ):
+                    continue
+                if not isinstance(layer.self_attention, Glm52SelfAttention):
+                    raise TypeError(
+                        "GLM-5.2 layer has unsupported attention "
+                        f"{type(layer.self_attention).__name__}."
+                    )
+                prefix = _adapter_model_prefix(layer)
+                apply_glm52_attention_lora(
+                    layer.self_attention,
+                    adapter_model_prefix=prefix,
+                    provider=provider,
+                    target_modules=targets,
+                    rank=rank,
+                    alpha=alpha,
+                )
+                experts = getattr(layer.mlp, "experts", None)
+                if experts is None:
+                    wrap_dense_mlp(
+                        layer.mlp,
+                        adapter_model_prefix=prefix,
+                        provider=provider,
+                        target_modules=targets,
+                        rank=rank,
+                        alpha=alpha,
+                        lora_cls=Glm52LoRA,
+                    )
+                    continue
+                wrap_glm52_grouped_moe_experts_3d(
+                    experts,
+                    adapter_model_prefix=prefix,
+                    target_modules=targets,
+                    rank=rank,
+                    alpha=alpha,
+                )
+                shared_experts = getattr(layer.mlp, "shared_experts", None)
+                if shared_experts is not None:
+                    wrap_shared_experts_mlp(
+                        shared_experts,
+                        adapter_model_prefix=prefix,
+                        provider=provider,
+                        target_modules=targets,
+                        rank=rank,
+                        alpha=alpha,
+                        lora_cls=Glm52LoRA,
+                    )
+
+    def build_adapter_weights_by_base(
+        self, model_chunks: Sequence[Any]
+    ) -> dict[str, list[Any]]:
+        from megatron.core.transformer.transformer_layer import TransformerLayer
+
+        from art.megatron.glm52.attention import Glm52SelfAttention
+        from art.megatron.glm52.lora import add_glm52_attention_adapter_weights
+        from art.megatron.weights.adapter_export import (
+            add_dense_mlp_adapter_weights,
+            add_grouped_moe_adapter_weights,
+            add_shared_experts_adapter_weights,
+            layer_base_prefix,
+        )
+
+        result: dict[str, list[Any]] = {}
+        for chunk in model_chunks:
+            for module_name, layer in chunk.named_modules():
+                if not isinstance(layer, TransformerLayer) or not isinstance(
+                    layer.self_attention, Glm52SelfAttention
+                ):
+                    continue
+                prefix = layer_base_prefix(layer, module_name=module_name)
+                add_glm52_attention_adapter_weights(
+                    result,
+                    layer_prefix=prefix,
+                    attention=layer.self_attention,
+                )
+                experts = getattr(layer.mlp, "experts", None)
+                if experts is None:
+                    add_dense_mlp_adapter_weights(
+                        result, layer_prefix=prefix, mlp=layer.mlp
+                    )
+                    continue
+                add_grouped_moe_adapter_weights(
+                    result, layer_prefix=prefix, experts=experts
+                )
+                shared_experts = getattr(layer.mlp, "shared_experts", None)
+                if shared_experts is not None:
+                    add_shared_experts_adapter_weights(
+                        result,
+                        layer_prefix=prefix,
+                        shared_experts=shared_experts,
+                    )
+        return result
+
+    def expert_packed_lora_groups(self) -> tuple[ExpertPackedLoraGroup, ...]:
+        return (
+            ExpertPackedLoraGroup(
+                art_group_suffix=".mlp.experts",
+                slots=(
+                    ExpertPackedLoraSlot(
+                        source_projection="gate_up_proj",
+                        source_lora="lora_A",
+                        output_suffix="base_layer.lora_A.weight",
+                        pack_layout="expert_rows",
+                    ),
+                    ExpertPackedLoraSlot(
+                        source_projection="gate_up_proj",
+                        source_lora="lora_B",
+                        output_suffix="base_layer.lora_B.weight",
+                        pack_layout="rank_major_expert_cols",
+                    ),
+                    ExpertPackedLoraSlot(
+                        source_projection="down_proj",
+                        source_lora="lora_A",
+                        output_suffix="lora_A.weight",
+                        pack_layout="expert_rows",
+                    ),
+                    ExpertPackedLoraSlot(
+                        source_projection="down_proj",
+                        source_lora="lora_B",
+                        output_suffix="lora_B.weight",
+                        pack_layout="rank_major_expert_cols",
+                    ),
+                ),
+            ),
+        )
+
+    def from_vllm_lora_tensors(
+        self,
+        tensors: dict[str, torch.Tensor],
+        *,
+        adapter_config: dict[str, Any],
+    ) -> dict[str, torch.Tensor]:
+        return _from_vllm_expert_lora(tensors, adapter_config)
+
+
+GLM52_HANDLER = Glm52Handler()
diff --git a/src/art/megatron/model_support/handlers/gpt_oss.py b/src/art/megatron/model_support/handlers/gpt_oss.py
index 93855e38c..320d223f7 100644
--- a/src/art/megatron/model_support/handlers/gpt_oss.py
+++ b/src/art/megatron/model_support/handlers/gpt_oss.py
@@ -8,7 +8,6 @@
 
 import torch
 
-from art.megatron import lora as art_lora
 from art.megatron.model_support.handlers.default_dense import (
     DefaultMoeHandler,
     _compile_workaround_flags_for_provider,
@@ -47,7 +46,6 @@
     ExpertPackedLoraSlot,
     HfWeightSource,
     LayerFamilyInstance,
-    RolloutWeightsMode,
 )
 
 _GPT_OSS_MOE_COMPILE_WORKAROUND_FLAGS = (
@@ -448,37 +446,6 @@ def mapping_registry(self: Any) -> Any:
     bridge_type.mapping_registry = mapping_registry
 
 
-def _patch_gpt_oss_weight_loader(target: Any) -> None:
-    bridge_type = type(target)
-    original = getattr(bridge_type, "maybe_modify_loaded_hf_weight", None)
-    if original is None or getattr(original, "_art_gpt_oss_bias_encoding", False):
-        return
-    original_loader = cast(Any, original)
-
-    def maybe_modify_loaded_hf_weight(
-        self: Any,
-        hf_param: str | dict[str, str],
-        hf_state_dict: Any,
-    ) -> Any:
-        def load_one(name: str) -> torch.Tensor:
-            loaded = original_loader(self, name, hf_state_dict)
-            if name.endswith(".mlp.experts.down_proj") and name not in hf_state_dict:
-                # This Bridge version documents MXFP4 down projection output as
-                # [E, hidden, ffn], but its dequantizer emits the checkpoint's
-                # [E, ffn, hidden] layout. GPT-OSS-20B is square, so shape-based
-                # alignment cannot detect the orientation. Normalize before the
-                # optimized loader caches the materialized logical tensor.
-                loaded = loaded.transpose(-1, -2).contiguous()
-            return loaded
-
-        if isinstance(hf_param, dict):
-            return {key: load_one(name) for key, name in hf_param.items()}
-        return load_one(hf_param)
-
-    setattr(maybe_modify_loaded_hf_weight, "_art_gpt_oss_bias_encoding", True)
-    bridge_type.maybe_modify_loaded_hf_weight = maybe_modify_loaded_hf_weight
-
-
 def _gpt_oss_padded_mapping_registry(
     upstream_registry: Any,
     *,
@@ -591,33 +558,42 @@ def megatron_to_hf(
             )
             if not converted:
                 return converted
-            tensor = _gate_up_from_etp_shard_order(
-                next(iter(converted.values())), self.tp_size
-            )
-            gate = tensor[:logical_ffn, :logical_hidden]
-            up = tensor[internal_ffn : internal_ffn + logical_ffn, :logical_hidden]
+            tensor = next(iter(converted.values()))
+            if self.ep_size > 1:
+                tensor = torch.stack(
+                    [
+                        _gate_up_from_etp_shard_order(expert, self.tp_size)
+                        for expert in tensor
+                    ]
+                )
+            else:
+                tensor = _gate_up_from_etp_shard_order(tensor, self.tp_size)
+            gate = tensor[..., :logical_ffn, :logical_hidden]
+            up = tensor[..., internal_ffn : internal_ffn + logical_ffn, :logical_hidden]
             interleaved = torch.empty(
+                *tensor.shape[:-2],
                 2 * logical_ffn,
                 logical_hidden,
                 dtype=tensor.dtype,
                 device=tensor.device,
             )
-            interleaved[::2] = gate
-            interleaved[1::2] = up
+            interleaved[..., 0::2, :] = gate
+            interleaved[..., 1::2, :] = up
             names = cast(dict[str, str], self.hf_param)
             return {
-                names["weight"]: interleaved.t().contiguous(),
+                names["weight"]: interleaved.transpose(-1, -2).contiguous(),
                 names["bias"]: torch.stack(
                     [
-                        tensor[:logical_ffn, logical_hidden],
+                        tensor[..., :logical_ffn, logical_hidden],
                         tensor[
+                            ...,
                             internal_ffn : internal_ffn + logical_ffn,
                             logical_hidden,
                         ],
                     ],
                     dim=-1,
                 )
-                .flatten()
+                .flatten(-2)
                 .contiguous(),
             }
 
@@ -660,6 +636,7 @@ def hf_to_megatron(
             )
 
             global_expert_number = extract_expert_number_from_param(self.megatron_param)
+            # Index through ExpertTensorSlice so global EP metadata is preserved.
             expert_weight = hf_weights["weight"][global_expert_number]
             expert_bias = hf_weights["bias"][global_expert_number]
             normalized_param = self._normalize_expert_param_name(self.megatron_param)
@@ -704,8 +681,10 @@ def megatron_to_hf(
             tensor = next(iter(converted.values()))
             names = cast(dict[str, str], self.hf_param)
             return {
-                names["weight"]: tensor[:logical_hidden, :logical_ffn].t().contiguous(),
-                names["bias"]: tensor[:logical_hidden, logical_ffn].contiguous(),
+                names["weight"]: tensor[..., :logical_hidden, :logical_ffn]
+                .transpose(-1, -2)
+                .contiguous(),
+                names["bias"]: tensor[..., :logical_hidden, logical_ffn].contiguous(),
             }
 
         def resolve(self, captures: tuple[str, ...]) -> Any:
@@ -789,7 +768,6 @@ def _hf_weight_source(
         setattr(bridge, "_art_hf_weight_source", _hf_weight_source)
         model_bridge = getattr(bridge, "_model_bridge", None)
         if model_bridge is not None and model_bridge is not bridge:
-            _patch_gpt_oss_weight_loader(model_bridge)
             _patch_gpt_oss_mapping_registry(model_bridge)
             if type(model_bridge) is object:
                 return
@@ -821,12 +799,7 @@ def hf_weight_source(
             kind="bridge_materialized",
         )
 
-    def vllm_engine_args(
-        self,
-        *,
-        rollout_weights_mode: RolloutWeightsMode,
-    ) -> dict[str, object]:
-        del rollout_weights_mode
+    def vllm_engine_args(self) -> dict[str, object]:
         return {"moe_backend": "triton_unfused"}
 
     def vllm_server_args(self) -> dict[str, object]:
@@ -1171,8 +1144,12 @@ def preprocess_hook(
                         setattr(rotary_module, "cp_group", rotary_cp_group)
                     if packed_cp_group is not None:
                         setattr(packed_seq_params, "cp_group", packed_cp_group)
-            decoder_input = cast(torch.Tensor, preproc_output[0])
-            if not decoder_input.requires_grad and decoder_input.is_leaf:
+            decoder_input = cast(torch.Tensor | None, preproc_output[0])
+            if (
+                decoder_input is not None
+                and not decoder_input.requires_grad
+                and decoder_input.is_leaf
+            ):
                 decoder_input.requires_grad_(True)
             rotary_pos_emb = preproc_output[1]
             if not isinstance(position_ids, torch.Tensor) or not torch.is_tensor(
@@ -1285,7 +1262,6 @@ def _vllm_moe_config(adapter_config: dict[str, Any]) -> dict[str, Any]:
     if "experts" not in target_modules:
         target_modules.append("experts")
     config["target_modules"] = target_modules
-    config["art_merged_lora_delta_unsupported_target_modules"] = ["experts"]
     return config
 
 
@@ -1392,47 +1368,53 @@ def _zero_gpt_oss_moe_lora_padding(
     if logical_hidden == internal_hidden and logical_ffn == internal_ffn:
         return
     with torch.no_grad():
-        for prefix, a_t, b_t in art_lora.iter_lora_sites(model_chunks):
-            if ".mlp.experts." not in prefix:
-                continue
-            if prefix.endswith(".gate_up_proj"):
-                _zero_gpt_oss_lora_padding_tensor_set(
-                    a_t,
-                    dim=-2,
-                    logical=logical_hidden,
-                    internal=internal_hidden,
-                    components=(internal_hidden,),
-                    grads=grads,
-                    params=params,
-                )
-                _zero_gpt_oss_lora_padding_tensor_set(
-                    b_t,
-                    dim=-1,
-                    logical=logical_ffn,
-                    internal=internal_ffn,
-                    components=(internal_ffn, internal_ffn),
-                    grads=grads,
-                    params=params,
-                )
-            elif prefix.endswith(".down_proj"):
-                _zero_gpt_oss_lora_padding_tensor_set(
-                    a_t,
-                    dim=-2,
-                    logical=logical_ffn,
-                    internal=internal_ffn,
-                    components=(internal_ffn,),
-                    grads=grads,
-                    params=params,
-                )
-                _zero_gpt_oss_lora_padding_tensor_set(
-                    b_t,
-                    dim=-1,
-                    logical=logical_hidden,
-                    internal=internal_hidden,
-                    components=(internal_hidden,),
-                    grads=grads,
-                    params=params,
-                )
+        for chunk in model_chunks:
+            for module in chunk.modules():
+                prefix = getattr(module, "adapter_model_prefix", None)
+                if not isinstance(prefix, str) or ".mlp.experts." not in prefix:
+                    continue
+                if prefix.endswith(".gate_up_proj"):
+                    if hasattr(module, "A_T"):
+                        _zero_gpt_oss_lora_padding_tensor_set(
+                            cast(torch.nn.Parameter, module.A_T),
+                            dim=-2,
+                            logical=logical_hidden,
+                            internal=internal_hidden,
+                            components=(internal_hidden,),
+                            grads=grads,
+                            params=params,
+                        )
+                    if hasattr(module, "B_T"):
+                        _zero_gpt_oss_lora_padding_tensor_set(
+                            cast(torch.nn.Parameter, module.B_T),
+                            dim=-1,
+                            logical=logical_ffn,
+                            internal=internal_ffn,
+                            components=(internal_ffn, internal_ffn),
+                            grads=grads,
+                            params=params,
+                        )
+                elif prefix.endswith(".down_proj"):
+                    if hasattr(module, "A_T"):
+                        _zero_gpt_oss_lora_padding_tensor_set(
+                            cast(torch.nn.Parameter, module.A_T),
+                            dim=-2,
+                            logical=logical_ffn,
+                            internal=internal_ffn,
+                            components=(internal_ffn,),
+                            grads=grads,
+                            params=params,
+                        )
+                    if hasattr(module, "B_T"):
+                        _zero_gpt_oss_lora_padding_tensor_set(
+                            cast(torch.nn.Parameter, module.B_T),
+                            dim=-1,
+                            logical=logical_hidden,
+                            internal=internal_hidden,
+                            components=(internal_hidden,),
+                            grads=grads,
+                            params=params,
+                        )
 
 
 def _zero_gpt_oss_lora_padding_state_tensor(
diff --git a/src/art/megatron/model_support/handlers/qwen3_5.py b/src/art/megatron/model_support/handlers/qwen3_5.py
index db72e0687..ac62c4f4c 100644
--- a/src/art/megatron/model_support/handlers/qwen3_5.py
+++ b/src/art/megatron/model_support/handlers/qwen3_5.py
@@ -28,9 +28,6 @@
 _QWEN35_MOE_COMPILE_WORKAROUND_FLAGS = (
     "moe_postprocess",
     "te_triton_permute_with_mask_map",
-    # Torch 2.11.0 compiles Megatron's weighted SwiGLU custom autograd
-    # function with zero cotangents when its forward casts internally.
-    "weighted_bias_swiglu_no_inner_forward_cast",
 )
 _QWEN35_MOE_UNCONDITIONAL_COMPILE_WORKAROUND_FLAGS: tuple[str, ...] = ()
 _ART_LAYER_PREFIX = "base_model.model.model.layers."
@@ -139,7 +136,9 @@ def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None:
                 raise RuntimeError("ART Qwen3.5 Megatron training does not use MTP.")
             preprocess = gpt_module._preprocess
 
-            def preprocess_hook(*args, _preprocess=preprocess, **kwargs):
+            def preprocess_hook(
+                *args, _preprocess=preprocess, _gpt=gpt_module, **kwargs
+            ):
                 position_ids = kwargs.get("position_ids")
                 if isinstance(position_ids, torch.Tensor) and position_ids.ndim == 2:
                     kwargs = dict(kwargs)
@@ -148,15 +147,12 @@ def preprocess_hook(*args, _preprocess=preprocess, **kwargs):
                         position_ids.shape[0],
                         position_ids.shape[1],
                     )
-                rotary_pos_emb = getattr(gpt_module, "rotary_pos_emb", None)
+                rotary_pos_emb = getattr(_gpt, "rotary_pos_emb", None)
                 rotary_cp_group = getattr(rotary_pos_emb, "cp_group", None)
                 dispatched_local_cp_positions = (
                     isinstance(position_ids, torch.Tensor)
                     and position_ids.ndim == 2
-                    and _context_parallel_world_size(
-                        getattr(gpt_module, "config", None)
-                    )
-                    > 1
+                    and _context_parallel_world_size(getattr(_gpt, "config", None)) > 1
                     and rotary_cp_group is not None
                 )
                 if dispatched_local_cp_positions:
@@ -166,8 +162,12 @@ def preprocess_hook(*args, _preprocess=preprocess, **kwargs):
                 finally:
                     if dispatched_local_cp_positions:
                         setattr(rotary_pos_emb, "cp_group", rotary_cp_group)
-                decoder_input = cast(torch.Tensor, preproc_output[0])
-                if not decoder_input.requires_grad and decoder_input.is_leaf:
+                decoder_input = cast(torch.Tensor | None, preproc_output[0])
+                if (
+                    decoder_input is not None
+                    and decoder_input.is_leaf
+                    and not decoder_input.requires_grad
+                ):
                     decoder_input.requires_grad_(True)
                 return tuple(preproc_output)
 
@@ -1176,6 +1176,11 @@ def _select_qwen35_expert_weight(
 _QWEN35_TEXT_ONLY_BRIDGE_REGISTERED = False
 
 
+def _propagate_qwen35_text_dtype(hf_pretrained: Any) -> None:
+    config = hf_pretrained.config
+    config.text_config.dtype = config.dtype
+
+
 def ensure_qwen35_text_only_bridge_registered() -> None:
     global _QWEN35_TEXT_ONLY_BRIDGE_REGISTERED
     if _QWEN35_TEXT_ONLY_BRIDGE_REGISTERED:
@@ -1201,6 +1206,10 @@ def ensure_qwen35_text_only_bridge_registered() -> None:
         model_type="qwen3_5",
     )
     class _ArtQwen35DenseTextOnlyBridge(Qwen35VLBridge):
+        def provider_bridge(self, hf_pretrained: Any) -> Any:
+            _propagate_qwen35_text_dtype(hf_pretrained)
+            return super().provider_bridge(hf_pretrained)
+
         def mapping_registry(self) -> Any:
             return _qwen35_text_only_mapping_registry(Qwen35VLBridge)
 
@@ -1211,6 +1220,10 @@ def mapping_registry(self) -> Any:
         model_type="qwen3_5_moe",
     )
     class _ArtQwen35TextOnlyBridge(Qwen35VLMoEBridge):
+        def provider_bridge(self, hf_pretrained: Any) -> Any:
+            _propagate_qwen35_text_dtype(hf_pretrained)
+            return super().provider_bridge(hf_pretrained)
+
         def mapping_registry(self) -> Any:
             return _qwen35_text_only_mapping_registry(Qwen35VLMoEBridge)
 
diff --git a/src/art/megatron/model_support/handlers/qwen3_common.py b/src/art/megatron/model_support/handlers/qwen3_common.py
index 0b0c56820..91932656c 100644
--- a/src/art/megatron/model_support/handlers/qwen3_common.py
+++ b/src/art/megatron/model_support/handlers/qwen3_common.py
@@ -81,11 +81,13 @@ def install_qwen3_text_preprocess_patch(model_chunks: Sequence[Any]) -> None:
         )
         preprocess = gpt_module._preprocess
 
-        def preprocess_hook(*args, _preprocess=preprocess, **kwargs):
+        def preprocess_hook(
+            *args, _preprocess=preprocess, _gpt_module=gpt_module, **kwargs
+        ):
             position_ids = kwargs.get("position_ids")
-            rotary_pos_emb = getattr(gpt_module, "rotary_pos_emb", None)
+            rotary_pos_emb = getattr(_gpt_module, "rotary_pos_emb", None)
             rotary_cp_group = getattr(rotary_pos_emb, "cp_group", None)
-            config = getattr(gpt_module, "config", None)
+            config = getattr(_gpt_module, "config", None)
             cp_world_size = _context_parallel_world_size(config)
             uses_dispatched_local_cp_positions = (
                 isinstance(position_ids, torch.Tensor)
@@ -100,8 +102,12 @@ def preprocess_hook(*args, _preprocess=preprocess, **kwargs):
             finally:
                 if uses_dispatched_local_cp_positions:
                     setattr(rotary_pos_emb, "cp_group", rotary_cp_group)
-            decoder_input = cast(torch.Tensor, preproc_output[0])
-            if not decoder_input.requires_grad and decoder_input.is_leaf:
+            decoder_input = cast(torch.Tensor | None, preproc_output[0])
+            if (
+                decoder_input is not None
+                and not decoder_input.requires_grad
+                and decoder_input.is_leaf
+            ):
                 decoder_input.requires_grad_(True)
             position_ids = cast(torch.Tensor, position_ids)
             table = cast(torch.Tensor, preproc_output[1])
@@ -110,15 +116,15 @@ def preprocess_hook(*args, _preprocess=preprocess, **kwargs):
             embedding_dim = int(table.shape[-1])
             if (
                 rotary_pos_emb is not None
-                and getattr(gpt_module, "position_embedding_type", None) == "rope"
+                and getattr(_gpt_module, "position_embedding_type", None) == "rope"
                 and cp_world_size > 1
             ):
                 rotary_seq_len = cast(
                     int,
-                    getattr(gpt_module, "_art_qwen3_rotary_seq_len", None),
+                    getattr(_gpt_module, "_art_qwen3_rotary_seq_len", None),
                 )
                 table_source = _build_absolute_rotary_pos_emb(
-                    gpt_module,
+                    _gpt_module,
                     max_position=int(rotary_seq_len) - 1,
                     dtype=table.dtype,
                     device=table.device,
diff --git a/src/art/megatron/model_support/handlers/qwen3_moe.py b/src/art/megatron/model_support/handlers/qwen3_moe.py
index 5aec937f4..513119efa 100644
--- a/src/art/megatron/model_support/handlers/qwen3_moe.py
+++ b/src/art/megatron/model_support/handlers/qwen3_moe.py
@@ -11,7 +11,11 @@
     install_qwen3_text_preprocess_patch,
     qwen3_forward_kwargs,
 )
-from art.megatron.model_support.spec import CompileWorkaroundConfig
+from art.megatron.model_support.spec import (
+    CompileWorkaroundConfig,
+    ExpertPackedLoraGroup,
+    ExpertPackedLoraSlot,
+)
 
 _QWEN3_MOE_COMPILE_WORKAROUND_FLAGS = (
     "moe_postprocess",
@@ -24,6 +28,23 @@ class Qwen3MoeHandler(DefaultMoeHandler):
     key = "qwen3_moe"
     native_vllm_lora_status = "validated"
 
+    def expert_packed_lora_groups(self) -> tuple[ExpertPackedLoraGroup, ...]:
+        return (
+            ExpertPackedLoraGroup(
+                art_group_suffix=".mlp.experts",
+                slots=tuple(
+                    ExpertPackedLoraSlot(
+                        source_projection=projection,
+                        source_lora=lora,
+                        output_suffix=f"{projection}.{lora}.weight",
+                        pack_layout="expert_rows",
+                    )
+                    for projection in ("gate_proj", "up_proj", "down_proj")
+                    for lora in ("lora_A", "lora_B")
+                ),
+            ),
+        )
+
     def to_vllm_lora_tensors(
         self,
         tensors: dict[str, torch.Tensor],
@@ -35,6 +56,9 @@ def to_vllm_lora_tensors(
     def to_vllm_lora_config(self, adapter_config: dict[str, Any]) -> dict[str, Any]:
         return _qwen3_moe_config(adapter_config)
 
+    def vllm_lora_conversion_is_view_only(self) -> bool:
+        return True
+
     def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None:
         install_qwen3_text_preprocess_patch(model_chunks)
 
@@ -65,6 +89,11 @@ def compile_workaround_config(
     r"^.*\.mlp\.experts\.\d+\."
     r"(?:gate_proj|up_proj|down_proj)\.lora_[AB]\.weight$"
 )
+_QWEN3_PACKED_MOE_KEY_RE = re.compile(
+    r"^(?P<prefix>.*\.mlp\.experts)\."
+    r"(?P<projection>gate_proj|up_proj|down_proj)\."
+    r"(?P<lora>lora_[AB])\.weight$"
+)
 
 
 def _qwen3_moe_config(adapter_config: dict[str, Any]) -> dict[str, Any]:
@@ -89,6 +118,52 @@ def _clone(tensor: torch.Tensor) -> torch.Tensor:
     return tensor.clone().contiguous()
 
 
+def _expand_packed_moe_lora(
+    prefix: str,
+    slots: dict[tuple[str, str], torch.Tensor],
+    *,
+    rank: int,
+) -> dict[str, torch.Tensor]:
+    expected = {
+        (projection, lora)
+        for projection in ("gate_proj", "up_proj", "down_proj")
+        for lora in ("lora_A", "lora_B")
+    }
+    if set(slots) != expected:
+        raise RuntimeError(f"Incomplete packed Qwen3 MoE LoRA block for {prefix}")
+    num_experts: int | None = None
+    shaped: dict[str, tuple[torch.Tensor, torch.Tensor]] = {}
+    for projection in ("gate_proj", "up_proj", "down_proj"):
+        a = slots[(projection, "lora_A")]
+        b = slots[(projection, "lora_B")]
+        if a.ndim != 2 or b.ndim != 2 or a.shape[0] % rank or b.shape[1] != rank:
+            raise RuntimeError(
+                f"Invalid packed Qwen3 MoE LoRA shapes for {prefix}.{projection}: "
+                f"A={tuple(a.shape)} B={tuple(b.shape)} rank={rank}"
+            )
+        projection_experts = a.shape[0] // rank
+        if projection_experts <= 0 or b.shape[0] % projection_experts:
+            raise RuntimeError(
+                f"Packed Qwen3 MoE LoRA expert shape does not divide for "
+                f"{prefix}.{projection}"
+            )
+        if num_experts is not None and projection_experts != num_experts:
+            raise RuntimeError(f"Packed Qwen3 MoE expert counts differ for {prefix}")
+        num_experts = projection_experts
+        shaped[projection] = (
+            a.reshape(projection_experts, rank, a.shape[1]),
+            b.reshape(projection_experts, b.shape[0] // projection_experts, rank),
+        )
+
+    assert num_experts is not None
+    return {
+        f"{prefix}.{expert}.{projection}.lora_{lora}.weight": tensor[expert]
+        for projection, pair in shaped.items()
+        for lora, tensor in zip(("A", "B"), pair, strict=True)
+        for expert in range(num_experts)
+    }
+
+
 def _expand_fused_moe_lora(
     prefix: str,
     slots: dict[str, torch.Tensor],
@@ -196,8 +271,15 @@ def _to_vllm_lora_tensors(
     *,
     adapter_config: dict[str, Any],
 ) -> tuple[dict[str, torch.Tensor], dict[str, Any]]:
+    packed: dict[str, dict[tuple[str, str], torch.Tensor]] = {}
     grouped: dict[str, dict[str, torch.Tensor]] = {}
     for key, tensor in tensors.items():
+        packed_match = _QWEN3_PACKED_MOE_KEY_RE.match(key)
+        if packed_match is not None:
+            packed.setdefault(packed_match.group("prefix"), {})[
+                (packed_match.group("projection"), packed_match.group("lora"))
+            ] = tensor
+            continue
         match = _QWEN3_FUSED_MOE_KEY_RE.match(key)
         if match is None:
             continue
@@ -206,6 +288,30 @@ def _to_vllm_lora_tensors(
         )
         grouped.setdefault(match.group("prefix"), {})[slot] = tensor
 
+    if packed and grouped:
+        raise RuntimeError("Qwen3 LoRA contains both packed and fused expert blocks")
+
+    if packed:
+        rank = int(adapter_config["r"])
+        transformed = {
+            key: tensor
+            for prefix, slots in packed.items()
+            for key, tensor in _expand_packed_moe_lora(prefix, slots, rank=rank).items()
+        }
+        used_keys = {
+            f"{prefix}.{projection}.{lora}.weight"
+            for prefix in packed
+            for projection in ("gate_proj", "up_proj", "down_proj")
+            for lora in ("lora_A", "lora_B")
+        }
+        for key, tensor in tensors.items():
+            if key in used_keys:
+                continue
+            if key in transformed:
+                raise RuntimeError(f"Duplicate expanded Qwen3 MoE LoRA key: {key}")
+            transformed[key] = tensor
+        return transformed, _qwen3_moe_config(adapter_config)
+
     if not grouped:
         if any(_QWEN3_EXPERT_MOE_KEY_RE.match(key) for key in tensors):
             return tensors, _qwen3_moe_config(adapter_config)
diff --git a/src/art/megatron/model_support/lora_disk.py b/src/art/megatron/model_support/lora_disk.py
index 46741d50a..f0b01183b 100644
--- a/src/art/megatron/model_support/lora_disk.py
+++ b/src/art/megatron/model_support/lora_disk.py
@@ -6,14 +6,17 @@
 import torch
 
 from art.megatron.model_support.spec import ModelSupportHandler
+from art.utils.safetensors import (
+    PreparedSafetensors,
+    prepare_safetensors,
+    save_prepared_safetensors,
+)
 
 ART_LORA_FORMAT_CONFIG_KEY = "art_lora_format"
 ART_LORA_FORMAT_VLLM = "vllm"
 
 safetensors = importlib.import_module("safetensors")
-safetensors_torch = importlib.import_module("safetensors.torch")
 safe_open = safetensors.safe_open
-save_file = safetensors_torch.save_file
 
 
 def _jsonable_config(value: Any) -> Any:
@@ -78,10 +81,15 @@ def save_vllm_lora_tensors(
     lora_path: str | Path,
     tensors: dict[str, torch.Tensor],
     adapter_config: dict[str, Any],
+    *,
+    prepared_tensors: PreparedSafetensors | None = None,
 ) -> None:
     base_dir = Path(lora_path)
     base_dir.mkdir(parents=True, exist_ok=True)
-    save_file(tensors, base_dir / "adapter_model.safetensors")
+    save_prepared_safetensors(
+        prepared_tensors or prepare_safetensors(tensors),
+        base_dir / "adapter_model.safetensors",
+    )
     save_adapter_config(
         base_dir,
         {**adapter_config, ART_LORA_FORMAT_CONFIG_KEY: ART_LORA_FORMAT_VLLM},
diff --git a/src/art/megatron/model_support/registry.py b/src/art/megatron/model_support/registry.py
index 5511205a9..d9ef70ad1 100644
--- a/src/art/megatron/model_support/registry.py
+++ b/src/art/megatron/model_support/registry.py
@@ -16,6 +16,7 @@
 _GEMMA4_DENSE_HANDLER_KEY = "gemma4_dense"
 _GEMMA4_MOE_HANDLER_KEY = "gemma4_moe"
 _DSV4_HANDLER_KEY = "dsv4"
+_GLM52_HANDLER_KEY = "glm52"
 _GPT_OSS_MOE_HANDLER_KEY = "gpt_oss_moe"
 _VALIDATED_NATIVE_VLLM_LORA_STATUS: NativeVllmLoraStatus = "validated"
 _WIP_NATIVE_VLLM_LORA_STATUS: NativeVllmLoraStatus = "wip"
@@ -74,6 +75,16 @@
     "down_proj",
     "experts",
 )
+_GLM52_TARGET_MODULES = (
+    "q_a_proj",
+    "q_b_proj",
+    "kv_a_proj_with_mqa",
+    "o_proj",
+    "gate_proj",
+    "up_proj",
+    "down_proj",
+    "experts",
+)
 
 DEFAULT_DENSE_SPEC = ModelSupportSpec(
     key="default_dense",
@@ -220,6 +231,19 @@
     dependency_floor=DependencyFloor(transformers="5.12.1"),
 )
 
+GLM52_SPEC = ModelSupportSpec(
+    key="glm52",
+    handler_key=_GLM52_HANDLER_KEY,
+    is_moe=True,
+    model_names=("zai-org/GLM-5.2",),
+    default_target_modules=_GLM52_TARGET_MODULES,
+    native_vllm_lora_status=_VALIDATED_NATIVE_VLLM_LORA_STATUS,
+    dependency_floor=DependencyFloor(
+        transformers="5.12.1",
+        megatron_bridge="e1a207ac757e5d0ed94d8ffbe1cbd28e81d8c084",
+    ),
+)
+
 GPT_OSS_MOE_SPEC = ModelSupportSpec(
     key="gpt_oss_moe",
     handler_key=_GPT_OSS_MOE_HANDLER_KEY,
@@ -245,9 +269,10 @@
     GEMMA4_MOE_SPEC,
     GEMMA4_DENSE_SPEC,
     DSV4_SPEC,
+    GLM52_SPEC,
     GPT_OSS_MOE_SPEC,
 )
-PROBE_ONLY_MODEL_SUPPORT_SPECS = ()
+PROBE_ONLY_MODEL_SUPPORT_SPECS: tuple[ModelSupportSpec, ...] = ()
 _ALL_MODEL_SUPPORT_SPECS = (
     DEFAULT_DENSE_SPEC,
     *VALIDATED_MODEL_SUPPORT_SPECS,
@@ -301,6 +326,10 @@
         "art.megatron.model_support.handlers.dsv4",
         "DSV4_HANDLER",
     ),
+    _GLM52_HANDLER_KEY: (
+        "art.megatron.model_support.handlers.glm52",
+        "GLM52_HANDLER",
+    ),
     _GPT_OSS_MOE_HANDLER_KEY: (
         "art.megatron.model_support.handlers.gpt_oss",
         "GPT_OSS_MOE_HANDLER",
@@ -340,6 +369,7 @@
 GEMMA4_MOE_MODELS = frozenset(GEMMA4_MOE_SPEC.model_names)
 GEMMA4_DENSE_MODELS = frozenset(GEMMA4_DENSE_SPEC.model_names)
 DSV4_MODELS = frozenset(DSV4_SPEC.model_names)
+GLM52_MODELS = frozenset(GLM52_SPEC.model_names)
 GPT_OSS_MOE_MODELS = frozenset(GPT_OSS_MOE_SPEC.model_names)
 
 
@@ -364,6 +394,13 @@ def get_model_support_spec(
     )
 
 
+def get_model_support_spec_by_key(key: str) -> ModelSupportSpec:
+    try:
+        return _SPECS_BY_KEY[key]
+    except KeyError as exc:
+        raise KeyError(f"No model support spec registered for {key!r}") from exc
+
+
 def get_model_support_handler(
     base_model: str,
     *,
@@ -447,20 +484,6 @@ def native_vllm_lora_status_for_model(
     ).native_vllm_lora_status
 
 
-def model_requires_merged_rollout(
-    base_model: str,
-    *,
-    allow_unvalidated_arch: bool = False,
-) -> bool:
-    return (
-        get_model_support_spec(
-            base_model,
-            allow_unvalidated_arch=allow_unvalidated_arch,
-        ).default_rollout_weights_mode
-        == "merged"
-    )
-
-
 def model_uses_expert_parallel(
     base_model: str,
     *,
diff --git a/src/art/megatron/model_support/spec.py b/src/art/megatron/model_support/spec.py
index f5ca3f16e..438a0481c 100644
--- a/src/art/megatron/model_support/spec.py
+++ b/src/art/megatron/model_support/spec.py
@@ -1,4 +1,13 @@
-from typing import TYPE_CHECKING, Any, Literal, Protocol, Sequence, runtime_checkable
+from contextlib import AbstractContextManager
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Literal,
+    Protocol,
+    Sequence,
+    runtime_checkable,
+)
 
 from pydantic import BaseModel, ConfigDict, Field
 
@@ -6,7 +15,6 @@
     from megatron.bridge import AutoBridge
     from megatron.bridge.models.gpt_provider import GPTModelProvider
 
-RolloutWeightsMode = Literal["lora", "merged"]
 NativeVllmLoraStatus = Literal["disabled", "wip", "validated"]
 SharedExpertCompileState = Literal[
     "none",
@@ -56,6 +64,7 @@ class PrefixTreeModelStateContext(BaseModel):
     attention_token_layout_index: Any | None = None
     attention_head_dim: int | None = None
     attention_value_head_dim: int | None = None
+    context_parallel_state: Any | None = None
 
 
 class CompileWorkaroundConfig(BaseModel):
@@ -95,7 +104,6 @@ class ModelSupportSpec(BaseModel):
     is_moe: bool = False
     model_names: tuple[str, ...] = ()
     default_target_modules: tuple[str, ...]
-    default_rollout_weights_mode: RolloutWeightsMode = "lora"
     native_vllm_lora_status: NativeVllmLoraStatus = "disabled"
     dependency_floor: DependencyFloor = Field(default_factory=DependencyFloor)
 
@@ -135,6 +143,8 @@ def patch_provider(
 
     def configure_provider_for_runtime(self, provider: "GPTModelProvider") -> None: ...
 
+    def context_parallel_workload_profile(self, provider: Any) -> Any | None: ...
+
     def default_chat_template(self) -> str | None: ...
 
     def configure_tokenizer(
@@ -144,16 +154,22 @@ def configure_tokenizer(
         internal_config: Any,
     ) -> Any: ...
 
-    def vllm_engine_args(
-        self,
-        *,
-        rollout_weights_mode: RolloutWeightsMode,
-    ) -> dict[str, object]: ...
+    def vllm_engine_args(self) -> dict[str, object]: ...
 
     def vllm_server_args(self) -> dict[str, object]: ...
 
     def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None: ...
 
+    def build_pipeline_microbatch_activator(
+        self,
+        model_chunks: Sequence[Any],
+    ) -> Callable[[Any, int], None] | None: ...
+
+    def preserve_pipeline_microbatch_activation(
+        self,
+        model_chunks: Sequence[Any],
+    ) -> AbstractContextManager[None]: ...
+
     def build_prefix_tree_model_state(
         self,
         context: PrefixTreeModelStateContext,
@@ -209,6 +225,8 @@ def to_vllm_lora_config(
         adapter_config: dict[str, Any],
     ) -> dict[str, Any]: ...
 
+    def vllm_lora_conversion_is_view_only(self) -> bool: ...
+
     def expert_packed_lora_groups(self) -> tuple[ExpertPackedLoraGroup, ...]: ...
 
     def from_vllm_lora_tensors(
diff --git a/src/art/megatron/optimizer_state.py b/src/art/megatron/optimizer_state.py
index e5df9ce6c..9ce355669 100644
--- a/src/art/megatron/optimizer_state.py
+++ b/src/art/megatron/optimizer_state.py
@@ -1,136 +1,1799 @@
 from __future__ import annotations
 
+import asyncio
+from contextlib import ExitStack, asynccontextmanager, contextmanager
+import copy
+import fcntl
+import hashlib
 import json
 import os
 from pathlib import Path
 import re
+import shutil
 import time
-from typing import Literal
+from typing import Any, AsyncIterator, Callable, Iterator, Literal, cast
+from uuid import uuid4
 
-from pydantic import BaseModel, Field, model_validator
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+import torch
 
 from ..utils.get_model_step import get_step_from_dir
 from ..utils.output_dirs import get_step_checkpoint_dir
+from .tensor_snapshot import (
+    PendingCpuSnapshot,
+    PinnedCpuSnapshotBuilder,
+    PinnedCpuSnapshotStager,
+)
+
+ALLOW_UNPAIRED_MEGATRON_RESUME_ENV = "ART_ALLOW_UNPAIRED_MEGATRON_RESUME"
+OPTIMIZER_GENERATIONS_DIR = "generations"
+OPTIMIZER_MANIFEST = "manifest.json"
+OPTIMIZER_POINTER = "committed.json"
+OPTIMIZER_POLICY_POINTER = "policy.json"
+OPTIMIZER_MODEL_LOCK = ".optimizer.lock"
+OPTIMIZER_WRITER_LOCK = ".writer.lock"
+OPTIMIZER_GENERATION_LEASE_PREFIX = ".lease-"
+OPTIMIZER_TRASH_PREFIX = ".trash-"
+OPTIMIZER_ORPHAN_GRACE_S = 3600.0
+ADAPTER_PUBLICATION_ACK = ".optimizer-published.json"
+ADAPTER_LATEST_POINTER = "latest-adapter.json"
+_ADAPTER_FILES = ("adapter_config.json", "adapter_model.safetensors")
+_GENERATION_PATTERN = r"step-\d{8,}-[0-9a-f]{32}"
+_GENERATION_RE = re.compile(f"^{_GENERATION_PATTERN}$")
+_TRASH_RE = re.compile(f"^\\.trash-({_GENERATION_PATTERN})-[0-9a-f]{{32}}$")
+_POINTER_TEMP_RE = re.compile(r"^\.committed\.json\.\d+\.[0-9a-f]{32}\.tmp$")
+_POLICY_TEMP_RE = re.compile(r"^\.policy\.json\.\d+\.[0-9a-f]{32}\.tmp$")
+_SHA256_PATTERN = r"^[0-9a-f]{64}$"
+_POINTER_UNSET = object()
+_SCHEDULE_PROVIDER_FIELDS = {
+    "batch_p2p_comm",
+    "batch_p2p_sync",
+    "finalize_model_grads_func",
+    "microbatch_group_size_per_vp_stage",
+    "overlap_p2p_comm",
+    "variable_seq_lengths",
+}
+
+
+class _OptimizerRecord(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class MegatronResumeStep(_OptimizerRecord):
+    step: int
+    latest_lora_step: int
+    optimizer_step: int | None
+    used_unpaired_override: bool = False
+    quarantined_lora_steps: tuple[int, ...] = ()
+
+
+class CheckpointFile(_OptimizerRecord):
+    name: Literal["adapter_config.json", "adapter_model.safetensors"]
+    size_bytes: int = Field(gt=0)
+
+
+class OptimizerAdapter(_OptimizerRecord):
+    identity: str = Field(min_length=1)
+    training_session_id: str = Field(min_length=1)
+    step: int = Field(ge=0)
+    generation_id: str = Field(pattern=f"^{_GENERATION_PATTERN}$")
+    files: tuple[CheckpointFile, ...]
+
+    @model_validator(mode="after")
+    def _validate_files(self) -> "OptimizerAdapter":
+        if _generation_step(self.generation_id) != self.step:
+            raise ValueError("adapter generation ID and policy step must match")
+        if tuple(file.name for file in self.files) != _ADAPTER_FILES:
+            raise ValueError("adapter manifest must cover every payload file once")
+        return self
+
+
+class OptimizerTopology(_OptimizerRecord):
+    world_size: int = Field(gt=0)
+    tp: int = Field(gt=0)
+    cp: int = Field(gt=0)
+    ep: int = Field(gt=0)
+    etp: int = Field(gt=0)
+    pp: int = Field(gt=0)
+    vpp: int = Field(gt=0)
+
+
+class OptimizerShard(_OptimizerRecord):
+    rank: int = Field(ge=0)
+    size_bytes: int = Field(gt=0)
+    layout_sha256: str = Field(pattern=_SHA256_PATTERN)
+
+
+class _PairedOptimizerRecord(_OptimizerRecord):
+    step: int = Field(ge=0)
+    adapter: OptimizerAdapter
+
+    @model_validator(mode="after")
+    def _validate_adapter_step(self) -> "_PairedOptimizerRecord":
+        if self.step != self.adapter.step:
+            raise ValueError("optimizer and adapter steps must match")
+        return self
+
+
+class _OptimizerGenerationRecord(_PairedOptimizerRecord):
+    generation: str = Field(pattern=f"^{_GENERATION_PATTERN}$")
+
+    @model_validator(mode="after")
+    def _validate_generation_step(self) -> "_OptimizerGenerationRecord":
+        if int(self.generation.split("-", 2)[1]) != self.step:
+            raise ValueError("optimizer generation name and step must match")
+        if self.generation != self.adapter.generation_id:
+            raise ValueError("optimizer and adapter generation IDs must match")
+        return self
+
+
+class OptimizerGenerationManifest(_OptimizerGenerationRecord):
+    format_version: Literal[3] = 3
+    runtime_sha256: str = Field(pattern=_SHA256_PATTERN)
+    topology: OptimizerTopology
+    shards: tuple[OptimizerShard, ...]
+
+
+class OptimizerGenerationPointer(_OptimizerGenerationRecord):
+    format_version: Literal[3] = 3
+
+
+class OptimizerPolicyPointer(_OptimizerRecord):
+    format_version: Literal[2] = 2
+    policy_adapter: OptimizerAdapter
+    optimizer_anchor: OptimizerGenerationPointer | None
+
+    @model_validator(mode="after")
+    def _validate_policy_alias(self) -> "OptimizerPolicyPointer":
+        if self.policy_adapter.step == 0:
+            raise ValueError("policy alias must advance beyond checkpoint 0")
+        if self.optimizer_anchor is not None and (
+            self.policy_adapter.step <= self.optimizer_anchor.step
+        ):
+            raise ValueError("policy alias must be newer than its optimizer anchor")
+        return self
+
+
+class CommittedOptimizerPolicy(_OptimizerRecord):
+    policy_adapter: OptimizerAdapter
+    state_adapter: OptimizerAdapter | None
+    optimizer_anchor: OptimizerGenerationPointer | None
+
+
+class OptimizerStateSnapshot(_OptimizerRecord):
+    model_config = ConfigDict(extra="forbid", frozen=True, arbitrary_types_allowed=True)
+
+    generation_id: str = Field(pattern=f"^{_GENERATION_PATTERN}$")
+    step: int = Field(ge=1)
+    rank: int = Field(ge=0)
+    world_size: int = Field(gt=0)
+    runtime_sha256: str = Field(pattern=_SHA256_PATTERN)
+    layout_sha256: str = Field(pattern=_SHA256_PATTERN)
+    topology: OptimizerTopology
+    state_dict: Any
+
+    @model_validator(mode="after")
+    def _validate_identity(self) -> "OptimizerStateSnapshot":
+        if _generation_step(self.generation_id) != self.step:
+            raise ValueError("optimizer snapshot generation and step must match")
+        if self.rank >= self.world_size or self.topology.world_size != self.world_size:
+            raise ValueError("optimizer snapshot rank/topology mismatch")
+        return self
+
+
+def optimizer_shard_name(rank: int, world_size: int) -> str:
+    if world_size <= 0 or rank < 0 or rank >= world_size:
+        raise ValueError(
+            f"Invalid optimizer shard rank {rank} for world size {world_size}"
+        )
+    return f"{rank + 1:02d}-of-{world_size:02d}.pt"
+
+
+def current_optimizer_topology(world_size: int) -> OptimizerTopology:
+    from megatron.core import parallel_state as ps
+
+    return OptimizerTopology(
+        world_size=world_size,
+        tp=int(ps.get_tensor_model_parallel_world_size()),
+        cp=int(ps.get_context_parallel_world_size()),
+        ep=int(ps.get_expert_model_parallel_world_size()),
+        etp=int(ps.get_expert_tensor_parallel_world_size()),
+        pp=int(ps.get_pipeline_model_parallel_world_size()),
+        vpp=int(ps.get_virtual_pipeline_model_parallel_world_size() or 1),
+    )
+
+
+def new_optimizer_generation(step: int) -> str:
+    if step < 0:
+        raise ValueError(f"Optimizer step must be non-negative, got {step}")
+    return f"step-{step:08d}-{uuid4().hex}"
+
+
+def _validate_generation_name(generation: str) -> None:
+    if _GENERATION_RE.fullmatch(generation) is None:
+        raise ValueError(f"Invalid optimizer generation name: {generation!r}")
+
+
+def optimizer_pending_generation_path(
+    optimizer_state_path: str, generation: str
+) -> Path:
+    _validate_generation_name(generation)
+    return (
+        Path(optimizer_state_path)
+        / OPTIMIZER_GENERATIONS_DIR
+        / f".pending-{generation}"
+    )
+
+
+def optimizer_generation_path(optimizer_state_path: str, generation: str) -> Path:
+    _validate_generation_name(generation)
+    return Path(optimizer_state_path) / OPTIMIZER_GENERATIONS_DIR / generation
+
+
+def _generation_lease_path(path: Path, generation: str) -> Path:
+    _validate_generation_name(generation)
+    return (
+        path
+        / OPTIMIZER_GENERATIONS_DIR
+        / f"{OPTIMIZER_GENERATION_LEASE_PREFIX}{generation}"
+    )
+
+
+def _generation_step(generation: str) -> int:
+    _validate_generation_name(generation)
+    return int(generation.split("-", 2)[1])
+
+
+def _adapter_generation_lease_path(output_dir: str | Path, generation: str) -> Path:
+    _validate_generation_name(generation)
+    return Path(output_dir).absolute() / "megatron_runtime" / "leases" / generation
+
+
+@contextmanager
+def adapter_generation_lease(adapter: OptimizerAdapter) -> Iterator[None]:
+    path = _adapter_generation_lease_path(
+        Path(adapter.identity).absolute().parent.parent,
+        adapter.generation_id,
+    )
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("a+b") as lease_file:
+        fcntl.flock(lease_file.fileno(), fcntl.LOCK_SH)
+        try:
+            yield
+        finally:
+            fcntl.flock(lease_file.fileno(), fcntl.LOCK_UN)
+
+
+@contextmanager
+def _adapter_retention_leases(
+    output_dir: str, protected_steps: set[int]
+) -> Iterator[set[int]]:
+    checkpoints = Path(output_dir) / "checkpoints"
+    with ExitStack() as leases:
+        if checkpoints.is_dir():
+            for checkpoint in checkpoints.iterdir():
+                if (
+                    not checkpoint.is_dir()
+                    or not checkpoint.name.isdigit()
+                    or (step := int(checkpoint.name)) in protected_steps
+                ):
+                    continue
+                publication = read_adapter_publication(
+                    checkpoint, step=step, verify_files=False
+                )
+                generation = (
+                    publication.generation_id
+                    if publication is not None
+                    else _initial_generation_id(checkpoint, step)
+                )
+                path = _adapter_generation_lease_path(output_dir, generation)
+                path.parent.mkdir(parents=True, exist_ok=True)
+                lease = leases.enter_context(path.open("a+b"))
+                try:
+                    fcntl.flock(lease.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+                except BlockingIOError:
+                    protected_steps.add(step)
+                else:
+                    leases.callback(fcntl.flock, lease.fileno(), fcntl.LOCK_UN)
+        yield protected_steps
+
+
+@contextmanager
+def optimizer_model_lease(optimizer_state_path: str | Path) -> Iterator[None]:
+    with _optimizer_model_lock_path(optimizer_state_path).open("a+b") as lock_file:
+        fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX)
+        try:
+            yield
+        finally:
+            fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
+
+
+@asynccontextmanager
+async def async_optimizer_model_lease(
+    optimizer_state_path: str | Path,
+) -> AsyncIterator[None]:
+    with _optimizer_model_lock_path(optimizer_state_path).open("a+b") as lock_file:
+        while True:
+            try:
+                fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+                break
+            except BlockingIOError:
+                await asyncio.sleep(0.05)
+        try:
+            yield
+        finally:
+            fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
+
+
+def _optimizer_model_lock_path(optimizer_state_path: str | Path) -> Path:
+    model_root = Path(optimizer_state_path).absolute().parent
+    model_root.mkdir(parents=True, exist_ok=True)
+    return model_root / OPTIMIZER_MODEL_LOCK
+
+
+def optimizer_shard_path(generation_path: Path, *, rank: int, world_size: int) -> Path:
+    return generation_path / optimizer_shard_name(rank, world_size)
+
+
+def _adapter_checkpoint_files(path: str | Path) -> tuple[Path, tuple[Path, ...]]:
+    adapter_path = Path(path)
+    files = tuple(adapter_path / name for name in _ADAPTER_FILES)
+    missing = [str(file) for file in files if not file.is_file()]
+    if missing:
+        raise RuntimeError(f"Adapter checkpoint is incomplete; missing {missing}")
+    return adapter_path, files
+
+
+def _adapter_file_records(path: str | Path) -> tuple[CheckpointFile, ...]:
+    _adapter_path, files = _adapter_checkpoint_files(path)
+    return tuple(
+        CheckpointFile(name=cast(Any, file.name), size_bytes=file.stat().st_size)
+        for file in files
+    )
+
+
+def _initial_generation_id(path: str | Path, step: int) -> str:
+    suffix = hashlib.sha256(str(Path(path).absolute()).encode()).hexdigest()[:32]
+    return f"step-{step:08d}-{suffix}"
+
+
+def optimizer_adapter(
+    path: str | Path,
+    step: int,
+    *,
+    training_session_id: str = "legacy",
+    generation_id: str | None = None,
+) -> OptimizerAdapter:
+    if step < 0:
+        raise ValueError(f"Adapter step must be non-negative, got {step}")
+    identity = str(Path(path).absolute())
+    return OptimizerAdapter(
+        identity=identity,
+        training_session_id=training_session_id,
+        step=step,
+        generation_id=generation_id or _initial_generation_id(identity, step),
+        files=_adapter_file_records(identity),
+    )
+
+
+def canonical_adapter_path(staging_path: str | Path, step: int) -> Path:
+    staging = Path(staging_path).absolute()
+    if (
+        staging.parent.name != "staging"
+        or staging.parent.parent.name != "megatron_runtime"
+    ):
+        raise RuntimeError(
+            "Megatron adapter publication requires the managed staging layout: "
+            f"{staging}"
+        )
+    return Path(
+        get_step_checkpoint_dir(str(staging.parent.parent.parent), step)
+    ).absolute()
+
+
+def _canonical_adapter_path(path: str | Path, step: int) -> Path:
+    candidate = Path(path).absolute()
+    if (
+        candidate.parent.name == "staging"
+        and candidate.parent.parent.name == "megatron_runtime"
+    ):
+        return canonical_adapter_path(candidate, step)
+    return candidate
+
+
+def publish_adapter_checkpoint(
+    staging_path: str | Path,
+    *,
+    step: int,
+    training_session_id: str = "legacy",
+    generation_id: str | None = None,
+) -> OptimizerAdapter:
+    staging = Path(staging_path).absolute()
+    canonical = canonical_adapter_path(staging, step)
+    if canonical.exists():
+        raise RuntimeError(f"Refusing to replace canonical adapter {canonical}")
+    _, files = _adapter_checkpoint_files(staging)
+    for path in files:
+        with path.open("rb") as adapter_file:
+            os.fsync(adapter_file.fileno())
+    _fsync_directory(staging)
+    adapter = OptimizerAdapter(
+        identity=str(canonical),
+        training_session_id=training_session_id,
+        step=step,
+        generation_id=generation_id or _initial_generation_id(canonical, step),
+        files=_adapter_file_records(staging),
+    )
+    _write_model_atomic(staging / ADAPTER_PUBLICATION_ACK, adapter)
+    canonical.parent.mkdir(parents=True, exist_ok=True)
+    os.replace(staging, canonical)
+    _fsync_directory(canonical.parent)
+    _write_model_atomic(
+        canonical.parent.parent / "megatron_runtime" / ADAPTER_LATEST_POINTER,
+        adapter,
+    )
+    return adapter
+
+
+def read_latest_adapter_pointer(output_dir: str | Path) -> OptimizerAdapter | None:
+    pointer = Path(output_dir) / "megatron_runtime" / ADAPTER_LATEST_POINTER
+    if not pointer.exists():
+        return None
+    try:
+        adapter = OptimizerAdapter.model_validate_json(pointer.read_text("utf-8"))
+    except Exception as error:
+        raise RuntimeError(f"Invalid adapter generation pointer: {pointer}") from error
+    _validate_adapter_publication(adapter, verify_files=True)
+    return adapter
+
+
+def read_adapter_publication(
+    adapter_path: str | Path,
+    *,
+    step: int,
+    verify_files: bool = True,
+) -> OptimizerAdapter | None:
+    canonical = _canonical_adapter_path(adapter_path, step)
+    acknowledgment = canonical / ADAPTER_PUBLICATION_ACK
+    try:
+        payload = acknowledgment.read_text("utf-8")
+    except FileNotFoundError:
+        return None
+    try:
+        adapter = OptimizerAdapter.model_validate_json(payload)
+    except Exception as exc:
+        raise RuntimeError(
+            f"Invalid adapter publication acknowledgment: {acknowledgment}"
+        ) from exc
+    expected_identity = str(canonical)
+    if (
+        adapter.identity != expected_identity
+        or adapter.step != step
+        or "staging" in Path(adapter.identity).parts
+    ):
+        raise RuntimeError(
+            "Adapter publication acknowledgment does not identify the canonical "
+            f"adapter: acknowledged={adapter.model_dump()}, "
+            f"expected_identity={expected_identity!r}, expected_step={step}"
+        )
+    if verify_files:
+        current_files = _adapter_file_records(canonical)
+        if adapter.files != current_files:
+            raise RuntimeError(
+                "Adapter publication acknowledgment does not match canonical "
+                f"file coverage and sizes: acknowledged={adapter.files}, "
+                f"current={current_files}"
+            )
+    return adapter
+
+
+def _validate_adapter_publication(
+    adapter: OptimizerAdapter, *, verify_files: bool = False
+) -> None:
+    if "staging" in Path(adapter.identity).parts:
+        raise RuntimeError(
+            f"Optimizer pointers cannot reference a staging adapter: {adapter.identity}"
+        )
+    if (
+        read_adapter_publication(
+            adapter.identity,
+            step=adapter.step,
+            verify_files=verify_files,
+        )
+        != adapter
+    ):
+        raise RuntimeError(
+            f"Optimizer adapter publication is not acknowledged: {adapter.model_dump()}"
+        )
+
+
+def _fsync_directory(path: Path) -> None:
+    directory_fd = os.open(path, os.O_RDONLY | os.O_DIRECTORY)
+    try:
+        os.fsync(directory_fd)
+    finally:
+        os.close(directory_fd)
+
+
+def _write_model_atomic(path: Path, model: BaseModel) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    temporary = path.with_name(f".{path.name}.{os.getpid()}.{uuid4().hex}.tmp")
+    try:
+        with temporary.open("w", encoding="utf-8") as output:
+            output.write(json.dumps(model.model_dump(mode="json"), sort_keys=True))
+            output.write("\n")
+            output.flush()
+            os.fsync(output.fileno())
+        os.replace(temporary, path)
+        _fsync_directory(path.parent)
+    finally:
+        temporary.unlink(missing_ok=True)
+
+
+def _read_pointer(path: Path) -> OptimizerGenerationPointer | None:
+    pointer_path = path / OPTIMIZER_POINTER
+    if pointer_path.is_file():
+        try:
+            return OptimizerGenerationPointer.model_validate_json(
+                pointer_path.read_text("utf-8")
+            )
+        except Exception as exc:
+            raise RuntimeError(
+                f"Invalid optimizer generation pointer: {pointer_path}"
+            ) from exc
+    if pointer_path.exists():
+        raise RuntimeError(
+            f"Invalid optimizer generation pointer: {pointer_path} is not a file"
+        )
+    if not path.exists():
+        return None
+    legacy = sorted(
+        entry.name
+        for entry in path.iterdir()
+        if entry.is_file()
+        and (
+            entry.name == OPTIMIZER_MANIFEST
+            or entry.name.isdigit()
+            or (entry.name.endswith(".pt") and "-of-" in entry.name)
+        )
+    )
+    if legacy:
+        raise RuntimeError(
+            "Legacy optimizer checkpoint format is unsupported; expected an atomic "
+            f"{OPTIMIZER_POINTER} pointer, found {legacy} in {path}"
+        )
+    return None
+
+
+def _read_policy_pointer(path: Path) -> OptimizerPolicyPointer | None:
+    policy_path = path / OPTIMIZER_POLICY_POINTER
+    if policy_path.is_file():
+        try:
+            return OptimizerPolicyPointer.model_validate_json(
+                policy_path.read_text("utf-8")
+            )
+        except Exception as exc:
+            raise RuntimeError(
+                f"Invalid optimizer policy pointer: {policy_path}"
+            ) from exc
+    if policy_path.exists():
+        raise RuntimeError(
+            f"Invalid optimizer policy pointer: {policy_path} is not a file"
+        )
+    return None
+
+
+def _resolve_policy_pointer(
+    path: Path,
+    pointer: OptimizerGenerationPointer | None,
+) -> OptimizerPolicyPointer | None:
+    policy = _read_policy_pointer(path)
+    if policy is None:
+        return None
+    if policy.optimizer_anchor != pointer:
+        if pointer is not None and pointer.step >= policy.policy_adapter.step:
+            return None
+        raise RuntimeError(
+            "Optimizer policy pointer lost or changed its optimizer anchor: "
+            f"policy={policy.model_dump()}, "
+            f"current={pointer.model_dump() if pointer else None}"
+        )
+    _validate_adapter_publication(policy.policy_adapter, verify_files=True)
+    if pointer is not None and any(
+        not os.path.samefile(
+            Path(policy.policy_adapter.identity) / name,
+            Path(pointer.adapter.identity) / name,
+        )
+        for name in _ADAPTER_FILES
+    ):
+        raise RuntimeError("Optimizer policy alias does not reuse its anchor payload")
+    expected = Path(
+        get_step_checkpoint_dir(str(path.absolute().parent), policy.policy_adapter.step)
+    ).absolute()
+    if policy.policy_adapter.identity != str(expected):
+        raise RuntimeError("Optimizer policy does not identify a canonical checkpoint")
+    return policy
+
+
+def _committed_policy(
+    path: Path,
+    pointer: OptimizerGenerationPointer | None,
+    *,
+    initial_adapter_path: str,
+) -> CommittedOptimizerPolicy:
+    if policy := _resolve_policy_pointer(path, pointer):
+        return CommittedOptimizerPolicy(
+            policy_adapter=policy.policy_adapter,
+            state_adapter=None if pointer is None else pointer.adapter,
+            optimizer_anchor=pointer,
+        )
+    if pointer is not None:
+        return CommittedOptimizerPolicy(
+            policy_adapter=pointer.adapter,
+            state_adapter=pointer.adapter,
+            optimizer_anchor=pointer,
+        )
+    if (
+        Path(initial_adapter_path).absolute()
+        != Path(get_step_checkpoint_dir(str(path.absolute().parent), 0)).absolute()
+    ):
+        raise RuntimeError("Initial optimizer policy must use canonical checkpoint 0")
+    initial = optimizer_adapter(initial_adapter_path, 0)
+    return CommittedOptimizerPolicy(
+        policy_adapter=initial,
+        state_adapter=None,
+        optimizer_anchor=None,
+    )
+
+
+def resolve_committed_optimizer_policy(
+    optimizer_state_path: str,
+    *,
+    initial_adapter_path: str,
+) -> CommittedOptimizerPolicy:
+    path = Path(optimizer_state_path)
+    with _committed_generation_lease(path) as pointer:
+        if pointer is not None:
+            generation_path = optimizer_generation_path(
+                optimizer_state_path, pointer.generation
+            )
+            manifest = _read_manifest(generation_path)
+            _validate_pointer_manifest(pointer, manifest)
+            _validate_generation_files(generation_path, manifest, local_rank=None)
+            _validate_adapter_publication(pointer.adapter, verify_files=True)
+        return _committed_policy(
+            path,
+            pointer,
+            initial_adapter_path=initial_adapter_path,
+        )
+
+
+def commit_optimizer_policy_advance(
+    optimizer_state_path: str,
+    *,
+    initial_adapter_path: str,
+    expected_step: int,
+    adapter: OptimizerAdapter,
+) -> OptimizerPolicyPointer:
+    path = Path(optimizer_state_path)
+    with _writer_lease(path) as pointer:
+        current = _committed_policy(
+            path,
+            pointer,
+            initial_adapter_path=initial_adapter_path,
+        )
+        if current.policy_adapter.step != expected_step:
+            raise RuntimeError(
+                "Stale no-op policy writer: "
+                f"expected={expected_step}, current={current.policy_adapter.step}"
+            )
+        if adapter.step != expected_step + 1:
+            raise RuntimeError("Policy checkpoint must advance exactly one step")
+        if any(
+            not os.path.samefile(
+                Path(current.policy_adapter.identity) / name,
+                Path(adapter.identity) / name,
+            )
+            for name in _ADAPTER_FILES
+        ):
+            raise RuntimeError(
+                "No-op policy checkpoint must reuse immutable adapter payloads"
+            )
+        _validate_adapter_publication(adapter, verify_files=True)
+        policy = OptimizerPolicyPointer(
+            policy_adapter=adapter,
+            optimizer_anchor=pointer,
+        )
+        _write_model_atomic(path / OPTIMIZER_POLICY_POINTER, policy)
+        return policy
+
+
+def read_committed_optimizer_pointer(
+    optimizer_state_path: str,
+) -> OptimizerGenerationPointer | None:
+    return _read_pointer(Path(optimizer_state_path))
+
+
+def read_committed_optimizer_step(optimizer_state_path: str) -> int | None:
+    pointer = read_committed_optimizer_pointer(optimizer_state_path)
+    return None if pointer is None else pointer.step
+
+
+def read_committed_optimizer_adapter_step(optimizer_state_path: str) -> int | None:
+    pointer = read_committed_optimizer_pointer(optimizer_state_path)
+    return None if pointer is None else pointer.adapter.step
+
+
+def _read_manifest(generation_path: Path) -> OptimizerGenerationManifest:
+    manifest_path = generation_path / OPTIMIZER_MANIFEST
+    try:
+        return OptimizerGenerationManifest.model_validate_json(
+            manifest_path.read_text("utf-8")
+        )
+    except Exception as exc:
+        raise RuntimeError(
+            f"Invalid optimizer generation manifest: {manifest_path}"
+        ) from exc
+
+
+def _ordered_manifest_shards(
+    manifest: OptimizerGenerationManifest,
+) -> tuple[OptimizerShard, ...]:
+    topology = manifest.topology
+    ordered = tuple(sorted(manifest.shards, key=lambda shard: shard.rank))
+    expected_ranks = tuple(range(topology.world_size))
+    actual_ranks = tuple(shard.rank for shard in ordered)
+    if actual_ranks != expected_ranks:
+        raise RuntimeError(
+            "Optimizer manifest shard coverage mismatch: "
+            f"expected_ranks={expected_ranks}, actual_ranks={actual_ranks}"
+        )
+    return ordered
+
+
+def build_optimizer_manifest(
+    *,
+    generation: str,
+    step: int,
+    adapter: OptimizerAdapter,
+    runtime_sha256: str,
+    world_size: int,
+    shards: list[OptimizerShard],
+    topology: OptimizerTopology | None = None,
+) -> OptimizerGenerationManifest:
+    manifest = OptimizerGenerationManifest(
+        generation=generation,
+        step=step,
+        adapter=adapter,
+        runtime_sha256=runtime_sha256,
+        topology=topology or current_optimizer_topology(world_size),
+        shards=tuple(shards),
+    )
+    _ordered_manifest_shards(manifest)
+    return manifest
+
+
+def _validate_pointer_manifest(
+    pointer: OptimizerGenerationPointer,
+    manifest: OptimizerGenerationManifest,
+) -> None:
+    if (
+        manifest.generation,
+        manifest.step,
+        manifest.adapter,
+    ) != (pointer.generation, pointer.step, pointer.adapter):
+        raise RuntimeError(
+            "Optimizer pointer/manifest identity mismatch: "
+            f"pointer={pointer.model_dump()}, manifest={manifest.model_dump()}"
+        )
+
+
+def _validate_generation_files(
+    generation_path: Path,
+    manifest: OptimizerGenerationManifest,
+    *,
+    local_rank: int | None,
+) -> tuple[OptimizerShard, ...]:
+    ordered = _ordered_manifest_shards(manifest)
+    names = tuple(
+        optimizer_shard_name(shard.rank, manifest.topology.world_size)
+        for shard in ordered
+    )
+    expected_entries = tuple(sorted((OPTIMIZER_MANIFEST, *names)))
+    if not generation_path.is_dir():
+        raise RuntimeError(
+            f"Optimizer generation directory is missing: {generation_path}"
+        )
+    actual_entries = tuple(sorted(entry.name for entry in generation_path.iterdir()))
+    if actual_entries != expected_entries:
+        raise RuntimeError(
+            "Optimizer generation shard coverage mismatch: "
+            f"expected={expected_entries}, actual={actual_entries}"
+        )
+    for shard in ordered:
+        name = optimizer_shard_name(shard.rank, manifest.topology.world_size)
+        actual_size = (generation_path / name).stat().st_size
+        if actual_size != shard.size_bytes:
+            raise RuntimeError(
+                f"Optimizer shard size mismatch for {name}: "
+                f"expected={shard.size_bytes}, actual={actual_size}"
+            )
+    if local_rank is not None:
+        if local_rank < 0 or local_rank >= len(ordered):
+            raise RuntimeError(
+                f"Invalid local optimizer rank {local_rank} for {len(ordered)} shards"
+            )
+    return ordered
+
+
+@contextmanager
+def _root_lease(
+    path: Path, operation: int
+) -> Iterator[OptimizerGenerationPointer | None]:
+    path.mkdir(parents=True, exist_ok=True)
+    with (path / OPTIMIZER_WRITER_LOCK).open("a+b") as lock_file:
+        fcntl.flock(lock_file.fileno(), operation)
+        try:
+            yield _read_pointer(path)
+        finally:
+            fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
+
+
+@contextmanager
+def _writer_lease(path: Path) -> Iterator[OptimizerGenerationPointer | None]:
+    with _root_lease(path, fcntl.LOCK_EX) as pointer:
+        yield _recover_optimizer_pointer_locked(path, pointer)
+
+
+@contextmanager
+def _generation_lease(
+    path: Path,
+    generation: str,
+    *,
+    exclusive: bool,
+    nonblocking: bool = False,
+) -> Iterator[bool]:
+    lease_path = _generation_lease_path(path, generation)
+    lease_path.parent.mkdir(parents=True, exist_ok=True)
+    with lease_path.open("a+b") as lease_file:
+        operation = fcntl.LOCK_EX if exclusive else fcntl.LOCK_SH
+        if nonblocking:
+            operation |= fcntl.LOCK_NB
+        try:
+            fcntl.flock(lease_file.fileno(), operation)
+        except BlockingIOError:
+            yield False
+            return
+        try:
+            yield True
+        finally:
+            fcntl.flock(lease_file.fileno(), fcntl.LOCK_UN)
+
+
+@contextmanager
+def _committed_generation_lease(
+    path: Path,
+) -> Iterator[OptimizerGenerationPointer | None]:
+    stack = ExitStack()
+    with _root_lease(path, fcntl.LOCK_SH) as pointer:
+        if pointer is not None and not stack.enter_context(
+            _generation_lease(path, pointer.generation, exclusive=False)
+        ):
+            raise RuntimeError(
+                f"Could not lease optimizer generation {pointer.generation}"
+            )
+    try:
+        yield pointer
+    finally:
+        stack.close()
+
+
+def commit_optimizer_generation(
+    optimizer_state_path: str,
+    manifest: OptimizerGenerationManifest,
+    *,
+    expected_pointer: OptimizerGenerationPointer | None,
+    expected_policy_step: int | None = None,
+    initial_adapter_path: str | None = None,
+) -> Path:
+    path = Path(optimizer_state_path)
+    pending = optimizer_pending_generation_path(
+        optimizer_state_path, manifest.generation
+    )
+    committed = optimizer_generation_path(optimizer_state_path, manifest.generation)
+    _write_model_atomic(pending / OPTIMIZER_MANIFEST, manifest)
+    _validate_generation_files(pending, manifest, local_rank=None)
+    with _writer_lease(path) as current_pointer:
+        if current_pointer != expected_pointer:
+            raise RuntimeError(
+                "Stale optimizer writer: committed pointer changed before publication; "
+                f"expected={expected_pointer.model_dump() if expected_pointer else None}, "
+                f"current={current_pointer.model_dump() if current_pointer else None}"
+            )
+        if expected_policy_step is not None:
+            if initial_adapter_path is None:
+                raise ValueError("initial_adapter_path is required for lineage checks")
+            current_policy = _committed_policy(
+                path,
+                current_pointer,
+                initial_adapter_path=initial_adapter_path,
+            )
+            if current_policy.policy_adapter.step != expected_policy_step:
+                raise RuntimeError(
+                    "Stale optimizer writer: policy lineage changed before publication; "
+                    f"expected={expected_policy_step}, "
+                    f"current={current_policy.policy_adapter.step}"
+                )
+        if current_pointer is not None and manifest.step <= current_pointer.step:
+            raise RuntimeError(
+                "Optimizer generation step must advance monotonically: "
+                f"current={current_pointer.step}, attempted={manifest.step}"
+            )
+        _validate_adapter_publication(manifest.adapter)
+        if committed.exists():
+            raise RuntimeError(f"Optimizer generation already exists: {committed}")
+        os.replace(pending, committed)
+        _fsync_directory(committed.parent)
+        pointer = OptimizerGenerationPointer(
+            generation=manifest.generation,
+            step=manifest.step,
+            adapter=manifest.adapter,
+        )
+        _write_model_atomic(path / OPTIMIZER_POINTER, pointer)
+        policy_path = path / OPTIMIZER_POLICY_POINTER
+        if policy_path.exists():
+            policy_path.unlink()
+            _fsync_directory(path)
+    return committed
+
+
+def _prune_optimizer_generations_locked(
+    optimizer_state_path: str,
+    *,
+    retain_adapter_steps: set[int],
+    orphan_grace_s: float = OPTIMIZER_ORPHAN_GRACE_S,
+) -> set[int]:
+    """Reclaim unretained generations and return adapter steps still in use."""
+    if orphan_grace_s < 0:
+        raise ValueError("orphan_grace_s must be non-negative")
+    path = Path(optimizer_state_path)
+    generations = path / OPTIMIZER_GENERATIONS_DIR
+    if not path.exists():
+        return set()
+
+    protected_steps: set[int] = set()
+    trash: list[Path] = []
+    now = time.time()
+    with _writer_lease(path) as pointer:
+        pointer_temps, candidates = _scan_optimizer_transactions(path)
+        policy = _resolve_policy_pointer(path, pointer)
+        if policy is not None:
+            protected_steps.add(policy.policy_adapter.step)
+        if not generations.exists():
+            if pointer is not None:
+                raise RuntimeError(
+                    "Optimizer pointer exists without a generations directory: "
+                    f"{generations}"
+                )
+            if pointer_temps:
+                raise RuntimeError(
+                    "Interrupted optimizer pointer has no committed generation"
+                )
+            return protected_steps
+        if not generations.is_dir():
+            raise RuntimeError(
+                f"Optimizer generations path is not a directory: {generations}"
+            )
+
+        if len(pointer_temps) > 1:
+            raise RuntimeError(
+                "Cannot collect optimizer generations with multiple interrupted "
+                "pointers"
+            )
+        records: list[tuple[Path, str, int, bool, bool]] = []
+        manifests: dict[str, OptimizerGenerationManifest] = {}
+        current_found = False
+        for entry, generation, pending in candidates:
+            step = _generation_step(generation)
+            manifest = None if pending else _read_manifest(entry)
+            if manifest is not None and (
+                manifest.generation != generation or manifest.step != step
+            ):
+                raise RuntimeError(
+                    f"Optimizer generation directory/manifest mismatch: {entry}"
+                )
+            if manifest is not None:
+                manifests[generation] = manifest
+            adapter_step = step if manifest is None else manifest.adapter.step
+            current = pointer is not None and pointer.generation == generation
+            young = now - entry.stat().st_mtime < orphan_grace_s
+            if current:
+                assert manifest is not None
+                _validate_pointer_manifest(pointer, manifest)
+                _validate_adapter_publication(pointer.adapter)
+                current_found = True
+            records.append((entry, generation, adapter_step, current, young))
+
+        if pointer is not None and not current_found:
+            raise RuntimeError(
+                f"Optimizer pointer generation is missing: {pointer.generation}"
+            )
+        interrupted_generation: str | None = None
+        if pointer_temps:
+            temporary_pointer = pointer_temps[0][1]
+            interrupted_generation = temporary_pointer.generation
+            manifest = manifests.get(interrupted_generation)
+            if manifest is None:
+                raise RuntimeError(
+                    "Interrupted optimizer pointer has no committed generation"
+                )
+            _validate_pointer_manifest(temporary_pointer, manifest)
+            if pointer is not None and temporary_pointer.step <= pointer.step:
+                raise RuntimeError(
+                    "Interrupted optimizer pointer does not advance the committed "
+                    "generation"
+                )
+        trash.extend(
+            entry
+            for entry in generations.iterdir()
+            if entry.name.startswith(OPTIMIZER_TRASH_PREFIX)
+            and now - entry.stat().st_mtime >= orphan_grace_s
+        )
+
+        for entry, generation, adapter_step, current, young in records:
+            if current or adapter_step in retain_adapter_steps or young:
+                protected_steps.add(adapter_step)
+                continue
+
+            with _generation_lease(
+                path,
+                generation,
+                exclusive=True,
+                nonblocking=True,
+            ) as acquired:
+                if not acquired:
+                    protected_steps.add(adapter_step)
+                    continue
+                destination = generations / (
+                    f"{OPTIMIZER_TRASH_PREFIX}{generation}-{uuid4().hex}"
+                )
+                if interrupted_generation == generation:
+                    pointer_temps[0][0].unlink()
+                    _fsync_directory(path)
+                os.replace(entry, destination)
+                os.utime(destination)
+                trash.append(destination)
+            _generation_lease_path(path, generation).unlink(missing_ok=True)
+
+        live = {generation for entry, generation, *_ in records if entry.exists()}
+        for lease in generations.iterdir():
+            if not lease.name.startswith(OPTIMIZER_GENERATION_LEASE_PREFIX):
+                continue
+            generation = lease.name.removeprefix(OPTIMIZER_GENERATION_LEASE_PREFIX)
+            _validate_generation_name(generation)
+            if generation in live:
+                continue
+            with _generation_lease(
+                path,
+                generation,
+                exclusive=True,
+                nonblocking=True,
+            ) as acquired:
+                if acquired:
+                    lease.unlink()
+        if trash:
+            _fsync_directory(generations)
+
+    for entry in trash:
+        shutil.rmtree(entry)
+    return protected_steps
+
+
+def prune_optimizer_generations(
+    optimizer_state_path: str,
+    *,
+    retain_adapter_steps: set[int],
+    orphan_grace_s: float = OPTIMIZER_ORPHAN_GRACE_S,
+) -> set[int]:
+    with optimizer_model_lease(optimizer_state_path):
+        return _prune_optimizer_generations_locked(
+            optimizer_state_path,
+            retain_adapter_steps=retain_adapter_steps,
+            orphan_grace_s=orphan_grace_s,
+        )
+
+
+@contextmanager
+def optimizer_retention_lease(
+    output_dir: str, retain_adapter_steps: set[int]
+) -> Iterator[set[int]]:
+    paths = tuple(
+        f"{output_dir}/optimizer_states_{job_type}" for job_type in ("rl", "sft")
+    )
+    with optimizer_model_lease(paths[0]):
+        protected = set(retain_adapter_steps)
+        for path in paths:
+            protected.update(
+                _prune_optimizer_generations_locked(
+                    path, retain_adapter_steps=protected
+                )
+            )
+        with _adapter_retention_leases(output_dir, protected):
+            yield protected
+
+
+def _validate_generation(
+    optimizer_state_path: str,
+    pointer: OptimizerGenerationPointer,
+    world_size: int,
+    local_rank: int | None,
+) -> tuple[Path, OptimizerGenerationManifest, tuple[OptimizerShard, ...]]:
+    path = optimizer_generation_path(optimizer_state_path, pointer.generation)
+    manifest = _read_manifest(path)
+    _validate_pointer_manifest(pointer, manifest)
+    current = current_optimizer_topology(world_size)
+    if manifest.topology != current:
+        raise RuntimeError(
+            "Optimizer checkpoint topology mismatch; optimizer state is topology-strict: "
+            f"saved={manifest.topology.model_dump()} current={current.model_dump()}"
+        )
+    return (
+        path,
+        manifest,
+        _validate_generation_files(path, manifest, local_rank=local_rank),
+    )
+
+
+def pin_optimizer_generation(
+    optimizer_state_path: str,
+    *,
+    world_size: int,
+    runtime_sha256: str,
+    layout_sha256_by_rank: tuple[str, ...],
+    adapter: OptimizerAdapter,
+    pointer: OptimizerGenerationPointer | None | object = _POINTER_UNSET,
+    verify_adapter_files: bool = True,
+) -> OptimizerGenerationPointer | None:
+    if pointer is _POINTER_UNSET:
+        pointer = read_committed_optimizer_pointer(optimizer_state_path)
+    if pointer is None:
+        return None
+    pointer = cast(OptimizerGenerationPointer, pointer)
+    _, manifest, ordered = _validate_generation(
+        optimizer_state_path, pointer, world_size, None
+    )
+    if manifest.runtime_sha256 != runtime_sha256:
+        raise RuntimeError(
+            "Optimizer checkpoint model-runtime mismatch: "
+            f"saved={manifest.runtime_sha256}, current={runtime_sha256}"
+        )
+    if pointer.adapter != adapter:
+        raise RuntimeError(
+            "Optimizer checkpoint adapter mismatch: "
+            f"saved={pointer.adapter.model_dump()}, current={adapter.model_dump()}"
+        )
+    _validate_adapter_publication(pointer.adapter, verify_files=verify_adapter_files)
+    saved_layouts = tuple(shard.layout_sha256 for shard in ordered)
+    if saved_layouts != layout_sha256_by_rank:
+        raise RuntimeError(
+            "Optimizer parameter ownership/layout mismatch: "
+            f"saved={saved_layouts}, current={layout_sha256_by_rank}"
+        )
+    return pointer
 
-ALLOW_UNPAIRED_MEGATRON_RESUME_ENV = "ART_ALLOW_UNPAIRED_MEGATRON_RESUME"
-OPTIMIZER_MANIFEST = "CURRENT.json"
-_GENERATION_SHARD_RE = re.compile(
-    r"^step-(?P<step>\d+)-(?P<rank>\d+)-of-(?P<world>\d+)\.pt$"
-)
+
+def resolve_optimizer_shard(
+    optimizer_state_path: str,
+    *,
+    rank: int,
+    world_size: int,
+    pointer: OptimizerGenerationPointer | None = None,
+) -> Path | None:
+    pointer = pointer or read_committed_optimizer_pointer(optimizer_state_path)
+    if pointer is None:
+        return None
+    generation_path, _, ordered = _validate_generation(
+        optimizer_state_path, pointer, world_size, rank
+    )
+    return generation_path / optimizer_shard_name(ordered[rank].rank, world_size)
 
 
-class OptimizerCommit(BaseModel):
-    schema_version: Literal[1] = 1
-    step: int = Field(ge=0)
-    world_size: int = Field(ge=1)
-    files: tuple[str, ...]
+def _type_identity(value: object) -> str:
+    value_type = value if isinstance(value, type) else type(value)
+    return f"{value_type.__module__}.{value_type.__qualname__}"
 
-    @model_validator(mode="after")
-    def validate_files(self) -> "OptimizerCommit":
-        if self.files != optimizer_generation_files(self.step, self.world_size):
-            raise ValueError(
-                "optimizer manifest files do not match its step/world size"
+
+def _runtime_json_default(value: Any) -> Any:
+    if isinstance(value, torch.dtype):
+        return str(value)
+    if isinstance(value, torch.Tensor):
+        return {"shape": list(value.shape), "dtype": str(value.dtype)}
+    if isinstance(value, BaseModel):
+        return value.model_dump(mode="json")
+    if isinstance(value, set):
+        return sorted(value, key=repr)
+    if callable(value):
+        module = getattr(value, "__module__", "")
+        name = getattr(value, "__qualname__", type(value).__qualname__)
+        return f"{module}.{name}"
+    return _type_identity(value)
+
+
+def _canonical_runtime_json(value: Any) -> Any:
+    if isinstance(value, dict):
+        keys = tuple(value)
+        try:
+            supported = all(
+                key is None or isinstance(key, (str, int, float, bool)) for key in keys
             )
-        return self
+            if supported:
+                sorted(keys)
+        except TypeError:
+            supported = False
+        if supported:
+            return {key: _canonical_runtime_json(item) for key, item in value.items()}
+        return [
+            "__art_typed_mapping__",
+            [
+                [_type_identity(key), repr(key), _canonical_runtime_json(item)]
+                for key, item in sorted(
+                    value.items(),
+                    key=lambda pair: (_type_identity(pair[0]), repr(pair[0])),
+                )
+            ],
+        ]
+    if isinstance(value, (list, tuple)):
+        return [_canonical_runtime_json(item) for item in value]
+    if isinstance(value, set):
+        return sorted(
+            (_canonical_runtime_json(item) for item in value),
+            key=repr,
+        )
+    if isinstance(value, BaseModel):
+        return _canonical_runtime_json(value.model_dump(mode="json"))
+    return value
 
 
-class MegatronResumeStep(BaseModel):
-    step: int
-    latest_lora_step: int
-    optimizer_step: int | None
-    used_unpaired_override: bool = False
-    quarantined_lora_steps: tuple[int, ...] = ()
+def _json_sha256(value: Any) -> str:
+    encoded = json.dumps(
+        _canonical_runtime_json(value),
+        default=_runtime_json_default,
+        sort_keys=True,
+        separators=(",", ":"),
+    ).encode()
+    return hashlib.sha256(encoded).hexdigest()
 
 
-def optimizer_generation_files(step: int, world_size: int) -> tuple[str, ...]:
-    return tuple(
-        f"step-{step:08d}-{rank:02d}-of-{world_size:02d}.pt"
-        for rank in range(1, world_size + 1)
+def _public_fields(value: object, *, exclude: set[str] | None = None) -> dict[str, Any]:
+    exclude = exclude or set()
+    return {
+        key: item
+        for key, item in sorted(vars(value).items())
+        if not key.startswith("_") and key not in exclude
+    }
+
+
+def _model_runtime_sha256(runtime: Any) -> str:
+    if runtime.optimizer_runtime_sha256 is not None:
+        return runtime.optimizer_runtime_sha256
+    runtime.optimizer_runtime_sha256 = _json_sha256(
+        {
+            "model_support": runtime.model_support_spec,
+            "provider": {
+                "type": _type_identity(runtime.provider),
+                "fields": _public_fields(
+                    runtime.provider,
+                    exclude=_SCHEDULE_PROVIDER_FIELDS,
+                ),
+            },
+            "optimizer": _type_identity(runtime.optimizer),
+            "optimizer_config": _public_fields(runtime.optimizer_config),
+            "compile": runtime.transformer_layers_compiled,
+            "topology": current_optimizer_topology(runtime.world_size),
+            "torch": torch.__version__,
+        }
     )
+    return runtime.optimizer_runtime_sha256
 
 
-def read_optimizer_commit(optimizer_state_path: str) -> OptimizerCommit | None:
-    path = Path(optimizer_state_path)
-    manifest_path = path / OPTIMIZER_MANIFEST
-    if not manifest_path.exists():
-        return None
-    commit = OptimizerCommit.model_validate_json(manifest_path.read_text())
-    missing = [name for name in commit.files if not (path / name).is_file()]
-    if missing:
+def _optimizer_layout_sha256(runtime: Any) -> str:
+    names_by_parameter: dict[int, list[str]] = {}
+    for chunk_index, chunk in enumerate(runtime.model):
+        for name, parameter in chunk.named_parameters(remove_duplicate=False):
+            qualified = f"chunk.{chunk_index}.{name}"
+            names_by_parameter.setdefault(id(parameter), []).append(qualified)
+            main_parameter = getattr(parameter, "main_param", None)
+            if main_parameter is not None:
+                names_by_parameter.setdefault(id(main_parameter), []).append(qualified)
+
+    groups = []
+    for group_index, group in enumerate(runtime.optimizer.param_groups):
+        parameters = []
+        for group_order, parameter in enumerate(group["params"]):
+            names = tuple(sorted(set(names_by_parameter.get(id(parameter), ()))))
+            if not names:
+                raise RuntimeError(
+                    "Optimizer parameter is not owned by a model chunk: "
+                    f"group={group_index}, order={group_order}, "
+                    f"shape={tuple(parameter.shape)}"
+                )
+            parameters.append(
+                {
+                    "names": names,
+                    "shape": tuple(parameter.shape),
+                    "dtype": str(parameter.dtype),
+                    "requires_grad": bool(parameter.requires_grad),
+                }
+            )
+        groups.append(parameters)
+    return _json_sha256(groups)
+
+
+def _distributed_rank(runtime: Any) -> int:
+    if torch.distributed.is_initialized():  # ty:ignore[possibly-missing-attribute]
+        return int(torch.distributed.get_rank())  # ty:ignore[possibly-missing-attribute]
+    if (runtime.rank, runtime.world_size) != (0, 1):
+        raise RuntimeError(
+            "Multi-rank optimizer durability requires an initialized process group: "
+            f"rank={runtime.rank}, world_size={runtime.world_size}"
+        )
+    return 0
+
+
+def _all_gather_objects(runtime: Any, value: Any) -> list[Any]:
+    if not torch.distributed.is_initialized():  # ty:ignore[possibly-missing-attribute]
+        _distributed_rank(runtime)
+        return [value]
+    gathered: list[Any] = [None] * int(
+        torch.distributed.get_world_size()  # ty:ignore[possibly-missing-attribute]
+    )
+    torch.distributed.all_gather_object(  # ty:ignore[possibly-missing-attribute]
+        gathered, value
+    )
+    return gathered
+
+
+def _error_text(exc: BaseException) -> str:
+    return f"{type(exc).__name__}: {exc}"
+
+
+def _result_errors(results: list[Any], missing: str) -> list[str]:
+    return [
+        f"rank {rank}: {missing}"
+        if result is None
+        else f"rank {rank}: {result['error']}"
+        for rank, result in enumerate(results)
+        if result is None or "error" in result
+    ]
+
+
+def optimizer_group_decision(
+    runtime: Any,
+    decide: Callable[[], Any],
+    *,
+    operation: str,
+) -> Any:
+    box: list[dict[str, Any] | None] = [None]
+    if _distributed_rank(runtime) == 0:
+        try:
+            box[0] = {"value": decide()}
+        except Exception as exc:
+            box[0] = {"error": _error_text(exc)}
+    if torch.distributed.is_initialized():  # ty:ignore[possibly-missing-attribute]
+        torch.distributed.broadcast_object_list(  # ty:ignore[possibly-missing-attribute]
+            box, src=0
+        )
+    result = box[0]
+    if result is None:
+        raise RuntimeError(f"Rank 0 returned no {operation} decision")
+    if "error" in result:
+        raise RuntimeError(f"{operation} failed: {result['error']}")
+    return result["value"]
+
+
+def _raise_rank_errors(runtime: Any, results: list[Any], *, operation: str) -> None:
+    def decide() -> None:
+        errors = _result_errors(results, "missing result")
+        if errors:
+            raise RuntimeError("; ".join(errors))
+
+    optimizer_group_decision(runtime, decide, operation=operation)
+
+
+def _run_rank_operation(runtime: Any, operation: str, run: Callable[[], Any]) -> Any:
+    value: Any = None
+    try:
+        value = run()
+        local_result: dict[str, str] = {}
+    except Exception as exc:
+        local_result = {"error": _error_text(exc)}
+    _raise_rank_errors(
+        runtime, _all_gather_objects(runtime, local_result), operation=operation
+    )
+    return value
+
+
+def _runtime_layout_record(runtime: Any) -> dict[str, Any]:
+    try:
+        return {
+            "rank": runtime.rank,
+            "runtime_sha256": _model_runtime_sha256(runtime),
+            "layout_sha256": _optimizer_layout_sha256(runtime),
+        }
+    except Exception as exc:
+        return {"rank": runtime.rank, "error": _error_text(exc)}
+
+
+def _validated_runtime_layouts(
+    runtime: Any, records: list[Any]
+) -> tuple[str, tuple[str, ...]]:
+    errors = _result_errors(records, "missing runtime metadata")
+    if errors:
+        raise RuntimeError("; ".join(errors))
+    ranks = tuple(record["rank"] for record in records)
+    expected_ranks = tuple(range(len(records)))
+    if ranks != expected_ranks or len(records) != runtime.world_size:
+        raise RuntimeError(
+            "Optimizer rank metadata mismatch: "
+            f"expected={expected_ranks}, actual={ranks}, "
+            f"runtime_world={runtime.world_size}"
+        )
+    runtime_digests = {record["runtime_sha256"] for record in records}
+    if len(runtime_digests) != 1:
         raise RuntimeError(
-            f"Optimizer manifest {manifest_path} references missing shard(s): {missing}"
+            f"Trainer ranks disagree on model-runtime digest: {sorted(runtime_digests)}"
         )
-    return commit
+    return runtime_digests.pop(), tuple(record["layout_sha256"] for record in records)
 
 
-def resolve_optimizer_shard_path(
+def _stage_optimizer_value(value: Any, stager: PinnedCpuSnapshotBuilder) -> Any:
+    if isinstance(value, torch.Tensor):
+        return stager.stage(value)
+    if isinstance(value, dict):
+        return {
+            _stage_optimizer_value(key, stager): _stage_optimizer_value(item, stager)
+            for key, item in value.items()
+        }
+    if isinstance(value, list):
+        return [_stage_optimizer_value(item, stager) for item in value]
+    if isinstance(value, tuple):
+        return (
+            type(value)(*(_stage_optimizer_value(item, stager) for item in value))
+            if hasattr(value, "_fields")
+            else tuple(_stage_optimizer_value(item, stager) for item in value)
+        )
+    return copy.deepcopy(value)
+
+
+def snapshot_optimizer_state(
+    runtime: Any,
+    *,
+    generation_id: str,
+    step: int,
+) -> OptimizerStateSnapshot:
+    return stage_optimizer_state_snapshot(
+        runtime,
+        generation_id=generation_id,
+        step=step,
+        stager=PinnedCpuSnapshotStager(),
+    ).resolve()
+
+
+def stage_optimizer_state_snapshot(
+    runtime: Any,
+    *,
+    generation_id: str,
+    step: int,
+    stager: PinnedCpuSnapshotStager,
+) -> PendingCpuSnapshot[OptimizerStateSnapshot]:
+    if runtime.optimizer is None:
+        raise RuntimeError("Cannot snapshot an uninitialized optimizer")
+    records = _all_gather_objects(runtime, _runtime_layout_record(runtime))
+    runtime_sha256, layouts = _validated_runtime_layouts(runtime, records)
+    builder = stager.begin()
+    return builder.finish(
+        OptimizerStateSnapshot(
+            generation_id=generation_id,
+            step=step,
+            rank=runtime.rank,
+            world_size=runtime.world_size,
+            runtime_sha256=runtime_sha256,
+            layout_sha256=layouts[runtime.rank],
+            topology=current_optimizer_topology(runtime.world_size),
+            state_dict=_stage_optimizer_value(runtime.optimizer.state_dict(), builder),
+        )
+    )
+
+
+def write_optimizer_snapshot_shard(
+    snapshot: OptimizerStateSnapshot,
+    *,
     optimizer_state_path: str,
+) -> OptimizerShard:
+    pending = optimizer_pending_generation_path(
+        optimizer_state_path, snapshot.generation_id
+    )
+    shard_path = optimizer_shard_path(
+        pending,
+        rank=snapshot.rank,
+        world_size=snapshot.world_size,
+    )
+    temporary = shard_path.with_name(f".{shard_path.name}.{os.getpid()}.tmp")
+    pending.mkdir(parents=True, exist_ok=True)
+    try:
+        with temporary.open("wb") as output:
+            torch.save(snapshot.state_dict, output)
+            output.flush()
+            os.fsync(output.fileno())
+        os.replace(temporary, shard_path)
+    finally:
+        temporary.unlink(missing_ok=True)
+    return OptimizerShard(
+        rank=snapshot.rank,
+        size_bytes=shard_path.stat().st_size,
+        layout_sha256=snapshot.layout_sha256,
+    )
+
+
+def _loaded_adapter(adapter_path: str, step: int) -> OptimizerAdapter:
+    path = Path(adapter_path).absolute()
+    canonical = _canonical_adapter_path(path, step)
+    adapter = read_adapter_publication(canonical, step=step, verify_files=True)
+    if adapter is None:
+        adapter = optimizer_adapter(canonical, step)
+    if path != canonical:
+        raise RuntimeError("Optimizer state must load an immutable canonical adapter")
+    return adapter
+
+
+def _write_optimizer_shard(
+    runtime: Any, generation_path: Path, *, layout_sha256: str
+) -> OptimizerShard:
+    shard_path = optimizer_shard_path(
+        generation_path,
+        rank=runtime.rank,
+        world_size=runtime.world_size,
+    )
+    temporary = shard_path.with_name(f".{shard_path.name}.{os.getpid()}.tmp")
+    generation_path.mkdir(parents=True, exist_ok=True)
+    try:
+        with temporary.open("wb") as output:
+            torch.save(runtime.optimizer.state_dict(), output)
+            output.flush()
+            os.fsync(output.fileno())
+        os.replace(temporary, shard_path)
+    finally:
+        temporary.unlink(missing_ok=True)
+    return OptimizerShard(
+        rank=runtime.rank,
+        size_bytes=shard_path.stat().st_size,
+        layout_sha256=layout_sha256,
+    )
+
+
+def _save_optimizer_state_locked(
+    runtime: Any,
     *,
-    rank: int,
-    world_size: int,
-    expected_step: int,
-) -> Path | None:
-    if not 0 <= rank < world_size:
-        raise ValueError(f"optimizer rank {rank} is outside world size {world_size}")
-    path = Path(optimizer_state_path)
-    commit = read_optimizer_commit(optimizer_state_path)
-    if commit is not None:
-        if commit.world_size != world_size:
-            raise RuntimeError(
-                "Optimizer world size does not match the active Megatron runtime: "
-                f"{commit.world_size} != {world_size}"
-            )
-        if commit.step != expected_step:
-            raise RuntimeError(
-                "Optimizer state does not match the source policy checkpoint: "
-                f"{commit.step} != {expected_step}"
+    optimizer_state_path: str,
+    step: int,
+    adapter: OptimizerAdapter,
+) -> None:
+    records = _all_gather_objects(runtime, _runtime_layout_record(runtime))
+
+    def select_generation() -> tuple[str, str, tuple[str, ...], dict[str, Any] | None]:
+        runtime_sha256, layouts = _validated_runtime_layouts(runtime, records)
+        path = Path(optimizer_state_path)
+        with _writer_lease(path) as expected:
+            if expected is not None and step <= expected.step:
+                raise RuntimeError(
+                    "Optimizer save step must advance the committed pointer: "
+                    f"current={expected.step}, attempted={step}"
+                )
+            expected_data = (
+                None if expected is None else expected.model_dump(mode="json")
             )
-        return path / commit.files[rank]
-    return None
+        return (
+            adapter.generation_id,
+            runtime_sha256,
+            layouts,
+            expected_data,
+        )
+
+    generation, runtime_sha256, layouts, expected_data = cast(
+        tuple[str, str, tuple[str, ...], dict[str, Any] | None],
+        optimizer_group_decision(
+            runtime, select_generation, operation="optimizer generation selection"
+        ),
+    )
+    expected = (
+        None
+        if expected_data is None
+        else OptimizerGenerationPointer.model_validate(expected_data)
+    )
+    pending = optimizer_pending_generation_path(optimizer_state_path, generation)
+    try:
+        shard = _write_optimizer_shard(
+            runtime, pending, layout_sha256=layouts[runtime.rank]
+        )
+        local_result: dict[str, Any] = {"shard": shard.model_dump(mode="json")}
+    except Exception as exc:
+        local_result = {"rank": runtime.rank, "error": _error_text(exc)}
+    gathered = _all_gather_objects(runtime, local_result)
+
+    def publish_generation() -> None:
+        errors = _result_errors(gathered, "missing shard metadata")
+        if errors:
+            raise RuntimeError("; ".join(errors))
+        manifest = build_optimizer_manifest(
+            generation=generation,
+            step=step,
+            adapter=adapter,
+            runtime_sha256=runtime_sha256,
+            world_size=runtime.world_size,
+            shards=[
+                OptimizerShard.model_validate(result["shard"]) for result in gathered
+            ],
+        )
+        commit_optimizer_generation(
+            optimizer_state_path, manifest, expected_pointer=expected
+        )
+
+    optimizer_group_decision(
+        runtime, publish_generation, operation="optimizer generation publication"
+    )
 
 
-def commit_optimizer_generation(
+def save_optimizer_state(
+    runtime: Any,
+    *,
     optimizer_state_path: str,
+    step: int,
+    adapter: OptimizerAdapter,
+) -> None:
+    with ExitStack() as leases:
+        optimizer_group_decision(
+            runtime,
+            lambda: leases.enter_context(optimizer_model_lease(optimizer_state_path)),
+            operation="optimizer model lease acquisition",
+        )
+        save_optimizer_state_under_model_lease(
+            runtime,
+            optimizer_state_path=optimizer_state_path,
+            step=step,
+            adapter=adapter,
+        )
+
+
+def save_optimizer_state_under_model_lease(
+    runtime: Any,
     *,
+    optimizer_state_path: str,
     step: int,
-    world_size: int,
-    files: tuple[str, ...],
+    adapter: OptimizerAdapter,
 ) -> None:
-    path = Path(optimizer_state_path)
-    path.mkdir(parents=True, exist_ok=True)
-    previous = read_optimizer_commit(optimizer_state_path)
-    missing = [name for name in files if not (path / name).is_file()]
-    if missing:
-        raise RuntimeError(f"Cannot commit missing optimizer shard(s): {missing}")
-    commit = OptimizerCommit(step=step, world_size=world_size, files=files)
-    _atomic_write(path / OPTIMIZER_MANIFEST, commit.model_dump_json())
-
-    retained = set(files) | {OPTIMIZER_MANIFEST}
-    obsolete = set(previous.files if previous is not None else ())
-    obsolete.update(
-        item.name
-        for item in path.iterdir()
-        if item.is_file()
-        and (_GENERATION_SHARD_RE.fullmatch(item.name) or item.name.isdigit())
-    )
-    for name in obsolete - retained:
-        candidate = path / name
-        if candidate.exists():
-            candidate.unlink()
-
-
-def _atomic_write(path: Path, content: str) -> None:
-    temporary = path.with_name(f"{path.name}.tmp")
-    with temporary.open("w", encoding="utf-8") as handle:
-        handle.write(content)
-        handle.flush()
-        os.fsync(handle.fileno())
-    os.replace(temporary, path)
-    directory_fd = os.open(path.parent, os.O_RDONLY)
-    try:
-        os.fsync(directory_fd)
-    finally:
-        os.close(directory_fd)
+    _save_optimizer_state_locked(
+        runtime,
+        optimizer_state_path=optimizer_state_path,
+        step=step,
+        adapter=adapter,
+    )
+
+
+def _sibling_optimizer_owns_adapter(
+    optimizer_state_path: str, adapter: OptimizerAdapter
+) -> bool:
+    current = Path(optimizer_state_path).absolute()
+    for sibling in (
+        current.parent / "optimizer_states_rl",
+        current.parent / "optimizer_states_sft",
+    ):
+        if sibling == current or not sibling.exists():
+            continue
+        with _committed_generation_lease(sibling) as pointer:
+            policy_pointer = _read_policy_pointer(sibling)
+            if pointer is None and policy_pointer is None:
+                continue
+            if pointer is not None:
+                manifest = _read_manifest(
+                    optimizer_generation_path(str(sibling), pointer.generation)
+                )
+                _validate_pointer_manifest(pointer, manifest)
+            policy = _committed_policy(
+                sibling,
+                pointer,
+                initial_adapter_path=get_step_checkpoint_dir(str(sibling.parent), 0),
+            )
+            if policy.policy_adapter == adapter:
+                return True
+    return False
+
+
+def load_optimizer_state(
+    runtime: Any,
+    *,
+    optimizer_state_path: str,
+    adapter_path: str,
+    adapter_step: int,
+    allow_missing: bool,
+    initialize: Callable[[Any], None],
+) -> Path | None:
+    records = _all_gather_objects(runtime, _runtime_layout_record(runtime))
+    with ExitStack() as leases:
+
+        def select_generation() -> dict[str, Any] | None:
+            runtime_sha256, layouts = _validated_runtime_layouts(runtime, records)
+            adapter = _loaded_adapter(adapter_path, adapter_step)
+            path = Path(optimizer_state_path)
+            leased_pointer = leases.enter_context(_committed_generation_lease(path))
+            policy = _committed_policy(
+                path,
+                leased_pointer,
+                initial_adapter_path=get_step_checkpoint_dir(
+                    str(path.absolute().parent), 0
+                ),
+            )
+            if policy.policy_adapter == adapter:
+                if policy.optimizer_anchor is None:
+                    return None
+                assert policy.state_adapter is not None
+                pinned_adapter = policy.state_adapter
+            else:
+                pinned_adapter = adapter
+            lineage_switch = (
+                leased_pointer is None or leased_pointer.adapter != pinned_adapter
+            ) and _sibling_optimizer_owns_adapter(optimizer_state_path, adapter)
+            if lineage_switch:
+                return None
+            pointer = pin_optimizer_generation(
+                optimizer_state_path,
+                world_size=runtime.world_size,
+                runtime_sha256=runtime_sha256,
+                layout_sha256_by_rank=layouts,
+                adapter=pinned_adapter,
+                pointer=leased_pointer,
+                verify_adapter_files=False,
+            )
+            if pointer is None and not allow_missing:
+                raise RuntimeError(
+                    "No optimizer generation is paired with canonical adapter "
+                    f"step {adapter_step}"
+                )
+            return None if pointer is None else pointer.model_dump(mode="json")
+
+        pointer_data = optimizer_group_decision(
+            runtime, select_generation, operation="optimizer load selection"
+        )
+        if pointer_data is None:
+            _run_rank_operation(
+                runtime, "optimizer reset", lambda: initialize(runtime.optimizer)
+            )
+            return None
+
+        pointer = OptimizerGenerationPointer.model_validate(pointer_data)
+
+        def load_shard() -> tuple[Path, Any]:
+            shard_path = resolve_optimizer_shard(
+                optimizer_state_path,
+                rank=runtime.rank,
+                world_size=runtime.world_size,
+                pointer=pointer,
+            )
+            assert shard_path is not None
+            return shard_path, torch.load(shard_path)
+
+        shard_path, loaded_state = cast(
+            tuple[Path, Any],
+            _run_rank_operation(runtime, "optimizer shard load", load_shard),
+        )
+        try:
+            _run_rank_operation(
+                runtime,
+                "optimizer state apply",
+                lambda: runtime.optimizer.load_state_dict(loaded_state),
+            )
+        finally:
+            del loaded_state
+        return shard_path
 
 
 def _allow_unpaired_resume() -> bool:
@@ -141,56 +1804,422 @@ def _allow_unpaired_resume() -> bool:
     }
 
 
+def _scan_optimizer_transactions(
+    path: Path,
+) -> tuple[
+    list[tuple[Path, OptimizerGenerationPointer]],
+    list[tuple[Path, str, bool]],
+]:
+    pointer_temps: list[tuple[Path, OptimizerGenerationPointer]] = []
+    allowed = {
+        OPTIMIZER_POINTER,
+        OPTIMIZER_POLICY_POINTER,
+        OPTIMIZER_WRITER_LOCK,
+        OPTIMIZER_GENERATIONS_DIR,
+        "uncommitted_generations",
+    }
+    for entry in sorted(path.iterdir()):
+        if entry.name in allowed:
+            continue
+        if _POINTER_TEMP_RE.fullmatch(entry.name) is None or not entry.is_file():
+            raise RuntimeError(
+                "Ambiguous interrupted optimizer transaction; unexpected root "
+                f"entry {entry}"
+            )
+        try:
+            pointer = OptimizerGenerationPointer.model_validate_json(
+                entry.read_text("utf-8")
+            )
+        except Exception as exc:
+            raise RuntimeError(
+                f"Invalid interrupted optimizer pointer: {entry}"
+            ) from exc
+        pointer_temps.append((entry, pointer))
+
+    candidates: list[tuple[Path, str, bool]] = []
+    generations = path / OPTIMIZER_GENERATIONS_DIR
+    if not generations.exists():
+        return pointer_temps, candidates
+    if not generations.is_dir():
+        raise RuntimeError(
+            f"Optimizer generations path is not a directory: {generations}"
+        )
+    for entry in sorted(generations.iterdir()):
+        name = entry.name
+        if name.startswith(OPTIMIZER_GENERATION_LEASE_PREFIX):
+            _validate_generation_name(
+                name.removeprefix(OPTIMIZER_GENERATION_LEASE_PREFIX)
+            )
+            if not entry.is_file():
+                raise RuntimeError(f"Invalid optimizer generation lease: {entry}")
+            continue
+        if name.startswith(OPTIMIZER_TRASH_PREFIX):
+            if _TRASH_RE.fullmatch(name) is None or not entry.is_dir():
+                raise RuntimeError(f"Invalid optimizer generation trash: {entry}")
+            continue
+        pending = name.startswith(".pending-")
+        generation = name.removeprefix(".pending-") if pending else name
+        if _GENERATION_RE.fullmatch(generation) is None or not entry.is_dir():
+            raise RuntimeError(
+                "Ambiguous interrupted optimizer transaction; unexpected generation "
+                f"entry {entry}"
+            )
+        candidates.append((entry, generation, pending))
+    return pointer_temps, candidates
+
+
+def _quarantine_pointer_temp(path: Path, temporary: Path) -> None:
+    quarantine = (
+        path
+        / "uncommitted_generations"
+        / f"invalid_pointer_{int(time.time())}_{uuid4().hex}"
+    )
+    quarantine.mkdir(parents=True)
+    os.replace(temporary, quarantine / temporary.name)
+    _fsync_directory(quarantine)
+    _fsync_directory(path)
+
+
+def _validate_committed_generation(
+    path: Path, pointer: OptimizerGenerationPointer
+) -> None:
+    generation_path = optimizer_generation_path(str(path), pointer.generation)
+    manifest = _read_manifest(generation_path)
+    _validate_pointer_manifest(pointer, manifest)
+    _validate_generation_files(generation_path, manifest, local_rank=None)
+    _validate_adapter_publication(pointer.adapter, verify_files=True)
+
+
+def _recover_optimizer_pointer_locked(
+    path: Path, current: OptimizerGenerationPointer | None
+) -> OptimizerGenerationPointer | None:
+    policy_temps = tuple(
+        entry
+        for entry in sorted(path.iterdir())
+        if _POLICY_TEMP_RE.fullmatch(entry.name) is not None and entry.is_file()
+    )
+    for temporary in policy_temps:
+        temporary.unlink()
+    if policy_temps:
+        _fsync_directory(path)
+    temporary_paths = tuple(
+        entry
+        for entry in sorted(path.iterdir())
+        if _POINTER_TEMP_RE.fullmatch(entry.name) is not None and entry.is_file()
+    )
+    if len(temporary_paths) > 1:
+        raise RuntimeError(
+            "Ambiguous interrupted optimizer transaction; found multiple temporary "
+            "pointers"
+        )
+    if temporary_paths:
+        try:
+            temporary = OptimizerGenerationPointer.model_validate_json(
+                temporary_paths[0].read_text("utf-8")
+            )
+        except Exception:
+            _quarantine_pointer_temp(path, temporary_paths[0])
+            temporary = None
+    else:
+        temporary = None
+
+    _, candidates = _scan_optimizer_transactions(path)
+    current_step = -1 if current is None else current.step
+    advancing = tuple(
+        (entry, generation)
+        for entry, generation, pending in candidates
+        if not pending
+        and generation != (None if current is None else current.generation)
+        and _generation_step(generation) > current_step
+    )
+    if temporary is not None and temporary.step <= current_step:
+        if temporary == current:
+            temporary_paths[0].unlink()
+            _fsync_directory(path)
+        else:
+            _quarantine_pointer_temp(path, temporary_paths[0])
+        temporary = None
+    if temporary is not None:
+        if len(advancing) != 1 or advancing[0][1] != temporary.generation:
+            raise RuntimeError(
+                "Interrupted optimizer pointer does not uniquely identify an "
+                "advancing committed generation"
+            )
+        pointer = temporary
+    elif not advancing:
+        return current
+    elif len(advancing) == 1:
+        manifest = _read_manifest(advancing[0][0])
+        pointer = OptimizerGenerationPointer(
+            generation=manifest.generation,
+            step=manifest.step,
+            adapter=manifest.adapter,
+        )
+    else:
+        raise RuntimeError(
+            "Ambiguous interrupted optimizer transaction; found multiple advancing "
+            "committed generations"
+        )
+
+    _validate_committed_generation(path, pointer)
+    if temporary is None:
+        _write_model_atomic(path / OPTIMIZER_POINTER, pointer)
+    else:
+        os.replace(temporary_paths[0], path / OPTIMIZER_POINTER)
+        _fsync_directory(path)
+    return pointer
+
+
+def _optimizer_state_paths(output_dir: str, current: str) -> tuple[Path, ...]:
+    selected = Path(current).absolute()
+    candidates = {selected} | {
+        (Path(output_dir) / f"optimizer_states_{kind}").absolute()
+        for kind in ("rl", "sft")
+    }
+    return tuple(
+        sorted(path for path in candidates if path == selected or path.exists())
+    )
+
+
+def _recover_optimizer_transactions(output_dir: str, current: str) -> None:
+    for path in _optimizer_state_paths(output_dir, current):
+        with _writer_lease(path):
+            pass
+
+
+def _recover_uncommitted_initial_transaction(
+    *,
+    output_dir: str,
+    optimizer_state_path: str,
+) -> tuple[int, ...]:
+    if get_step_from_dir(output_dir) != 1:
+        return ()
+    path = Path(optimizer_state_path).absolute()
+    checkpoint = Path(get_step_checkpoint_dir(output_dir, 1)).absolute()
+    roots = _optimizer_state_paths(output_dir, optimizer_state_path)
+    with ExitStack() as locks:
+        pointers = {root: locks.enter_context(_writer_lease(root)) for root in roots}
+        pointer = pointers[path]
+        if pointer is not None:
+            return ()
+        policy = _resolve_policy_pointer(path, pointer)
+        if policy is not None and policy.policy_adapter.step == 1:
+            return ()
+        adapter = read_adapter_publication(checkpoint, step=1, verify_files=True)
+        if adapter is None:
+            return ()
+
+        pointer_temps, candidates = _scan_optimizer_transactions(path)
+        for sibling in roots:
+            if sibling == path:
+                continue
+            sibling_temps, sibling_candidates = _scan_optimizer_transactions(sibling)
+            sibling_pointer = pointers[sibling]
+            if sibling_pointer is not None and sibling_pointer.adapter.step == 1:
+                return ()
+            if any(pointer.adapter.step == 1 for _, pointer in sibling_temps) or any(
+                _generation_step(generation) == 1
+                for _, generation, _ in sibling_candidates
+            ):
+                raise RuntimeError(
+                    "Cannot recover interrupted initial optimizer transaction; "
+                    f"sibling optimizer state may own checkpoint 0001: {sibling}"
+                )
+        if len(candidates) > 1:
+            raise RuntimeError(
+                "Ambiguous interrupted initial optimizer transaction; found "
+                f"{len(candidates)} candidate generations"
+            )
+        manifest: OptimizerGenerationManifest | None = None
+        if candidates:
+            entry, generation, pending = candidates[0]
+            if _generation_step(generation) != 1:
+                raise RuntimeError(
+                    "Ambiguous interrupted initial optimizer transaction; "
+                    f"unexpected generation {generation}"
+                )
+            manifest_path = entry / OPTIMIZER_MANIFEST
+            if not pending or manifest_path.exists():
+                manifest = _read_manifest(entry)
+                if (
+                    manifest.generation != generation
+                    or manifest.step != 1
+                    or manifest.adapter != adapter
+                ):
+                    raise RuntimeError(
+                        "Interrupted initial optimizer generation does not match "
+                        f"the published adapter: {entry}"
+                    )
+        if len(pointer_temps) > 1:
+            raise RuntimeError(
+                "Ambiguous interrupted initial optimizer transaction; found "
+                f"{len(pointer_temps)} temporary pointers"
+            )
+        if pointer_temps:
+            if not candidates or candidates[0][2] or manifest is None:
+                raise RuntimeError(
+                    "Interrupted optimizer pointer has no committed generation"
+                )
+            expected = OptimizerGenerationPointer(
+                generation=manifest.generation,
+                step=manifest.step,
+                adapter=manifest.adapter,
+            )
+            if pointer_temps[0][1] != expected:
+                raise RuntimeError(
+                    "Interrupted optimizer pointer does not match its generation"
+                )
+        if candidates and not locks.enter_context(
+            _generation_lease(
+                path,
+                candidates[0][1],
+                exclusive=True,
+                nonblocking=True,
+            )
+        ):
+            raise RuntimeError(
+                "Interrupted initial optimizer generation is still in use: "
+                f"{candidates[0][1]}"
+            )
+        if _allow_unpaired_resume() and (pointer_temps or candidates):
+            raise RuntimeError(
+                f"{ALLOW_UNPAIRED_MEGATRON_RESUME_ENV} cannot bypass an interrupted "
+                "optimizer transaction"
+            )
+        if _allow_unpaired_resume():
+            return ()
+
+        tag = f"initial_step_0001_{adapter.generation_id.rsplit('-', 1)[-1][:16]}"
+        previous = Path(output_dir) / "unpaired_checkpoints" / tag / checkpoint.name
+        if previous.exists():
+            tag = f"{tag}_{uuid4().hex}"
+        quarantine = path / "uncommitted_generations" / tag
+        quarantine.mkdir(parents=True, exist_ok=True)
+        if pointer_temps:
+            pointer_temp = pointer_temps[0][0]
+            destination = quarantine / pointer_temp.name
+            if destination.exists():
+                raise RuntimeError(f"Optimizer quarantine entry exists: {destination}")
+            os.replace(pointer_temp, destination)
+            _fsync_directory(path)
+        if candidates:
+            entry = candidates[0][0]
+            destination = quarantine / entry.name
+            if destination.exists():
+                raise RuntimeError(f"Optimizer quarantine entry exists: {destination}")
+            os.replace(entry, destination)
+            _fsync_directory(quarantine)
+            _fsync_directory(entry.parent)
+
+        checkpoint_quarantine = Path(output_dir) / "unpaired_checkpoints" / tag
+        checkpoint_quarantine.mkdir(parents=True, exist_ok=True)
+        destination = checkpoint_quarantine / checkpoint.name
+        if destination.exists():
+            raise RuntimeError(f"Checkpoint quarantine entry exists: {destination}")
+        os.replace(checkpoint, destination)
+        _fsync_directory(checkpoint_quarantine)
+        _fsync_directory(checkpoint.parent)
+    return (1,)
+
+
 def resolve_megatron_resume_step(
     *,
     output_dir: str,
     optimizer_state_path: str,
 ) -> MegatronResumeStep:
     latest_lora_step = get_step_from_dir(output_dir)
-    commit = read_optimizer_commit(optimizer_state_path)
-    optimizer_step = commit.step if commit is not None else None
+    with _committed_generation_lease(Path(optimizer_state_path)) as pointer:
+        if pointer is not None:
+            _validate_committed_generation(Path(optimizer_state_path), pointer)
+            expected_path = Path(
+                get_step_checkpoint_dir(output_dir, pointer.adapter.step)
+            ).absolute()
+            if pointer.adapter.identity != str(expected_path):
+                raise RuntimeError(
+                    "Optimizer pointer does not identify the canonical adapter path: "
+                    f"saved={pointer.adapter.identity}, expected={expected_path}"
+                )
+        policy = _resolve_policy_pointer(Path(optimizer_state_path), pointer)
+        if policy is not None:
+            expected_path = Path(
+                get_step_checkpoint_dir(output_dir, policy.policy_adapter.step)
+            ).absolute()
+            if policy.policy_adapter.identity != str(expected_path):
+                raise RuntimeError(
+                    "Optimizer policy pointer does not identify the canonical "
+                    f"adapter path: saved={policy.policy_adapter.identity}, "
+                    f"expected={expected_path}"
+                )
+            return MegatronResumeStep(
+                step=policy.policy_adapter.step,
+                latest_lora_step=latest_lora_step,
+                optimizer_step=None if pointer is None else pointer.step,
+            )
+        if pointer is not None:
+            return MegatronResumeStep(
+                step=pointer.step,
+                latest_lora_step=latest_lora_step,
+                optimizer_step=pointer.step,
+            )
     if latest_lora_step == 0:
         return MegatronResumeStep(
             step=0,
             latest_lora_step=latest_lora_step,
-            optimizer_step=optimizer_step,
-        )
-    if optimizer_step is not None and os.path.isdir(
-        get_step_checkpoint_dir(output_dir, optimizer_step)
-    ):
-        return MegatronResumeStep(
-            step=optimizer_step,
-            latest_lora_step=latest_lora_step,
-            optimizer_step=optimizer_step,
+            optimizer_step=None,
         )
     if _allow_unpaired_resume():
         return MegatronResumeStep(
             step=latest_lora_step,
             latest_lora_step=latest_lora_step,
-            optimizer_step=optimizer_step,
+            optimizer_step=None,
             used_unpaired_override=True,
         )
-    marker = (
-        "no optimizer step marker"
-        if optimizer_step is None
-        else f"optimizer marker step {optimizer_step:04d} has no matching LoRA checkpoint"
-    )
     raise RuntimeError(
         "Cannot resume Megatron training from an unpaired LoRA/optimizer state: "
-        f"latest LoRA checkpoint is {latest_lora_step:04d}, {marker}. "
+        f"latest LoRA checkpoint is {latest_lora_step:04d}, no optimizer pointer. "
         f"Set {ALLOW_UNPAIRED_MEGATRON_RESUME_ENV}=1 to override."
     )
 
 
-def prepare_megatron_resume_state(
+def _resolve_model_resume_step(
+    *, output_dir: str, optimizer_state_path: str
+) -> MegatronResumeStep:
+    paired = []
+    for path in _optimizer_state_paths(output_dir, optimizer_state_path):
+        if (
+            read_committed_optimizer_pointer(str(path)) is not None
+            or _read_policy_pointer(path) is not None
+        ):
+            paired.append(
+                resolve_megatron_resume_step(
+                    output_dir=output_dir,
+                    optimizer_state_path=str(path),
+                )
+            )
+    if paired:
+        return max(paired, key=lambda info: info.step)
+    return resolve_megatron_resume_step(
+        output_dir=output_dir,
+        optimizer_state_path=optimizer_state_path,
+    )
+
+
+def _prepare_megatron_resume_state_locked(
     *,
     output_dir: str,
     optimizer_state_path: str,
 ) -> MegatronResumeStep:
-    info = resolve_megatron_resume_step(
+    _recover_optimizer_transactions(output_dir, optimizer_state_path)
+    recovered_steps = _recover_uncommitted_initial_transaction(
+        output_dir=output_dir,
+        optimizer_state_path=optimizer_state_path,
+    )
+    info = _resolve_model_resume_step(
         output_dir=output_dir,
         optimizer_state_path=optimizer_state_path,
     )
+    if recovered_steps:
+        info = info.model_copy(update={"quarantined_lora_steps": recovered_steps})
     if info.used_unpaired_override or info.latest_lora_step <= info.step:
         return info
 
@@ -200,30 +2229,74 @@ def prepare_megatron_resume_state(
         / "unpaired_checkpoints"
         / f"resume_from_{info.step:04d}_{int(time.time())}_{os.getpid()}"
     )
+    to_move = [
+        checkpoint_dir
+        for checkpoint_dir in sorted(checkpoints_dir.iterdir())
+        if checkpoint_dir.is_dir()
+        and checkpoint_dir.name.isdigit()
+        and int(checkpoint_dir.name) > info.step
+    ]
     moved_steps: list[int] = []
-    for checkpoint_dir in sorted(checkpoints_dir.iterdir()):
-        if not checkpoint_dir.is_dir() or not checkpoint_dir.name.isdigit():
-            continue
-        step = int(checkpoint_dir.name)
-        if step <= info.step:
-            continue
+    for checkpoint_dir in to_move:
         quarantine_dir.mkdir(parents=True, exist_ok=True)
-        checkpoint_dir.rename(quarantine_dir / checkpoint_dir.name)
-        moved_steps.append(step)
+        os.replace(checkpoint_dir, quarantine_dir / checkpoint_dir.name)
+        moved_steps.append(int(checkpoint_dir.name))
+    if moved_steps:
+        _fsync_directory(checkpoints_dir)
+        _fsync_directory(quarantine_dir)
     return info.model_copy(update={"quarantined_lora_steps": tuple(moved_steps)})
 
 
+def prepare_megatron_resume_state(
+    *,
+    output_dir: str,
+    optimizer_state_path: str,
+) -> MegatronResumeStep:
+    with optimizer_model_lease(optimizer_state_path):
+        info = _prepare_megatron_resume_state_locked(
+            output_dir=output_dir,
+            optimizer_state_path=optimizer_state_path,
+        )
+        latest = Path(output_dir) / "megatron_runtime" / ADAPTER_LATEST_POINTER
+        if info.step == 0:
+            if latest.exists():
+                latest.unlink()
+                _fsync_directory(latest.parent)
+        else:
+            policy = resolve_committed_optimizer_policy(
+                optimizer_state_path,
+                initial_adapter_path=get_step_checkpoint_dir(output_dir, 0),
+            )
+            _write_model_atomic(latest, policy.policy_adapter)
+        return info
+
+
 def format_megatron_resume_message(info: MegatronResumeStep) -> str:
     if info.used_unpaired_override:
         return (
             "Resuming Megatron from unpaired LoRA checkpoint "
             f"{info.step} because {ALLOW_UNPAIRED_MEGATRON_RESUME_ENV} is set"
         )
+    suffix = ""
+    if info.quarantined_lora_steps:
+        moved = ", ".join(f"{step:04d}" for step in info.quarantined_lora_steps)
+        suffix = f"; quarantined unpaired LoRA checkpoint(s): {moved}"
+    if info.step > 0 and info.optimizer_step != info.step:
+        optimizer = (
+            "an uninitialized optimizer"
+            if info.optimizer_step is None
+            else f"optimizer state {info.optimizer_step}"
+        )
+        latest = (
+            ""
+            if info.step == info.latest_lora_step
+            else f" instead of latest LoRA checkpoint {info.latest_lora_step}"
+        )
+        return (
+            f"Resuming no-op policy checkpoint {info.step} with {optimizer}"
+            f"{latest}{suffix}"
+        )
     if info.step != info.latest_lora_step:
-        suffix = ""
-        if info.quarantined_lora_steps:
-            moved = ", ".join(f"{step:04d}" for step in info.quarantined_lora_steps)
-            suffix = f"; quarantined unpaired LoRA checkpoint(s): {moved}"
         return (
             "Resuming Megatron from paired LoRA/optimizer checkpoint "
             f"{info.step} instead of latest LoRA checkpoint "
diff --git a/src/art/megatron/provider.py b/src/art/megatron/provider.py
index 52bc7ff7c..555e2591c 100644
--- a/src/art/megatron/provider.py
+++ b/src/art/megatron/provider.py
@@ -12,11 +12,18 @@
 from megatron.core.transformer.enums import AttnBackend
 from pydantic import BaseModel, ConfigDict
 import torch
+from transformers import AutoConfig
 
+from art.megatron.expert_parallel import (
+    activate_expert_parallel_layout,
+    configure_expert_parallel_layout,
+    patch_moe_routers,
+)
 from art.megatron.model_support.registry import (
     ensure_model_support_bridge_registered_for_spec,
     get_model_support_handler_for_spec,
     get_model_support_spec,
+    get_model_support_spec_by_key,
 )
 from art.megatron.model_support.spec import ModelSupportSpec
 from art.megatron.runtime.bridge_runtime import install_art_bridge_runtime_patches
@@ -56,6 +63,10 @@
         "virtual_pipeline_model_parallel_size",
         "ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE",
     ),
+    (
+        "microbatch_group_size_per_vp_stage",
+        "ART_MEGATRON_VPP_MICROBATCH_GROUP_SIZE",
+    ),
     ("expert_model_parallel_size", "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE"),
     ("recompute_num_layers", "ART_MEGATRON_RECOMPUTE_NUM_LAYERS"),
 )
@@ -91,11 +102,12 @@ def resolve_layer_spec(
     module_spec_type = _optional_module_spec_type()
     if module_spec_type is not None and isinstance(base_layer_spec, module_spec_type):
         return copy.deepcopy(base_layer_spec)
-    kwargs = (
-        {"vp_stage": vp_stage}
-        if vp_stage in inspect.signature(base_layer_spec).parameters
-        else {}
+    parameters = inspect.signature(base_layer_spec).parameters
+    accepts_vp_stage = "vp_stage" in parameters or any(
+        parameter.kind is inspect.Parameter.VAR_KEYWORD
+        for parameter in parameters.values()
     )
+    kwargs = {"vp_stage": vp_stage} if accepts_vp_stage else {}
     return base_layer_spec(config, **kwargs)
 
 
@@ -180,6 +192,7 @@ class _ProviderRuntimeEnv(BaseModel):
     context_parallel_size: int | None = None
     pipeline_model_parallel_size: int | None = None
     virtual_pipeline_model_parallel_size: int | None = None
+    microbatch_group_size_per_vp_stage: int | None = None
     expert_model_parallel_size: int | None = None
     expert_tensor_parallel_size: int | None = None
     recompute_granularity: Literal["full", "selective"] | None = None
@@ -370,6 +383,12 @@ def _apply_art_training_runtime_prepare_defaults(
     provider: GPTModelProvider,
     handler: Any,
 ) -> None:
+    # Apex does not build its CUDA extensions in the CUDA 13 environment.
+    if (
+        torch.version.cuda is not None
+        and int(torch.version.cuda.partition(".")[0]) >= 13
+    ):
+        provider.gradient_accumulation_fusion = False
     provider.recompute_granularity = "full"
     provider.recompute_method = "uniform"
     provider.recompute_num_layers = 1
@@ -540,6 +559,11 @@ def _apply_runtime_env_overrides(
         runtime_env,
         "virtual_pipeline_model_parallel_size",
     )
+    _apply_provider_attr_if_set(
+        provider,
+        runtime_env,
+        "microbatch_group_size_per_vp_stage",
+    )
     _apply_provider_attr_if_value(provider, runtime_env, "expert_model_parallel_size")
     _apply_provider_attr_if_value(provider, runtime_env, "expert_tensor_parallel_size")
     _apply_provider_attr_if_set(provider, runtime_env, "recompute_granularity")
@@ -609,20 +633,39 @@ def _build_provider_bundle(
     model: str,
     *,
     torch_dtype: torch.dtype,
+    load_weights: bool = True,
     allow_unvalidated_arch: bool = False,
+    model_support_key: str | None = None,
 ) -> ProviderBundle:
-    spec = get_model_support_spec(
-        model,
-        allow_unvalidated_arch=allow_unvalidated_arch,
+    spec = (
+        get_model_support_spec_by_key(model_support_key)
+        if model_support_key is not None
+        else get_model_support_spec(
+            model,
+            allow_unvalidated_arch=allow_unvalidated_arch,
+        )
     )
     ensure_model_support_bridge_registered_for_spec(spec)
     handler = get_model_support_handler_for_spec(spec)
-    bridge = AutoBridge.from_hf_pretrained(
-        model,
-        dtype=torch_dtype,
-        trust_remote_code=True,
+    if load_weights:
+        bridge = AutoBridge.from_hf_pretrained(
+            model,
+            dtype=torch_dtype,
+            trust_remote_code=True,
+        )
+    else:
+        bridge = AutoBridge.from_hf_config(
+            AutoConfig.from_pretrained(
+                model,
+                dtype=torch_dtype,
+                trust_remote_code=True,
+            )
+        )
+    provider = (
+        bridge.to_megatron_provider()
+        if load_weights
+        else bridge.to_megatron_provider(load_weights=False)
     )
-    provider = bridge.to_megatron_provider()
     handler.patch_bridge(bridge)
     return ProviderBundle(
         provider=provider,
@@ -636,13 +679,17 @@ def prepare_provider_bundle(
     model: str,
     *,
     torch_dtype: torch.dtype = torch.bfloat16,
+    load_weights: bool = True,
     allow_unvalidated_arch: bool = False,
+    model_support_key: str | None = None,
 ) -> ProviderBundle:
     runtime_env = _ProviderRuntimeEnv.from_environ()
     bundle = _build_provider_bundle(
         model,
         torch_dtype=torch_dtype,
+        load_weights=load_weights,
         allow_unvalidated_arch=allow_unvalidated_arch,
+        model_support_key=model_support_key,
     )
     provider = bundle.provider
     setattr(provider, "_art_model_support_handler", bundle.handler)
@@ -672,15 +719,34 @@ def prepare_provider_bundle(
 
 
 def finalize_provider_bundle(provider_bundle: ProviderBundle) -> ProviderBundle:
-    runtime_env = _ProviderRuntimeEnv.from_environ()
+    _ProviderRuntimeEnv.from_environ()
     provider = cast(GPTModelProvider, provider_bundle.provider)
     _apply_art_training_runtime_finalize_defaults(provider)
     _enforce_art_moe_grouped_gemm_fast_path(provider)
+    configure_expert_parallel_layout(provider)
     _finalize_provider_with_art_overrides(provider)
+    if activate_expert_parallel_layout(provider) is not None:
+        _install_nonuniform_expert_parallel(provider)
     _normalize_recompute_settings(provider)
     return provider_bundle
 
 
+def _install_nonuniform_expert_parallel(provider: GPTModelProvider) -> None:
+    base_layer_spec = provider.transformer_layer_spec
+
+    def _nonuniform_expert_layer_spec(
+        config: GPTModelProvider, vp_stage: int | None = None
+    ) -> object:
+        layer_spec = resolve_layer_spec(base_layer_spec, config, vp_stage)
+        if patch_moe_routers(layer_spec) == 0:
+            raise RuntimeError(
+                "non-uniform expert parallelism found no MoE router in the layer spec"
+            )
+        return layer_spec
+
+    provider.transformer_layer_spec = cast(Any, _nonuniform_expert_layer_spec)
+
+
 def _finalize_provider_with_art_overrides(provider: GPTModelProvider) -> None:
     if not _is_art_gdn_context_parallel_provider(provider):
         _finalize_provider_config(provider)
@@ -759,13 +825,17 @@ def get_provider_bundle(
     model: str,
     *,
     torch_dtype: torch.dtype = torch.bfloat16,
+    load_weights: bool = True,
     allow_unvalidated_arch: bool = False,
+    model_support_key: str | None = None,
 ) -> ProviderBundle:
     return finalize_provider_bundle(
         prepare_provider_bundle(
             model,
             torch_dtype=torch_dtype,
+            load_weights=load_weights,
             allow_unvalidated_arch=allow_unvalidated_arch,
+            model_support_key=model_support_key,
         )
     )
 
@@ -774,10 +844,14 @@ def get_provider(
     model: str,
     *,
     torch_dtype: torch.dtype = torch.bfloat16,
+    load_weights: bool = True,
     allow_unvalidated_arch: bool = False,
+    model_support_key: str | None = None,
 ) -> GPTModelProvider:
     return get_provider_bundle(
         model,
         torch_dtype=torch_dtype,
+        load_weights=load_weights,
         allow_unvalidated_arch=allow_unvalidated_arch,
+        model_support_key=model_support_key,
     ).provider
diff --git a/src/art/megatron/routing_replay.py b/src/art/megatron/routing_replay.py
index 39efa4101..61b6150b0 100644
--- a/src/art/megatron/routing_replay.py
+++ b/src/art/megatron/routing_replay.py
@@ -10,7 +10,7 @@
 import types
 from typing import TYPE_CHECKING, Any, Protocol
 
-from pydantic import BaseModel, ConfigDict, model_validator
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 from safetensors.torch import load_file, save_file
 import torch
 
@@ -20,10 +20,11 @@
     from art.preprocessing.pack import PackedTensors
 
 ROUTER_NAME_TOKEN = ".mlp.router"
-ROUTER_KEY_FORMAT_VERSION = "moe_routing_replay_v3"
+ROUTER_KEY_FORMAT_VERSION = "moe_routing_replay_v4"
 GLOBAL_TOKEN_UIDS_KEY = "global_token_uids"
 
 _ROUTER_LAYER_PATTERN = re.compile(r"decoder\.layers\.(?P<layer>\d+)\.mlp\.router$")
+_ROUTER_KEY_PATTERN = re.compile(r"^chunk_\d+\.layer_(?P<layer>\d+)\.mlp\.router$")
 _TRACE_CHUNK_PREFIX_PATTERN = re.compile(r"^chunk(?P<chunk>\d+)\.(?P<name>.+)$")
 logger = logging.getLogger(__name__)
 _ACTIVE_ROUTING_REPLAY_CONTROLLER: Any | None = None
@@ -33,6 +34,21 @@ def _active_routing_replay_controller() -> Any | None:
     return _ACTIVE_ROUTING_REPLAY_CONTROLLER
 
 
+@torch.compiler.disable
+def _routing_with_replay_boundary(
+    router_module: Any,
+    *args: Any,
+    **kwargs: Any,
+) -> Any:
+    controller = _active_routing_replay_controller()
+    if controller is not None:
+        controller._prepare_native_target_for_router(
+            router_module._art_routing_replay_router_key,
+            logits=args[0],
+        )
+    return router_module._art_routing_replay_original(*args, **kwargs)
+
+
 def _to_tensor_cpu_contiguous(
     tensor: torch.Tensor, *, dtype: torch.dtype
 ) -> torch.Tensor:
@@ -64,6 +80,115 @@ def build_router_key_from_module_name(*, chunk_index: int, module_name: str) ->
     return f"chunk_{chunk_index:02d}.layer_{layer_index:04d}.mlp.router"
 
 
+def _router_key_for_model_module(
+    *,
+    module_name: str,
+    layer_prefixes: list[tuple[str, int]],
+    fallback_chunk_index: int | None,
+) -> str:
+    for prefix, global_layer_index in layer_prefixes:
+        if module_name.startswith(f"{prefix}."):
+            return f"chunk_00.layer_{global_layer_index:04d}.mlp.router"
+    if fallback_chunk_index is not None:
+        return build_router_key_from_module_name(
+            chunk_index=fallback_chunk_index,
+            module_name=module_name,
+        )
+    raise RuntimeError(
+        "PP/VPP routing replay requires every router to have an owning "
+        f"TransformerLayer; router='{module_name}'"
+    )
+
+
+def _global_layer_prefixes(chunk: Any) -> list[tuple[str, int]]:
+    from megatron.core.transformer.transformer_layer import TransformerLayer
+
+    prefixes: dict[str, int] = {}
+    for module_name, module in chunk.named_modules():
+        original = getattr(module, "_orig_mod", None)
+        layer = (
+            original
+            if isinstance(original, TransformerLayer)
+            else module
+            if isinstance(module, TransformerLayer)
+            else None
+        )
+        if layer is not None:
+            prefixes[module_name] = int(layer.layer_number) - 1
+    return sorted(prefixes.items(), key=lambda item: len(item[0]), reverse=True)
+
+
+def prepare_moe_routing_replay_boundaries(
+    model_chunks: list[Any],
+    *,
+    pipeline_model: bool | None = None,
+) -> dict[str, dict[str, Any]]:
+    """Install stable eager router boundaries before model compilation."""
+    if pipeline_model is None:
+        from megatron.core import parallel_state as ps
+
+        pipeline_model = len(model_chunks) > 1 or (
+            ps.model_parallel_is_initialized()
+            and int(ps.get_pipeline_model_parallel_world_size()) > 1
+        )
+    bindings: dict[str, dict[str, Any]] = {}
+    for chunk_index, chunk in enumerate(model_chunks):
+        layer_prefixes = _global_layer_prefixes(chunk)
+        for module_name, module in chunk.named_modules():
+            if ROUTER_NAME_TOKEN not in module_name or not hasattr(module, "routing"):
+                continue
+            router_key = _router_key_for_model_module(
+                module_name=module_name,
+                layer_prefixes=layer_prefixes,
+                fallback_chunk_index=None if pipeline_model else chunk_index,
+            )
+            if router_key in bindings:
+                raise RuntimeError(
+                    "Multiple local model chunks own the same replay router: "
+                    f"router_key='{router_key}'"
+                )
+            config = getattr(module, "config", None)
+            if bool(getattr(config, "moe_router_fusion", False)):
+                raise RuntimeError(
+                    "MoE routing replay requires moe_router_fusion=False because "
+                    "Megatron Core fused routing bypasses RouterReplay: "
+                    f"router_key='{router_key}'"
+                )
+            router_replay = getattr(module, "router_replay", None)
+            if router_replay is None:
+                raise RuntimeError(
+                    "MoE routing replay requires provider.moe_enable_routing_replay=True "
+                    "before model construction: "
+                    f"router_key='{router_key}'"
+                )
+            installed_key = getattr(
+                module, "_art_routing_replay_router_key", router_key
+            )
+            if installed_key != router_key:
+                raise RuntimeError(
+                    "Routing replay boundary key changed after model construction: "
+                    f"{installed_key!r} != {router_key!r}"
+                )
+            if not getattr(module, "_art_routing_replay_target_patched", False):
+                module._art_routing_replay_original = module.routing
+                module._art_routing_replay_router_key = router_key
+                module.routing = types.MethodType(_routing_with_replay_boundary, module)
+                module._art_routing_replay_target_patched = True
+            bindings[router_key] = {
+                "module": module,
+                "router_replay": router_replay,
+                "sequence_parallel": bool(getattr(config, "sequence_parallel", False)),
+                "context_parallel_size": int(
+                    getattr(config, "context_parallel_size", 1)
+                ),
+                "topk": int(getattr(module, "topk")),
+                "chunk_index": chunk_index,
+                "layer_index": _global_layer_from_router_key(router_key),
+                "num_experts": int(getattr(config, "num_moe_experts", 0) or 0),
+            }
+    return bindings
+
+
 def build_router_key_from_trace_name(trace_module_name: str) -> str:
     chunk_match = _TRACE_CHUNK_PREFIX_PATTERN.match(trace_module_name)
     if chunk_match is None:
@@ -77,6 +202,13 @@ def build_router_key_from_trace_name(trace_module_name: str) -> str:
     )
 
 
+def _global_layer_from_router_key(router_key: str) -> int:
+    match = _ROUTER_KEY_PATTERN.fullmatch(router_key)
+    if match is None:
+        raise RuntimeError(f"Invalid routing replay router key: {router_key!r}")
+    return int(match.group("layer"))
+
+
 class ParallelTopology(BaseModel):
     tp: int
     ep: int
@@ -250,7 +382,10 @@ class MoeRoutingReplayBundle(BaseModel):
     num_steps: int
     max_topk: int
     router_keys: list[str]
-    steps: dict[int, StepRoutes]
+    steps: dict[int, StepRoutes] = Field(default_factory=dict)
+    expert_indices: torch.Tensor | None = None
+    num_experts: int | None = None
+    global_grad_accumulation_sequences: int | None = None
 
     @model_validator(mode="after")
     def _validate(self) -> "MoeRoutingReplayBundle":
@@ -267,6 +402,14 @@ def _validate(self) -> "MoeRoutingReplayBundle":
             raise RuntimeError("router_keys cannot be empty")
         if len(set(self.router_keys)) != len(self.router_keys):
             raise RuntimeError("router_keys must be unique")
+        if self.expert_indices is not None:
+            self._validate_tensor_storage()
+            return self
+        if (
+            self.num_experts is not None
+            or self.global_grad_accumulation_sequences is not None
+        ):
+            raise RuntimeError("Legacy replay bundles cannot carry tensor metadata")
         expected_steps = set(range(self.num_steps))
         if set(self.steps) != expected_steps:
             raise RuntimeError(
@@ -290,6 +433,41 @@ def _validate(self) -> "MoeRoutingReplayBundle":
                         )
         return self
 
+    @property
+    def tensor_backed(self) -> bool:
+        return self.expert_indices is not None
+
+    def _validate_tensor_storage(self) -> None:
+        indices = self.expert_indices
+        assert indices is not None
+        if self.steps:
+            raise RuntimeError("Tensor-backed replay cannot also contain route calls")
+        if indices.device.type != "cpu" or not indices.is_contiguous():
+            raise RuntimeError("Tensor-backed replay requires contiguous CPU storage")
+        if indices.ndim != 4 or min(map(int, indices.shape)) <= 0:
+            raise RuntimeError(
+                "Tensor-backed replay requires [layer, row, position, topk]"
+            )
+        num_experts = int(self.num_experts or 0)
+        expected_dtype = torch.uint8 if num_experts <= 256 else torch.uint16
+        if not 1 <= num_experts <= 65_536 or indices.dtype != expected_dtype:
+            raise RuntimeError("Tensor-backed replay expert count and dtype disagree")
+        accumulation = int(self.global_grad_accumulation_sequences or 0)
+        if accumulation <= 0:
+            raise RuntimeError("Tensor-backed replay requires positive accumulation")
+        layers, sequences, _sequence_length, topk = map(int, indices.shape)
+        if (
+            layers != len(self.router_keys)
+            or topk != self.max_topk
+            or self.num_steps != math.ceil(sequences / accumulation)
+        ):
+            raise RuntimeError("Tensor-backed replay metadata disagrees with its shape")
+        expected_keys = [
+            f"chunk_00.layer_{layer:04d}.mlp.router" for layer in range(layers)
+        ]
+        if self.router_keys != expected_keys:
+            raise RuntimeError("Tensor-backed replay router keys are not layer-major")
+
     @classmethod
     def from_dir(cls, bundle_dir: str | Path) -> "MoeRoutingReplayBundle":
         base_dir = Path(bundle_dir)
@@ -304,6 +482,24 @@ def from_dir(cls, bundle_dir: str | Path) -> "MoeRoutingReplayBundle":
                 f"{manifest.get('format_version')!r}; expected "
                 f"{ROUTER_KEY_FORMAT_VERSION!r}"
             )
+        if manifest.get("storage") == "layer_major":
+            loaded = load_file(str(base_dir / manifest["file"]))
+            indices = loaded["expert_indices"].detach().clone().contiguous()
+            del loaded
+            return cls(
+                format_version=manifest["format_version"],
+                topology=ParallelTopology.model_validate(manifest["topology"]),
+                num_steps=int(manifest["num_steps"]),
+                max_topk=int(manifest["max_topk"]),
+                router_keys=list(manifest["router_keys"]),
+                expert_indices=indices,
+                num_experts=int(manifest["num_experts"]),
+                global_grad_accumulation_sequences=int(
+                    manifest["global_grad_accumulation_sequences"]
+                ),
+            )
+        if manifest.get("storage") != "calls":
+            raise RuntimeError("Unknown MoE routing replay storage format")
 
         steps: dict[int, StepRoutes] = {}
         for step_index_str, step_info in manifest["steps"].items():
@@ -364,6 +560,28 @@ def from_dir(cls, bundle_dir: str | Path) -> "MoeRoutingReplayBundle":
     def to_dir(self, bundle_dir: str | Path) -> None:
         base_dir = Path(bundle_dir)
         base_dir.mkdir(parents=True, exist_ok=True)
+        if self.tensor_backed:
+            assert self.expert_indices is not None
+            tensor_file = "layer_major.safetensors"
+            save_file(
+                {"expert_indices": self.expert_indices}, str(base_dir / tensor_file)
+            )
+            manifest = {
+                "format_version": self.format_version,
+                "storage": "layer_major",
+                "file": tensor_file,
+                "topology": self.topology.model_dump(mode="json"),
+                "num_steps": self.num_steps,
+                "max_topk": self.max_topk,
+                "router_keys": self.router_keys,
+                "num_experts": self.num_experts,
+                "global_grad_accumulation_sequences": (
+                    self.global_grad_accumulation_sequences
+                ),
+            }
+            with (base_dir / "manifest.json").open("w", encoding="utf-8") as handle:
+                json.dump(manifest, handle, indent=2, sort_keys=True)
+            return
         manifest_steps: dict[str, Any] = {}
 
         for step_index, step_routes in sorted(self.steps.items()):
@@ -405,6 +623,7 @@ def to_dir(self, bundle_dir: str | Path) -> None:
 
         manifest = {
             "format_version": self.format_version,
+            "storage": "calls",
             "topology": self.topology.model_dump(mode="json"),
             "num_steps": self.num_steps,
             "max_topk": self.max_topk,
@@ -429,127 +648,22 @@ def build_moe_routing_replay_bundle_from_packed_tensors(
             "global_grad_accumulation_sequences must be positive when building "
             f"MoE routing replay bundles, got {global_grad_accumulation_sequences}"
         )
-    expert_indices = _to_tensor_cpu_contiguous(
-        routing_replay.expert_indices, dtype=torch.int32
-    )
-    token_mask = _to_tensor_cpu_contiguous(routing_replay.token_mask, dtype=torch.bool)
-    num_experts = int(routing_replay.num_experts)
-    num_sequences = int(expert_indices.shape[0])
-    sequence_length = int(expert_indices.shape[1])
-    num_layers = int(expert_indices.shape[2])
-    topk = int(expert_indices.shape[3])
+    expert_indices = routing_replay.expert_indices
+    num_layers, num_sequences, _sequence_length, topk = map(int, expert_indices.shape)
 
     router_keys = [
         f"chunk_00.layer_{layer_index:04d}.mlp.router"
         for layer_index in range(num_layers)
     ]
-    steps: dict[int, StepRoutes] = {}
     num_steps = math.ceil(num_sequences / global_grad_accumulation_sequences)
-    global_token_uids = torch.arange(sequence_length, dtype=torch.int64)
-    all_row_positions = torch.arange(sequence_length, dtype=torch.long)
-    for step_index in range(num_steps):
-        start = step_index * global_grad_accumulation_sequences
-        end = start + global_grad_accumulation_sequences
-        calls_by_router: dict[str, dict[int, RouterCallRoute]] = {
-            router_key: {} for router_key in router_keys
-        }
-        for offset, sample_index in enumerate(range(start, end)):
-            if sample_index < num_sequences:
-                routes_by_layer = _sample_routes_by_layer(
-                    expert_indices=expert_indices,
-                    token_mask=token_mask,
-                    sample_index=sample_index,
-                    num_experts=num_experts,
-                    topk=topk,
-                )
-                sample_route_index: int | None = sample_index
-                micro_slot: int | None = None
-            else:
-                routes_by_layer = _synthetic_replay_layer_rows(
-                    row_positions=all_row_positions,
-                    layer_seeds=_layer_replay_seeds(
-                        num_layers=num_layers,
-                        base_seed=(step_index + 1) * 1_000_003 + (offset + 1) * 9_176,
-                    ),
-                    num_experts=num_experts,
-                    topk=topk,
-                    dtype=expert_indices.dtype,
-                )
-                sample_route_index = None
-                micro_slot = offset
-            for layer_index, router_key in enumerate(router_keys):
-                calls_by_router[router_key][offset] = _full_mask_router_call_route(
-                    expert_indices=routes_by_layer[layer_index],
-                    num_experts=num_experts,
-                    sample_index=sample_route_index,
-                    micro_slot=micro_slot,
-                )
-        routers = {
-            router_key: StepRouterRoutes.model_construct(calls=calls)
-            for router_key, calls in calls_by_router.items()
-        }
-        steps[step_index] = StepRoutes.model_construct(
-            routers=routers,
-            global_token_uids=global_token_uids,
-        )
-    return MoeRoutingReplayBundle.model_construct(
+    return MoeRoutingReplayBundle(
         topology=topology or parallel_topology_from_env(),
         num_steps=num_steps,
         max_topk=topk,
         router_keys=router_keys,
-        steps=steps,
-    )
-
-
-def _sample_routes_by_layer(
-    *,
-    expert_indices: torch.Tensor,
-    token_mask: torch.Tensor,
-    sample_index: int,
-    num_experts: int,
-    topk: int,
-) -> torch.Tensor:
-    routes_by_layer = expert_indices[sample_index].permute(1, 0, 2).contiguous()
-    missing_positions = torch.nonzero(~token_mask[sample_index], as_tuple=False).view(
-        -1
-    )
-    if int(missing_positions.numel()) == 0:
-        return routes_by_layer
-    # Megatron Core RouterReplay requires concrete top-k ids. The packer leaves
-    # only padding and terminal query rows missing, so materialize deterministic
-    # values for those rows without rescanning the bundle here.
-    routes_by_layer[:, missing_positions, :] = _synthetic_replay_layer_rows(
-        row_positions=missing_positions,
-        layer_seeds=_layer_replay_seeds(
-            num_layers=int(expert_indices.shape[2]),
-            base_seed=(sample_index + 1) * 1_000_003,
-        ),
-        num_experts=num_experts,
-        topk=topk,
-        dtype=expert_indices.dtype,
-    )
-    return routes_by_layer
-
-
-def _layer_replay_seeds(*, num_layers: int, base_seed: int) -> torch.Tensor:
-    return base_seed + (torch.arange(num_layers, dtype=torch.long) + 1) * 97_003
-
-
-def _full_mask_router_call_route(
-    *,
-    expert_indices: torch.Tensor,
-    num_experts: int,
-    sample_index: int | None = None,
-    micro_slot: int | None = None,
-) -> RouterCallRoute:
-    return RouterCallRoute.model_construct(
         expert_indices=expert_indices,
-        expert_probs=None,
-        expert_mask=None,
-        num_experts=int(num_experts),
-        sample_index=None if sample_index is None else int(sample_index),
-        micro_slot=None if micro_slot is None else int(micro_slot),
-        rank_token_counts=None,
+        num_experts=routing_replay.num_experts,
+        global_grad_accumulation_sequences=global_grad_accumulation_sequences,
     )
 
 
@@ -562,7 +676,24 @@ def parallel_topology_from_env() -> ParallelTopology:
     )
     cp = _env_int("ART_MEGATRON_CONTEXT_PARALLEL_SIZE", 1)
     pp = _env_int("ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE", 1)
-    return ParallelTopology(tp=tp, ep=ep, etp=etp, dp=1, sp=tp > 1, cp=cp, pp=pp)
+    vpp = _env_int("ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE", 1)
+    world_size = _env_int("WORLD_SIZE", tp * cp * pp)
+    model_parallel_size = tp * cp * pp
+    if world_size % model_parallel_size:
+        raise RuntimeError(
+            f"WORLD_SIZE={world_size} is not divisible by TP*CP*PP="
+            f"{model_parallel_size}"
+        )
+    return ParallelTopology(
+        tp=tp,
+        ep=ep,
+        etp=etp,
+        dp=world_size // model_parallel_size,
+        sp=tp > 1,
+        cp=cp,
+        pp=pp,
+        vpp=vpp,
+    )
 
 
 def _env_int(name: str, default: int) -> int:
@@ -711,33 +842,41 @@ def __init__(
         self._device = torch.device(device) if device is not None else None
 
         self._active_step_index: int | None = None
+        self._active_step_samples: list[int | None] = []
         self._active_sample_index: int | None = None
         self._active_step_routes: StepRoutes | None = None
         self._active_micro_order: int | None = None
+        self._active_chunk_index: int | None = None
         self._router_call_cursors: dict[str, int] = {}
         self._router_call_sequences: dict[str, list[int]] = {}
         self._router_last_call_indices: dict[str, int] = {}
         self._router_last_call_keys: dict[str, tuple[str, int] | None] = {}
+        self._router_consumed_calls: dict[str, dict[tuple[str, int], int]] = {}
         self._router_reuse_counts: dict[str, int] = {}
         self._global_uid_to_row_index: dict[int, int] = {}
         self._global_uid_dense_start: int | None = None
         self._global_uid_count: int = 0
         self._local_router_keys: set[str] = set()
+        self._local_router_keys_by_chunk: dict[int, set[str]] = {}
         self._router_bindings: dict[str, dict[str, Any]] = {}
+        self._runtime_topology: ParallelTopology | None = None
+        self._expect_recompute_reuse = False
         self._prepared_uid_sets: dict[str, torch.Tensor] = {}
         self._prepared_targets: dict[tuple[str, str, int], torch.Tensor] = {}
         self._router_prepared_target_keys: dict[str, tuple[str, int]] = {}
-        self._target_buffers: dict[tuple[str, str, int], torch.Tensor] = {}
+        self._step_targets: dict[tuple[str, str, int], torch.Tensor] = {}
+        self._step_target_ready_events: dict[
+            tuple[str, str, int], torch.cuda.Event
+        ] = {}
         self._host_target_staging: list[torch.Tensor] = []
         self._target_copy_stream: torch.cuda.Stream | None = None
-        self._target_copy_event: torch.cuda.Event | None = None
-        self._target_copy_waited: bool = True
         self._active_token_uid_key: str | None = None
 
     def update_bundle(self, *, bundle: MoeRoutingReplayBundle, strict: bool) -> None:
         self.bundle = bundle
         self.strict = strict
         self.clear_replay_state()
+        self._validate_runtime_topology()
         if self.strict:
             missing = sorted(
                 router_key
@@ -749,6 +888,7 @@ def update_bundle(self, *, bundle: MoeRoutingReplayBundle, strict: bool) -> None
                     "Router keys from model are missing in replay bundle: "
                     f"router_keys={missing}"
                 )
+        self._validate_local_routes()
 
     def clear_replay_state(self) -> None:
         self._clear_native_router_replay_state()
@@ -765,147 +905,174 @@ def install_router_patches(self, model_chunks: list[Any]) -> None:
         global _ACTIVE_ROUTING_REPLAY_CONTROLLER
         if self._router_bindings:
             return
-        for chunk_index, chunk in enumerate(model_chunks):
-            for module_name, module in chunk.named_modules():
-                if ROUTER_NAME_TOKEN not in module_name or not hasattr(
-                    module, "routing"
-                ):
-                    continue
-                router_key = build_router_key_from_module_name(
-                    chunk_index=chunk_index,
-                    module_name=module_name,
-                )
-                if self.strict and router_key not in self.bundle.router_keys:
-                    raise RuntimeError(
-                        "Router key from model is missing in replay bundle: "
-                        f"router_key='{router_key}'"
-                    )
-                config = getattr(module, "config", None)
-                if bool(getattr(config, "moe_router_fusion", False)):
-                    raise RuntimeError(
-                        "MoE routing replay requires moe_router_fusion=False because "
-                        "Megatron Core fused routing bypasses RouterReplay: "
-                        f"router_key='{router_key}'"
-                    )
-                router_replay = getattr(module, "router_replay", None)
-                if router_replay is None:
-                    raise RuntimeError(
-                        "MoE routing replay requires provider.moe_enable_routing_replay=True "
-                        "before model construction: "
-                        f"router_key='{router_key}'"
-                    )
-                if getattr(router_replay, "_art_routing_replay_patched", False):
-                    raise RuntimeError(
-                        "RouterReplay instance is already patched: "
-                        f"router_key='{router_key}'"
-                    )
-                if getattr(module, "_art_routing_replay_target_patched", False):
-                    raise RuntimeError(
-                        "Router module routing method is already patched: "
-                        f"router_key='{router_key}'"
-                    )
-
-                sequence_parallel = bool(getattr(config, "sequence_parallel", False))
-                context_parallel_size = int(getattr(config, "context_parallel_size", 1))
-                topk = int(getattr(module, "topk"))
-                original_routing = module.routing
-
-                def _prepare_native_target_for_bound_router(
-                    _controller: MoeRoutingReplayController = self,
-                    _router_key: str = router_key,
-                ) -> None:
-                    _controller._prepare_native_target_for_router(_router_key)
-
-                prepare_native_target = torch.compiler.disable(
-                    _prepare_native_target_for_bound_router
-                )
-
-                def _hash_routing_with_replay_target(
-                    router_module: Any,
-                    *args: Any,
-                    _original_routing: Any = original_routing,
-                    _prepare_native_target: Any = prepare_native_target,
-                    **kwargs: Any,
-                ) -> Any:
-                    del router_module
-                    _prepare_native_target()
-                    return _original_routing(*args, **kwargs)
-
-                def _moe_routing_with_replay_target(
-                    router_module: Any,
-                    *args: Any,
-                    _original_routing: Any = original_routing,
-                    _prepare_native_target: Any = prepare_native_target,
-                    **kwargs: Any,
-                ) -> Any:
-                    del router_module
-                    _prepare_native_target()
-                    return _original_routing(*args, **kwargs)
-
-                def _routing_with_replay_target(
-                    router_module: Any,
-                    *args: Any,
-                    _original_routing: Any = original_routing,
-                    _prepare_native_target: Any = prepare_native_target,
-                    **kwargs: Any,
-                ) -> Any:
-                    del router_module
-                    # Target selection mutates Python replay cursors and Megatron's
-                    # RouterReplay state; keep it out of Dynamo while preserving
-                    # compiled routing compute below.
-                    _prepare_native_target()
-                    return _original_routing(*args, **kwargs)
-
-                original_routing_name = getattr(
-                    getattr(original_routing, "__func__", None), "__name__", ""
-                )
-                if original_routing_name == "_hash_routing":
-                    routing_wrapper = _hash_routing_with_replay_target
-                elif original_routing_name == "_moe_routing":
-                    routing_wrapper = _moe_routing_with_replay_target
-                else:
-                    routing_wrapper = _routing_with_replay_target
-                # Routing replay mutates Python replay cursors and Megatron's
-                # RouterReplay target state immediately before routing consumes
-                # it. Keep the whole patched routing boundary eager so Dynamo
-                # cannot specialize or reorder around that mutable replay state.
-                module.routing = types.MethodType(
-                    torch.compiler.disable(routing_wrapper),
-                    module,
+        pipeline_model = self.bundle.topology.pp > 1 or len(model_chunks) > 1
+        bindings = prepare_moe_routing_replay_boundaries(
+            model_chunks,
+            pipeline_model=pipeline_model,
+        )
+        self._local_router_keys_by_chunk = {
+            chunk_index: set() for chunk_index in range(len(model_chunks))
+        }
+        for router_key, binding in bindings.items():
+            chunk_index = int(binding["chunk_index"])
+            if self.strict and router_key not in self.bundle.router_keys:
+                raise RuntimeError(
+                    "Router key from model is missing in replay bundle: "
+                    f"router_key='{router_key}'"
                 )
-                setattr(module, "_art_routing_replay_target_patched", True)
-                self._router_bindings[router_key] = {
-                    "module": module,
-                    "original_routing": original_routing,
-                    "router_replay": router_replay,
-                    "sequence_parallel": sequence_parallel,
-                    "context_parallel_size": context_parallel_size,
-                    "topk": topk,
-                }
-                self._local_router_keys.add(router_key)
+            self._router_bindings[router_key] = binding
+            self._local_router_keys.add(router_key)
+            self._local_router_keys_by_chunk[chunk_index].add(router_key)
+        self._runtime_topology = self._runtime_parallel_topology(model_chunks)
+        self._validate_runtime_topology()
+        self._validate_local_routes()
+        self._expect_recompute_reuse = bool(self._router_bindings) and all(
+            getattr(binding["module"].config, "recompute_granularity", None) == "full"
+            and getattr(binding["module"].config, "recompute_method", None) == "uniform"
+            and int(getattr(binding["module"].config, "recompute_num_layers", 0) or 0)
+            == 1
+            for binding in self._router_bindings.values()
+        )
         _ACTIVE_ROUTING_REPLAY_CONTROLLER = self
 
     def remove_router_patches(self) -> None:
         global _ACTIVE_ROUTING_REPLAY_CONTROLLER
         if _ACTIVE_ROUTING_REPLAY_CONTROLLER is self:
             _ACTIVE_ROUTING_REPLAY_CONTROLLER = None
-        for binding in self._router_bindings.values():
-            module = binding["module"]
-            original_routing = binding.get("original_routing")
-            if original_routing is not None:
-                module.routing = original_routing
-            if hasattr(module, "_art_routing_replay_target_patched"):
-                delattr(module, "_art_routing_replay_target_patched")
         self._router_bindings.clear()
         self._local_router_keys.clear()
-        self._target_buffers.clear()
+        self._local_router_keys_by_chunk.clear()
+        self._runtime_topology = None
+        self._expect_recompute_reuse = False
+        self._step_targets.clear()
         self._clear_native_router_replay_state()
         self._reset_step_state()
 
-    def begin_micro(self, sample_index: int | None, micro_order: int) -> None:
+    @staticmethod
+    def _runtime_parallel_topology(
+        model_chunks: list[Any],
+    ) -> ParallelTopology | None:
+        if not torch.distributed.is_initialized():  # ty: ignore[possibly-missing-attribute]
+            return None
+        from megatron.core import parallel_state as ps
+        from megatron.core.utils import get_model_config
+
+        sequence_parallel = {
+            bool(getattr(get_model_config(chunk), "sequence_parallel", False))
+            for chunk in model_chunks
+        }
+        if len(sequence_parallel) != 1:
+            raise RuntimeError(
+                "Model chunks disagree on sequence_parallel: "
+                f"values={sorted(sequence_parallel)}"
+            )
+        return ParallelTopology(
+            tp=int(ps.get_tensor_model_parallel_world_size()),
+            ep=int(ps.get_expert_model_parallel_world_size()),
+            etp=int(ps.get_expert_tensor_parallel_world_size()),
+            dp=int(ps.get_data_parallel_world_size()),
+            sp=sequence_parallel.pop(),
+            cp=int(ps.get_context_parallel_world_size()),
+            pp=int(ps.get_pipeline_model_parallel_world_size()),
+            vpp=int(ps.get_virtual_pipeline_model_parallel_world_size() or 1),
+        )
+
+    def _validate_runtime_topology(self) -> None:
+        if (
+            self._runtime_topology is not None
+            and self.bundle.topology != self._runtime_topology
+        ):
+            raise RuntimeError(
+                "Routing replay bundle topology differs from the active trainer: "
+                f"bundle={self.bundle.topology.model_dump()}, "
+                f"runtime={self._runtime_topology.model_dump()}"
+            )
+
+    def _validate_local_routes(self) -> None:
+        if self.bundle.tensor_backed:
+            assert self.bundle.expert_indices is not None
+            for router_key, binding in self._router_bindings.items():
+                if router_key not in self.bundle.router_keys:
+                    continue
+                model_num_experts = int(binding["num_experts"])
+                if model_num_experts and model_num_experts != self.bundle.num_experts:
+                    raise RuntimeError(
+                        "Replay expert count does not match the model router: "
+                        f"router='{router_key}', replay={self.bundle.num_experts}, "
+                        f"model={model_num_experts}"
+                    )
+                if int(binding["topk"]) != self.bundle.max_topk:
+                    raise RuntimeError(
+                        "Replay route topk does not match Megatron router topk: "
+                        f"router='{router_key}', replay={self.bundle.max_topk}, "
+                        f"router_topk={binding['topk']}"
+                    )
+                if int(binding["layer_index"]) >= int(
+                    self.bundle.expert_indices.shape[0]
+                ):
+                    raise RuntimeError(
+                        f"Replay has no global layer for router '{router_key}'"
+                    )
+            return
+        for router_key, binding in self._router_bindings.items():
+            if router_key not in self.bundle.router_keys:
+                continue
+            model_num_experts = int(binding["num_experts"])
+            for step_index, step in self.bundle.steps.items():
+                for call_index, route in step.routers[router_key].calls.items():
+                    selected = (
+                        route.expert_indices
+                        if route.expert_mask is None
+                        else route.expert_indices[route.expert_mask]
+                    )
+                    if int(selected.numel()) == 0:
+                        continue
+                    minimum = int(selected.min().item())
+                    maximum = int(selected.max().item())
+                    limit = model_num_experts or int(route.num_experts)
+                    if minimum < 0 or maximum >= limit:
+                        raise RuntimeError(
+                            "Replay route expert id is outside the model router: "
+                            f"step={step_index}, router='{router_key}', "
+                            f"call={call_index}, range=[{minimum}, {maximum}], "
+                            f"num_experts={limit}"
+                        )
+
+    def _active_local_router_keys(self) -> set[str]:
+        if self._active_chunk_index is None:
+            raise RuntimeError("Routing replay chunk is not active")
+        try:
+            return self._local_router_keys_by_chunk[self._active_chunk_index]
+        except KeyError as exc:
+            raise RuntimeError(
+                f"Routing replay received unknown model chunk {self._active_chunk_index}"
+            ) from exc
+
+    def begin_micro(
+        self,
+        sample_index: int | None,
+        micro_order: int,
+        chunk_index: int = 0,
+    ) -> None:
+        if self._active_step_index is None:
+            raise RuntimeError("Routing replay begin_micro called before set_step")
+        if self.bundle.tensor_backed:
+            if not 0 <= micro_order < len(self._active_step_samples):
+                raise RuntimeError(
+                    f"Routing replay micro order is out of range: {micro_order}"
+                )
+            expected_sample = self._active_step_samples[micro_order]
+            if sample_index != expected_sample:
+                raise RuntimeError(
+                    "Routing replay micro sample differs from set_step: "
+                    f"micro={micro_order}, expected={expected_sample}, "
+                    f"actual={sample_index}"
+                )
         self._active_sample_index = sample_index
         self._active_micro_order = micro_order
-        for router_key in sorted(self._local_router_keys):
+        self._active_chunk_index = chunk_index
+        self._reset_staged_micro_targets()
+        for router_key in sorted(self._active_local_router_keys()):
             call_indices = self._active_micro_call_indices(router_key)
             if len(call_indices) != 1:
                 raise RuntimeError(
@@ -925,7 +1092,7 @@ def prepare_micro_targets(
         *,
         active_token_uid_key: str = "attention",
     ) -> None:
-        if self._active_step_routes is None or self._active_micro_order is None:
+        if self._active_step_index is None or self._active_micro_order is None:
             raise RuntimeError(
                 "Routing replay target staging requires set_step and begin_micro"
             )
@@ -943,11 +1110,13 @@ def prepare_micro_targets(
                 f"key='{active_token_uid_key}', prepared={sorted(prepared_uid_sets)}"
             )
         self._prepared_uid_sets = prepared_uid_sets
-        if not self._local_router_keys:
+        active_router_keys = self._active_local_router_keys()
+        if not active_router_keys:
             self._active_token_uid_key = active_token_uid_key
             return
+        new_target_keys: list[tuple[str, str, int]] = []
         for token_uid_key, token_uids in prepared_uid_sets.items():
-            for router_key in sorted(self._local_router_keys):
+            for router_key in sorted(active_router_keys):
                 call_indices = self._active_micro_call_indices(router_key)
                 if len(call_indices) != 1:
                     raise RuntimeError(
@@ -956,6 +1125,11 @@ def prepare_micro_targets(
                     )
                 call_index = call_indices[0]
                 binding = self._router_bindings[router_key]
+                target_key = (token_uid_key, router_key, call_index)
+                cached_target = self._step_targets.get(target_key)
+                if cached_target is not None:
+                    self._prepared_targets[target_key] = cached_target
+                    continue
                 router_token_uids = self._token_uids_for_router_binding(
                     token_uids,
                     sequence_parallel=bool(binding["sequence_parallel"]),
@@ -966,14 +1140,16 @@ def prepare_micro_targets(
                     explicit_uids=router_token_uids,
                 )
                 self._stage_prepared_target(
-                    target_key=(token_uid_key, router_key, call_index),
+                    target_key=target_key,
                     target_cpu=target_cpu,
                 )
-        self._record_target_copy_event()
+                self._step_targets[target_key] = self._prepared_targets[target_key]
+                new_target_keys.append(target_key)
+        self._record_target_copy_event(new_target_keys)
         self.set_active_token_uid_key(active_token_uid_key)
 
     def set_active_token_uid_key(self, token_uid_key: str) -> None:
-        if not self._local_router_keys:
+        if not self._active_local_router_keys():
             self._active_token_uid_key = token_uid_key
             return
         prepared_keys = {
@@ -1023,6 +1199,14 @@ def set_step(
         step_index: int,
         sample_index: int | list[int | None] | None,
     ) -> None:
+        if self.bundle.tensor_backed:
+            self._set_tensor_step(step_index=step_index, sample_index=sample_index)
+            RouterReplay, RouterReplayAction = _router_replay_classes()
+            RouterReplay.clear_global_indices()
+            RouterReplay.set_global_router_replay_action(
+                RouterReplayAction.REPLAY_FORWARD
+            )
+            return
         if step_index not in self.bundle.steps:
             raise RuntimeError(
                 f"Replay bundle missing step_index={step_index}. "
@@ -1036,12 +1220,17 @@ def set_step(
             else sample_index
         )
         self._active_micro_order = None
+        self._active_chunk_index = None
         self._active_step_routes = step_routes
         self._reset_staged_micro_targets()
+        self._step_targets = {}
+        self._step_target_ready_events = {}
+        self._host_target_staging = []
         self._router_call_cursors = {}
         self._router_call_sequences = {}
         self._router_last_call_indices = {}
         self._router_last_call_keys = {}
+        self._router_consumed_calls = {}
         self._router_reuse_counts = {}
         self._global_uid_count = int(step_routes.global_token_uids.numel())
         self._global_uid_dense_start = self._dense_global_uid_start(
@@ -1079,6 +1268,7 @@ def set_step(
                         f"route_topk={route.max_topk}, router_topk={binding_topk}"
                     )
             self._router_call_cursors[router_key] = 0
+            self._router_consumed_calls[router_key] = {}
             self._router_call_sequences[router_key] = self._build_call_sequence(
                 router_key=router_key,
                 sample_index=sample_index,
@@ -1087,8 +1277,46 @@ def set_step(
         RouterReplay.clear_global_indices()
         RouterReplay.set_global_router_replay_action(RouterReplayAction.REPLAY_FORWARD)
 
-    def finalize_step(self) -> None:
-        if self._active_step_routes is None:
+    def _set_tensor_step(
+        self,
+        *,
+        step_index: int,
+        sample_index: int | list[int | None] | None,
+    ) -> None:
+        if not 0 <= step_index < self.bundle.num_steps:
+            raise RuntimeError(
+                f"Replay bundle missing step_index={step_index}. "
+                f"Available steps={list(range(self.bundle.num_steps))}"
+            )
+        samples = sample_index if isinstance(sample_index, list) else [sample_index]
+        if not samples:
+            raise RuntimeError("Routing replay step requires at least one microbatch")
+        assert self.bundle.expert_indices is not None
+        accumulation = int(self.bundle.global_grad_accumulation_sequences or 0)
+        start = step_index * accumulation
+        stop = min(start + accumulation, int(self.bundle.expert_indices.shape[1]))
+        real_samples = [sample for sample in samples if sample is not None]
+        if len(real_samples) != len(set(real_samples)) or any(
+            not start <= sample < stop for sample in real_samples
+        ):
+            raise RuntimeError(
+                "Routing replay samples do not belong to the active step: "
+                f"step={step_index}, span=[{start}, {stop}), samples={samples}"
+            )
+
+        self._reset_step_state()
+        self._active_step_index = step_index
+        self._active_step_samples = list(samples)
+        self._global_uid_dense_start = 0
+        self._global_uid_count = int(self.bundle.expert_indices.shape[2])
+        call_sequence = list(range(len(samples)))
+        for router_key in self._local_router_keys:
+            self._router_call_cursors[router_key] = 0
+            self._router_call_sequences[router_key] = call_sequence
+            self._router_consumed_calls[router_key] = {}
+
+    def finalize_step(self, *, expect_recompute: bool = False) -> None:
+        if self._active_step_index is None:
             raise RuntimeError("finalize_step called before set_step")
         for router_key in sorted(self._local_router_keys):
             consumed = self._router_call_cursors.get(router_key, 0)
@@ -1104,6 +1332,14 @@ def finalize_step(self) -> None:
                     f"step={self._active_step_index}, router='{router_key}', "
                     f"consumed={consumed}, expected={len(call_sequence)}"
                 )
+            if expect_recompute and self._expect_recompute_reuse:
+                reused = self._router_reuse_counts.get(router_key, 0)
+                if reused != len(call_sequence):
+                    raise RuntimeError(
+                        "Routing replay recompute consumption mismatch: "
+                        f"step={self._active_step_index}, router='{router_key}', "
+                        f"reused={reused}, expected={len(call_sequence)}"
+                    )
         if self._router_reuse_counts:
             logger.info(
                 "Routing replay reused routes for recompute: step=%s counts=%s",
@@ -1115,15 +1351,21 @@ def finalize_step(self) -> None:
 
     def _reset_step_state(self) -> None:
         self._active_step_index = None
+        self._active_step_samples = []
         self._active_sample_index = None
         self._active_step_routes = None
         self._active_micro_order = None
+        self._active_chunk_index = None
         self._router_call_cursors = {}
         self._router_call_sequences = {}
         self._router_last_call_indices = {}
         self._router_last_call_keys = {}
+        self._router_consumed_calls = {}
         self._router_reuse_counts = {}
         self._reset_staged_micro_targets()
+        self._step_targets = {}
+        self._step_target_ready_events = {}
+        self._host_target_staging = []
         self._global_uid_to_row_index = {}
         self._global_uid_dense_start = None
         self._global_uid_count = 0
@@ -1132,9 +1374,6 @@ def _reset_staged_micro_targets(self) -> None:
         self._prepared_uid_sets = {}
         self._prepared_targets = {}
         self._router_prepared_target_keys = {}
-        self._host_target_staging = []
-        self._target_copy_event = None
-        self._target_copy_waited = True
         self._active_token_uid_key = None
 
     @staticmethod
@@ -1223,12 +1462,21 @@ def _active_router_call_key(self) -> tuple[str, int] | None:
         )
 
     def _active_micro_call_indices(self, router_key: str) -> list[int]:
+        if self.bundle.tensor_backed:
+            if self._active_step_index is None or self._active_micro_order is None:
+                raise RuntimeError("Routing replay begin_micro called before set_step")
+            return [self._active_micro_order]
         if self._active_step_routes is None:
             raise RuntimeError("Routing replay begin_micro called before set_step")
         router_calls = self._active_step_routes.routers[router_key].calls
         call_sequence = self._router_call_sequences[router_key]
         cursor = self._router_call_cursors.get(router_key, 0)
         active_call_key = self._active_router_call_key()
+        consumed_call = self._router_consumed_calls.get(router_key, {}).get(
+            active_call_key
+        )
+        if consumed_call is not None:
+            return [consumed_call]
         if cursor >= len(call_sequence):
             last_index = self._router_last_call_indices.get(router_key)
             last_key = self._router_last_call_keys.get(router_key)
@@ -1259,6 +1507,33 @@ def _active_micro_call_indices(self, router_key: str) -> list[int]:
         return indices
 
     def _next_route_call_index(self, router_key: str) -> int:
+        if self.bundle.tensor_backed:
+            if self._active_step_index is None or self._active_micro_order is None:
+                raise RuntimeError(
+                    "Routing replay router call occurred before set_step"
+                )
+            call_index = self._active_micro_order
+            call_key = ("micro", call_index)
+            consumed = self._router_consumed_calls[router_key]
+            if call_key in consumed:
+                if not self.allow_recompute_reuse:
+                    raise RuntimeError(
+                        "Routing replay recompute reuse is disabled: "
+                        f"step={self._active_step_index}, router='{router_key}', "
+                        f"micro={call_index}"
+                    )
+                self._router_reuse_counts[router_key] = (
+                    self._router_reuse_counts.get(router_key, 0) + 1
+                )
+                return call_index
+            if call_index not in self._router_call_sequences[router_key]:
+                raise RuntimeError(
+                    "Routing replay micro is outside the local call sequence: "
+                    f"router='{router_key}', micro={call_index}"
+                )
+            consumed[call_key] = call_index
+            self._router_call_cursors[router_key] += 1
+            return call_index
         if self._active_step_routes is None:
             raise RuntimeError("Routing replay router call occurred before set_step")
         router_calls = self._active_step_routes.routers[router_key].calls
@@ -1270,6 +1545,20 @@ def _next_route_call_index(self, router_key: str) -> int:
             )
         cursor = self._router_call_cursors.get(router_key, 0)
         active_call_key = self._active_router_call_key()
+        consumed_call = self._router_consumed_calls.get(router_key, {}).get(
+            active_call_key
+        )
+        if consumed_call is not None:
+            if not self.allow_recompute_reuse:
+                raise RuntimeError(
+                    "Routing replay recompute reuse is disabled: "
+                    f"step={self._active_step_index}, router='{router_key}', "
+                    f"call_key={active_call_key}"
+                )
+            self._router_reuse_counts[router_key] = (
+                self._router_reuse_counts.get(router_key, 0) + 1
+            )
+            return consumed_call
         last_index = self._router_last_call_indices.get(router_key)
         last_key = self._router_last_call_keys.get(router_key)
         next_key = (
@@ -1302,14 +1591,16 @@ def _next_route_call_index(self, router_key: str) -> int:
         call_index = call_sequence[cursor]
         self._router_call_cursors[router_key] = cursor + 1
         self._router_last_call_indices[router_key] = call_index
-        self._router_last_call_keys[router_key] = _router_call_key(
-            router_calls[call_index]
-        )
+        call_key = _router_call_key(router_calls[call_index])
+        self._router_last_call_keys[router_key] = call_key
+        self._router_consumed_calls[router_key][call_key] = call_index
         return call_index
 
-    def _prepare_native_target_for_router(self, router_key: str) -> None:
+    def _prepare_native_target_for_router(
+        self, router_key: str, *, logits: torch.Tensor
+    ) -> None:
         if (
-            self._active_step_routes is None
+            self._active_step_index is None
             or self._active_micro_order is None
             or self._active_token_uid_key is None
         ):
@@ -1317,6 +1608,13 @@ def _prepare_native_target_for_router(self, router_key: str) -> None:
                 "Routing replay router call occurred before staged targets were ready: "
                 f"router='{router_key}'"
             )
+        binding = self._router_bindings[router_key]
+        if int(binding["chunk_index"]) != self._active_chunk_index:
+            raise RuntimeError(
+                "Routing replay router ran under the wrong VPP chunk: "
+                f"router='{router_key}', owner={binding['chunk_index']}, "
+                f"active={self._active_chunk_index}"
+            )
         call_indices = self._active_micro_call_indices(router_key)
         if len(call_indices) != 1:
             raise RuntimeError(
@@ -1331,9 +1629,6 @@ def _prepare_native_target_for_router(self, router_key: str) -> None:
                 f"actual={call_index}"
             )
         target_key = (self._active_token_uid_key, call_index)
-        if self._router_prepared_target_keys.get(router_key) == target_key:
-            return
-        self.wait_for_staged_targets()
         staged_key = (self._active_token_uid_key, router_key, call_index)
         target = self._prepared_targets.get(staged_key)
         if target is None:
@@ -1342,14 +1637,34 @@ def _prepare_native_target_for_router(self, router_key: str) -> None:
                 f"step={self._active_step_index}, router='{router_key}', "
                 f"call={call_index}, token_uid_key='{self._active_token_uid_key}'"
             )
-        topk = int(self._router_bindings[router_key]["topk"])
+        self._wait_for_staged_target(staged_key, target)
+        if target.device.type == "cuda":
+            target.record_stream(torch.cuda.current_stream(target.device))
+        if self._router_prepared_target_keys.get(router_key) == target_key:
+            return
+        topk = int(binding["topk"])
+        logit_experts = int(logits.shape[-1])
+        model_num_experts = int(binding["num_experts"])
+        if model_num_experts and model_num_experts != logit_experts:
+            raise RuntimeError(
+                "Routing replay router expert count differs from logits: "
+                f"router='{router_key}', model_experts={model_num_experts}, "
+                f"logit_experts={logit_experts}"
+            )
+        expected_tokens = int(logits.numel()) // logit_experts
+        if int(target.shape[0]) != expected_tokens:
+            raise RuntimeError(
+                "Routing replay target token count differs from router logits: "
+                f"router='{router_key}', target_tokens={int(target.shape[0])}, "
+                f"logit_tokens={expected_tokens}"
+            )
         if int(target.shape[1]) != topk:
             raise RuntimeError(
                 "Routing replay target topk mismatch at router call: "
                 f"router='{router_key}', call={call_index}, "
                 f"target_topk={int(target.shape[1])}, router_topk={topk}"
             )
-        router_replay = self._router_bindings[router_key]["router_replay"]
+        router_replay = binding["router_replay"]
         router_replay.set_target_indices(target)
         router_replay.set_router_replay_action(
             _router_replay_classes()[1].REPLAY_FORWARD
@@ -1363,6 +1678,52 @@ def _explicit_target_for_router_call(
         call_index: int,
         explicit_uids: torch.Tensor,
     ) -> torch.Tensor:
+        if self.bundle.tensor_backed:
+            assert self.bundle.expert_indices is not None
+            num_experts = int(self.bundle.num_experts or 0)
+            topk = self.bundle.max_topk
+            layer_index = int(self._router_bindings[router_key]["layer_index"])
+            sample_index = self._active_step_samples[call_index]
+            source = (
+                None
+                if sample_index is None
+                else self.bundle.expert_indices[layer_index, sample_index]
+            )
+            local_uids = explicit_uids.reshape(-1).contiguous()
+            target_cpu = torch.empty(
+                (int(local_uids.numel()), topk),
+                dtype=(torch.uint8 if num_experts <= 256 else torch.uint16),
+            )
+            valid_positions = torch.nonzero(local_uids >= 0, as_tuple=False).reshape(-1)
+            if int(valid_positions.numel()) > 0:
+                valid_uids = local_uids[valid_positions]
+                if source is None:
+                    target_cpu[valid_positions] = _synthetic_replay_rows(
+                        row_positions=valid_uids,
+                        num_experts=num_experts,
+                        topk=topk,
+                        dtype=target_cpu.dtype,
+                        seed=self._tensor_synthetic_seed(layer_index, call_index),
+                    )
+                else:
+                    row_indices = self._row_indices_for_explicit_uids(
+                        valid_uids=valid_uids,
+                        router_key=router_key,
+                        call_index=call_index,
+                    )
+                    target_cpu[valid_positions] = source.index_select(0, row_indices)
+            invalid_positions = torch.nonzero(local_uids < 0, as_tuple=False).reshape(
+                -1
+            )
+            if int(invalid_positions.numel()) > 0:
+                target_cpu[invalid_positions] = _synthetic_replay_rows(
+                    row_positions=invalid_positions,
+                    num_experts=num_experts,
+                    topk=topk,
+                    dtype=target_cpu.dtype,
+                    seed=self._tensor_synthetic_seed(layer_index, call_index),
+                )
+            return target_cpu.contiguous()
         if self._active_step_routes is None:
             raise RuntimeError("Routing replay explicit target used before set_step")
         route = self._active_step_routes.routers[router_key].calls[call_index]
@@ -1395,6 +1756,13 @@ def _explicit_target_for_router_call(
             )
         return target_cpu.contiguous()
 
+    def _tensor_synthetic_seed(self, layer_index: int, call_index: int) -> int:
+        return (
+            (int(self._active_step_index or 0) + 1) * 1_000_003
+            + (layer_index + 1) * 97_003
+            + (call_index + 1) * 9_176
+        )
+
     def _row_indices_for_explicit_uids(
         self,
         *,
@@ -1455,10 +1823,10 @@ def _stage_prepared_target(
         target_key: tuple[str, str, int],
         target_cpu: torch.Tensor,
     ) -> None:
-        target_cpu = target_cpu.to(dtype=torch.long).contiguous()
+        target_cpu = target_cpu.contiguous()
         device = self._target_device()
         if device.type != "cuda":
-            self._prepared_targets[target_key] = target_cpu
+            self._prepared_targets[target_key] = target_cpu.to(dtype=torch.long)
             return
         if self._target_copy_stream is None:
             self._target_copy_stream = torch.cuda.Stream(device=device)
@@ -1466,36 +1834,36 @@ def _stage_prepared_target(
             target_cpu if target_cpu.is_pinned() else target_cpu.pin_memory()
         ).contiguous()
         self._host_target_staging.append(host_target)
-        buffer = self._target_buffers.get(target_key)
-        if (
-            buffer is None
-            or buffer.shape != host_target.shape
-            or buffer.device != device
-            or buffer.dtype != torch.long
-        ):
-            buffer = torch.empty(
+        with torch.cuda.stream(self._target_copy_stream):
+            narrow_buffer = torch.empty(
                 tuple(host_target.shape),
                 device=device,
-                dtype=torch.long,
+                dtype=host_target.dtype,
             )
-            self._target_buffers[target_key] = buffer
-        with torch.cuda.stream(self._target_copy_stream):
-            buffer.copy_(host_target, non_blocking=True)
+            narrow_buffer.copy_(host_target, non_blocking=True)
+            buffer = narrow_buffer.to(dtype=torch.long)
+            narrow_buffer.record_stream(self._target_copy_stream)
             buffer.record_stream(self._target_copy_stream)
         self._prepared_targets[target_key] = buffer
-        self._target_copy_waited = False
 
-    def _record_target_copy_event(self) -> None:
-        if self._target_copy_stream is None or self._target_copy_waited:
+    def _record_target_copy_event(
+        self,
+        target_keys: list[tuple[str, str, int]],
+    ) -> None:
+        if self._target_copy_stream is None or not target_keys:
             return
-        self._target_copy_event = torch.cuda.Event()
+        ready = torch.cuda.Event()
         with torch.cuda.stream(self._target_copy_stream):
-            self._target_copy_event.record()
+            ready.record()
+        for target_key in target_keys:
+            self._step_target_ready_events[target_key] = ready
 
-    def wait_for_staged_targets(self) -> None:
-        if self._target_copy_event is None or self._target_copy_waited:
+    def _wait_for_staged_target(
+        self,
+        target_key: tuple[str, str, int],
+        target: torch.Tensor,
+    ) -> None:
+        ready = self._step_target_ready_events.get(target_key)
+        if ready is None:
             return
-        torch.cuda.current_stream(self._target_device()).wait_event(
-            self._target_copy_event
-        )
-        self._target_copy_waited = True
+        torch.cuda.current_stream(target.device).wait_event(ready)
diff --git a/src/art/megatron/runtime/__init__.py b/src/art/megatron/runtime/__init__.py
index 8b1378917..d5323abb4 100644
--- a/src/art/megatron/runtime/__init__.py
+++ b/src/art/megatron/runtime/__init__.py
@@ -1 +1,37 @@
+from art.distributed.data_plane import PackedBatchLeaseSet, PackedBatchRef
 
+from .data_plane import InMemoryPackedBatch
+from .specs import (
+    AdapterReady,
+    CurrentTrainConfig,
+    DurableTrainOutput,
+    ExperimentalTrainConfig,
+    TrainAccepted,
+    TrainCancelled,
+    TrainCompleted,
+    TrainerRuntimeSpec,
+    TrainEvent,
+    TrainFailed,
+    TrainingRunSpec,
+    TrainJobSpec,
+    TrainProgress,
+)
+
+__all__ = [
+    "AdapterReady",
+    "CurrentTrainConfig",
+    "DurableTrainOutput",
+    "ExperimentalTrainConfig",
+    "InMemoryPackedBatch",
+    "PackedBatchRef",
+    "PackedBatchLeaseSet",
+    "TrainAccepted",
+    "TrainCancelled",
+    "TrainCompleted",
+    "TrainEvent",
+    "TrainFailed",
+    "TrainJobSpec",
+    "TrainProgress",
+    "TrainerRuntimeSpec",
+    "TrainingRunSpec",
+]
diff --git a/src/art/megatron/runtime/bridge_runtime.py b/src/art/megatron/runtime/bridge_runtime.py
index 4a0d8f5c8..3a1cc3358 100644
--- a/src/art/megatron/runtime/bridge_runtime.py
+++ b/src/art/megatron/runtime/bridge_runtime.py
@@ -2,7 +2,10 @@
 
 from collections.abc import Callable, Iterable, Mapping
 import contextlib
+import copy
+from dataclasses import replace
 import fnmatch
+import re
 from typing import Any, cast
 
 from megatron.bridge.models.common.unimodal import to_empty_if_meta_device
@@ -11,8 +14,10 @@
     ColumnParallelMapping,
     MegatronParamMapping,
     ReplicatedMapping,
+    extract_expert_number_from_param,
     get_module_and_param_from_name,
 )
+from megatron.bridge.models.conversion.utils import unwrap_model
 from megatron.bridge.models.model_provider import ModelProviderMixin
 from megatron.core.distributed import DistributedDataParallelConfig
 from megatron.core.enums import ModelType
@@ -21,13 +26,22 @@
 from megatron.core.utils import get_model_config
 import torch
 
+from art.megatron.expert_parallel import (
+    ExpertParallelLayout,
+    get_expert_parallel_layout,
+)
 from art.megatron.model_support.spec import HfWeightSource
 
 _Fp32PreservedTensor = tuple[torch.nn.Module, str, torch.Tensor, bool]
 
 
 class ExpertTensorSlice:
-    __slots__ = ("global_start", "global_stop", "tensor")
+    __slots__ = (
+        "global_start",
+        "global_stop",
+        "physical_to_logical",
+        "tensor",
+    )
 
     def __init__(
         self,
@@ -35,13 +49,22 @@ def __init__(
         *,
         global_start: int,
         global_stop: int,
+        physical_to_logical: tuple[int | None, ...] | None = None,
     ) -> None:
         self.tensor = tensor
         self.global_start = int(global_start)
         self.global_stop = int(global_stop)
+        self.physical_to_logical = physical_to_logical
 
     def get(self, global_expert: int) -> torch.Tensor:
         global_expert = int(global_expert)
+        if self.physical_to_logical is not None:
+            logical_expert = self.physical_to_logical[global_expert]
+            if logical_expert is None:
+                raise RuntimeError(
+                    f"masked physical expert {global_expert} has no checkpoint tensor"
+                )
+            global_expert = logical_expert
         if not self.global_start <= global_expert < self.global_stop:
             raise RuntimeError(
                 "expert slice cache miss for global expert "
@@ -114,13 +137,23 @@ def _needs_expert_slice_prefetch(task: Any) -> bool:
         int(getattr(mapping, "ep_size", 1)) > 1
         and bool(getattr(mapping, "is_expert", False))
         and bool(getattr(mapping, "is_grouped_export", False))
-        and isinstance(getattr(mapping, "hf_param", None), str)
+        and isinstance(getattr(mapping, "hf_param", None), (str, Mapping))
     )
 
 
 def _expert_slice_range(task: Any) -> tuple[int, int]:
     mapping = task.mapping
     config = getattr(task.megatron_module, "config", None)
+    layout = get_expert_parallel_layout(config)
+    if layout is not None:
+        local_experts = tuple(
+            expert
+            for expert in layout.local_logical_experts(int(mapping.ep_rank))
+            if expert is not None
+        )
+        if not local_experts:
+            raise RuntimeError(f"EP rank {mapping.ep_rank} owns no logical experts")
+        return local_experts[0], local_experts[-1] + 1
     num_experts = int(getattr(config, "num_moe_experts", 0) or 0)
     ep_size = int(getattr(mapping, "ep_size", 1))
     ep_rank = int(getattr(mapping, "ep_rank", 0))
@@ -168,6 +201,71 @@ def _direct_hf_weight_source(key: str) -> HfWeightSource:
     return HfWeightSource(logical_key=key, physical_key_options=((key,),))
 
 
+_HF_EXPERT_RE = re.compile(r"(?P<prefix>(?:^|\.)experts\.)(?P<expert>\d+)(?=\.|$)")
+
+
+def _logical_hf_param(
+    hf_param: Any,
+    *,
+    physical_expert: int,
+    logical_expert: int,
+) -> Any:
+    if isinstance(hf_param, str):
+        return _HF_EXPERT_RE.sub(
+            lambda match: (
+                f"{match.group('prefix')}{logical_expert}"
+                if int(match.group("expert")) == physical_expert
+                else match.group(0)
+            ),
+            hf_param,
+        )
+    if isinstance(hf_param, Mapping):
+        return {
+            key: _logical_hf_param(
+                value,
+                physical_expert=physical_expert,
+                logical_expert=logical_expert,
+            )
+            for key, value in hf_param.items()
+        }
+    return hf_param
+
+
+def _prepare_nonuniform_expert_tasks(tasks: Iterable[Any]) -> list[Any]:
+    prepared: list[Any] = []
+    for task in tasks:
+        if (
+            task is None
+            or task.megatron_module is None
+            or not bool(getattr(task.mapping, "is_expert", False))
+        ):
+            prepared.append(task)
+            continue
+        layout = get_expert_parallel_layout(
+            getattr(task.megatron_module, "config", None)
+        )
+        if layout is None:
+            prepared.append(task)
+            continue
+        physical_expert = extract_expert_number_from_param(task.mapping.megatron_param)
+        logical_expert = layout.logical_expert(physical_expert)
+        if logical_expert is None:
+            if task.param_weight is None:
+                raise RuntimeError(
+                    f"masked physical expert {physical_expert} has no target parameter"
+                )
+            task.param_weight.data.zero_()
+            continue
+        mapping = copy.copy(task.mapping)
+        mapping.hf_param = _logical_hf_param(
+            mapping.hf_param,
+            physical_expert=physical_expert,
+            logical_expert=logical_expert,
+        )
+        prepared.append(replace(task, mapping=mapping))
+    return prepared
+
+
 def _planned_hf_weight_source(
     bridge: MegatronModelBridge | None,
     key: str,
@@ -264,14 +362,14 @@ def load_unique_hf_keys_once(
         if not _needs_expert_slice_prefetch(task):
             continue
         start, stop = _expert_slice_range(task)
-        key = cast(str, task.mapping.hf_param)
-        previous = expert_slice_ranges.get(key)
-        expert_slice_ranges[key] = (
-            (start, stop)
-            if previous is None
-            else (min(previous[0], start), max(previous[1], stop))
-        )
-        expert_slice_task_by_key.setdefault(key, task)
+        for key in _iter_hf_param_names(task.mapping.hf_param):
+            previous = expert_slice_ranges.get(key)
+            expert_slice_ranges[key] = (
+                (start, stop)
+                if previous is None
+                else (min(previous[0], start), max(previous[1], stop))
+            )
+            expert_slice_task_by_key.setdefault(key, task)
     cache: dict[str, torch.Tensor | ExpertTensorSlice] = {}
     direct_physical_by_logical: dict[str, str] = {}
     materialized_source_by_key: dict[str, tuple[HfWeightSource, tuple[str, ...]]] = {}
@@ -319,10 +417,14 @@ def load_unique_hf_keys_once(
             )
         )
     for key, (start, stop) in expert_slice_ranges.items():
+        task = expert_slice_task_by_key.get(key)
+        layout = get_expert_parallel_layout(
+            getattr(getattr(task, "megatron_module", None), "config", None)
+        )
         source = _planned_hf_weight_source(
             bridge,
             key,
-            task=expert_slice_task_by_key.get(key),
+            task=task,
         )
         selected_option = _select_physical_key_option(source, hf_state_dict)
         if source.kind != "direct":
@@ -341,6 +443,9 @@ def load_unique_hf_keys_once(
                 _pin_cpu_tensor(tensor[start:stop]),
                 global_start=start,
                 global_stop=stop,
+                physical_to_logical=(
+                    None if layout is None else layout.physical_to_logical
+                ),
             )
             continue
         if len(selected_option) != 1:
@@ -359,6 +464,9 @@ def load_unique_hf_keys_once(
             ),
             global_start=start,
             global_stop=stop,
+            physical_to_logical=(
+                None if layout is None else layout.physical_to_logical
+            ),
         )
     return cache
 
@@ -686,6 +794,58 @@ def _replicated_hf_to_megatron(
     return self.broadcast_tensor_to_tp_ranks(tensor, src_rank=0)
 
 
+def _shared_embedding_broadcast_model(
+    megatron_model: list[MegatronModule],
+) -> list[MegatronModule]:
+    if len(megatron_model) == 1:
+        return megatron_model
+    for chunk in megatron_model:
+        model = unwrap_model(chunk)
+        language_model = getattr(model, "language_model", None)
+        if language_model is not None:
+            model = language_model
+        embedding = getattr(model, "embedding", None)
+        if (
+            getattr(embedding, "word_embeddings", None) is not None
+            or getattr(model, "output_layer", None) is not None
+        ):
+            return [chunk]
+    return megatron_model
+
+
+def _validate_local_pretrained_tasks(
+    bridge: MegatronModelBridge,
+    megatron_model: list[Any],
+    tasks: Iterable[Any],
+) -> None:
+    covered = {
+        id(task.param_weight)
+        for task in tasks
+        if task is not None
+        and task.megatron_module is not None
+        and task.param_weight is not None
+    }
+    config = getattr(unwrap_model(megatron_model)[0], "config", None)
+    tied_output = bool(
+        config is not None and bridge._share_embeddings_and_output_weights(config)
+    )
+    missing = [
+        name
+        for model in megatron_model
+        for name, param in model.named_parameters()
+        if not bridge._is_adapter_param_name(name)
+        and not (tied_output and "output_layer" in name)
+        and id(param) not in covered
+    ]
+    if missing:
+        preview = ", ".join(missing[:8])
+        remainder = f" (+{len(missing) - 8} more)" if len(missing) > 8 else ""
+        raise RuntimeError(
+            "Megatron Bridge did not create pretrained load tasks for "
+            f"{len(missing)} required local parameter(s): {preview}{remainder}"
+        )
+
+
 def _optimized_load_weights_hf_to_megatron(
     self: MegatronModelBridge,
     hf_pretrained: Any,
@@ -700,6 +860,8 @@ def _optimized_load_weights_hf_to_megatron(
         if hasattr(megatron_model[0], "hide_loss_modules"):
             stack.enter_context(megatron_model[0].hide_loss_modules())
         tasks = self.build_conversion_tasks(hf_pretrained, megatron_model)
+        _validate_local_pretrained_tasks(self, megatron_model, tasks)
+        tasks = _prepare_nonuniform_expert_tasks(tasks)
     hf_state_dict = hf_pretrained.state
     raw_cache = load_unique_hf_keys_once(
         tasks,
@@ -756,7 +918,7 @@ def _optimized_load_weights_hf_to_megatron(
             pending_device_copy = True
     if pending_device_copy and torch.cuda.is_available():
         torch.cuda.synchronize()
-    self._broadcast_shared_embeddings(megatron_model)
+    self._broadcast_shared_embeddings(_shared_embedding_broadcast_model(megatron_model))
     return megatron_model
 
 
@@ -766,6 +928,7 @@ def install_art_bridge_runtime_patches() -> None:
     _patch_router_gating_linear_empty_input()
     _patch_bias_swiglu_empty_input()
     _patch_moe_unpermute_empty_input()
+    _patch_nonuniform_expert_export()
     if not getattr(
         model_provider_module.get_model, "__art_meta_materialization__", False
     ):
@@ -795,6 +958,77 @@ def install_art_bridge_runtime_patches() -> None:
         )
 
 
+def _patch_nonuniform_expert_export() -> None:
+    original = MegatronParamMapping.gather_from_ep_ranks
+    if getattr(original, "__art_nonuniform_experts__", False):
+        return
+
+    def _gather_from_ep_ranks(
+        self: MegatronParamMapping,
+        megatron_weights: torch.Tensor | None,
+        megatron_module: MegatronModule | None,
+        hf_param_name: Any,
+    ) -> dict[str, torch.Tensor]:
+        if megatron_module is None:
+            payload = self.broadcast_obj_from_pp_rank(
+                None, "art_expert_parallel_layout"
+            )
+            layout = (
+                None
+                if payload is None
+                else ExpertParallelLayout.model_validate(payload)
+            )
+        else:
+            layout = get_expert_parallel_layout(
+                getattr(megatron_module, "config", None)
+            )
+            self.broadcast_obj_from_pp_rank(
+                None if layout is None else layout.model_dump(mode="python"),
+                "art_expert_parallel_layout",
+            )
+        if layout is None or hf_param_name is None:
+            return original(self, megatron_weights, megatron_module, hf_param_name)
+        if isinstance(hf_param_name, Mapping):
+            if megatron_weights is None:
+                return {}
+            gathered = [
+                torch.empty_like(megatron_weights) for _ in range(layout.ep_size)
+            ]
+            torch.distributed.all_gather(
+                gathered, megatron_weights, group=self.ep_group
+            )
+            return {str(hf_param_name): torch.stack(gathered)}
+        if not _HF_EXPERT_RE.search(hf_param_name):
+            return original(self, megatron_weights, megatron_module, hf_param_name)
+        if megatron_weights is None:
+            return {}
+
+        physical_expert = extract_expert_number_from_param(self.megatron_param)
+        local_expert = physical_expert % layout.slots_per_rank
+        gathered = [torch.empty_like(megatron_weights) for _ in range(layout.ep_size)]
+        torch.distributed.all_gather(gathered, megatron_weights, group=self.ep_group)
+        result: dict[str, torch.Tensor] = {}
+        for ep_rank, weight in enumerate(gathered):
+            logical_expert = layout.logical_expert(
+                ep_rank * layout.slots_per_rank + local_expert
+            )
+            if logical_expert is None:
+                continue
+            key = _HF_EXPERT_RE.sub(
+                lambda match: f"{match.group('prefix')}{logical_expert}",
+                hf_param_name,
+            )
+            result[key] = weight
+        return result
+
+    setattr(_gather_from_ep_ranks, "__art_nonuniform_experts__", True)
+    setattr(
+        MegatronParamMapping,
+        "gather_from_ep_ranks",
+        _gather_from_ep_ranks,
+    )
+
+
 def _patch_router_gating_linear_empty_input() -> None:
     from megatron.core.transformer.moe import moe_utils, router
 
diff --git a/src/art/megatron/runtime/client.py b/src/art/megatron/runtime/client.py
deleted file mode 100644
index c01b146c9..000000000
--- a/src/art/megatron/runtime/client.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import asyncio
-import datetime
-import json
-import os
-from typing import Any, AsyncIterator
-
-from .jobs import DEFAULT_JOBS_DIR, MegatronJob, dump_megatron_job
-
-DEFAULT_TRAINING_LOG_DIR = "/tmp/megatron_training_logs"
-
-
-def create_megatron_job_paths(
-    *,
-    jobs_dir: str = DEFAULT_JOBS_DIR,
-    training_log_dir: str = DEFAULT_TRAINING_LOG_DIR,
-) -> tuple[str, str]:
-    timestamp = datetime.datetime.now().isoformat()
-    os.makedirs(jobs_dir, exist_ok=True)
-    os.makedirs(training_log_dir, exist_ok=True)
-    return (
-        os.path.join(jobs_dir, f"{timestamp}.json"),
-        os.path.join(training_log_dir, f"{timestamp}.jsonl"),
-    )
-
-
-def write_megatron_job(job: MegatronJob, *, job_path: str) -> None:
-    os.makedirs(os.path.dirname(job_path), exist_ok=True)
-    with open(job_path, "w", encoding="utf-8") as handle:
-        handle.write(dump_megatron_job(job))
-
-
-async def stream_megatron_job(
-    job: MegatronJob,
-    *,
-    job_path: str,
-    process: Any | None = None,
-    process_log_path: str | None = None,
-    poll_interval: float = 0.05,
-) -> AsyncIterator[dict[str, Any]]:
-    num_lines = 0
-    try:
-        while True:
-            await asyncio.sleep(poll_interval)
-            process_returncode = None
-            if process is not None:
-                process_returncode = process.returncode
-                poll = getattr(process, "poll", None)
-                if process_returncode is None and callable(poll):
-                    process_returncode = poll()
-            if process_returncode is not None:
-                raise RuntimeError(
-                    f"Megatron worker exited with code {process_returncode}. "
-                    f"Check logs at {process_log_path or job.log_path}"
-                )
-            try:
-                with open(job.log_path, "a+", encoding="utf-8") as log_file:
-                    log_file.seek(0)
-                    lines = log_file.readlines()[num_lines:]
-            except FileNotFoundError:
-                continue
-
-            for line in lines:
-                if not (line := line.strip()):
-                    continue
-                if line == "all done":
-                    return
-                num_lines += 1
-                yield json.loads(line)
-    finally:
-        if os.path.exists(job_path):
-            os.remove(job_path)
-        if os.path.exists(job.log_path):
-            os.remove(job.log_path)
diff --git a/src/art/megatron/runtime/compile_cache.py b/src/art/megatron/runtime/compile_cache.py
new file mode 100644
index 000000000..3b4aa1fba
--- /dev/null
+++ b/src/art/megatron/runtime/compile_cache.py
@@ -0,0 +1,145 @@
+from __future__ import annotations
+
+import hashlib
+from importlib.metadata import PackageNotFoundError, version
+import json
+import os
+from pathlib import Path
+import sys
+import time
+from typing import Any, Literal
+import uuid
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from .specs import TrainerRuntimeSpec
+
+_PACKAGES = ("megatron-core", "torchmonarch", "transformer-engine", "transformers")
+
+
+class CompileCacheEvent(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    status: Literal["miss", "hit", "published", "existing"]
+    key: str = Field(pattern=r"^[0-9a-f]{64}$")
+    elapsed_s: float = Field(ge=0)
+    artifact_bytes: int = Field(default=0, ge=0)
+
+
+def _package_versions() -> dict[str, str]:
+    result = {}
+    for package in _PACKAGES:
+        try:
+            result[package] = version(package)
+        except PackageNotFoundError:
+            result[package] = "missing"
+    return result
+
+
+def _compile_cache_key(spec: TrainerRuntimeSpec, rank: int) -> str:
+    import torch
+    import triton
+
+    runtime = spec.model_dump(
+        mode="json",
+        exclude={
+            "cache_root",
+            "compile_cache",
+            "compile_fingerprint",
+            "optimizer_layout_fingerprint",
+            "snapshot_pool_capacity",
+            "trainer_mesh",
+        },
+    )
+    runtime.update(
+        {
+            "rank": rank,
+            "topology": spec.trainer_mesh.topology.model_dump(mode="json"),
+            "hybrid_ep": (
+                None
+                if spec.hybrid_ep is None
+                else {
+                    "multinode": spec.hybrid_ep.multinode,
+                    "ranks_per_nvlink_domain": spec.hybrid_ep.ranks_per_nvlink_domain,
+                }
+            ),
+        }
+    )
+    payload: dict[str, Any] = {
+        "schema": 1,
+        "runtime": runtime,
+        "environment": {
+            "python": sys.implementation.cache_tag,
+            "torch": torch.__version__,
+            "torch_git": torch.version.git_version,
+            "triton": triton.__version__,
+            "cuda": torch.version.cuda,
+            "sm": torch.cuda.get_device_capability(),
+            "packages": _package_versions(),
+            "compile_workarounds": os.environ.get(
+                "ART_MEGATRON_COMPILE_WORKAROUNDS", "1"
+            ),
+        },
+    }
+    encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode()
+    return hashlib.sha256(encoded).hexdigest()
+
+
+class TrainerCompileCache:
+    """Trusted rank-local PyTorch compiler cache for one exact runtime shape."""
+
+    def __init__(
+        self, spec: TrainerRuntimeSpec, *, rank: int, cache_root: Path
+    ) -> None:
+        self.key = _compile_cache_key(spec, rank)
+        self.path = cache_root / "megatron" / "compile_cache" / "v1" / self.key
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+        self.loaded = False
+
+    def load(self) -> CompileCacheEvent:
+        import torch
+
+        started = time.perf_counter()
+        if not self.path.is_file():
+            return CompileCacheEvent(
+                status="miss", key=self.key, elapsed_s=time.perf_counter() - started
+            )
+        artifact = self.path.read_bytes()
+        if torch.compiler.load_cache_artifacts(artifact) is None:
+            raise RuntimeError(f"PyTorch rejected compile cache {self.key}")
+        self.loaded = True
+        return CompileCacheEvent(
+            status="hit",
+            key=self.key,
+            elapsed_s=time.perf_counter() - started,
+            artifact_bytes=len(artifact),
+        )
+
+    def publish(self) -> CompileCacheEvent:
+        import torch
+
+        started = time.perf_counter()
+        if self.loaded or self.path.is_file():
+            return CompileCacheEvent(
+                status="existing",
+                key=self.key,
+                elapsed_s=time.perf_counter() - started,
+                artifact_bytes=self.path.stat().st_size,
+            )
+        saved = torch.compiler.save_cache_artifacts()
+        if saved is None:
+            raise RuntimeError("PyTorch produced no compiler cache after training")
+        artifact, _info = saved
+        staging = self.path.with_name(f".{self.key}.{os.getpid()}.{uuid.uuid4().hex}")
+        try:
+            staging.write_bytes(artifact)
+            os.replace(staging, self.path)
+        finally:
+            staging.unlink(missing_ok=True)
+        self.loaded = True
+        return CompileCacheEvent(
+            status="published",
+            key=self.key,
+            elapsed_s=time.perf_counter() - started,
+            artifact_bytes=len(artifact),
+        )
diff --git a/src/art/megatron/runtime/data_plane.py b/src/art/megatron/runtime/data_plane.py
new file mode 100644
index 000000000..a99876209
--- /dev/null
+++ b/src/art/megatron/runtime/data_plane.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, PrivateAttr, model_validator
+
+from art.distributed.data_plane import MappedPackedBatch, PackedBatchRef
+from art.preprocessing.pack import PackedTensors
+
+
+class InMemoryPackedBatch(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid")
+
+    ref: PackedBatchRef
+    tensors: PackedTensors
+    _mapped: MappedPackedBatch | None = PrivateAttr(default=None)
+
+    @classmethod
+    def open(
+        cls, ref: PackedBatchRef, local_ref: PackedBatchRef
+    ) -> "InMemoryPackedBatch":
+        mapped = MappedPackedBatch.open(local_ref)
+        batch = cls(ref=ref, tensors=mapped.tensors)
+        batch._mapped = mapped
+        return batch
+
+    def close(self) -> None:
+        if self._mapped is not None:
+            self._mapped.close()
+            self._mapped = None
+
+
+class SFTBatchData(BaseModel):
+    """Typed in-memory SFT payload sent directly to warm trainer actors."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid", frozen=True)
+
+    trajectory_tensors: tuple[dict[str, Any], ...]
+    learning_rate: float
+    num_trajectories: int
+    num_tokens: int
+    num_trainable_tokens: int
+
+    @model_validator(mode="after")
+    def _validate_trajectories(self) -> "SFTBatchData":
+        if not self.trajectory_tensors:
+            raise ValueError("SFT batch must contain at least one trajectory")
+        if self.num_trajectories != len(self.trajectory_tensors):
+            raise ValueError("SFT trajectory count does not match its tensor payload")
+        required = {"input_ids", "attention_mask", "labels"}
+        if any(not required <= tensors.keys() for tensors in self.trajectory_tensors):
+            raise ValueError("SFT trajectory tensors are incomplete")
+        if self.num_tokens < 1 or self.num_trainable_tokens < 1:
+            raise ValueError("SFT batch must contain trainable tokens")
+        return self
+
+
+def validate_packed_batch(batch: InMemoryPackedBatch) -> None:
+    tokens = batch.tensors["tokens"]
+    shape = tuple(int(size) for size in tokens.shape)
+    expected = (batch.ref.num_sequences, batch.ref.sequence_length)
+    if shape != expected:
+        raise ValueError(
+            f"packed token shape {shape} does not match batch ref {expected}"
+        )
+    for key, tensor in batch.tensors.items():
+        is_contiguous = getattr(tensor, "is_contiguous", None)
+        if callable(is_contiguous) and not is_contiguous():
+            raise ValueError(f"packed tensor {key!r} must be contiguous")
diff --git a/src/art/megatron/runtime/executor.py b/src/art/megatron/runtime/executor.py
new file mode 100644
index 000000000..77ec83cce
--- /dev/null
+++ b/src/art/megatron/runtime/executor.py
@@ -0,0 +1,658 @@
+from __future__ import annotations
+
+from concurrent.futures import Future, ThreadPoolExecutor
+import gc
+from pathlib import Path
+from threading import BoundedSemaphore, Event, Lock
+import time
+from typing import TYPE_CHECKING, Any
+
+from art.utils.safetensors import PreparedSafetensors, SafetensorsLayout
+
+from ..tensor_snapshot import PinnedCpuSnapshotStager
+from .data_plane import InMemoryPackedBatch, SFTBatchData, validate_packed_batch
+from .publication import (
+    TrainerPublicationFailed,
+    TrainerPublicationSucceeded,
+    TrainerRankPublication,
+)
+from .specs import (
+    ResidentLoraInspectionShard,
+    ResidentLoraInspectionSpec,
+    ResidentScoreJobSpec,
+    ResidentScoreShard,
+    SFTJobSpec,
+    TrainerGeneration,
+    TrainerJobSpec,
+    TrainJobSpec,
+)
+from .trainer_run import EventSink
+
+if TYPE_CHECKING:
+    from art.megatron.optimizer_state import OptimizerAdapter
+
+
+class MegatronTrainJobExecutor:
+    """Thin adapter around the warm runtime's in-memory job entrypoint."""
+
+    def __init__(self, runtime: Any) -> None:
+        self.runtime = runtime
+        self._publisher = _GenerationPublisher(
+            runtime,
+            capacity=int(runtime.snapshot_pool_capacity),
+        )
+        self._python_gc_stabilized = False
+        self._closed = False
+
+    def execute(
+        self,
+        job: TrainJobSpec,
+        batch: InMemoryPackedBatch,
+        sink: EventSink,
+        cancelled: Event,
+    ) -> dict[str, float]:
+        if self._closed:
+            raise RuntimeError("Megatron executor is closed")
+        timing = self.runtime.inter_forward_backward_timing
+        timing.current_job_start_s = time.monotonic()
+        validate_packed_batch(batch)
+        self._publisher.raise_if_failed()
+        from art.megatron.train import execute_megatron_rl_job
+
+        metrics = execute_megatron_rl_job(
+            self.runtime,
+            job,
+            batch.tensors,
+            progress_sink=lambda step_index, num_steps, metrics: sink.progress(
+                step_index=step_index,
+                num_steps=num_steps,
+                metrics=metrics,
+            ),
+            adapter_ready_sink=lambda: sink.adapter_ready(
+                learner_version=job.learner_version,
+                adapter_path=job.output_adapter_path,
+            ),
+            snapshot_sink=lambda *args: self._publisher.submit(*args, sink=sink),
+            cancelled=cancelled,
+        )
+        metrics.update(self._stabilize_python_gc())
+        timing.previous_job_complete_s = time.monotonic()
+        return metrics
+
+    def execute_sft(
+        self,
+        job: SFTJobSpec,
+        batches: tuple[SFTBatchData, ...],
+        sink: EventSink,
+        cancelled: Event,
+    ) -> dict[str, float]:
+        if self._closed:
+            raise RuntimeError("Megatron executor is closed")
+        timing = self.runtime.inter_forward_backward_timing
+        timing.current_job_start_s = time.monotonic()
+        self._publisher.raise_if_failed()
+        from art.megatron.train import execute_megatron_sft_job
+
+        metrics = execute_megatron_sft_job(
+            self.runtime,
+            job,
+            batches,
+            progress_sink=lambda step_index, num_steps, metrics: sink.progress(
+                step_index=step_index,
+                num_steps=num_steps,
+                metrics=metrics,
+            ),
+            adapter_ready_sink=lambda: sink.adapter_ready(
+                learner_version=job.learner_version,
+                adapter_path=job.output_adapter_path,
+            ),
+            snapshot_sink=lambda *args: self._publisher.submit(*args, sink=sink),
+            cancelled=cancelled,
+        )
+        metrics.update(self._stabilize_python_gc())
+        timing.previous_job_complete_s = time.monotonic()
+        return metrics
+
+    def _stabilize_python_gc(self) -> dict[str, float]:
+        if self._python_gc_stabilized or not self.runtime.transformer_layers_compiled:
+            return {}
+        started = time.perf_counter()
+        collected = gc.collect()
+        gc.freeze()
+        self._python_gc_stabilized = True
+        return {
+            "python_gc_stabilize_s": time.perf_counter() - started,
+            "python_gc_collected_objects": float(collected),
+            "python_gc_frozen_objects": float(gc.get_freeze_count()),
+        }
+
+    def score(
+        self,
+        job: ResidentScoreJobSpec,
+        batch: InMemoryPackedBatch,
+    ) -> ResidentScoreShard:
+        self._validate_resident_score(job.run_id, job.learner)
+        validate_packed_batch(batch)
+        from art.megatron.train import execute_megatron_score_job
+
+        return execute_megatron_score_job(self.runtime, job, batch.tensors)
+
+    def inspect_resident_lora(
+        self,
+        request: ResidentLoraInspectionSpec,
+    ) -> ResidentLoraInspectionShard:
+        self._validate_resident_inspection(request.run_id, request.learner)
+        from art.megatron.train import inspect_resident_lora
+
+        return inspect_resident_lora(self.runtime, request)
+
+    def _validate_diagnostic_runtime(self) -> None:
+        if self._closed:
+            raise RuntimeError("Megatron executor is closed")
+        self._publisher.raise_if_failed()
+
+    def _validate_resident_score(self, run_id: str, learner: TrainerGeneration) -> None:
+        self._validate_diagnostic_runtime()
+        runtime = self.runtime
+        if (
+            runtime.resident_run_id != run_id
+            or runtime.resident_training_session_id != learner.training_session_id
+            or runtime.resident_policy_step != learner.policy_step
+            or runtime.resident_generation_id != learner.generation_id
+            or not runtime.optimizer_state_loaded
+            or runtime.optimizer is None
+        ):
+            raise RuntimeError("resident trainer state does not match score learner")
+
+    def _validate_resident_inspection(
+        self, run_id: str, learner: TrainerGeneration
+    ) -> None:
+        self._validate_diagnostic_runtime()
+        runtime = self.runtime
+        if runtime.resident_run_id != run_id:
+            raise RuntimeError("resident trainer run does not match inspection")
+        unhydrated = (
+            runtime.resident_training_session_id is None
+            and runtime.resident_policy_step is None
+            and runtime.resident_generation_id is None
+            and not runtime.optimizer_state_loaded
+        )
+        hydrated = (
+            runtime.resident_training_session_id == learner.training_session_id
+            and runtime.resident_policy_step == learner.policy_step
+            and runtime.resident_generation_id == learner.generation_id
+            and runtime.optimizer_state_loaded
+        )
+        if not (unhydrated or hydrated):
+            raise RuntimeError(
+                "resident trainer hydration markers are partial or do not match "
+                "the inspection learner"
+            )
+
+    def advance_without_training(
+        self,
+        *,
+        source: TrainerGeneration,
+        output: TrainerGeneration,
+        optimizer_state_path: str,
+        adapter: "OptimizerAdapter | None",
+    ) -> dict[str, float]:
+        if self._closed:
+            raise RuntimeError("Megatron executor is closed")
+        if (
+            output.training_session_id != source.training_session_id
+            or output.policy_step != source.policy_step + 1
+        ):
+            raise ValueError(
+                "a no-op transition must preserve session and advance one step"
+            )
+        runtime = self.runtime
+        if (
+            runtime.resident_training_session_id != source.training_session_id
+            or runtime.resident_policy_step != source.policy_step
+            or runtime.resident_generation_id != source.generation_id
+            or not runtime.optimizer_state_loaded
+            or runtime.optimizer is None
+        ):
+            raise RuntimeError("resident trainer state does not match no-op transition")
+        del optimizer_state_path, adapter
+        runtime.resident_policy_step = output.policy_step
+        runtime.resident_generation_id = output.generation_id
+        return {}
+
+    def close(self) -> None:
+        if self._closed:
+            return
+        self._closed = True
+        failures: list[BaseException] = []
+        try:
+            self._publisher.close()
+            self.runtime.optimizer_snapshot_barrier.synchronize()
+        except BaseException as error:
+            failures.append(error)
+        controller = getattr(self.runtime, "moe_routing_replay_controller", None)
+        if controller is not None:
+            try:
+                controller.remove_router_patches()
+            except BaseException as error:
+                failures.append(error)
+            finally:
+                self.runtime.moe_routing_replay_controller = None
+        try:
+            import torch
+
+            if torch.distributed.is_initialized():
+                torch.distributed.destroy_process_group()
+        except BaseException as error:
+            failures.append(error)
+        if len(failures) == 1:
+            raise failures[0]
+        if failures:
+            raise BaseExceptionGroup("Megatron executor close failed", failures)
+
+
+class _GenerationPublisher:
+    def __init__(
+        self,
+        runtime: Any,
+        *,
+        capacity: int,
+    ) -> None:
+        if capacity < 1:
+            raise ValueError("snapshot pool capacity must be positive")
+        self.runtime = runtime
+        self.capacity = capacity
+        self._slots = BoundedSemaphore(capacity)
+        self._lock = Lock()
+        self._available_stagers = [
+            PinnedCpuSnapshotStager(reusable=True) for _ in range(capacity)
+        ]
+        self._transport_pool = ThreadPoolExecutor(
+            max_workers=1, thread_name_prefix="art-publish-transport"
+        )
+        self._durability_pool = ThreadPoolExecutor(
+            max_workers=1, thread_name_prefix="art-publish-durable"
+        )
+        self._transport_sender: Any | None = None
+        self._lora_layout: SafetensorsLayout | None = None
+        self._failures: list[BaseException] = []
+        self._in_flight = 0
+
+    def submit(
+        self,
+        job: TrainerJobSpec,
+        adapter_dtypes: dict[str, Any],
+        adapter_config: dict[str, Any],
+        save_optimizer: bool,
+        *,
+        sink: EventSink,
+    ) -> dict[str, float]:
+        from art.megatron.optimizer_state import stage_optimizer_state_snapshot
+        from art.megatron.weights.lora_publish import (
+            stage_vllm_lora_snapshot_from_model,
+        )
+
+        wait_s, in_flight, stager = self._acquire_slot()
+        prepare_started = time.perf_counter()
+        optimizer_handoff: Future[Any] = Future()
+        transport: Future[Future[TrainerRankPublication]] | None = None
+        try:
+            lora = stage_vllm_lora_snapshot_from_model(
+                model=self.runtime.model,
+                adapter_dtypes=adapter_dtypes,
+                handler=self.runtime.model_support_handler,
+                adapter_config=adapter_config,
+                rank=self.runtime.rank,
+                world_size=self.runtime.world_size,
+                stager=stager,
+            )
+            lora_launch_s = time.perf_counter() - prepare_started
+            lora_resolve_started = time.perf_counter()
+            lora = None if lora is None else lora.resolve()
+            lora_resolve_s = time.perf_counter() - lora_resolve_started
+            transport = self._enqueue_transport(
+                generation=job.output.generation,
+                optimizer_state_path=job.output.optimizer_state_path,
+                staging_adapter_path=job.output.staging_adapter_path,
+                lora=lora,
+                adapter=None,
+                optimizer=optimizer_handoff,
+                publication_targets=getattr(job, "publication_targets", ()),
+            )
+            optimizer_started = time.perf_counter()
+            optimizer = (
+                stage_optimizer_state_snapshot(
+                    self.runtime,
+                    generation_id=job.output_generation_id,
+                    step=job.learner_version,
+                    stager=stager,
+                )
+                if save_optimizer
+                else None
+            )
+            if optimizer is not None:
+                self.runtime.optimizer_snapshot_barrier.register(optimizer)
+            optimizer_handoff.set_result(optimizer)
+            optimizer_launch_s = time.perf_counter() - optimizer_started
+            handoff_started = time.perf_counter()
+            transport.add_done_callback(
+                lambda done: self._transport_ready(
+                    done,
+                    sink=sink,
+                    generation=job.output.generation,
+                    stager=stager,
+                )
+            )
+            transport_handoff_wait_s = time.perf_counter() - handoff_started
+        except BaseException as error:
+            publication_error = error
+            if transport is not None:
+                optimizer_handoff.set_exception(error)
+                publication_error = self._drain_transport(transport, error)
+            self._report_failure(
+                publication_error,
+                sink=sink,
+                generation=job.output.generation,
+                remember=False,
+                stager=stager,
+            )
+            raise
+        return {
+            "snapshot_pool_wait_s": wait_s,
+            "snapshot_pool_in_use": float(in_flight),
+            "snapshot_pool_pressure": in_flight / self.capacity,
+            "snapshot_lora_launch_s": lora_launch_s,
+            "snapshot_lora_resolve_s": lora_resolve_s,
+            "snapshot_optimizer_launch_s": optimizer_launch_s,
+            "snapshot_transport_handoff_wait_s": transport_handoff_wait_s,
+            "snapshot_launch_s": time.perf_counter() - prepare_started,
+        }
+
+    def _transport_ready(
+        self,
+        future: Future[Future[TrainerRankPublication]],
+        *,
+        sink: EventSink,
+        generation: TrainerGeneration,
+        stager: PinnedCpuSnapshotStager,
+    ) -> None:
+        try:
+            persistence = future.result()
+        except BaseException as error:
+            self._failed(error, sink=sink, generation=generation, stager=stager)
+            return
+        persistence.add_done_callback(
+            lambda done: self._completed(
+                done,
+                sink=sink,
+                generation=generation,
+                stager=stager,
+            )
+        )
+
+    def _acquire_slot(self) -> tuple[float, int, PinnedCpuSnapshotStager]:
+        self.raise_if_failed()
+        started = time.perf_counter()
+        self._slots.acquire()
+        wait_s = time.perf_counter() - started
+        with self._lock:
+            stager = self._available_stagers.pop()
+            stager.reset()
+            self._in_flight += 1
+            return wait_s, self._in_flight, stager
+
+    def _enqueue_transport(
+        self,
+        **kwargs: Any,
+    ) -> Future[Future[TrainerRankPublication]]:
+        return self._transport_pool.submit(self._transport_snapshot, **kwargs)
+
+    @staticmethod
+    def _drain_transport(
+        transport: Future[Future[TrainerRankPublication]],
+        fallback: BaseException,
+    ) -> BaseException:
+        try:
+            transport.result().result()
+        except BaseException as error:
+            return error
+        return fallback
+
+    def _transport_snapshot(
+        self,
+        *,
+        generation: TrainerGeneration,
+        optimizer_state_path: str,
+        staging_adapter_path: str | None,
+        lora: Any,
+        adapter: "OptimizerAdapter | None",
+        optimizer: Future[Any],
+        publication_targets: tuple[Any, ...],
+    ) -> Future[TrainerRankPublication]:
+        prepared_tensors = None
+        if lora is not None:
+            if self._lora_layout is None:
+                self._lora_layout = SafetensorsLayout(lora.tensors)
+            prepared_tensors = self._lora_layout.bind(lora.tensors)
+        failures: list[BaseException] = []
+        if int(self.runtime.rank) == 0 and publication_targets:
+            if lora is None or prepared_tensors is None:
+                raise RuntimeError("rank zero has no LoRA snapshot to transfer")
+            try:
+                self._transfer_lora_snapshot(
+                    lora,
+                    publication_targets,
+                    prepared_tensors=prepared_tensors,
+                )
+            except BaseException as error:
+                failures.append(error)
+        return self._durability_pool.submit(
+            self._persist_snapshot,
+            generation=generation,
+            optimizer_state_path=optimizer_state_path,
+            staging_adapter_path=staging_adapter_path,
+            lora=lora,
+            adapter=adapter,
+            optimizer=optimizer,
+            prepared_tensors=prepared_tensors,
+            failures=failures,
+        )
+
+    def _persist_snapshot(
+        self,
+        *,
+        generation: TrainerGeneration,
+        optimizer_state_path: str,
+        staging_adapter_path: str | None,
+        lora: Any,
+        adapter: "OptimizerAdapter | None",
+        optimizer: Future[Any],
+        prepared_tensors: PreparedSafetensors | None,
+        failures: list[BaseException],
+    ) -> TrainerRankPublication:
+        record: TrainerRankPublication | None = None
+        try:
+            pending_optimizer = optimizer.result()
+            resolved_optimizer = (
+                None if pending_optimizer is None else pending_optimizer.resolve()
+            )
+            record = self._persist_generation(
+                generation=generation,
+                optimizer_state_path=optimizer_state_path,
+                staging_adapter_path=staging_adapter_path,
+                lora=lora,
+                adapter=adapter,
+                optimizer=resolved_optimizer,
+                prepared_tensors=prepared_tensors,
+            )
+        except BaseException as error:
+            failures.append(error)
+        if len(failures) == 1:
+            raise failures[0]
+        if failures:
+            raise BaseExceptionGroup(
+                "adapter persistence and transport failed", failures
+            )
+        if record is None:
+            raise RuntimeError("trainer rank produced no publication record")
+        return record
+
+    def _transfer_lora_snapshot(
+        self,
+        lora: Any,
+        targets: tuple[Any, ...],
+        *,
+        prepared_tensors: PreparedSafetensors,
+    ) -> None:
+        from art.distributed.adapter_transport import AdapterSnapshotSender
+
+        if self._transport_sender is None:
+            self._transport_sender = AdapterSnapshotSender()
+        self._transport_sender.send(
+            lora,
+            targets,
+            prepared_tensors=prepared_tensors,
+        )
+
+    def _persist_generation(
+        self,
+        *,
+        generation: TrainerGeneration,
+        optimizer_state_path: str,
+        staging_adapter_path: str | None,
+        lora: Any,
+        adapter: "OptimizerAdapter | None",
+        optimizer: Any,
+        prepared_tensors: PreparedSafetensors | None,
+    ) -> TrainerRankPublication:
+        from art.megatron.optimizer_state import (
+            publish_adapter_checkpoint,
+            write_optimizer_snapshot_shard,
+        )
+        from art.megatron.weights.lora_publish import save_vllm_lora_snapshot
+
+        rank = int(self.runtime.rank)
+        if rank == 0:
+            if lora is not None:
+                if staging_adapter_path is None or adapter is not None:
+                    raise RuntimeError("new adapter publication is inconsistent")
+                staging = Path(staging_adapter_path)
+                if staging.exists():
+                    raise RuntimeError(f"Adapter staging generation exists: {staging}")
+                save_vllm_lora_snapshot(
+                    lora,
+                    str(staging),
+                    prepared_tensors=prepared_tensors,
+                )
+                adapter = publish_adapter_checkpoint(
+                    staging,
+                    step=generation.policy_step,
+                    training_session_id=generation.training_session_id,
+                    generation_id=generation.generation_id,
+                )
+            if adapter is None:
+                raise RuntimeError("rank zero has no immutable adapter")
+        shard = (
+            write_optimizer_snapshot_shard(
+                optimizer,
+                optimizer_state_path=optimizer_state_path,
+            )
+            if optimizer is not None
+            else None
+        )
+        return TrainerRankPublication(
+            generation=generation,
+            rank=rank,
+            adapter=adapter,
+            shard=shard,
+            runtime_sha256=None if optimizer is None else optimizer.runtime_sha256,
+            topology=None if optimizer is None else optimizer.topology,
+            saves_optimizer=optimizer is not None,
+        )
+
+    def _completed(
+        self,
+        future: Future[TrainerRankPublication],
+        *,
+        sink: EventSink,
+        generation: TrainerGeneration,
+        stager: PinnedCpuSnapshotStager,
+    ) -> None:
+        try:
+            event = TrainerPublicationSucceeded(record=future.result())
+        except BaseException as error:
+            self._failed(error, sink=sink, generation=generation, stager=stager)
+            return
+        try:
+            sink.publication(event)
+        except BaseException as error:
+            with self._lock:
+                self._failures.append(error)
+        finally:
+            self._release_slot(stager)
+
+    def _failed(
+        self,
+        error: BaseException,
+        *,
+        sink: EventSink,
+        generation: TrainerGeneration,
+        stager: PinnedCpuSnapshotStager,
+    ) -> None:
+        self._report_failure(
+            error,
+            sink=sink,
+            generation=generation,
+            remember=True,
+            stager=stager,
+        )
+
+    def _report_failure(
+        self,
+        error: BaseException,
+        *,
+        sink: EventSink,
+        generation: TrainerGeneration,
+        remember: bool,
+        stager: PinnedCpuSnapshotStager,
+    ) -> None:
+        if remember:
+            with self._lock:
+                self._failures.append(error)
+        event = TrainerPublicationFailed(
+            generation_id=generation.generation_id,
+            rank=int(self.runtime.rank),
+            error_type=type(error).__name__,
+            message=str(error) or type(error).__name__,
+        )
+        try:
+            sink.publication(event)
+        except BaseException as sink_error:
+            with self._lock:
+                self._failures.append(sink_error)
+        finally:
+            self._release_slot(stager)
+
+    def _release_slot(self, stager: PinnedCpuSnapshotStager) -> None:
+        with self._lock:
+            self._in_flight -= 1
+            self._available_stagers.append(stager)
+        self._slots.release()
+
+    def raise_if_failed(self) -> None:
+        with self._lock:
+            failures = tuple(self._failures)
+        if failures:
+            raise BaseExceptionGroup("trainer generation publication failed", failures)
+
+    def close(self) -> None:
+        self._transport_pool.shutdown(wait=True)
+        self._durability_pool.shutdown(wait=True)
+        if self._transport_sender is not None:
+            self._transport_sender.close()
+            self._transport_sender = None
+        with self._lock:
+            in_flight = self._in_flight
+        if in_flight:
+            raise RuntimeError(f"publication close retained {in_flight} snapshots")
+        self.raise_if_failed()
diff --git a/src/art/megatron/runtime/jobs.py b/src/art/megatron/runtime/jobs.py
deleted file mode 100644
index 4285c88bc..000000000
--- a/src/art/megatron/runtime/jobs.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from typing import Annotated, Any, Literal, TypeAlias
-
-from pydantic import BaseModel, Field, TypeAdapter
-
-from ... import types
-from ...preprocessing.pack import DiskPackedTensors
-
-DEFAULT_TRAINING_LOG_PATH = "/tmp/megatron_training_log.jsonl"
-DEFAULT_JOBS_DIR = "/tmp/megatron_training_jobs"
-DEFAULT_VLLM_WAKE_LOCK_PATH = "/tmp/megatron_vllm_waking"
-LORA_READY_EVENT = "lora_ready"
-OPTIMIZER_READY_EVENT = "optimizer_ready"
-
-
-class MergedWeightTransferInitInfo(BaseModel):
-    master_address: str
-    master_port: int
-    rank_offset: int
-    world_size: int
-
-
-class MergedWeightTransferSpec(BaseModel):
-    init_info: MergedWeightTransferInitInfo
-    vllm_base_url: str
-    served_model_name: str
-    api_key: str | None = None
-    nccl_so_path: str | None = None
-
-
-class _MegatronTrainingJobBase(BaseModel):
-    step: int = Field(default=0, ge=0)
-    source_policy_step: int = Field(ge=0)
-    training_session_id: str
-    lora_path: str
-    allow_unvalidated_arch: bool = False
-    optimizer_state_path: str
-    disk_packed_tensors: DiskPackedTensors
-    config: types.TrainConfig
-    experimental_config: dict[str, Any]
-    moe_routing_replay_path: str | None = None
-    moe_routing_replay_strict: bool = True
-    log_path: str = DEFAULT_TRAINING_LOG_PATH
-
-
-class MegatronTrainingJob(_MegatronTrainingJobBase):
-    kind: Literal["train_lora"] = "train_lora"
-
-
-class MegatronMergedTrainingJob(_MegatronTrainingJobBase):
-    kind: Literal["train_merged"] = "train_merged"
-    merged_weight_transfer: MergedWeightTransferSpec
-
-
-class MegatronSyncJob(BaseModel):
-    kind: Literal["sync"] = "sync"
-    lora_path: str
-    allow_unvalidated_arch: bool = False
-    merged_weight_transfer: MergedWeightTransferSpec
-    log_path: str = DEFAULT_TRAINING_LOG_PATH
-
-
-class MegatronOptimizerSaveJob(BaseModel):
-    kind: Literal["save_optimizer"] = "save_optimizer"
-    step: int = Field(ge=0)
-    training_session_id: str
-    optimizer_state_path: str
-    log_path: str = DEFAULT_TRAINING_LOG_PATH
-
-
-class MegatronSFTTrainingJob(BaseModel):
-    kind: Literal["sft"] = "sft"
-    step: int = Field(ge=0)
-    source_policy_step: int = Field(ge=0)
-    training_session_id: str
-    lora_path: str
-    allow_unvalidated_arch: bool = False
-    optimizer_state_path: str
-    sft_data_dir: str
-    num_batches: int
-    learning_rates: list[float]
-    grad_accumulation_sequences: int | None = None
-    weight_decay: float = 0.0
-    max_grad_norm: float = 1.0
-    internal_checkpoint_interval: int | None = Field(default=None, ge=1)
-    log_path: str = DEFAULT_TRAINING_LOG_PATH
-
-
-MegatronJob: TypeAlias = Annotated[
-    MegatronTrainingJob
-    | MegatronMergedTrainingJob
-    | MegatronSyncJob
-    | MegatronOptimizerSaveJob
-    | MegatronSFTTrainingJob,
-    Field(discriminator="kind"),
-]
-
-_MEGATRON_JOB_ADAPTER = TypeAdapter(MegatronJob)
-
-
-def dump_megatron_job(job: MegatronJob) -> str:
-    return _MEGATRON_JOB_ADAPTER.dump_json(job).decode()
-
-
-def load_megatron_job(raw: str | bytes) -> MegatronJob:
-    return _MEGATRON_JOB_ADAPTER.validate_json(raw)
diff --git a/src/art/megatron/runtime/local.py b/src/art/megatron/runtime/local.py
new file mode 100644
index 000000000..58a646b33
--- /dev/null
+++ b/src/art/megatron/runtime/local.py
@@ -0,0 +1,290 @@
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import re
+import socket
+import threading
+
+from art import dev
+from art.distributed.specs import (
+    CUDA_DEVICE_UUID_PATTERN,
+    ClusterSpec,
+    EndpointSpec,
+    GpuPlacement,
+    HostSpec,
+    ModelServiceMemberSpec,
+    ModelServiceSpec,
+    RuntimeTopology,
+    TrainerMeshSpec,
+    VllmParallelSpec,
+)
+
+from ..runtime_config import get_megatron_runtime_config
+
+LocalServicePorts = tuple[int, int]
+
+
+def _bind_loopback_port(port: int = 0) -> socket.socket:
+    reservation = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    try:
+        reservation.bind(("127.0.0.1", port))
+    except BaseException:
+        reservation.close()
+        raise
+    return reservation
+
+
+class LocalEndpointAllocator:
+    """Owns unique API and rendezvous ports for backend-local runtimes."""
+
+    _lock = threading.Lock()
+    _reserved: set[int] = set()
+
+    def __init__(self) -> None:
+        self._owned: set[int] = set()
+
+    def reserve(self) -> LocalServicePorts:
+        with self._lock:
+            sockets: list[socket.socket] = []
+            try:
+                while len(sockets) < 2:
+                    reservation = _bind_loopback_port()
+                    if reservation.getsockname()[1] in self._reserved:
+                        reservation.close()
+                        continue
+                    sockets.append(reservation)
+                ports = tuple(reservation.getsockname()[1] for reservation in sockets)
+                assert len(ports) == 2
+                self._reserved.update(ports)
+                self._owned.update(ports)
+                return ports
+            finally:
+                for reservation in sockets:
+                    reservation.close()
+
+    def replace_api_port(
+        self, ports: LocalServicePorts, api_port: int
+    ) -> LocalServicePorts:
+        with self._lock:
+            if ports[0] == api_port:
+                return ports
+            if not 1 <= api_port <= 65535:
+                raise ValueError("OpenAI server port must be between 1 and 65535")
+            if not set(ports) <= self._owned:
+                raise RuntimeError("local service endpoint ownership was lost")
+            if api_port in self._reserved:
+                raise ValueError(f"local service port {api_port} is already reserved")
+            api = _bind_loopback_port(api_port)
+            try:
+                configured = (api_port, ports[1])
+                self._reserved.difference_update(ports)
+                self._reserved.update(configured)
+                self._owned.difference_update(ports)
+                self._owned.update(configured)
+                return configured
+            finally:
+                api.close()
+
+    def release(self, ports: LocalServicePorts) -> None:
+        with self._lock:
+            if not set(ports) <= self._owned:
+                raise RuntimeError("local service endpoint ownership was lost")
+            self._reserved.difference_update(ports)
+            self._owned.difference_update(ports)
+
+
+def _host_gpu_ids(
+    gpu_ids: tuple[int, ...], *, visible_gpu_count: int
+) -> tuple[int | str, ...]:
+    raw_visible = os.environ.get("CUDA_VISIBLE_DEVICES")
+    if raw_visible is None:
+        return gpu_ids
+    visible = tuple(part.strip() for part in raw_visible.split(",") if part.strip())
+    if len(visible) != visible_gpu_count or len(
+        {value.casefold() for value in visible}
+    ) != len(visible):
+        raise RuntimeError(
+            "local Monarch requires unique CUDA_VISIBLE_DEVICES matching the "
+            f"visible CUDA count, got {raw_visible!r} for {visible_gpu_count} GPUs"
+        )
+    if any(
+        not (value.isdecimal() or re.fullmatch(CUDA_DEVICE_UUID_PATTERN, value))
+        for value in visible
+    ):
+        raise RuntimeError(
+            "CUDA_VISIBLE_DEVICES must contain only numeric, full GPU UUID, or MIG "
+            "tokens"
+        )
+    invalid = [gpu_id for gpu_id in gpu_ids if gpu_id < 0 or gpu_id >= len(visible)]
+    if invalid:
+        raise ValueError(
+            f"GPU ids {invalid} exceed the controller's visible CUDA devices"
+        )
+    return tuple(
+        int(visible[gpu_id]) if visible[gpu_id].isdecimal() else visible[gpu_id]
+        for gpu_id in gpu_ids
+    )
+
+
+def with_local_serving_port(
+    topology: RuntimeTopology,
+    *,
+    model_name: str,
+    port: int,
+    rendezvous_port: int | None = None,
+) -> RuntimeTopology:
+    services = tuple(
+        service for service in topology.model_services if service.name == model_name
+    )
+    if len(services) != 1:
+        raise ValueError(f"runtime topology has no unique service {model_name!r}")
+    service = services[0]
+    endpoint = EndpointSpec(host=service.leader_endpoint.host, port=port)
+    if endpoint == service.leader_endpoint and rendezvous_port is None:
+        return topology
+    if (
+        len(topology.cluster.hosts) != 1
+        or len(service.members) != 1
+        or not service.leader_endpoint.is_loopback
+    ):
+        raise ValueError("OpenAI server port conflicts with the compiled topology")
+    rendezvous = service.rendezvous
+    if rendezvous_port is not None:
+        rendezvous = EndpointSpec(host=rendezvous.host, port=rendezvous_port)
+    elif endpoint.port == rendezvous.port:
+        reservation = _bind_loopback_port()
+        try:
+            rendezvous = EndpointSpec(
+                host=rendezvous.host, port=reservation.getsockname()[1]
+            )
+        finally:
+            reservation.close()
+    configured = service.model_copy(
+        update={"leader_endpoint": endpoint, "rendezvous": rendezvous}
+    )
+    return RuntimeTopology(
+        cluster=topology.cluster,
+        rollout_host_ids=topology.rollout_host_ids,
+        trainer=topology.trainer,
+        model_services=tuple(
+            configured if value is service else value
+            for value in topology.model_services
+        ),
+    )
+
+
+def compile_local_runtime_topology(
+    config: dev.BackendModelConfig,
+    *,
+    model_name: str,
+    base_model: str,
+    artifact_root: str,
+    visible_gpu_count: int,
+    service_ports: LocalServicePorts | None = None,
+) -> RuntimeTopology:
+    if visible_gpu_count < 1:
+        raise RuntimeError("MegatronBackend requires at least one visible CUDA GPU")
+    trainer_gpu_ids = _host_gpu_ids(
+        tuple(map(int, config.get("trainer_gpu_ids", range(visible_gpu_count)))),
+        visible_gpu_count=visible_gpu_count,
+    )
+    if not trainer_gpu_ids:
+        raise ValueError("Megatron trainer GPU placement must not be empty")
+    from art.dev.validate import is_dedicated_mode, is_external_vllm_mode
+
+    engine = config.get("engine_args", {})
+    parallel = VllmParallelSpec(
+        tp=int(engine.get("tensor_parallel_size", 1)),
+        pp=int(engine.get("pipeline_parallel_size", 1)),
+        dp=int(engine.get("data_parallel_size", 1)),
+        enable_expert_parallel=bool(engine.get("enable_expert_parallel", False)),
+    )
+    dedicated = is_dedicated_mode(config)
+    external = is_external_vllm_mode(config)
+    inference_gpu_ids = ()
+    if not external:
+        inference_gpu_ids = _host_gpu_ids(
+            tuple(map(int, config.get("inference_gpu_ids", ()))),
+            visible_gpu_count=visible_gpu_count,
+        )
+        candidates = inference_gpu_ids if dedicated else trainer_gpu_ids
+        if len(candidates) < parallel.world_size:
+            raise ValueError("vLLM parallelism exceeds local inference GPU placement")
+        inference_gpu_ids = candidates[: parallel.world_size]
+    available_gpu_ids = tuple(dict.fromkeys((*trainer_gpu_ids, *inference_gpu_ids)))
+    host_id = "local"
+    init_args = config.get("init_args", {})
+    provider_model = str(init_args.get("model_name", base_model))
+    configured_revision = init_args.get("revision")
+    revision = str(configured_revision) if configured_revision is not None else None
+    model_services = ()
+    if not external:
+        if service_ports is None:
+            reservations = (_bind_loopback_port(), _bind_loopback_port())
+            try:
+                service_ports = tuple(
+                    reservation.getsockname()[1] for reservation in reservations
+                )
+            finally:
+                for reservation in reservations:
+                    reservation.close()
+        api_port, rendezvous_port = service_ports
+        if api_port == rendezvous_port:
+            raise ValueError("local API and rendezvous ports must differ")
+        fingerprint = hashlib.sha256(
+            json.dumps(
+                {
+                    "base_model": provider_model,
+                    "parallel": parallel.model_dump(mode="json"),
+                    "revision": revision or "<default>",
+                },
+                sort_keys=True,
+            ).encode()
+        ).hexdigest()
+        model_services = (
+            ModelServiceSpec(
+                name=model_name,
+                members=(
+                    ModelServiceMemberSpec(
+                        member_id=host_id,
+                        host_id=host_id,
+                        node_rank=0,
+                        gpu_ids=inference_gpu_ids,
+                    ),
+                ),
+                leader_endpoint=EndpointSpec(host="127.0.0.1", port=api_port),
+                rendezvous=EndpointSpec(host="127.0.0.1", port=rendezvous_port),
+                base_model=provider_model,
+                model_revision=revision,
+                runtime_fingerprint=fingerprint,
+                parallel=parallel,
+                temporal_gpu_sharing=not dedicated,
+            ),
+        )
+    return RuntimeTopology(
+        cluster=ClusterSpec(
+            hosts=(
+                HostSpec(
+                    host_id=host_id,
+                    node_rank=0,
+                    worker_address="tcp://127.0.0.1:0",
+                    cpu_slots=max(1, os.cpu_count() or 1),
+                    gpu_ids=available_gpu_ids,
+                ),
+            ),
+            controller_host_id=host_id,
+            artifact_root=artifact_root,
+            cache_root=os.environ.get("ART_MEGATRON_CACHE_ROOT"),
+        ),
+        rollout_host_ids=(),
+        trainer=TrainerMeshSpec(
+            ranks=tuple(
+                GpuPlacement(host_id=host_id, gpu_id=gpu_id)
+                for gpu_id in trainer_gpu_ids
+            ),
+            topology=get_megatron_runtime_config().topology,
+        ),
+        model_services=model_services,
+    )
diff --git a/src/art/megatron/runtime/managed.py b/src/art/megatron/runtime/managed.py
new file mode 100644
index 000000000..314b1c900
--- /dev/null
+++ b/src/art/megatron/runtime/managed.py
@@ -0,0 +1,481 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+import fcntl
+import hashlib
+from importlib import metadata
+import json
+import os
+from pathlib import Path
+import platform
+import shlex
+import shutil
+import subprocess
+import sys
+import tempfile
+from typing import Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from art.distributed.host_admission import (
+    RuntimeFingerprint,
+    runtime_package_names,
+)
+from art.utils.cache_dirs import configure_model_cache_env
+
+RUNTIME_INSTALL_MARKER = "openpipe-art-megatron-runtime"
+RUNTIME_LAUNCHER = "art-megatron-python"
+RUNTIME_PROTOCOL_VERSION = 1
+RuntimeProfile = Literal["cuda12", "cuda13"]
+RuntimeVariant = Literal["base", "hybrid_ep", "hybrid_ep_multinode"]
+
+
+class MegatronRuntimeAsset(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    filename: str = Field(min_length=1)
+    sha256: str = Field(pattern=r"^[0-9a-f]{64}$")
+
+
+class MegatronRuntimeManifest(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    art_package: str
+    art_version: str
+    runtime_package: str
+    runtime_version: str
+    protocol_version: int
+    python: str
+    pyproject: MegatronRuntimeAsset
+    lockfile: MegatronRuntimeAsset
+    source_archives: tuple[MegatronRuntimeAsset, ...] = ()
+
+
+class MegatronRuntimeInstallMarker(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    managed_by: Literal["openpipe-art-megatron-runtime"]
+    manifest_hash: str = Field(pattern=r"^[0-9a-f]{64}$")
+    profile: RuntimeProfile
+    variant: RuntimeVariant
+    cache_root: str
+
+
+class MegatronRuntimeInfo(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    python: str = Field(min_length=1)
+    profile: RuntimeProfile
+    variant: RuntimeVariant
+    manifest_hash: str = Field(pattern=r"^[0-9a-f]{64}$")
+    runtime: RuntimeFingerprint
+
+
+def _runtime_profile() -> RuntimeProfile:
+    if override := os.environ.get("ART_MEGATRON_RUNTIME_CUDA_PROFILE"):
+        if override in ("cuda12", "cuda13"):
+            return override
+        raise ValueError(
+            "ART_MEGATRON_RUNTIME_CUDA_PROFILE must be 'cuda12' or 'cuda13'"
+        )
+    torch_profile: RuntimeProfile | None = None
+    try:
+        import torch
+
+        if str(torch.version.cuda).startswith("13."):
+            torch_profile = "cuda13"
+        elif str(torch.version.cuda).startswith("12."):
+            torch_profile = "cuda12"
+    except ImportError:
+        pass
+    cuda_home = Path(os.environ.get("CUDA_HOME", "/usr/local/cuda"))
+    toolkit_profile: RuntimeProfile | None = None
+    for command in ([str(cuda_home / "bin/nvcc"), "--version"], ["nvidia-smi"]):
+        try:
+            output = subprocess.run(
+                command, capture_output=True, text=True, check=False
+            ).stdout
+        except FileNotFoundError:
+            continue
+        if "release 13." in output or "CUDA Version: 13." in output:
+            toolkit_profile = "cuda13"
+            break
+        if "release 12." in output or "CUDA Version: 12." in output:
+            toolkit_profile = "cuda12"
+            break
+    if torch_profile and toolkit_profile and torch_profile != toolkit_profile:
+        raise RuntimeError(
+            f"ART has {torch_profile} PyTorch but CUDA_HOME is {toolkit_profile}; "
+            "install the matching megatron or megatron-cu130 profile"
+        )
+    return torch_profile or toolkit_profile or "cuda12"
+
+
+def _bundled_runtime_dir() -> Path:
+    return Path(__file__).resolve().parents[2] / "_megatron_runtime"
+
+
+def _source_runtime_python() -> Path:
+    root = Path(__file__).resolve().parents[4]
+    return root / "megatron_runtime" / ".venv" / "bin" / "python"
+
+
+def _runtime_cache_root() -> Path:
+    if override := os.environ.get("ART_MEGATRON_RUNTIME_CACHE_DIR"):
+        return Path(override).expanduser()
+    return configure_model_cache_env(os.environ.copy()) / "megatron_runtime"
+
+
+def _sha256_file(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as source:
+        for chunk in iter(lambda: source.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _load_manifest(bundle: Path) -> MegatronRuntimeManifest:
+    path = bundle / "manifest.json"
+    if not path.is_file():
+        raise RuntimeError(
+            "ART's Megatron runtime bundle is missing; install a release wheel built "
+            "with scripts/build_package.py"
+        )
+    manifest = MegatronRuntimeManifest.model_validate_json(path.read_text())
+    if manifest.protocol_version != RUNTIME_PROTOCOL_VERSION:
+        raise RuntimeError(
+            f"Unsupported Megatron runtime protocol {manifest.protocol_version}"
+        )
+    if (
+        manifest.art_package != "openpipe-art"
+        or metadata.version(manifest.art_package) != manifest.art_version
+    ):
+        raise RuntimeError("Megatron runtime bundle does not match the ART wheel")
+    for asset in (manifest.pyproject, manifest.lockfile, *manifest.source_archives):
+        if _sha256_file(bundle / asset.filename) != asset.sha256:
+            raise RuntimeError(
+                f"Bundled Megatron runtime asset is corrupt: {asset.filename}"
+            )
+    return manifest
+
+
+def _manifest_hash(
+    manifest: MegatronRuntimeManifest,
+    profile: RuntimeProfile,
+    variant: RuntimeVariant,
+    art_build_sha256: str,
+) -> str:
+    payload = json.dumps(
+        {
+            "manifest": manifest.model_dump(mode="json"),
+            "profile": profile,
+            "variant": variant,
+            "art_build_sha256": art_build_sha256,
+        },
+        sort_keys=True,
+    ).encode()
+    return hashlib.sha256(payload).hexdigest()
+
+
+def _run(command: list[str], *, cwd: Path | None = None) -> str:
+    result = subprocess.run(command, cwd=cwd, capture_output=True, text=True)
+    if result.returncode:
+        detail = (result.stdout + result.stderr)[-8000:]
+        raise RuntimeError(
+            f"Megatron runtime command failed: {shlex.join(command)}\n{detail}"
+        )
+    return result.stdout
+
+
+def _uv() -> str:
+    candidates = (Path(sys.executable).parent / "uv", shutil.which("uv"))
+    for candidate in candidates:
+        if candidate and Path(candidate).is_file():
+            return str(candidate)
+    raise RuntimeError(
+        "The Megatron install profile requires uv; reinstall openpipe-art with "
+        "the megatron or megatron-cu130 extra"
+    )
+
+
+@contextmanager
+def _install_lock(cache_root: Path):
+    cache_root.mkdir(parents=True, exist_ok=True)
+    with (cache_root / ".install.lock").open("w") as lock:
+        fcntl.flock(lock, fcntl.LOCK_EX)
+        try:
+            yield
+        finally:
+            fcntl.flock(lock, fcntl.LOCK_UN)
+
+
+def _marker(runtime_dir: Path) -> MegatronRuntimeInstallMarker | None:
+    try:
+        return MegatronRuntimeInstallMarker.model_validate_json(
+            (runtime_dir / "install.json").read_text()
+        )
+    except (OSError, ValueError):
+        return None
+
+
+def _runtime_python(runtime_dir: Path) -> Path:
+    return runtime_dir / ".venv" / "bin" / "python"
+
+
+def _runtime_launcher(runtime_dir: Path) -> Path:
+    return runtime_dir / ".venv" / "bin" / RUNTIME_LAUNCHER
+
+
+def _write_runtime_launcher(runtime_dir: Path) -> Path:
+    launcher = _runtime_launcher(runtime_dir)
+    python_directory = f"python{sys.version_info.major}.{sys.version_info.minor}"
+    launcher.write_text(
+        "#!/bin/sh\n"
+        "set -eu\n"
+        'runtime_root=$(CDPATH= cd -- "$(dirname -- "$0")/.." && pwd)\n'
+        f'site_packages="$runtime_root/lib/{python_directory}/site-packages"\n'
+        "library_path=\n"
+        'for directory in "$site_packages"/nvidia/*/lib; do\n'
+        '    [ -d "$directory" ] || continue\n'
+        '    library_path="${library_path}${library_path:+:}${directory}"\n'
+        "done\n"
+        'export LD_LIBRARY_PATH="${library_path}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}"\n'
+        'if [ -n "${ART_MONARCH_PROGRAM_PYTHONPATH:-}" ]; then\n'
+        '    export PYTHONPATH="$ART_MONARCH_PROGRAM_PYTHONPATH"\n'
+        "else\n"
+        "    unset PYTHONPATH\n"
+        "fi\n"
+        'exec "$runtime_root/bin/python" "$@"\n'
+    )
+    launcher.chmod(0o755)
+    return launcher
+
+
+def _valid_runtime(
+    runtime_dir: Path,
+    *,
+    cache_root: Path,
+    manifest_hash: str,
+    profile: RuntimeProfile,
+    variant: RuntimeVariant,
+) -> Path | None:
+    marker = _marker(runtime_dir)
+    python = _runtime_python(runtime_dir)
+    launcher = _runtime_launcher(runtime_dir)
+    if (
+        marker is None
+        or marker.managed_by != RUNTIME_INSTALL_MARKER
+        or marker.manifest_hash != manifest_hash
+        or marker.profile != profile
+        or marker.variant != variant
+        or marker.cache_root != str(cache_root.resolve())
+        or runtime_dir.resolve().parent != cache_root.resolve()
+        or not os.access(python, os.X_OK)
+        or not os.access(launcher, os.X_OK)
+        or not (runtime_dir / ".venv" / "pyvenv.cfg").is_file()
+    ):
+        return None
+    return launcher
+
+
+def _site_packages(python: Path) -> Path:
+    value = _run(
+        [
+            str(python),
+            "-c",
+            "import sysconfig; print(sysconfig.get_paths()['purelib'])",
+        ]
+    ).strip()
+    path = Path(value)
+    if not path.is_dir():
+        raise RuntimeError(f"Megatron runtime site-packages does not exist: {path}")
+    return path
+
+
+def _copy_art(runtime_python: Path) -> None:
+    import art
+
+    source = Path(art.__file__).resolve().parent
+    destination = _site_packages(runtime_python)
+    shutil.copytree(
+        source,
+        destination / "art",
+        ignore=shutil.ignore_patterns("__pycache__", "*.pyc", "_vllm_runtime"),
+    )
+    mp_actors = source.parent / "mp_actors"
+    if mp_actors.is_dir():
+        shutil.copytree(mp_actors, destination / "mp_actors")
+    dist_info = tuple(source.parent.glob("openpipe_art-*.dist-info"))
+    if len(dist_info) != 1:
+        raise RuntimeError(
+            f"Expected one installed openpipe-art dist-info, found {dist_info}"
+        )
+    shutil.copytree(dist_info[0], destination / dist_info[0].name)
+
+
+def _install_runtime(
+    bundle: Path,
+    manifest: MegatronRuntimeManifest,
+    profile: RuntimeProfile,
+    variant: RuntimeVariant,
+    cache_root: Path,
+    manifest_hash: str,
+) -> Path:
+    stage = Path(tempfile.mkdtemp(prefix=f".{manifest_hash}.tmp-", dir=cache_root))
+    runtime_dir = cache_root / manifest_hash
+    promoted = False
+    try:
+        shutil.copy2(bundle / manifest.pyproject.filename, stage / "pyproject.toml")
+        shutil.copy2(bundle / manifest.lockfile.filename, stage / "uv.lock")
+        _run(
+            [
+                _uv(),
+                "sync",
+                "--project",
+                str(stage),
+                "--extra",
+                profile,
+                "--frozen",
+                "--no-dev",
+                "--no-install-project",
+                "--python",
+                sys.executable,
+            ]
+        )
+        python = _runtime_python(stage)
+        _copy_art(python)
+        launcher = _write_runtime_launcher(stage)
+        if variant != "base":
+            _prepare_hybrid_ep(launcher, multinode=variant == "hybrid_ep_multinode")
+        if runtime_dir.exists():
+            if existing := _valid_runtime(
+                runtime_dir,
+                cache_root=cache_root,
+                manifest_hash=manifest_hash,
+                profile=profile,
+                variant=variant,
+            ):
+                return existing
+            raise RuntimeError(
+                f"Refusing to replace invalid Megatron runtime directory: {runtime_dir}"
+            )
+        (stage / "install.json").write_text(
+            MegatronRuntimeInstallMarker(
+                managed_by=RUNTIME_INSTALL_MARKER,
+                manifest_hash=manifest_hash,
+                profile=profile,
+                variant=variant,
+                cache_root=str(cache_root.resolve()),
+            ).model_dump_json(indent=2)
+            + "\n"
+        )
+        stage.rename(runtime_dir)
+        promoted = True
+        return _runtime_launcher(runtime_dir)
+    finally:
+        if not promoted and stage.exists():
+            shutil.rmtree(stage)
+
+
+def _fingerprint(
+    python: Path, profile: RuntimeProfile, *, hybrid_ep: bool
+) -> RuntimeFingerprint:
+    nixl_package = {"cuda12": "nixl-cu12", "cuda13": "nixl-cu13"}[profile]
+    packages = [*runtime_package_names(trainer=True), nixl_package]
+    if profile == "cuda12":
+        packages.append("apex")
+    if hybrid_ep:
+        packages.append("art-deep-ep")
+    script = (
+        "import json; from art.distributed.host_admission import "
+        "build_runtime_fingerprint; print(build_runtime_fingerprint("
+        "json.loads(__import__('sys').argv[1])).model_dump_json())"
+    )
+    return RuntimeFingerprint.model_validate_json(
+        _run([str(python), "-c", script, json.dumps(packages)]).strip()
+    )
+
+
+def _prepare_hybrid_ep(python: Path, *, multinode: bool) -> None:
+    environment = os.environ.copy()
+    environment.update(
+        HYBRID_EP_MULTINODE="1" if multinode else "0",
+        USE_NIXL="1" if multinode else "0",
+    )
+    result = subprocess.run(
+        [str(python), "-m", "art.megatron.hybrid_ep_setup"],
+        env=environment,
+        capture_output=True,
+        text=True,
+    )
+    if result.returncode:
+        detail = (result.stdout + result.stderr)[-8000:]
+        raise RuntimeError(f"HybridEP runtime preparation failed:\n{detail}")
+
+
+def ensure_megatron_runtime(
+    *,
+    art_build_sha256: str,
+    require_hybrid_ep: bool = False,
+    multinode: bool = False,
+) -> MegatronRuntimeInfo:
+    if multinode and not require_hybrid_ep:
+        raise ValueError("multi-node HybridEP requires require_hybrid_ep=True")
+    profile = _runtime_profile()
+    variant: RuntimeVariant = (
+        "hybrid_ep_multinode"
+        if multinode
+        else "hybrid_ep"
+        if require_hybrid_ep
+        else "base"
+    )
+    managed = False
+    if override := os.environ.get("ART_MEGATRON_RUNTIME_PYTHON"):
+        python = Path(override).expanduser().resolve()
+        identity = hashlib.sha256(
+            f"{art_build_sha256}:{python}:{variant}".encode()
+        ).hexdigest()
+    else:
+        bundle = _bundled_runtime_dir()
+        if not (bundle / "manifest.json").is_file():
+            source_python = _source_runtime_python()
+            python = (
+                _write_runtime_launcher(source_python.parents[2])
+                if source_python.is_file()
+                else Path(sys.executable)
+            )
+            identity = hashlib.sha256(
+                f"source:{art_build_sha256}:{python}:{platform.python_version()}:{variant}".encode()
+            ).hexdigest()
+        else:
+            managed = True
+            manifest = _load_manifest(bundle)
+            identity = _manifest_hash(manifest, profile, variant, art_build_sha256)
+            cache_root = _runtime_cache_root()
+            runtime_dir = cache_root / identity
+            with _install_lock(cache_root):
+                python = _valid_runtime(
+                    runtime_dir,
+                    cache_root=cache_root,
+                    manifest_hash=identity,
+                    profile=profile,
+                    variant=variant,
+                ) or _install_runtime(
+                    bundle,
+                    manifest,
+                    profile,
+                    variant,
+                    cache_root,
+                    identity,
+                )
+    if not os.access(python, os.X_OK):
+        raise RuntimeError(f"Megatron runtime Python is not executable: {python}")
+    if require_hybrid_ep and not managed:
+        _prepare_hybrid_ep(python, multinode=multinode)
+    return MegatronRuntimeInfo(
+        python=str(python),
+        profile=profile,
+        variant=variant,
+        manifest_hash=identity,
+        runtime=_fingerprint(python, profile, hybrid_ep=require_hybrid_ep),
+    )
diff --git a/src/art/megatron/runtime/monarch.py b/src/art/megatron/runtime/monarch.py
new file mode 100644
index 000000000..bdda472b4
--- /dev/null
+++ b/src/art/megatron/runtime/monarch.py
@@ -0,0 +1,1811 @@
+from __future__ import annotations
+
+import asyncio
+from collections.abc import AsyncIterator, Awaitable
+import hashlib
+import json
+import os
+import socket
+from threading import Event, Lock, Thread
+import time
+import traceback
+from typing import Any, Callable
+
+import monarch.actor as monarch_actor
+from monarch.actor import Actor, Channel, MeshFailure, Port, ProcMesh, endpoint
+from monarch.spmd import SPMDActor
+from pydantic import BaseModel, ConfigDict
+
+from art.distributed.data_plane import PackedBatchLeaseSet
+from art.distributed.monarch_bootstrap import activate_cuda_device
+from art.distributed.specs import GpuId
+from art.utils.cache_dirs import configure_model_cache_env
+from art.utils.lifecycle import cleanup_after_failure, consume_future_exception
+
+from .data_plane import InMemoryPackedBatch, SFTBatchData
+from .publication import (
+    TRAINER_PUBLICATION_EVENT_ADAPTER,
+    TrainerPublicationEvent,
+    TrainerPublicationFailed,
+    TrainerPublicationSucceeded,
+    TrainerRankPublication,
+)
+from .specs import (
+    TRAIN_EVENT_ADAPTER,
+    AdapterReady,
+    HybridEpRuntimeSpec,
+    ResidentLoraExport,
+    ResidentLoraInspectionResult,
+    ResidentLoraInspectionShard,
+    ResidentLoraInspectionSpec,
+    ResidentLoraRankSummary,
+    ResidentScoreJobSpec,
+    ResidentScoreResult,
+    ResidentScoreShard,
+    SFTJobSpec,
+    TrainAccepted,
+    TrainCancelled,
+    TrainCompleted,
+    TrainerGeneration,
+    TrainerJobSpec,
+    TrainerRuntimeSpec,
+    TrainEvent,
+    TrainFailed,
+    TrainingRunSpec,
+    TrainJobSpec,
+    TrainProgress,
+)
+
+
+class _ActorEventSink:
+    def __init__(self, port: Port[dict[str, Any]], *, coordinator: bool) -> None:
+        self._port = port
+        self._coordinator = coordinator
+
+    def progress(
+        self, *, step_index: int, num_steps: int, metrics: dict[str, float]
+    ) -> None:
+        if self._coordinator:
+            self._port.send(
+                {
+                    "kind": "progress",
+                    "step_index": step_index,
+                    "num_steps": num_steps,
+                    "metrics": metrics,
+                }
+            )
+
+    def adapter_ready(self, *, learner_version: int, adapter_path: str) -> None:
+        if self._coordinator:
+            self._port.send(
+                {
+                    "kind": "adapter_ready",
+                    "learner_version": learner_version,
+                    "adapter_path": adapter_path,
+                }
+            )
+
+    def publication(self, event: TrainerPublicationEvent) -> None:
+        self._port.send(event.model_dump(mode="json"))
+
+
+_SUPERVISION_LOCK = Lock()
+_SUPERVISION_HANDLERS: dict[str, "MonarchTrainerSupervision"] = {}
+_SUPERVISION_MESHES: dict[str, "MonarchTrainerSupervision"] = {}
+_PREVIOUS_FAULT_HOOK: Callable[[MeshFailure], None] | None = None
+
+
+def _configure_hybrid_ep_env(
+    spec: HybridEpRuntimeSpec, *, run_id: str | None = None
+) -> None:
+    os.environ["NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN"] = str(
+        spec.ranks_per_nvlink_domain
+    )
+    transport = spec.nixl_transport
+    metadata_store = transport.metadata_store if transport is not None else None
+    nixl_paths = None
+    if transport is not None:
+        if metadata_store is None:
+            raise RuntimeError(
+                "NIXL metadata store was not resolved before trainer launch"
+            )
+        from art.distributed.nixl_runtime import configure_nixl_environment
+
+        nixl_paths = configure_nixl_environment()
+    values = {
+        "HYBRID_EP_MULTINODE": "1" if transport else None,
+        "USE_NIXL": "1" if transport else None,
+        "DEEPEP_NIXL_RUN_ID": (run_id or spec.run_id) if transport else None,
+        "NIXL_ETCD_ENDPOINTS": metadata_store.url if metadata_store else None,
+        "NIXL_HOME": transport.nixl_home if transport else None,
+        "UCX_HOME": transport.ucx_home if transport else None,
+        "NIXL_PLUGIN_DIR": (
+            transport.nixl_plugin_dir or str(nixl_paths.plugin_dir)
+            if transport and nixl_paths
+            else None
+        ),
+        "UCX_MODULE_DIR": (
+            transport.ucx_module_dir or str(nixl_paths.ucx_module_dir)
+            if transport and nixl_paths
+            else None
+        ),
+        "UCX_NET_DEVICES": transport.ucx_net_devices if transport else None,
+        "UCX_TLS": transport.ucx_tls if transport else None,
+        "UCX_IB_GDA_RETAIN_INACTIVE_CTX": "yes" if transport else None,
+        "UCX_CUDA_COPY_ENABLE_FABRIC": (
+            "yes" if transport and transport.enable_cuda_fabric else "no"
+        )
+        if transport
+        else None,
+    }
+    for name, value in values.items():
+        if value is None:
+            os.environ.pop(name, None)
+        else:
+            os.environ[name] = value
+
+
+def _build_training_runtime(spec: TrainerRuntimeSpec, *, rank: int) -> Any:
+    import torch
+
+    from art.megatron.train import build_training_runtime
+
+    return build_training_runtime(
+        model_identifier=spec.model_identifier,
+        model_initialization=spec.model_initialization,
+        provider_torch_dtype={
+            "bfloat16": torch.bfloat16,
+            "float16": torch.float16,
+            "float32": torch.float32,
+        }[spec.dtype],
+        print_env=rank == 0,
+        model_support_key=spec.model_support_key,
+        snapshot_pool_capacity=spec.snapshot_pool_capacity,
+    )
+
+
+class _TrainerRankReady(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    rank: int
+    host_id: str
+    gpu_id: GpuId
+    hostname: str
+    process_id: int
+
+
+class _CpLookaheadResult(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    rank: int
+    batch_id: str
+    planned_sequences: int = 0
+    elapsed_s: float = 0.0
+    error_type: str | None = None
+    message: str | None = None
+    traceback_text: str | None = None
+
+
+def _dispatch_trainer_fault(failure: MeshFailure) -> None:
+    message = str(failure)
+    with _SUPERVISION_LOCK:
+        owner = _SUPERVISION_MESHES.get(failure.mesh_name)
+        handlers = (
+            (owner,)
+            if owner is not None
+            else tuple(
+                handler
+                for token, handler in _SUPERVISION_HANDLERS.items()
+                if token in message
+            )
+        )
+        previous = _PREVIOUS_FAULT_HOOK
+    if handlers:
+        for handler in handlers:
+            handler.notify(message)
+        return
+    if previous is not None:
+        previous(failure)
+
+
+class MonarchTrainerSupervision:
+    """Route one owned trainer mesh failure without masking unrelated faults."""
+
+    def __init__(self, run_id: str) -> None:
+        self.run_id = run_id
+        self.token = hashlib.sha256(run_id.encode()).hexdigest()[:16]
+        self._loop = asyncio.get_running_loop()
+        self._failure: asyncio.Future[str] = self._loop.create_future()
+        self._mesh_names: set[str] = set()
+        self._closed = False
+        global _PREVIOUS_FAULT_HOOK
+        with _SUPERVISION_LOCK:
+            if self.token in _SUPERVISION_HANDLERS:
+                raise RuntimeError(f"trainer run {run_id!r} is already supervised")
+            if not _SUPERVISION_HANDLERS:
+                _PREVIOUS_FAULT_HOOK = monarch_actor.unhandled_fault_hook
+                setattr(
+                    monarch_actor,
+                    "unhandled_fault_hook",
+                    _dispatch_trainer_fault,
+                )
+            _SUPERVISION_HANDLERS[self.token] = self
+
+    def own_mesh(self, mesh_name: str) -> None:
+        if not mesh_name:
+            raise ValueError("trainer mesh name must not be empty")
+        with _SUPERVISION_LOCK:
+            if self._closed:
+                raise RuntimeError(f"trainer run {self.run_id!r} is closed")
+            owner = _SUPERVISION_MESHES.get(mesh_name)
+            if owner is not None and owner is not self:
+                raise RuntimeError(f"Monarch mesh {mesh_name!r} already has an owner")
+            self._mesh_names.add(mesh_name)
+            _SUPERVISION_MESHES[mesh_name] = self
+
+    def notify(self, failure: str) -> None:
+        def set_failure() -> None:
+            if not self._failure.done():
+                self._failure.set_result(failure)
+
+        self._loop.call_soon_threadsafe(set_failure)
+
+    async def wait(self) -> str:
+        return await asyncio.shield(self._failure)
+
+    def close(self) -> None:
+        global _PREVIOUS_FAULT_HOOK
+        with _SUPERVISION_LOCK:
+            if self._closed:
+                return
+            self._closed = True
+            if _SUPERVISION_HANDLERS.get(self.token) is self:
+                _SUPERVISION_HANDLERS.pop(self.token)
+            for mesh_name in self._mesh_names:
+                if _SUPERVISION_MESHES.get(mesh_name) is self:
+                    _SUPERVISION_MESHES.pop(mesh_name)
+            if not _SUPERVISION_HANDLERS:
+                if monarch_actor.unhandled_fault_hook is _dispatch_trainer_fault:
+                    assert _PREVIOUS_FAULT_HOOK is not None
+                    setattr(
+                        monarch_actor,
+                        "unhandled_fault_hook",
+                        _PREVIOUS_FAULT_HOOK,
+                    )
+                _PREVIOUS_FAULT_HOOK = None
+
+
+class _TrainerSPMDActor(SPMDActor):
+    """Own the rendezvous store until the warm trainer mesh is stopped."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._store: Any = None
+
+    @endpoint
+    def start_store(self, _request: None) -> tuple[str, int]:
+        if self._store is not None:
+            raise RuntimeError("trainer rendezvous store is already running")
+        from torch.distributed import TCPStore
+
+        hostname = socket.gethostname()
+        self._store = TCPStore(
+            hostname,
+            0,
+            self.world_size,
+            True,
+            wait_for_workers=False,
+        )
+        return hostname, int(self._store.port)
+
+    @endpoint
+    def setup_agent_store_env(self, master_addr: str, master_port: int) -> None:
+        self._setup_env(master_addr, master_port)
+        os.environ["TORCHELASTIC_USE_AGENT_STORE"] = "True"
+
+    def __cleanup__(self, exc: Exception | None) -> None:
+        del exc
+        self._store = None
+
+
+class MonarchTrainerActor(Actor):
+    """One warm Megatron rank, spawned once on every trainer ProcMesh process."""
+
+    def __init__(
+        self,
+        runtime_spec_json: str,
+        run_id: str,
+    ) -> None:
+        runtime_spec = TrainerRuntimeSpec.model_validate_json(runtime_spec_json)
+        topology = runtime_spec.trainer_mesh.topology
+        cache_root = configure_model_cache_env(cache_root=runtime_spec.cache_root)
+        os.environ.update(
+            {
+                "MODEL_IDENTIFIER": runtime_spec.model_identifier,
+                "ART_MEGATRON_TENSOR_MODEL_PARALLEL_SIZE": str(topology.tp),
+                "ART_MEGATRON_CONTEXT_PARALLEL_SIZE": str(topology.cp),
+                "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE": str(topology.ep),
+                "ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE": str(topology.pp),
+                "ART_MEGATRON_EXPERT_TENSOR_PARALLEL_SIZE": str(topology.etp),
+                "ART_MEGATRON_LORA_RANK": str(runtime_spec.lora_rank),
+                "ART_MEGATRON_LORA_TARGET_MODULES": json.dumps(
+                    runtime_spec.lora_target_modules
+                ),
+                "ART_DISABLE_MEGATRON_COMPILE": (
+                    "0" if runtime_spec.compile_enabled else "1"
+                ),
+                "ART_MEGATRON_ALLOW_UNVALIDATED_ARCH": str(
+                    int(runtime_spec.allow_unvalidated_arch)
+                ),
+                "ART_MEGATRON_ENABLE_MOE_ROUTING_REPLAY": str(
+                    int(runtime_spec.enable_moe_routing_replay)
+                ),
+                "ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD": str(
+                    int(runtime_spec.streaming_weight_offload)
+                ),
+                "ART_MEGATRON_OFFLOAD_BETWEEN_JOBS": str(
+                    int(runtime_spec.offload_between_jobs)
+                ),
+            }
+        )
+        if runtime_spec.random_state is not None:
+            os.environ["ART_MEGATRON_RANDOM_STATE"] = str(runtime_spec.random_state)
+        if topology.vpp is not None:
+            os.environ["ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE"] = str(
+                topology.vpp
+            )
+        if topology.vpp_microbatch_group_size is not None:
+            os.environ["ART_MEGATRON_VPP_MICROBATCH_GROUP_SIZE"] = str(
+                topology.vpp_microbatch_group_size
+            )
+        world_size = int(os.environ["WORLD_SIZE"])
+        if world_size != len(runtime_spec.trainer_mesh.ranks):
+            raise RuntimeError(
+                "Monarch ProcMesh world does not match TrainerRuntimeSpec: "
+                f"{world_size} != {len(runtime_spec.trainer_mesh.ranks)}"
+            )
+
+        rank = int(os.environ["RANK"])
+        placement = runtime_spec.trainer_mesh.ranks[rank]
+        self._host_id = placement.host_id
+        self._gpu_id = placement.gpu_id
+        local_rank = activate_cuda_device(placement.gpu_id)
+        os.environ["LOCAL_RANK"] = str(local_rank)
+
+        import torch
+
+        torch.set_num_threads(int(os.environ["OMP_NUM_THREADS"]))
+        torch.cuda.set_device(local_rank)
+        self._compile_cache = None
+        self._compile_cache_metrics: dict[str, float] = {}
+        if runtime_spec.compile_cache:
+            from .compile_cache import TrainerCompileCache
+
+            self._compile_cache = TrainerCompileCache(
+                runtime_spec, rank=rank, cache_root=cache_root
+            )
+            event = self._compile_cache.load()
+            self._compile_cache_metrics.update(
+                {
+                    "hit": float(event.status == "hit"),
+                    "load_s": event.elapsed_s,
+                    "artifact_bytes": float(event.artifact_bytes),
+                }
+            )
+        if topology.ep > 1:
+            from art.megatron.hybrid_ep_setup import validate_hybrid_ep
+
+            hybrid_ep = runtime_spec.hybrid_ep
+            if hybrid_ep is None:
+                raise RuntimeError(
+                    "expert parallelism requires a HybridEP runtime spec"
+                )
+            group_index = rank // (topology.etp * topology.ep)
+            _configure_hybrid_ep_env(
+                hybrid_ep,
+                run_id=f"{hybrid_ep.run_id}-{run_id}-g{group_index}",
+            )
+            validate_hybrid_ep(require_multinode=hybrid_ep.multinode)
+        self._runtime = _build_training_runtime(runtime_spec, rank=rank)
+        self._runtime.resident_run_id = run_id
+        if self._runtime.model_support_handler.key != runtime_spec.handler_name:
+            raise RuntimeError(
+                "resolved model-support handler does not match TrainerRuntimeSpec: "
+                f"{self._runtime.model_support_handler.key!r} != "
+                f"{runtime_spec.handler_name!r}"
+            )
+        from art.megatron.training.streaming_weight_offload import (
+            streaming_weight_offload_config_from_env,
+        )
+        from art.megatron.training.weight_offload import WeightOffloadManager
+
+        from .executor import MegatronTrainJobExecutor
+
+        self._executor = MegatronTrainJobExecutor(self._runtime)
+        self._weight_offload = WeightOffloadManager.from_config(
+            model=self._runtime.model,
+            rank=self._runtime.rank,
+            compile_enabled=self._runtime.transformer_layers_compiled,
+            offload_between_jobs=runtime_spec.offload_between_jobs,
+            streaming_config=streaming_weight_offload_config_from_env(),
+        )
+        self._weight_offload.install()
+        self._cp_preplanner = None
+        self._cp_lookahead_port = None
+        self._cp_lookahead_thread = None
+        if topology.cp > 1:
+            from art.megatron.training.microbatches import CpBatchPreplanner
+
+            self._cp_preplanner = CpBatchPreplanner.from_runtime(
+                self._runtime,
+                device=torch.device("cuda", local_rank),
+            )
+            if self._cp_preplanner is None:
+                raise RuntimeError("CP trainer did not create a batch preplanner")
+            self._cp_lookahead_port, receiver = Channel.open()
+            self._cp_lookahead_thread = Thread(
+                target=self._run_cp_lookahead,
+                args=(receiver,),
+                name=f"art-cp-lookahead-rank-{rank}",
+                daemon=True,
+            )
+            self._cp_lookahead_thread.start()
+        self._valid = True
+
+    def _run_cp_lookahead(self, receiver: Any) -> None:
+        while (request := receiver.recv().get()) is not None:
+            batch_json, batch_id, accumulation, reply = request
+            batch = None
+            started = time.perf_counter()
+            try:
+                leases = PackedBatchLeaseSet.model_validate_json(batch_json)
+                if leases.ref.batch_id != batch_id:
+                    raise RuntimeError("CP lookahead request batch ID mismatch")
+                if self._cp_preplanner is None:
+                    raise RuntimeError("CP lookahead preplanner is unavailable")
+                batch = InMemoryPackedBatch.open(
+                    leases.ref, leases.host_refs[self._host_id]
+                )
+                planned = self._cp_preplanner.preplan(
+                    batch.tensors,
+                    global_grad_accumulation_sequences=accumulation,
+                )
+                result = _CpLookaheadResult(
+                    rank=self._runtime.rank,
+                    batch_id=batch_id,
+                    planned_sequences=planned,
+                    elapsed_s=time.perf_counter() - started,
+                )
+            except BaseException as error:
+                result = _CpLookaheadResult(
+                    rank=self._runtime.rank,
+                    batch_id=batch_id,
+                    elapsed_s=time.perf_counter() - started,
+                    error_type=type(error).__name__,
+                    message=str(error),
+                    traceback_text=traceback.format_exc(),
+                )
+            finally:
+                if batch is not None:
+                    batch.close()
+            reply.send(result.model_dump(mode="json"))
+
+    def _stop_cp_lookahead(self) -> None:
+        thread, self._cp_lookahead_thread = self._cp_lookahead_thread, None
+        if thread is None:
+            return
+        port = self._cp_lookahead_port
+        if port is None:
+            raise RuntimeError("CP lookahead thread has no request port")
+        port.send(None)
+        thread.join(timeout=30.0)
+        if thread.is_alive():
+            raise RuntimeError("CP lookahead service did not stop within 30 seconds")
+
+    def _publish_compile_cache(self) -> None:
+        if self._compile_cache is None or "publish_s" in self._compile_cache_metrics:
+            return
+        event = self._compile_cache.publish()
+        self._compile_cache_metrics.update(
+            {
+                "publish_s": event.elapsed_s,
+                "published": float(event.status == "published"),
+                "artifact_bytes": float(event.artifact_bytes),
+            }
+        )
+
+    @endpoint
+    def ready(self) -> dict[str, Any]:
+        return _TrainerRankReady(
+            rank=self._runtime.rank,
+            host_id=self._host_id,
+            gpu_id=self._gpu_id,
+            hostname=socket.gethostname(),
+            process_id=os.getpid(),
+        ).model_dump(mode="json")
+
+    @endpoint
+    def cp_lookahead_port(self) -> dict[str, Any] | None:
+        if self._cp_lookahead_port is None:
+            return None
+        return {"rank": self._runtime.rank, "port": self._cp_lookahead_port}
+
+    @endpoint
+    def execute(
+        self,
+        job_json: str,
+        batch_json: str,
+        event_port: Port[dict[str, Any]],
+    ) -> dict[str, Any]:
+        batch = None
+        try:
+            if not self._valid:
+                raise RuntimeError("trainer actor runtime is invalid")
+            job = TrainJobSpec.model_validate_json(job_json)
+            leases = PackedBatchLeaseSet.model_validate_json(batch_json)
+            batch = InMemoryPackedBatch.open(job.batch, leases.host_refs[self._host_id])
+            coordinator = self._runtime.rank == 0
+            with self._weight_offload.job():
+                metrics = self._executor.execute(
+                    job,
+                    batch,
+                    _ActorEventSink(event_port, coordinator=coordinator),
+                    Event(),
+                )
+                self._publish_compile_cache()
+            if coordinator:
+                event_port.send({"kind": "actor_completed", "metrics": metrics})
+            return {
+                "rank": self._runtime.rank,
+                "learner_version": job.learner_version,
+                "metrics": metrics if coordinator else {},
+                "compile_cache": self._compile_cache_metrics,
+            }
+        except BaseException as error:
+            self._valid = False
+            event_port.send(
+                {
+                    "kind": "rank_failed",
+                    "rank": self._runtime.rank,
+                    "error_type": type(error).__name__,
+                    "message": str(error),
+                    "traceback": traceback.format_exc(),
+                }
+            )
+            raise
+        finally:
+            if batch is not None:
+                batch.close()
+
+    @endpoint
+    def execute_sft(
+        self,
+        job_json: str,
+        batches: tuple[SFTBatchData, ...],
+        event_port: Port[dict[str, Any]],
+    ) -> dict[str, Any]:
+        try:
+            if not self._valid:
+                raise RuntimeError("trainer actor runtime is invalid")
+            job = SFTJobSpec.model_validate_json(job_json)
+            coordinator = self._runtime.rank == 0
+            with self._weight_offload.job():
+                metrics = self._executor.execute_sft(
+                    job,
+                    batches,
+                    _ActorEventSink(event_port, coordinator=coordinator),
+                    Event(),
+                )
+                self._publish_compile_cache()
+            if coordinator:
+                event_port.send({"kind": "actor_completed", "metrics": metrics})
+            return {
+                "rank": self._runtime.rank,
+                "learner_version": job.learner_version,
+                "metrics": metrics if coordinator else {},
+                "compile_cache": self._compile_cache_metrics,
+            }
+        except BaseException as error:
+            self._valid = False
+            event_port.send(
+                {
+                    "kind": "rank_failed",
+                    "rank": self._runtime.rank,
+                    "error_type": type(error).__name__,
+                    "message": str(error),
+                    "traceback": traceback.format_exc(),
+                }
+            )
+            raise
+
+    @endpoint
+    def score(self, job_json: str, batch_json: str) -> dict[str, Any]:
+        batch = None
+        try:
+            if not self._valid:
+                raise RuntimeError("trainer actor runtime is invalid")
+            job = ResidentScoreJobSpec.model_validate_json(job_json)
+            leases = PackedBatchLeaseSet.model_validate_json(batch_json)
+            batch = InMemoryPackedBatch.open(job.batch, leases.host_refs[self._host_id])
+            with self._weight_offload.job():
+                result = self._executor.score(job, batch)
+            return result.model_dump(mode="json")
+        except BaseException:
+            self._valid = False
+            raise
+        finally:
+            if batch is not None:
+                batch.close()
+
+    @endpoint
+    def inspect_resident_lora(self, request_json: str) -> dict[str, Any]:
+        if not self._valid:
+            raise RuntimeError("trainer actor runtime is invalid")
+        request = ResidentLoraInspectionSpec.model_validate_json(request_json)
+        with self._weight_offload.job():
+            result = self._executor.inspect_resident_lora(request)
+        return result.model_dump(mode="json")
+
+    @endpoint
+    def close(self) -> None:
+        self._stop_cp_lookahead()
+        self._executor.close()
+        import torch
+
+        if torch.distributed.is_initialized():
+            torch.distributed.destroy_process_group()
+
+    @endpoint
+    def advance_without_training(
+        self,
+        source_json: str,
+        output_json: str,
+        optimizer_state_path: str,
+        adapter_json: str | None,
+    ) -> dict[str, Any]:
+        if not self._valid:
+            raise RuntimeError("trainer actor runtime is invalid")
+        from art.megatron.optimizer_state import OptimizerAdapter
+
+        source = TrainerGeneration.model_validate_json(source_json)
+        output = TrainerGeneration.model_validate_json(output_json)
+        adapter = (
+            None
+            if adapter_json is None
+            else OptimizerAdapter.model_validate_json(adapter_json)
+        )
+        try:
+            with self._weight_offload.job():
+                metrics = self._executor.advance_without_training(
+                    source=source,
+                    output=output,
+                    optimizer_state_path=optimizer_state_path,
+                    adapter=adapter,
+                )
+            return {
+                "rank": self._runtime.rank,
+                "learner_version": output.policy_step,
+                "metrics": metrics,
+            }
+        except BaseException:
+            self._valid = False
+            raise
+
+    def __cleanup__(self, exc: Exception | None) -> None:
+        if exc is not None:
+            self._valid = False
+        self._stop_cp_lookahead()
+        self._executor.close()
+
+
+async def spawn_monarch_trainer_actors(
+    proc_mesh: ProcMesh,
+    runtime_spec: TrainerRuntimeSpec,
+    supervision: MonarchTrainerSupervision,
+) -> tuple[Any, tuple[_TrainerRankReady, ...], tuple[Port[Any], ...]]:
+    """Configure torch-elastic first, then initialize exactly one actor per rank."""
+    spmd: Any = proc_mesh.spawn(
+        f"art_torch_elastic_{supervision.token}", _TrainerSPMDActor
+    )
+    supervision.own_mesh(await spmd._name)
+    first_rank = dict.fromkeys(proc_mesh._labels, 0)
+    master_addr, master_port = await spmd.slice(**first_rank).start_store.call_one(None)
+    await spmd.setup_agent_store_env.call(master_addr, master_port)
+    actors: Any = proc_mesh.spawn(
+        f"art_megatron_trainer_{supervision.token}",
+        MonarchTrainerActor,
+        runtime_spec.model_dump_json(),
+        supervision.run_id,
+    )
+    supervision.own_mesh(await actors._name)
+    await actors.initialized
+    values = await actors.ready.call()
+    ready = tuple(
+        sorted(
+            (_TrainerRankReady.model_validate(value) for value in values.values()),
+            key=lambda value: value.rank,
+        )
+    )
+    placements = runtime_spec.trainer_mesh.ranks
+    if len(ready) != len(placements) or any(
+        (value.rank, value.host_id, value.gpu_id)
+        != (rank, placement.host_id, placement.gpu_id)
+        for rank, (value, placement) in enumerate(zip(ready, placements, strict=True))
+    ):
+        raise RuntimeError(
+            "trainer startup did not return the configured rank placement"
+        )
+    port_values = await actors.cp_lookahead_port.call()
+    lookahead_ports = tuple(
+        value["port"]
+        for value in sorted(
+            (value for value in port_values.values() if value is not None),
+            key=lambda value: value["rank"],
+        )
+    )
+    expected_port_count = (
+        len(placements) if runtime_spec.trainer_mesh.topology.cp > 1 else 0
+    )
+    if len(lookahead_ports) != expected_port_count:
+        raise RuntimeError("trainer ranks returned an incomplete CP lookahead service")
+    return actors, ready, lookahead_ports
+
+
+class _PublicationState:
+    __slots__ = (
+        "active_waiters",
+        "drain_done",
+        "future",
+        "generation_id",
+        "late_waitable",
+        "outcome_observed",
+        "records",
+        "train_done",
+    )
+
+    def __init__(
+        self,
+        generation_id: str,
+        future: asyncio.Future[tuple[TrainerRankPublication, ...]],
+    ) -> None:
+        self.generation_id = generation_id
+        self.future = future
+        self.records: dict[int, TrainerRankPublication] = {}
+        self.train_done = False
+        self.drain_done = True
+        self.active_waiters = 0
+        self.late_waitable = True
+        self.outcome_observed = False
+
+
+def _merge_resident_score_shards(
+    shards: tuple[ResidentScoreShard, ...],
+    *,
+    job: ResidentScoreJobSpec,
+    world_size: int,
+) -> ResidentScoreResult:
+    by_rank = {shard.rank: shard for shard in shards}
+    expected_ranks = set(range(world_size))
+    if len(by_rank) != len(shards) or set(by_rank) != expected_ranks:
+        raise RuntimeError("resident score did not return exactly one shard per rank")
+    ordered = tuple(by_rank[rank] for rank in range(world_size))
+    first = ordered[0]
+    for shard in ordered:
+        if (
+            shard.job_id != job.job_id
+            or shard.run_id != job.run_id
+            or shard.learner != job.learner
+            or shard.batch_id != job.batch.batch_id
+            or shard.batch_fingerprint != first.batch_fingerprint
+            or shard.top_k != job.top_k
+            or shard.expected_score_count != first.expected_score_count
+            or shard.routing_replay_packed_tokens != first.routing_replay_packed_tokens
+        ):
+            raise RuntimeError("resident score rank shards disagree on provenance")
+    expected_replay_tokens = (
+        0
+        if job.batch.moe_routing_replay is None
+        else job.batch.moe_routing_replay.packed_tokens
+    )
+    if first.routing_replay_packed_tokens != expected_replay_tokens:
+        raise RuntimeError("resident score routing replay does not match packed data")
+
+    scores: dict[tuple[int, int], Any] = {}
+    for shard in ordered:
+        for score in shard.scores:
+            key = score.sample_index, score.logit_index
+            previous = scores.get(key)
+            if previous is not None and previous != score:
+                raise RuntimeError(
+                    f"resident score replicas disagree at coordinate {key}"
+                )
+            scores[key] = score
+    merged = tuple(scores[key] for key in sorted(scores))
+    if len(merged) != first.expected_score_count:
+        raise RuntimeError(
+            "resident score did not cover every packed target: "
+            f"expected={first.expected_score_count}, got={len(merged)}"
+        )
+    return ResidentScoreResult(
+        job_id=job.job_id,
+        run_id=job.run_id,
+        learner=job.learner,
+        batch_id=job.batch.batch_id,
+        batch_fingerprint=first.batch_fingerprint,
+        ranks=tuple(range(world_size)),
+        top_k=job.top_k,
+        expected_score_count=first.expected_score_count,
+        routing_replay_packed_tokens=first.routing_replay_packed_tokens,
+        scores=merged,
+    )
+
+
+def _merge_resident_lora_shards(
+    shards: tuple[ResidentLoraInspectionShard, ...],
+    *,
+    request: ResidentLoraInspectionSpec,
+    world_size: int,
+) -> ResidentLoraInspectionResult:
+    by_rank = {shard.rank: shard for shard in shards}
+    expected_ranks = set(range(world_size))
+    if len(by_rank) != len(shards) or set(by_rank) != expected_ranks:
+        raise RuntimeError("resident LoRA inspection did not return one shard per rank")
+    ordered = tuple(by_rank[rank] for rank in range(world_size))
+    for shard in ordered:
+        if (
+            shard.request_id != request.request_id
+            or shard.run_id != request.run_id
+            or shard.learner != request.learner
+            or shard.target_modules != request.target_modules
+        ):
+            raise RuntimeError("resident LoRA rank shards disagree on provenance")
+
+    exports: dict[str, set[str | None]] = {}
+    for shard in ordered:
+        for export in shard.exports:
+            exports.setdefault(export.base_name, set()).update(export.adapter_keys)
+    return ResidentLoraInspectionResult(
+        request_id=request.request_id,
+        run_id=request.run_id,
+        learner=request.learner,
+        target_modules=request.target_modules,
+        rank_summaries=tuple(
+            ResidentLoraRankSummary(
+                rank=shard.rank,
+                module_count=shard.module_count,
+                trainable_parameter_count=len(shard.trainable_lora_parameter_names),
+                trainable_numel=shard.trainable_numel,
+            )
+            for shard in ordered
+        ),
+        wrapped_adapter_prefixes=tuple(
+            sorted(
+                {
+                    prefix
+                    for shard in ordered
+                    for prefix in shard.wrapped_adapter_prefixes
+                }
+            )
+        ),
+        exports=tuple(
+            ResidentLoraExport(
+                base_name=base_name,
+                adapter_keys=tuple(
+                    sorted(
+                        adapter_keys,
+                        key=lambda value: "" if value is None else value,
+                    )
+                ),
+            )
+            for base_name, adapter_keys in sorted(exports.items())
+        ),
+        trainable_lora_parameter_names=tuple(
+            sorted(
+                {
+                    name
+                    for shard in ordered
+                    for name in shard.trainable_lora_parameter_names
+                }
+            )
+        ),
+        unexpected_trainable_parameter_names=tuple(
+            sorted(
+                {
+                    name
+                    for shard in ordered
+                    for name in shard.unexpected_trainable_parameter_names
+                }
+            )
+        ),
+    )
+
+
+class MonarchTrainerRun:
+    def __init__(
+        self,
+        runtime_spec: TrainerRuntimeSpec,
+        run_spec: TrainingRunSpec,
+        actors: Any,
+        proc_mesh: ProcMesh,
+        supervision: MonarchTrainerSupervision,
+        rank_processes: tuple[_TrainerRankReady, ...],
+        cp_lookahead_ports: tuple[Port[Any], ...],
+    ) -> None:
+        if run_spec.runtime_fingerprint != runtime_spec.fingerprint:
+            raise ValueError(
+                "training run does not match the trainer runtime fingerprint"
+            )
+        self.runtime_spec = runtime_spec
+        self.run_spec = run_spec
+        self._actors = actors
+        self._proc_mesh = proc_mesh
+        self._supervision = supervision
+        self._rank_processes = rank_processes
+        self._cp_lookahead_ports = cp_lookahead_ports
+        self._learner_version = run_spec.initial_learner_version
+        self._jobs: dict[str, tuple[str, tuple[TrainEvent, ...]]] = {}
+        self._lock = asyncio.Lock()
+        self._cp_lookahead_lock = asyncio.Lock()
+        self._active_job_id: str | None = None
+        self._active_collective: asyncio.Future[Any] | None = None
+        self._active_receive: asyncio.Future[Any] | None = None
+        self._publications: dict[str, _PublicationState] = {}
+        self._publication_drains: set[asyncio.Task[None]] = set()
+        self._stop_task: asyncio.Task[None] | None = None
+        self._close_task: asyncio.Task[None] | None = None
+        self._closed = False
+        self._valid = True
+
+    @property
+    def learner_version(self) -> int:
+        return self._learner_version
+
+    @property
+    def valid(self) -> bool:
+        return self._valid
+
+    async def prepare_cp_lookahead(
+        self,
+        batch: PackedBatchLeaseSet,
+        *,
+        global_grad_accumulation_sequences: int | None,
+    ) -> dict[str, float]:
+        if not self._cp_lookahead_ports:
+            return {}
+        async with self._cp_lookahead_lock:
+            if self._closed or not self._valid:
+                raise RuntimeError("trainer run is not available for CP lookahead")
+            reply, receiver = Channel.open()
+            request = (
+                batch.model_dump_json(),
+                batch.ref.batch_id,
+                global_grad_accumulation_sequences,
+                reply,
+            )
+            started = time.perf_counter()
+            for port in self._cp_lookahead_ports:
+                port.send(request)
+            async with asyncio.timeout(self.run_spec.event_timeout_s):
+                results = []
+                for _ in self._cp_lookahead_ports:
+                    results.append(
+                        _CpLookaheadResult.model_validate(await receiver.recv())
+                    )
+            expected_ranks = set(range(len(self._cp_lookahead_ports)))
+            if {result.rank for result in results} != expected_ranks or any(
+                result.batch_id != batch.ref.batch_id for result in results
+            ):
+                raise RuntimeError("CP lookahead returned mismatched rank or batch IDs")
+            failures = [result for result in results if result.error_type is not None]
+            if failures:
+                details = "\n".join(
+                    f"rank {result.rank}: {result.error_type}: {result.message}\n"
+                    f"{result.traceback_text or ''}"
+                    for result in failures
+                )
+                raise RuntimeError(f"CP lookahead failed:\n{details}")
+            return {
+                "time/step_cp_lookahead_wait_s": time.perf_counter() - started,
+                "time/step_cp_lookahead_rank_max_s": max(
+                    result.elapsed_s for result in results
+                ),
+                "data/step_cp_preplanned_sequences_rank_max": float(
+                    max(result.planned_sequences for result in results)
+                ),
+            }
+
+    async def score(
+        self,
+        job: ResidentScoreJobSpec,
+        batch: PackedBatchLeaseSet,
+    ) -> ResidentScoreResult:
+        async with self._lock:
+            if error := self._validate_resident_score(job, batch):
+                raise error
+            values = await self._run_resident_collective(
+                job.job_id,
+                self._actors.score.call(job.model_dump_json(), batch.model_dump_json()),
+                invalidate_on_error=True,
+            )
+            shards = tuple(
+                ResidentScoreShard.model_validate(value) for value in values.values()
+            )
+            return _merge_resident_score_shards(
+                shards,
+                job=job,
+                world_size=len(self.runtime_spec.trainer_mesh.ranks),
+            )
+
+    async def inspect_resident_lora(
+        self,
+        request: ResidentLoraInspectionSpec,
+    ) -> ResidentLoraInspectionResult:
+        async with self._lock:
+            if error := self._validate_resident_inspection(request):
+                raise error
+            values = await self._run_resident_collective(
+                request.request_id,
+                self._actors.inspect_resident_lora.call(request.model_dump_json()),
+                invalidate_on_error=False,
+            )
+            shards = tuple(
+                ResidentLoraInspectionShard.model_validate(value)
+                for value in values.values()
+            )
+            return _merge_resident_lora_shards(
+                shards,
+                request=request,
+                world_size=len(self.runtime_spec.trainer_mesh.ranks),
+            )
+
+    async def _run_resident_collective(
+        self,
+        request_id: str,
+        operation: Awaitable[Any],
+        *,
+        invalidate_on_error: bool,
+    ) -> Any:
+        collective = asyncio.ensure_future(operation)
+        supervision = asyncio.create_task(self._supervision.wait())
+        self._active_job_id = request_id
+        self._active_collective = collective
+        try:
+            done, _ = await asyncio.wait(
+                {collective, supervision},
+                timeout=self.run_spec.event_timeout_s,
+                return_when=asyncio.FIRST_COMPLETED,
+            )
+            if not done:
+                raise TimeoutError(
+                    "trainer ranks produced no resident diagnostic result for "
+                    f"{self.run_spec.event_timeout_s:g}s"
+                )
+            if supervision in done:
+                raise RuntimeError("trainer mesh failed: " + supervision.result())
+            return await collective
+        except BaseException as exc:
+            if invalidate_on_error or not collective.done() or supervision.done():
+                self._valid = False
+                self._closed = True
+                self._cancel_active()
+                await cleanup_after_failure(
+                    exc,
+                    self._force_stop,
+                    message="resident diagnostic and trainer cleanup failed",
+                )
+            raise
+        finally:
+            supervision.cancel()
+            supervision.add_done_callback(consume_future_exception)
+            self._clear_active(request_id)
+
+    async def train(
+        self,
+        job: TrainJobSpec,
+        batch: PackedBatchLeaseSet,
+        *,
+        on_dispatched: Callable[[], None] | None = None,
+    ) -> AsyncIterator[TrainEvent]:
+        async for event in self._train(
+            job,
+            lambda port: self._actors.execute.call(
+                job.model_dump_json(), batch.model_dump_json(), port
+            ),
+            lambda: self._validate_rl(job, batch),
+            on_dispatched=on_dispatched,
+        ):
+            yield event
+
+    async def train_sft(
+        self, job: SFTJobSpec, batches: tuple[SFTBatchData, ...]
+    ) -> AsyncIterator[TrainEvent]:
+        async for event in self._train(
+            job,
+            lambda port: self._actors.execute_sft.call(
+                job.model_dump_json(), batches, port
+            ),
+            lambda: self._validate_sft(job, batches),
+        ):
+            yield event
+
+    async def _train(
+        self,
+        job: TrainerJobSpec,
+        start: Callable[[Port[dict[str, Any]]], Awaitable[Any]],
+        validate: Callable[[], BaseException | None],
+        *,
+        on_dispatched: Callable[[], None] | None = None,
+    ) -> AsyncIterator[TrainEvent]:
+        def signal_dispatched() -> None:
+            nonlocal on_dispatched
+            callback, on_dispatched = on_dispatched, None
+            if callback is not None:
+                callback()
+
+        cached = self._jobs.get(job.job_id)
+        if cached is not None and cached[0] == job.fingerprint:
+            signal_dispatched()
+            for event in cached[1]:
+                yield event
+            return
+
+        async with self._lock:
+            cached = self._jobs.get(job.job_id)
+            if cached is not None:
+                if cached[0] == job.fingerprint:
+                    signal_dispatched()
+                    for event in cached[1]:
+                        yield event
+                    return
+                yield TrainAccepted(
+                    job_id=job.job_id,
+                    run_id=job.run_id,
+                    sequence=0,
+                    expected_learner_version=job.expected_learner_version,
+                )
+                yield self._failed(
+                    job,
+                    1,
+                    RuntimeError("job_id was already used with a different job"),
+                    False,
+                )
+                return
+            events: list[TrainEvent] = []
+
+            def emit(event: TrainEvent) -> TrainEvent:
+                events.append(event)
+                return event
+
+            yield emit(
+                TrainAccepted(
+                    job_id=job.job_id,
+                    run_id=job.run_id,
+                    sequence=0,
+                    expected_learner_version=job.expected_learner_version,
+                )
+            )
+            error = validate()
+            if error is not None:
+                yield emit(self._failed(job, len(events), error, not self._valid))
+                return
+
+            publication = asyncio.get_running_loop().create_future()
+            publication.add_done_callback(consume_future_exception)
+            generation_id = job.output_generation_id
+            if generation_id in self._publications:
+                raise RuntimeError(
+                    f"publication generation already exists: {generation_id}"
+                )
+            self._expire_prior_publications()
+            publication_state = _PublicationState(generation_id, publication)
+            self._publications[generation_id] = publication_state
+            supervision: asyncio.Task[str] | None = None
+            try:
+                send_port, receiver = Channel[dict[str, Any]].open()
+                dispatch_started = time.perf_counter()
+                final_progress_received: float | None = None
+                collective = asyncio.ensure_future(start(send_port))
+                signal_dispatched()
+                receive = asyncio.ensure_future(receiver.recv())
+                supervision = asyncio.create_task(self._supervision.wait())
+                self._active_job_id = job.job_id
+                self._active_collective = collective
+                self._active_receive = receive
+                while True:
+                    waiters = {receive, supervision}
+                    if not collective.done():
+                        waiters.add(collective)
+                    event_timeout_s = (
+                        self.run_spec.initial_event_timeout_s
+                        if len(events) == 1
+                        and self.run_spec.initial_event_timeout_s is not None
+                        else self.run_spec.event_timeout_s
+                    )
+                    done, _ = await asyncio.wait(
+                        waiters,
+                        timeout=event_timeout_s,
+                        return_when=asyncio.FIRST_COMPLETED,
+                    )
+                    if not done:
+                        raise TimeoutError(
+                            f"trainer ranks produced no event for {event_timeout_s:g}s"
+                        )
+                    if supervision in done:
+                        raise RuntimeError(
+                            "trainer mesh failed: " + supervision.result()
+                        )
+                    if collective in done:
+                        await collective
+                        if receive not in done:
+                            continue
+                    payload = receive.result()
+                    if payload["kind"] in {
+                        "publication_succeeded",
+                        "publication_failed",
+                    }:
+                        self._record_publication(payload)
+                        receive = asyncio.ensure_future(receiver.recv())
+                        self._active_receive = receive
+                        continue
+                    if payload["kind"] == "rank_failed":
+                        raise RuntimeError(
+                            f"trainer rank {payload['rank']} failed: "
+                            f"{payload['error_type']}: {payload['message']}\n"
+                            f"{payload['traceback']}"
+                        )
+                    if payload["kind"] == "progress":
+                        if payload["step_index"] + 1 == payload["num_steps"]:
+                            final_progress_received = time.perf_counter()
+                        event = TrainProgress(
+                            job_id=job.job_id,
+                            run_id=job.run_id,
+                            sequence=len(events),
+                            step_index=payload["step_index"],
+                            num_steps=payload["num_steps"],
+                            metrics=payload["metrics"],
+                        )
+                    elif payload["kind"] == "adapter_ready":
+                        event = AdapterReady(
+                            job_id=job.job_id,
+                            run_id=job.run_id,
+                            sequence=len(events),
+                            learner_version=payload["learner_version"],
+                            adapter_path=payload["adapter_path"],
+                        )
+                    elif payload["kind"] == "actor_completed":
+                        actor_completed_received = time.perf_counter()
+                        values = await collective
+                        collective_completed = time.perf_counter()
+                        results = list(values.values())
+                        versions = {result["learner_version"] for result in results}
+                        ranks = {result["rank"] for result in results}
+                        expected_ranks = set(
+                            range(len(self.runtime_spec.trainer_mesh.ranks))
+                        )
+                        if versions != {job.learner_version} or ranks != expected_ranks:
+                            raise RuntimeError(
+                                "trainer ranks did not agree on job completion"
+                            )
+                        metrics = dict(payload["metrics"])
+                        cache_metrics = [
+                            result.get("compile_cache", {}) for result in results
+                        ]
+                        if any(cache_metrics):
+                            metrics.update(
+                                {
+                                    "trainer/compile_cache_hit_fraction": sum(
+                                        value.get("hit", 0.0) for value in cache_metrics
+                                    )
+                                    / len(cache_metrics),
+                                    "trainer/compile_cache_published_fraction": sum(
+                                        value.get("published", 0.0)
+                                        for value in cache_metrics
+                                    )
+                                    / len(cache_metrics),
+                                    "trainer/compile_cache_artifact_bytes_max": max(
+                                        value.get("artifact_bytes", 0.0)
+                                        for value in cache_metrics
+                                    ),
+                                    "time/trainer_compile_cache_load_max_s": max(
+                                        value.get("load_s", 0.0)
+                                        for value in cache_metrics
+                                    ),
+                                    "time/trainer_compile_cache_publish_max_s": max(
+                                        value.get("publish_s", 0.0)
+                                        for value in cache_metrics
+                                    ),
+                                }
+                            )
+                        if final_progress_received is not None:
+                            metrics.update(
+                                {
+                                    "time/step_monarch_dispatch_to_progress_s": (
+                                        final_progress_received - dispatch_started
+                                    ),
+                                    "time/step_monarch_progress_to_completed_s": (
+                                        actor_completed_received
+                                        - final_progress_received
+                                    ),
+                                }
+                            )
+                        metrics["time/step_monarch_collective_tail_s"] = (
+                            collective_completed - actor_completed_received
+                        )
+                        completed = TrainCompleted(
+                            job_id=job.job_id,
+                            run_id=job.run_id,
+                            sequence=len(events),
+                            learner_version=job.learner_version,
+                            metrics=metrics,
+                        )
+                        if not publication.done():
+                            publication_state.drain_done = False
+                            drain = asyncio.create_task(
+                                self._drain_publication(receiver, publication_state)
+                            )
+                            self._publication_drains.add(drain)
+                            drain.add_done_callback(self._publication_drains.discard)
+                            drain.add_done_callback(consume_future_exception)
+                        yield completed
+                        self._learner_version = job.learner_version
+                        emit(completed)
+                        self._clear_active(job.job_id)
+                        break
+                    else:
+                        raise RuntimeError(
+                            f"trainer rank sent unknown event {payload['kind']!r}"
+                        )
+                    yield emit(TRAIN_EVENT_ADAPTER.validate_python(event))
+                    receive = asyncio.ensure_future(receiver.recv())
+                    self._active_receive = receive
+            except BaseException as exc:
+                if not publication.done():
+                    publication.set_exception(exc)
+                    publication_state.records.clear()
+                closed_by_caller = self._closed
+                self._valid = False
+                self._closed = True
+                self._cancel_active()
+                await cleanup_after_failure(
+                    exc,
+                    self._force_stop,
+                    message="training and forced trainer ProcMesh cleanup failed",
+                )
+                caller_cancelled = isinstance(exc, GeneratorExit) or (
+                    isinstance(exc, asyncio.CancelledError)
+                    and _current_task_is_cancelling()
+                )
+                if caller_cancelled or (
+                    isinstance(exc, asyncio.CancelledError) and closed_by_caller
+                ):
+                    cancelled = TrainCancelled(
+                        job_id=job.job_id,
+                        run_id=job.run_id,
+                        sequence=len(events),
+                        reason="train stream was cancelled",
+                    )
+                    events.append(cancelled)
+                    if caller_cancelled:
+                        raise
+                    yield cancelled
+                    return
+                failure = self._failed(job, len(events), exc, True)
+                events.append(failure)
+                yield failure
+            finally:
+                if supervision is not None:
+                    supervision.cancel()
+                    supervision.add_done_callback(consume_future_exception)
+                self._clear_active(job.job_id)
+                # Older jobs cannot be retried after the sequential learner advances.
+                self._jobs = {job.job_id: (job.fingerprint, tuple(events))}
+                publication_state.train_done = True
+                self._retire_publication(publication_state)
+
+    def wait_for_publication(
+        self, generation_id: str
+    ) -> Awaitable[tuple[TrainerRankPublication, ...]]:
+        state = self._publications.get(generation_id)
+        if state is None:
+            raise RuntimeError(f"trainer has no publication {generation_id}")
+        if not state.late_waitable:
+            raise RuntimeError(
+                f"trainer publication {generation_id} is no longer waitable"
+            )
+        # Reserve before returning control; the next train may expire late waiters
+        # without yielding to the task which awaits this publication.
+        state.active_waiters += 1
+        return self._await_publication(state)
+
+    async def _await_publication(
+        self, state: "_PublicationState"
+    ) -> tuple[TrainerRankPublication, ...]:
+        observed = False
+        try:
+            result = await asyncio.shield(state.future)
+            observed = True
+            return result
+        except asyncio.CancelledError:
+            observed = state.future.cancelled()
+            raise
+        except BaseException:
+            observed = True
+            raise
+        finally:
+            state.active_waiters -= 1
+            state.outcome_observed |= observed
+            self._retire_publication(state)
+
+    def _record_publication(self, payload: dict[str, Any]) -> None:
+        event = TRAINER_PUBLICATION_EVENT_ADAPTER.validate_python(payload)
+        generation_id = (
+            event.record.generation.generation_id
+            if isinstance(event, TrainerPublicationSucceeded)
+            else event.generation_id
+        )
+        state = self._publications.get(generation_id)
+        if state is None:
+            raise RuntimeError(
+                f"trainer rank reported unknown publication {generation_id}"
+            )
+        future = state.future
+        if future.done():
+            if not future.cancelled() and future.exception() is not None:
+                return
+            raise RuntimeError(
+                f"trainer publication {generation_id} is already terminal"
+            )
+        if isinstance(event, TrainerPublicationFailed):
+            future.set_exception(
+                RuntimeError(
+                    f"trainer rank {event.rank} publication failed "
+                    f"({event.error_type}): {event.message}"
+                )
+            )
+            state.records.clear()
+            self._retire_publication(state)
+            return
+        record = event.record
+        world_size = len(self.runtime_spec.trainer_mesh.ranks)
+        if record.rank >= world_size:
+            raise RuntimeError(f"publication reported invalid rank {record.rank}")
+        records = state.records
+        if record.rank in records:
+            raise RuntimeError(
+                f"trainer rank {record.rank} published {generation_id} twice"
+            )
+        records[record.rank] = record
+        if len(records) == world_size:
+            future.set_result(tuple(records[rank] for rank in range(world_size)))
+            records.clear()
+            self._retire_publication(state)
+
+    async def _drain_publication(
+        self, receiver: Any, state: "_PublicationState"
+    ) -> None:
+        publication = state.future
+        supervision = asyncio.create_task(self._supervision.wait())
+        receive: asyncio.Future[Any] | None = None
+        try:
+            while not publication.done():
+                receive = asyncio.ensure_future(receiver.recv())
+                done, _ = await asyncio.wait(
+                    {receive, supervision},
+                    timeout=self.run_spec.shutdown_timeout_s,
+                    return_when=asyncio.FIRST_COMPLETED,
+                )
+                if not done:
+                    raise TimeoutError(
+                        f"trainer ranks produced no publication event for "
+                        f"{self.run_spec.shutdown_timeout_s:g}s"
+                    )
+                if supervision in done:
+                    raise RuntimeError("trainer mesh failed: " + supervision.result())
+                payload = receive.result()
+                if payload["kind"] == "rank_failed":
+                    raise RuntimeError(
+                        f"trainer rank {payload['rank']} failed after training: "
+                        f"{payload['error_type']}: {payload['message']}"
+                    )
+                self._record_publication(payload)
+        except BaseException as exc:
+            if not publication.done():
+                publication.set_exception(exc)
+                state.records.clear()
+            raise
+        finally:
+            supervision.cancel()
+            supervision.add_done_callback(consume_future_exception)
+            if receive is not None and not receive.done():
+                receive.cancel()
+                receive.add_done_callback(consume_future_exception)
+            state.drain_done = True
+            self._retire_publication(state)
+
+    def _expire_prior_publications(self) -> None:
+        for state in tuple(self._publications.values()):
+            state.late_waitable = False
+            self._retire_publication(state)
+
+    def _retire_publication(self, state: "_PublicationState") -> None:
+        # A waiter can observe a terminal event before the train/drain producer exits.
+        if not (
+            state.future.done()
+            and (state.outcome_observed or not state.late_waitable)
+            and state.active_waiters == 0
+            and state.train_done
+            and state.drain_done
+        ):
+            return
+        if self._publications.get(state.generation_id) is state:
+            self._publications.pop(state.generation_id)
+
+    async def advance_without_training(
+        self,
+        *,
+        source: TrainerGeneration,
+        output: TrainerGeneration,
+        optimizer_state_path: str,
+        adapter: Any | None,
+    ) -> dict[str, float]:
+        async with self._lock:
+            if self._closed or not self._valid:
+                raise RuntimeError("trainer runtime is invalid")
+            if self._active_job_id is not None:
+                raise RuntimeError("trainer has an active job")
+            if (
+                source.training_session_id != self.run_spec.training_session_id
+                or source.policy_step != self._learner_version
+            ):
+                raise ValueError(
+                    "expected learner version mismatch: "
+                    f"transition={source.policy_step}, "
+                    f"runtime={self._learner_version}"
+                )
+            if (
+                output.training_session_id != source.training_session_id
+                or output.policy_step != source.policy_step + 1
+            ):
+                raise ValueError("a no-op transition must advance exactly one step")
+            try:
+                values = await asyncio.wait_for(
+                    self._actors.advance_without_training.call(
+                        source.model_dump_json(),
+                        output.model_dump_json(),
+                        optimizer_state_path,
+                        None if adapter is None else adapter.model_dump_json(),
+                    ),
+                    timeout=self.run_spec.event_timeout_s,
+                )
+                results = list(values.values())
+                if {result["rank"] for result in results} != set(
+                    range(len(self.runtime_spec.trainer_mesh.ranks))
+                ) or {result["learner_version"] for result in results} != {
+                    output.policy_step
+                }:
+                    raise RuntimeError("trainer ranks rejected no-op transition")
+            except BaseException as exc:
+                self._valid = False
+                self._closed = True
+                await cleanup_after_failure(
+                    exc,
+                    self._force_stop,
+                    message="no-op transition and trainer cleanup failed",
+                )
+                raise
+            self._learner_version = output.policy_step
+            return next(result["metrics"] for result in results if result["rank"] == 0)
+
+    def _validate_resident_learner(
+        self,
+        *,
+        run_id: str,
+        learner: TrainerGeneration,
+    ) -> BaseException | None:
+        if self._closed or not self._valid:
+            return RuntimeError("trainer runtime is invalid")
+        if self._active_job_id is not None:
+            return RuntimeError("trainer has an active job")
+        if run_id != self.run_spec.run_id:
+            return ValueError("diagnostic run_id does not match this training run")
+        if learner.training_session_id != self.run_spec.training_session_id:
+            return ValueError("diagnostic learner does not match the training session")
+        if learner.policy_step != self._learner_version:
+            return ValueError(
+                "diagnostic learner version mismatch: "
+                f"request={learner.policy_step}, runtime={self._learner_version}"
+            )
+        return None
+
+    def _validate_resident_score(
+        self,
+        job: ResidentScoreJobSpec,
+        batch: PackedBatchLeaseSet,
+    ) -> BaseException | None:
+        if error := self._validate_resident_learner(
+            run_id=job.run_id,
+            learner=job.learner,
+        ):
+            return error
+        if batch.ref != job.batch:
+            return ValueError("resident score batch ref does not match its leases")
+        if job.batch.sequence_length != self.runtime_spec.packed_sequence_length:
+            return ValueError(
+                "resident score batch length does not match the trainer runtime"
+            )
+        if bool(job.batch.moe_routing_replay) != bool(
+            self.runtime_spec.enable_moe_routing_replay
+        ):
+            return ValueError(
+                "resident score routing replay does not match the trainer runtime"
+            )
+        return None
+
+    def _validate_resident_inspection(
+        self,
+        request: ResidentLoraInspectionSpec,
+    ) -> BaseException | None:
+        if error := self._validate_resident_learner(
+            run_id=request.run_id,
+            learner=request.learner,
+        ):
+            return error
+        if request.target_modules != self.runtime_spec.lora_target_modules:
+            return ValueError("resident LoRA targets do not match the trainer runtime")
+        return None
+
+    def _validate_common(self, job: TrainerJobSpec) -> BaseException | None:
+        if self._closed:
+            return RuntimeError("trainer run is closed")
+        if not self._valid:
+            return RuntimeError("trainer runtime is invalid")
+        if job.job_id in self._jobs:
+            return RuntimeError("job_id was already used with a different job")
+        if job.run_id != self.run_spec.run_id:
+            return ValueError("job run_id does not match this training run")
+        if job.training_session_id != self.run_spec.training_session_id:
+            return ValueError(
+                "job training_session_id does not match this training run"
+            )
+        if job.output.optimizer_state_path != self.run_spec.optimizer_state_path:
+            return ValueError(
+                "job optimizer state path does not match this training run"
+            )
+        if job.expected_learner_version != self._learner_version:
+            return ValueError(
+                "expected learner version mismatch: "
+                f"job={job.expected_learner_version}, runtime={self._learner_version}"
+            )
+        return None
+
+    def _validate_rl(
+        self, job: TrainJobSpec, batch: PackedBatchLeaseSet
+    ) -> BaseException | None:
+        if error := self._validate_common(job):
+            return error
+        if batch.ref != job.batch:
+            return ValueError("job batch ref does not match supplied packed batch")
+        if job.batch.sequence_length != self.runtime_spec.packed_sequence_length:
+            return ValueError(
+                "packed batch sequence length does not match the trainer runtime"
+            )
+        return None
+
+    def _validate_sft(
+        self, job: SFTJobSpec, batches: tuple[SFTBatchData, ...]
+    ) -> BaseException | None:
+        if error := self._validate_common(job):
+            return error
+        if len(batches) != job.num_batches:
+            return ValueError("SFT job batch count does not match its payload")
+        return None
+
+    @staticmethod
+    def _failed(
+        job: TrainerJobSpec,
+        sequence: int,
+        exc: BaseException,
+        invalidated: bool,
+    ) -> TrainFailed:
+        return TrainFailed(
+            job_id=job.job_id,
+            run_id=job.run_id,
+            sequence=sequence,
+            error_type=type(exc).__name__,
+            message=str(exc) or type(exc).__name__,
+            runtime_invalidated=invalidated,
+        )
+
+    async def close(self) -> None:
+        if self._close_task is not None and self._close_task.done():
+            try:
+                self._close_task.result()
+            except BaseException:
+                self._close_task = None
+        if self._close_task is None:
+            graceful = self._valid and self._active_job_id is None
+            self._closed = True
+            self._valid = False
+            self._cancel_active()
+            self._close_task = asyncio.create_task(self._close(graceful))
+            self._close_task.add_done_callback(consume_future_exception)
+        await asyncio.shield(self._close_task)
+
+    async def _close(self, graceful: bool) -> None:
+        loop = asyncio.get_running_loop()
+        deadline = loop.time() + self.run_spec.shutdown_timeout_s
+        primary: BaseException | None = None
+        if graceful:
+            publications = tuple(self._publications.values())
+            try:
+                async with asyncio.timeout(self.run_spec.shutdown_timeout_s / 2):
+                    await asyncio.gather(
+                        _remote_teardown(self._actors.close.call()),
+                        *(
+                            self._await_publication(publication)
+                            for publication in publications
+                        ),
+                        *tuple(self._publication_drains),
+                    )
+            except BaseException as exc:
+                primary = exc
+        try:
+            await self._force_stop(max(0.0, deadline - loop.time()))
+        except BaseException as exc:
+            if primary is None:
+                primary = exc
+            else:
+                primary.add_note(
+                    f"trainer ProcMesh cleanup failed: {type(exc).__name__}: {exc}"
+                )
+        if primary is not None:
+            raise primary
+
+    async def _force_stop(self, timeout_s: float | None = None) -> None:
+        if self._stop_task is not None and self._stop_task.done():
+            try:
+                self._stop_task.result()
+            except BaseException:
+                self._stop_task = None
+        if self._stop_task is None:
+            self._stop_task = asyncio.create_task(
+                _remote_teardown(self._proc_mesh.stop())
+            )
+
+            def stopped(task: asyncio.Task[None]) -> None:
+                if not task.cancelled() and task.exception() is None:
+                    self._supervision.close()
+
+            self._stop_task.add_done_callback(stopped)
+        await asyncio.wait_for(
+            asyncio.shield(self._stop_task),
+            self.run_spec.shutdown_timeout_s if timeout_s is None else timeout_s,
+        )
+
+    def _cancel_active(self) -> None:
+        # Monarch 0.2 only cancels these local waiters; ProcMesh.stop invalidates ranks.
+        for future in (self._active_receive, self._active_collective):
+            if future is not None and not future.done():
+                future.cancel()
+            if future is not None:
+                future.add_done_callback(consume_future_exception)
+
+    def _clear_active(self, job_id: str) -> None:
+        if self._active_job_id == job_id:
+            self._active_job_id = None
+            self._active_collective = None
+            self._active_receive = None
+
+
+async def _remote_teardown(operation: Awaitable[Any]) -> None:
+    try:
+        await operation
+    except asyncio.CancelledError:
+        task = asyncio.current_task()
+        if task is not None and task.cancelling():
+            raise
+
+
+def _current_task_is_cancelling() -> bool:
+    task = asyncio.current_task()
+    return task is not None and bool(task.cancelling())
diff --git a/src/art/megatron/runtime/native_assets.json b/src/art/megatron/runtime/native_assets.json
new file mode 100644
index 000000000..9d9fe6397
--- /dev/null
+++ b/src/art/megatron/runtime/native_assets.json
@@ -0,0 +1,10 @@
+{
+  "nixl-de8115ca.tar.gz": {
+    "sha256": "a9d88772935e91181733f00df0a7e93b6be5f1d29300e5c06cfc6b4ad2f6dbdb",
+    "url": "https://github.com/ai-dynamo/nixl/archive/de8115ca97d3f8fb63a4988e9b4d4a038b2e0f72.tar.gz"
+  },
+  "ucx-1.21.0.tar.gz": {
+    "sha256": "2374d2fcf3186fbfd5e27633ab153aabaeb6b4f503a88563d2aca67cf51ed2c1",
+    "url": "https://github.com/openucx/ucx/releases/download/v1.21.0/ucx-1.21.0.tar.gz"
+  }
+}
diff --git a/src/art/megatron/runtime/publication.py b/src/art/megatron/runtime/publication.py
new file mode 100644
index 000000000..c7d3941a4
--- /dev/null
+++ b/src/art/megatron/runtime/publication.py
@@ -0,0 +1,139 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Annotated, Literal
+
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator
+
+from art.megatron.optimizer_state import (
+    OptimizerAdapter,
+    OptimizerShard,
+    OptimizerTopology,
+    build_optimizer_manifest,
+    commit_optimizer_generation,
+    read_committed_optimizer_pointer,
+)
+
+from .specs import TrainerGeneration
+
+
+class _PublicationModel(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class TrainerRankPublication(_PublicationModel):
+    generation: TrainerGeneration
+    rank: int = Field(ge=0)
+    adapter: OptimizerAdapter | None = None
+    shard: OptimizerShard | None = None
+    runtime_sha256: str | None = None
+    topology: OptimizerTopology | None = None
+    saves_optimizer: bool
+
+    @model_validator(mode="after")
+    def _validate_payload(self) -> "TrainerRankPublication":
+        optimizer_values = (self.shard, self.runtime_sha256, self.topology)
+        if (
+            self.saves_optimizer
+            and not all(value is not None for value in optimizer_values)
+        ) or (
+            not self.saves_optimizer
+            and any(value is not None for value in optimizer_values)
+        ):
+            raise ValueError("optimizer publication fields must be present together")
+        if self.rank == 0:
+            if self.adapter is None:
+                raise ValueError("rank zero publication requires an adapter")
+            if (
+                self.adapter.training_session_id,
+                self.adapter.step,
+                self.adapter.generation_id,
+                self.adapter.identity,
+            ) != (
+                self.generation.training_session_id,
+                self.generation.policy_step,
+                self.generation.generation_id,
+                str(Path(self.generation.adapter_path).absolute()),
+            ):
+                raise ValueError("adapter and trainer generation identities differ")
+        elif self.adapter is not None:
+            raise ValueError("only rank zero may publish the adapter manifest")
+        if self.shard is not None and self.shard.rank != self.rank:
+            raise ValueError("optimizer shard identifies another trainer rank")
+        return self
+
+
+class TrainerPublicationSucceeded(_PublicationModel):
+    kind: Literal["publication_succeeded"] = "publication_succeeded"
+    record: TrainerRankPublication
+
+
+class TrainerPublicationFailed(_PublicationModel):
+    kind: Literal["publication_failed"] = "publication_failed"
+    generation_id: str = Field(min_length=1)
+    rank: int = Field(ge=0)
+    error_type: str = Field(min_length=1)
+    message: str = Field(min_length=1)
+
+
+TrainerPublicationEvent = Annotated[
+    TrainerPublicationSucceeded | TrainerPublicationFailed,
+    Field(discriminator="kind"),
+]
+TRAINER_PUBLICATION_EVENT_ADAPTER = TypeAdapter(TrainerPublicationEvent)
+
+
+class DurableTrainerPublication(_PublicationModel):
+    adapter: OptimizerAdapter
+    resume_step: int = Field(ge=0)
+    optimizer_step: int = Field(ge=0)
+
+
+def commit_trainer_publication(
+    optimizer_state_path: str,
+    generation: TrainerGeneration,
+    records: tuple[TrainerRankPublication, ...],
+) -> DurableTrainerPublication:
+    ordered = tuple(sorted(records, key=lambda record: record.rank))
+    if tuple(record.rank for record in ordered) != tuple(range(len(ordered))):
+        raise RuntimeError("trainer publication does not cover every rank exactly once")
+    if not ordered or {record.generation for record in ordered} != {generation}:
+        raise RuntimeError("trainer ranks published another generation")
+    if len({record.saves_optimizer for record in ordered}) != 1:
+        raise RuntimeError("trainer ranks disagree on optimizer persistence")
+    adapter = ordered[0].adapter
+    if adapter is None:
+        raise RuntimeError("trainer publication has no rank-zero adapter")
+    saves_optimizer = ordered[0].saves_optimizer
+    if saves_optimizer:
+        runtime_ids = {record.runtime_sha256 for record in ordered}
+        topologies = {record.topology for record in ordered}
+        if len(runtime_ids) != 1 or len(topologies) != 1:
+            raise RuntimeError(
+                "trainer ranks produced incompatible optimizer snapshots"
+            )
+        runtime_sha256 = runtime_ids.pop()
+        topology = topologies.pop()
+        if runtime_sha256 is None or topology is None:
+            raise RuntimeError("optimizer publication metadata is incomplete")
+        expected = read_committed_optimizer_pointer(optimizer_state_path)
+        commit_optimizer_generation(
+            optimizer_state_path,
+            build_optimizer_manifest(
+                generation=generation.generation_id,
+                step=generation.policy_step,
+                adapter=adapter,
+                runtime_sha256=runtime_sha256,
+                world_size=len(ordered),
+                shards=[record.shard for record in ordered if record.shard is not None],
+                topology=topology,
+            ),
+            expected_pointer=expected,
+        )
+    committed = read_committed_optimizer_pointer(optimizer_state_path)
+    optimizer_step = 0 if committed is None else committed.step
+    return DurableTrainerPublication(
+        adapter=adapter,
+        resume_step=generation.policy_step if saves_optimizer else optimizer_step,
+        optimizer_step=optimizer_step,
+    )
diff --git a/src/art/megatron/runtime/runtime_env.py b/src/art/megatron/runtime/runtime_env.py
index 7c66f5cab..0bf6223c5 100644
--- a/src/art/megatron/runtime/runtime_env.py
+++ b/src/art/megatron/runtime/runtime_env.py
@@ -4,25 +4,43 @@
     force_te_cutlass_grouped_gemm_env,
     install_te_cutlass_grouped_gemm_guard,
 )
+from art.utils.cache_dirs import compiler_cache_root, configure_model_cache_env
 
 
 def _set_cache_dir(env_var: str, default_path: str) -> None:
-    if not os.environ.get(env_var):
-        os.environ[env_var] = os.path.expanduser(default_path)
-    os.makedirs(os.environ[env_var], exist_ok=True)
+    path = os.path.expanduser(os.environ.get(env_var) or default_path)
+    os.environ[env_var] = path
+    os.makedirs(path, exist_ok=True)
+
+
+def _cache_path(name: str, cache_root: str) -> str:
+    return os.path.join(cache_root, name)
+
+
+def _set_inductor_cache_dir(cache_root: str) -> None:
+    from torch._inductor.runtime.cache_dir_utils import default_cache_dir
+
+    if os.environ.get("TORCHINDUCTOR_CACHE_DIR") == default_cache_dir():
+        del os.environ["TORCHINDUCTOR_CACHE_DIR"]
+    _set_cache_dir(
+        "TORCHINDUCTOR_CACHE_DIR",
+        _cache_path("torchinductor", cache_root),
+    )
 
 
 def configure_megatron_runtime_env() -> None:
+    cache_root = str(configure_model_cache_env())
+    compiled_root = str(compiler_cache_root(cache_root))
     force_te_cutlass_grouped_gemm_env()
     os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = os.environ.get(
         "ART_MEGATRON_CUDA_DEVICE_MAX_CONNECTIONS",
         os.environ.get("CUDA_DEVICE_MAX_CONNECTIONS", "1"),
     )
-    os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-    # The currently validated ART MoE grouped-GEMM runtime is SM90. Future
-    # SM100 support should come from the TE grouped-GEMM implementation, not
-    # ART-side kernel special casing.
-    os.environ["TORCH_CUDA_ARCH_LIST"] = "9.0"
-    _set_cache_dir("TORCHINDUCTOR_CACHE_DIR", "~/.cache/torchinductor")
-    _set_cache_dir("TRITON_CACHE_DIR", "~/.triton/cache")
+    _set_inductor_cache_dir(compiled_root)
+    _set_cache_dir("TRITON_CACHE_DIR", _cache_path("triton", compiled_root))
+    os.environ.setdefault("FLASH_ATTENTION_CUTE_DSL_CACHE_ENABLED", "1")
+    _set_cache_dir(
+        "FLASH_ATTENTION_CUTE_DSL_CACHE_DIR",
+        _cache_path("flash_attention_cute_dsl", compiled_root),
+    )
     install_te_cutlass_grouped_gemm_guard()
diff --git a/src/art/megatron/runtime/specs.py b/src/art/megatron/runtime/specs.py
new file mode 100644
index 000000000..855359c66
--- /dev/null
+++ b/src/art/megatron/runtime/specs.py
@@ -0,0 +1,484 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+import hashlib
+import json
+import math
+from typing import Annotated, Literal, TypeAlias
+
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator
+
+from art.distributed.adapter_transport import AdapterTransferTarget
+from art.distributed.data_plane import PackedBatchRef
+from art.distributed.specs import NixlTransportSpec, TrainerMeshSpec
+from art.types import TrainConfig, TrainSFTConfig
+
+
+class _Spec(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class HybridEpRuntimeSpec(_Spec):
+    ranks_per_nvlink_domain: int = Field(ge=1)
+    run_id: str = Field(min_length=1)
+    nixl_transport: NixlTransportSpec | None = None
+
+    @property
+    def multinode(self) -> bool:
+        return self.nixl_transport is not None
+
+
+class TrainerRuntimeSpec(_Spec):
+    art_revision: str = Field(min_length=1)
+    model_identifier: str = Field(min_length=1)
+    model_revision: str = Field(min_length=1)
+    model_initialization: Literal["pretrained", "random"] = "pretrained"
+    cache_root: str | None = Field(default=None, min_length=1)
+    model_support_key: str = Field(min_length=1)
+    handler_name: str = Field(min_length=1)
+    lora_rank: int = Field(ge=1)
+    lora_alpha: float = Field(default=32.0, gt=0)
+    lora_target_modules: tuple[str, ...]
+    dtype: Literal["bfloat16", "float16", "float32"]
+    trainer_mesh: TrainerMeshSpec
+    packed_sequence_length: int = Field(ge=1)
+    compile_enabled: bool
+    compile_cache: bool = False
+    compile_fingerprint: str = Field(min_length=1)
+    optimizer_layout_fingerprint: str = Field(min_length=1)
+    allow_unvalidated_arch: bool = False
+    enable_moe_routing_replay: bool = False
+    streaming_weight_offload: bool = False
+    offload_between_jobs: bool = False
+    random_state: int | None = None
+    hybrid_ep: HybridEpRuntimeSpec | None = None
+    snapshot_pool_capacity: int = Field(default=2, ge=1, le=4)
+
+    @model_validator(mode="after")
+    def _validate_lora_targets(self) -> "TrainerRuntimeSpec":
+        if self.lora_alpha != 32.0:
+            raise ValueError("current Megatron LoRA semantics require lora_alpha=32")
+        if self.compile_cache and not self.compile_enabled:
+            raise ValueError("compile_cache requires compile_enabled")
+        if not self.lora_target_modules:
+            raise ValueError("lora_target_modules must not be empty")
+        if len(set(self.lora_target_modules)) != len(self.lora_target_modules):
+            raise ValueError("lora_target_modules must be unique")
+        return self
+
+    @property
+    def fingerprint(self) -> str:
+        return _fingerprint(self)
+
+
+class TrainingRunSpec(_Spec):
+    run_id: str = Field(min_length=1)
+    runtime_fingerprint: str = Field(min_length=1)
+    training_session_id: str = Field(min_length=1)
+    initial_learner_version: int = Field(ge=0)
+    initial_adapter_path: str = Field(min_length=1)
+    optimizer_state_path: str = Field(min_length=1)
+    initial_event_timeout_s: float | None = Field(default=None, gt=0)
+    event_timeout_s: float = Field(default=300.0, gt=0)
+    shutdown_timeout_s: float = Field(default=240.0, gt=0)
+
+
+class CurrentTrainConfig(TrainConfig):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class CurrentSFTConfig(TrainSFTConfig):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class ExperimentalTrainConfig(_Spec):
+    advantage_balance: float = 0.0
+    allow_training_without_logprobs: bool | None = None
+    epsilon: float | None = None
+    epsilon_high: float | None = None
+    importance_sampling_level: Literal[
+        "token", "sequence", "average", "geometric_average"
+    ] = "token"
+    kimi_k2_tau: float | None = None
+    kl_penalty_coef: float = Field(default=0.0, ge=0)
+    kl_penalty_reference_step: int | None = Field(default=None, ge=0)
+    kl_penalty_source: Literal["current_learner", "sample"] = "current_learner"
+    kl_penalty_step_lag: int | None = Field(default=None, ge=0)
+    kl_ref_adapter_path: str | None = None
+    logprob_calculation_chunk_size: int | None = Field(default=None, ge=1)
+    mask_prob_ratio: bool = False
+    max_negative_advantage_importance_sampling_weight: float | None = None
+    num_trajectories_learning_rate_multiplier_power: float | None = None
+    packed_sequence_length: int | None = Field(default=None, ge=1)
+    plot_tensors: bool | None = None
+    ppo: bool = False
+    precalculate_logprobs: bool = False
+    scale_learning_rate_by_reward_std_dev: bool | None = None
+    scale_rewards: bool = True
+    truncated_importance_sampling: float | None = None
+    moe_routing_replay_strict: bool = True
+
+
+class TrainerGeneration(_Spec):
+    training_session_id: str = Field(min_length=1)
+    policy_step: int = Field(ge=0)
+    generation_id: str = Field(pattern=r"^step-\d{8,}-[0-9a-f]{32}$")
+    adapter_path: str = Field(min_length=1)
+
+    @model_validator(mode="after")
+    def _validate_generation_step(self) -> "TrainerGeneration":
+        if int(self.generation_id.split("-", 2)[1]) != self.policy_step:
+            raise ValueError("generation ID and policy step must match")
+        return self
+
+
+class DurableTrainOutput(_Spec):
+    generation: TrainerGeneration
+    staging_adapter_path: str = Field(min_length=1)
+    optimizer_state_path: str = Field(min_length=1)
+
+
+class _TrainerJobSpec(_Spec):
+    job_id: str = Field(min_length=1)
+    run_id: str = Field(min_length=1)
+    training_session_id: str = Field(min_length=1)
+    expected_learner_version: int = Field(ge=0)
+    learner_version: int = Field(ge=1)
+    source: TrainerGeneration
+    output: DurableTrainOutput
+    publication_targets: tuple[AdapterTransferTarget, ...] = ()
+
+    @model_validator(mode="after")
+    def _validate_versions(self) -> "_TrainerJobSpec":
+        if self.learner_version != self.expected_learner_version + 1:
+            raise ValueError(
+                "learner_version must immediately follow expected_learner_version"
+            )
+        if (
+            self.source.training_session_id != self.training_session_id
+            or self.source.policy_step != self.expected_learner_version
+        ):
+            raise ValueError("source generation does not identify the expected learner")
+        if (
+            self.output.generation.training_session_id != self.training_session_id
+            or self.output.generation.policy_step != self.learner_version
+        ):
+            raise ValueError("output generation does not identify the new learner")
+        if self.source.generation_id == self.output.generation.generation_id:
+            raise ValueError("source and output generation IDs must differ")
+        if self.source.adapter_path == self.output.staging_adapter_path:
+            raise ValueError("source adapter and output staging paths must differ")
+        if self.output.generation.adapter_path == self.output.staging_adapter_path:
+            raise ValueError("final and staging adapter paths must differ")
+        return self
+
+    @property
+    def fingerprint(self) -> str:
+        return _fingerprint(self)
+
+    # These aliases keep the Megatron executor on the current train semantics.
+    @property
+    def step(self) -> int:
+        return self.learner_version
+
+    @property
+    def source_policy_step(self) -> int:
+        return self.expected_learner_version
+
+    @property
+    def source_adapter_path(self) -> str:
+        return self.source.adapter_path
+
+    @property
+    def output_adapter_path(self) -> str:
+        return self.output.generation.adapter_path
+
+    @property
+    def output_generation_id(self) -> str:
+        return self.output.generation.generation_id
+
+    @property
+    def optimizer_state_path(self) -> str:
+        return self.output.optimizer_state_path
+
+
+class TrainJobSpec(_TrainerJobSpec):
+    kind: Literal["rl"] = "rl"
+    batch: PackedBatchRef
+    config: CurrentTrainConfig
+    experimental_config: ExperimentalTrainConfig = ExperimentalTrainConfig()
+
+    @model_validator(mode="after")
+    def _validate_batch_version(self) -> "TrainJobSpec":
+        if self.batch.max_source_version > self.expected_learner_version:
+            raise ValueError(
+                "batch source policy version cannot be newer than the learner"
+            )
+        return self
+
+
+class SFTJobSpec(_TrainerJobSpec):
+    kind: Literal["sft"] = "sft"
+    batch_id: str = Field(min_length=1)
+    num_batches: int = Field(ge=1)
+    config: CurrentSFTConfig
+    weight_decay: float = Field(default=0.0, ge=0)
+    max_grad_norm: float = Field(default=1.0, gt=0)
+
+    @model_validator(mode="after")
+    def _validate_batch_size(self) -> "SFTJobSpec":
+        if not isinstance(self.config.batch_size, int):
+            raise ValueError("typed SFT jobs require a resolved integer batch size")
+        return self
+
+
+class ResidentScoreJobSpec(_Spec):
+    job_id: str = Field(min_length=1)
+    run_id: str = Field(min_length=1)
+    learner: TrainerGeneration
+    batch: PackedBatchRef
+    global_grad_accumulation_sequences: int = Field(ge=1)
+    top_k: int = Field(default=20, ge=1, le=1024)
+
+    @model_validator(mode="after")
+    def _validate_batch_generation(self) -> "ResidentScoreJobSpec":
+        if not (
+            self.batch.min_source_version
+            == self.batch.max_source_version
+            == self.learner.policy_step
+        ):
+            raise ValueError(
+                "resident score batch must come exclusively from the requested learner"
+            )
+        return self
+
+
+class PackedTokenScore(_Spec):
+    sample_index: int = Field(ge=0)
+    logit_index: int = Field(ge=0)
+    target_token_id: int = Field(ge=0)
+    target_logprob: float
+    top_token_ids: tuple[int, ...]
+    top_logprobs: tuple[float, ...]
+
+    @model_validator(mode="after")
+    def _validate_score(self) -> "PackedTokenScore":
+        if not math.isfinite(self.target_logprob) or any(
+            not math.isfinite(value) for value in self.top_logprobs
+        ):
+            raise ValueError("resident token scores must be finite")
+        if not self.top_token_ids or len(self.top_token_ids) != len(self.top_logprobs):
+            raise ValueError("resident top-k token IDs and logprobs must align")
+        if any(token_id < 0 for token_id in self.top_token_ids):
+            raise ValueError("resident top-k token IDs must be non-negative")
+        if len(set(self.top_token_ids)) != len(self.top_token_ids):
+            raise ValueError("resident top-k token IDs must be unique")
+        if any(
+            left < right
+            for left, right in zip(
+                self.top_logprobs, self.top_logprobs[1:], strict=False
+            )
+        ):
+            raise ValueError("resident top-k logprobs must be descending")
+        return self
+
+
+def _validate_score_records(scores: tuple[PackedTokenScore, ...], top_k: int) -> None:
+    keys = [(score.sample_index, score.logit_index) for score in scores]
+    if keys != sorted(keys) or len(keys) != len(set(keys)):
+        raise ValueError("resident token scores must have unique sorted coordinates")
+    if any(len(score.top_token_ids) != top_k for score in scores):
+        raise ValueError("resident token score width does not match top_k")
+
+
+class ResidentScoreShard(_Spec):
+    rank: int = Field(ge=0)
+    job_id: str = Field(min_length=1)
+    run_id: str = Field(min_length=1)
+    learner: TrainerGeneration
+    batch_id: str = Field(min_length=1)
+    batch_fingerprint: str = Field(pattern=r"^[0-9a-f]{64}$")
+    top_k: int = Field(ge=1)
+    expected_score_count: int = Field(ge=1)
+    routing_replay_packed_tokens: int = Field(ge=0)
+    scores: tuple[PackedTokenScore, ...]
+
+    @model_validator(mode="after")
+    def _validate_scores(self) -> "ResidentScoreShard":
+        _validate_score_records(self.scores, self.top_k)
+        if len(self.scores) > self.expected_score_count:
+            raise ValueError("resident score shard exceeds the packed target count")
+        return self
+
+
+class ResidentScoreResult(_Spec):
+    job_id: str = Field(min_length=1)
+    run_id: str = Field(min_length=1)
+    learner: TrainerGeneration
+    batch_id: str = Field(min_length=1)
+    batch_fingerprint: str = Field(pattern=r"^[0-9a-f]{64}$")
+    ranks: tuple[int, ...]
+    top_k: int = Field(ge=1)
+    expected_score_count: int = Field(ge=1)
+    routing_replay_packed_tokens: int = Field(ge=0)
+    scores: tuple[PackedTokenScore, ...]
+
+    @model_validator(mode="after")
+    def _validate_result(self) -> "ResidentScoreResult":
+        if not self.ranks or len(self.ranks) != len(set(self.ranks)):
+            raise ValueError("resident score result ranks must be unique and nonempty")
+        _validate_score_records(self.scores, self.top_k)
+        if len(self.scores) != self.expected_score_count:
+            raise ValueError("resident score result does not cover every packed target")
+        return self
+
+
+class ResidentLoraInspectionSpec(_Spec):
+    request_id: str = Field(min_length=1)
+    run_id: str = Field(min_length=1)
+    learner: TrainerGeneration
+    target_modules: tuple[str, ...]
+
+    @model_validator(mode="after")
+    def _validate_targets(self) -> "ResidentLoraInspectionSpec":
+        if not self.target_modules or len(self.target_modules) != len(
+            set(self.target_modules)
+        ):
+            raise ValueError("resident LoRA target modules must be unique and nonempty")
+        return self
+
+
+class ResidentLoraExport(_Spec):
+    base_name: str = Field(min_length=1)
+    adapter_keys: tuple[str | None, ...]
+
+    @model_validator(mode="after")
+    def _validate_keys(self) -> "ResidentLoraExport":
+        if not self.adapter_keys or len(self.adapter_keys) != len(
+            set(self.adapter_keys)
+        ):
+            raise ValueError("resident LoRA export keys must be unique and nonempty")
+        return self
+
+
+class ResidentLoraRankSummary(_Spec):
+    rank: int = Field(ge=0)
+    module_count: int = Field(ge=0)
+    trainable_parameter_count: int = Field(ge=0)
+    trainable_numel: int = Field(ge=0)
+
+
+class ResidentLoraInspectionShard(_Spec):
+    rank: int = Field(ge=0)
+    request_id: str = Field(min_length=1)
+    run_id: str = Field(min_length=1)
+    learner: TrainerGeneration
+    target_modules: tuple[str, ...]
+    module_count: int = Field(ge=0)
+    wrapped_adapter_prefixes: tuple[str, ...]
+    exports: tuple[ResidentLoraExport, ...]
+    trainable_lora_parameter_names: tuple[str, ...]
+    unexpected_trainable_parameter_names: tuple[str, ...]
+    trainable_numel: int = Field(ge=0)
+
+
+class ResidentLoraInspectionResult(_Spec):
+    request_id: str = Field(min_length=1)
+    run_id: str = Field(min_length=1)
+    learner: TrainerGeneration
+    target_modules: tuple[str, ...]
+    rank_summaries: tuple[ResidentLoraRankSummary, ...]
+    wrapped_adapter_prefixes: tuple[str, ...]
+    exports: tuple[ResidentLoraExport, ...]
+    trainable_lora_parameter_names: tuple[str, ...]
+    unexpected_trainable_parameter_names: tuple[str, ...]
+
+
+TrainerJobSpec: TypeAlias = Annotated[
+    TrainJobSpec | SFTJobSpec,
+    Field(discriminator="kind"),
+]
+TRAIN_JOB_ADAPTER = TypeAdapter(TrainerJobSpec)
+
+
+class _TrainEvent(_Spec):
+    kind: str
+    job_id: str
+    run_id: str
+    sequence: int = Field(ge=0)
+
+
+class TrainAccepted(_TrainEvent):
+    kind: Literal["accepted"] = "accepted"
+    expected_learner_version: int = Field(ge=0)
+
+
+class TrainProgress(_TrainEvent):
+    kind: Literal["progress"] = "progress"
+    step_index: int = Field(ge=0)
+    num_steps: int = Field(ge=1)
+    metrics: dict[str, float]
+
+
+class AdapterReady(_TrainEvent):
+    kind: Literal["adapter_ready"] = "adapter_ready"
+    learner_version: int = Field(ge=1)
+    adapter_path: str = Field(min_length=1)
+
+
+class TrainCompleted(_TrainEvent):
+    kind: Literal["completed"] = "completed"
+    learner_version: int = Field(ge=1)
+    metrics: dict[str, float] = Field(default_factory=dict)
+
+
+class TrainFailed(_TrainEvent):
+    kind: Literal["failed"] = "failed"
+    error_type: str = Field(min_length=1)
+    message: str = Field(min_length=1)
+    runtime_invalidated: bool
+
+
+class TrainCancelled(_TrainEvent):
+    kind: Literal["cancelled"] = "cancelled"
+    reason: str = Field(min_length=1)
+    runtime_invalidated: bool = True
+
+
+TrainEvent: TypeAlias = Annotated[
+    TrainAccepted
+    | TrainProgress
+    | AdapterReady
+    | TrainCompleted
+    | TrainFailed
+    | TrainCancelled,
+    Field(discriminator="kind"),
+]
+TRAIN_EVENT_ADAPTER = TypeAdapter(TrainEvent)
+TERMINAL_EVENT_KINDS = frozenset({"completed", "failed", "cancelled"})
+
+
+def is_terminal_event(event: TrainEvent) -> bool:
+    return event.kind in TERMINAL_EVENT_KINDS
+
+
+def validate_event_stream(events: Sequence[TrainEvent]) -> None:
+    if not events:
+        raise ValueError("train event stream must not be empty")
+    if not isinstance(events[0], TrainAccepted):
+        raise ValueError("train event stream must begin with accepted")
+    if [event.sequence for event in events] != list(range(len(events))):
+        raise ValueError("train event sequence must be contiguous from zero")
+    terminals = [event for event in events if is_terminal_event(event)]
+    if len(terminals) != 1 or events[-1] is not terminals[0]:
+        raise ValueError("train event stream must end with exactly one terminal event")
+    identity = {(event.run_id, event.job_id) for event in events}
+    if len(identity) != 1:
+        raise ValueError("all train events must identify the same run and job")
+
+
+def _fingerprint(value: BaseModel) -> str:
+    payload = json.dumps(
+        value.model_dump(mode="json"), separators=(",", ":"), sort_keys=True
+    ).encode()
+    return hashlib.sha256(payload).hexdigest()
diff --git a/src/art/megatron/runtime/te_cutlass_grouped_gemm.py b/src/art/megatron/runtime/te_cutlass_grouped_gemm.py
index 23602fd8f..99983fedd 100644
--- a/src/art/megatron/runtime/te_cutlass_grouped_gemm.py
+++ b/src/art/megatron/runtime/te_cutlass_grouped_gemm.py
@@ -102,11 +102,11 @@ def _raise_if_te_cutlass_grouped_gemm_would_fallback(
     if reason is None:
         return
     raise RuntimeError(
-        "ART requires Transformer Engine CUTLASS grouped GEMM, but this "
+        "ART requires optimized Transformer Engine grouped GEMM, but this "
         f"grouped GEMM call would use the fallback path: {reason}. "
-        "Required shape: Hopper SM90, BF16/FP16 A/B/out tensors with matching "
-        "dtypes, no grouped bias/GELU/debug quantizer path, and uniform B K "
-        "dimension divisible by 128."
+        "Required shape: Hopper SM90 or Blackwell SM100+, BF16/FP16 A/B/out "
+        "tensors with matching dtypes, no grouped bias/GELU/debug quantizer "
+        "path, and uniform B K dimension divisible by 128."
     )
 
 
@@ -121,16 +121,14 @@ def _te_cutlass_grouped_gemm_fallback_reason(
     use_bias: bool,
 ) -> str | None:
     torch = _torch()
-    # Keep this in sync with TE's validated CUTLASS grouped-GEMM selector. ART
-    # currently supports the TE 2.11 SM90/Hopper path; SM100/Blackwell support
-    # should come from an upgraded Transformer Engine build using this same API.
+    # TE 2.14 adds BF16 grouped GEMM through cuBLAS 13.2 on SM100 and newer.
     if not A or not B or not out:
         return "A, B, and out must all be non-empty"
     if len(layout) < 2:
         return f"invalid layout {layout!r}"
     if not torch.cuda.is_available():
         return "CUDA is not available"
-    if (device_reason := _sm90_device_reason(A[0])) is not None:
+    if (device_reason := _grouped_gemm_device_reason(A[0])) is not None:
         return device_reason
     if gelu:
         return "grouped GELU pre-activation output is not supported"
@@ -153,7 +151,7 @@ def _te_cutlass_grouped_gemm_fallback_reason(
     return _uniform_b_k128_reason(B, transb=layout[1] == "T")
 
 
-def _sm90_device_reason(tensor: torch.Tensor) -> str | None:
+def _grouped_gemm_device_reason(tensor: torch.Tensor) -> str | None:
     torch = _torch()
     device = getattr(tensor, "device", None)
     device_index = torch.cuda.current_device()
@@ -163,8 +161,11 @@ def _sm90_device_reason(tensor: torch.Tensor) -> str | None:
     if capability is None:
         capability = torch.cuda.get_device_capability(device_index)
         _DEVICE_CAPABILITIES[device_index] = capability
-    if capability != (9, 0):
-        return f"CUDA device {device_index} has capability {capability}, not SM90"
+    if capability != (9, 0) and capability < (10, 0):
+        return (
+            f"CUDA device {device_index} has capability {capability}, "
+            "not SM90 or SM100+"
+        )
     return None
 
 
diff --git a/src/art/megatron/runtime/trainer_run.py b/src/art/megatron/runtime/trainer_run.py
new file mode 100644
index 000000000..79a4c2f34
--- /dev/null
+++ b/src/art/megatron/runtime/trainer_run.py
@@ -0,0 +1,17 @@
+from typing import Protocol
+
+from .publication import TrainerPublicationEvent
+
+
+class TrainingCancelledError(RuntimeError):
+    pass
+
+
+class EventSink(Protocol):
+    def progress(
+        self, *, step_index: int, num_steps: int, metrics: dict[str, float]
+    ) -> None: ...
+
+    def adapter_ready(self, *, learner_version: int, adapter_path: str) -> None: ...
+
+    def publication(self, event: TrainerPublicationEvent) -> None: ...
diff --git a/src/art/megatron/runtime_config.py b/src/art/megatron/runtime_config.py
index df1e25bd3..dba0ce7f2 100644
--- a/src/art/megatron/runtime_config.py
+++ b/src/art/megatron/runtime_config.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 from collections.abc import Mapping
+import os
 from typing import Any
 
 from ..types import MegatronRuntimeConfig, MegatronTopologyConfig
@@ -13,6 +14,8 @@ def init_megatron_runtime_config(
     *,
     topology: MegatronTopologyConfig | Mapping[str, int | None] | None = None,
     packed_sequence_length: int | None = None,
+    snapshot_pool_capacity: int = 2,
+    compile_cache: bool | None = None,
     streaming_weight_offload: bool = False,
 ) -> MegatronRuntimeConfig:
     global _MEGATRON_RUNTIME_CONFIG
@@ -20,6 +23,13 @@ def init_megatron_runtime_config(
         config = {
             "topology": topology,
             "packed_sequence_length": packed_sequence_length,
+            "snapshot_pool_capacity": snapshot_pool_capacity,
+            "compile_cache": (
+                os.environ.get("ART_MEGATRON_COMPILE_CACHE", "0").lower()
+                in {"1", "true", "yes", "on"}
+                if compile_cache is None
+                else compile_cache
+            ),
             "streaming_weight_offload": streaming_weight_offload,
         }
     runtime_config = MegatronRuntimeConfig.model_validate(config)
diff --git a/src/art/megatron/selective_lm_head.py b/src/art/megatron/selective_lm_head.py
new file mode 100644
index 000000000..e243ab459
--- /dev/null
+++ b/src/art/megatron/selective_lm_head.py
@@ -0,0 +1,335 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+import os
+from typing import Any, Iterator
+
+from megatron.core.tensor_parallel.mappings import (
+    gather_from_sequence_parallel_region,
+)
+from pydantic import BaseModel, ConfigDict
+import torch
+
+from art.loss import AlignedLossInputs, LossInputs
+
+_ENABLE_ENV = "ART_MEGATRON_SELECTIVE_LM_HEAD"
+
+
+class LmHeadTokenSelection(BaseModel):
+    """Rows projected by the LM head, derived from already-shifted labels."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+    flat_indices: torch.Tensor
+    full_shape: tuple[int, int]
+
+    @classmethod
+    def from_labels(
+        cls,
+        labels: torch.Tensor,
+        *,
+        target_device: torch.device | None = None,
+    ) -> "LmHeadTokenSelection":
+        if labels.ndim != 2:
+            raise ValueError(
+                f"LM-head labels must be [B, S], got {tuple(labels.shape)}"
+            )
+        indices = torch.nonzero(labels.reshape(-1) != -100, as_tuple=False).reshape(-1)
+        if labels.numel() and not indices.numel():
+            # Keep one ignored row so zero-contribution microbatches retain a graph.
+            indices = torch.zeros(1, dtype=torch.long, device=labels.device)
+        if target_device is not None:
+            indices = indices.to(device=target_device, non_blocking=True)
+        return cls(
+            flat_indices=indices.to(dtype=torch.long).contiguous(),
+            full_shape=(int(labels.shape[0]), int(labels.shape[1])),
+        )
+
+    def select(self, tensor: torch.Tensor) -> torch.Tensor:
+        expected = self.full_shape[0] * self.full_shape[1]
+        if tensor.numel() != expected:
+            raise ValueError(
+                "selected token tensor must match the label shape: "
+                f"tensor={tuple(tensor.shape)} labels={self.full_shape}"
+            )
+        return tensor.reshape(-1).index_select(0, self.flat_indices).unsqueeze(0)
+
+    def select_optional(self, tensor: torch.Tensor | None) -> torch.Tensor | None:
+        return None if tensor is None else self.select(tensor)
+
+    def restore(self, tensor: torch.Tensor, *, fill_value: float = 0.0) -> torch.Tensor:
+        if tensor.numel() != self.flat_indices.numel():
+            raise ValueError(
+                "selected tensor length does not match LM-head selection: "
+                f"tensor={tensor.numel()} selection={self.flat_indices.numel()}"
+            )
+        restored = tensor.new_full(self.full_shape, fill_value)
+        restored.reshape(-1).index_copy_(
+            0,
+            self.flat_indices,
+            tensor.reshape(-1),
+        )
+        return restored
+
+    def compact_loss_inputs(
+        self,
+        inputs: LossInputs | AlignedLossInputs,
+    ) -> AlignedLossInputs:
+        aligned = inputs.align_inputs()
+        return aligned.model_copy(
+            update={
+                "assistant_mask": self.select(aligned.assistant_mask),
+                "old_logprobs": self.select(aligned.old_logprobs),
+                "advantages": self.select(aligned.advantages),
+                "weights": self.select(aligned.weights),
+                "group_ids": self.select(aligned.group_ids),
+                "original_logprobs": self.select_optional(aligned.original_logprobs),
+                "entropies_are_aligned": True,
+            }
+        )
+
+
+class TokenLossOutput(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    token_losses: torch.Tensor
+    selection: LmHeadTokenSelection | None = None
+
+    def select(self, tensor: torch.Tensor) -> torch.Tensor:
+        return tensor if self.selection is None else self.selection.select(tensor)
+
+    def select_optional(self, tensor: torch.Tensor | None) -> torch.Tensor | None:
+        return (
+            tensor if self.selection is None else self.selection.select_optional(tensor)
+        )
+
+    def compact_loss_inputs(
+        self,
+        inputs: LossInputs | AlignedLossInputs,
+    ) -> LossInputs | AlignedLossInputs:
+        if self.selection is None:
+            return inputs
+        return self.selection.compact_loss_inputs(inputs)
+
+    def restore(self, tensor: torch.Tensor) -> torch.Tensor:
+        return tensor if self.selection is None else self.selection.restore(tensor)
+
+    def masked_sum(self, mask: torch.Tensor) -> torch.Tensor:
+        selected_mask = self.select(mask).to(dtype=torch.bool)
+        return self.token_losses[selected_mask].sum() + self.token_losses.sum() * 0.0
+
+
+def selective_lm_head_enabled() -> bool:
+    raw = os.environ.get(_ENABLE_ENV, "1").strip().lower()
+    if raw in {"1", "true", "yes", "on"}:
+        return True
+    if raw in {"0", "false", "no", "off"}:
+        return False
+    raise ValueError(f"{_ENABLE_ENV} must be a boolean, got {raw!r}")
+
+
+def forward_token_losses(
+    model: torch.nn.Module,
+    *,
+    labels: torch.Tensor,
+    selection: LmHeadTokenSelection,
+    forward_kwargs: dict[str, Any],
+    enabled: bool | None = None,
+) -> TokenLossOutput:
+    """Run the normal model path while projecting only labeled token rows.
+
+    Sequence-parallel hidden states are gathered before selection, matching the
+    communication performed by Megatron's output linear. The output linear's
+    own gather is disabled for this call; gather autograd performs the matching
+    reduce-scatter in backward.
+    """
+    if "labels" in forward_kwargs:
+        raise ValueError("forward_kwargs must not contain labels")
+    if enabled is None:
+        enabled = selective_lm_head_enabled()
+    if not enabled:
+        return TokenLossOutput(
+            token_losses=model(**forward_kwargs, labels=labels),
+        )
+    if tuple(labels.shape) != selection.full_shape:
+        raise ValueError(
+            f"labels={tuple(labels.shape)} selection={selection.full_shape}"
+        )
+
+    language_model = _language_model(model)
+    _validate_language_model(language_model)
+    if not labels.numel():
+        with _select_output_rows(language_model.output_layer, selection):
+            logits = model(**forward_kwargs, labels=None)
+        if not isinstance(logits, torch.Tensor):
+            raise TypeError(f"model must return logits, got {type(logits).__name__}")
+        return TokenLossOutput(
+            token_losses=_empty_token_losses(logits, labels),
+            selection=selection,
+        )
+    compact_labels = selection.select(labels)
+    with _select_output_rows(language_model.output_layer, selection):
+        with _restore_root_output(model, selection):
+            token_losses = model(**forward_kwargs, labels=compact_labels)
+    if not isinstance(token_losses, torch.Tensor):
+        raise TypeError(
+            f"model must return token losses, got {type(token_losses).__name__}"
+        )
+    return TokenLossOutput(
+        token_losses=selection.select(token_losses),
+        selection=selection,
+    )
+
+
+def forward_token_logits(
+    model: torch.nn.Module,
+    *,
+    selection: LmHeadTokenSelection,
+    forward_kwargs: dict[str, Any],
+) -> torch.Tensor:
+    """Project only selected token rows and return local vocabulary logits."""
+    if "labels" in forward_kwargs:
+        raise ValueError("forward_kwargs must not contain labels")
+    language_model = _language_model(model)
+    _validate_language_model(language_model)
+    with _select_output_rows(language_model.output_layer, selection):
+        logits = model(**forward_kwargs, labels=None)
+    if not isinstance(logits, torch.Tensor) or logits.ndim != 3:
+        raise TypeError("selected model output must be a [tokens, batch, vocab] tensor")
+    selected_tokens = int(selection.flat_indices.numel())
+    if tuple(logits.shape[:2]) == (selected_tokens, 1):
+        return logits[:, 0, :].contiguous()
+    if tuple(logits.shape[:2]) == (1, selected_tokens):
+        return logits[0, :, :].contiguous()
+    raise ValueError(
+        "selected logits do not match LM-head selection: "
+        f"logits={tuple(logits.shape)} selected_tokens={selected_tokens}"
+    )
+
+
+def _language_model(model: torch.nn.Module) -> Any:
+    module: Any = model
+    seen: set[int] = set()
+    while id(module) not in seen:
+        seen.add(id(module))
+        if hasattr(module, "module"):
+            module = module.module
+            continue
+        language_model = getattr(module, "language_model", None)
+        if language_model is not None:
+            module = language_model
+            continue
+        break
+    if not hasattr(module, "output_layer") or not hasattr(
+        module, "compute_language_model_loss"
+    ):
+        raise TypeError(
+            "selective LM head requires a GPT-compatible language model with "
+            "output_layer and compute_language_model_loss"
+        )
+    return module
+
+
+def _validate_language_model(language_model: Any) -> None:
+    if not bool(getattr(language_model, "post_process", False)):
+        raise RuntimeError("selective LM head requires the post-process model stage")
+    config = language_model.config
+    if bool(getattr(language_model, "mtp_process", False)) or int(
+        getattr(config, "mtp_num_layers", 0) or 0
+    ):
+        raise RuntimeError("selective LM head does not support MTP training")
+    if bool(getattr(language_model.output_layer, "gather_output", False)):
+        raise RuntimeError("selective LM head requires vocabulary-parallel logits")
+
+
+@contextmanager
+def _restore_root_output(
+    model: torch.nn.Module,
+    selection: LmHeadTokenSelection,
+) -> Iterator[None]:
+    def restore(
+        _module: torch.nn.Module,
+        _args: tuple[Any, ...],
+        output: Any,
+    ) -> torch.Tensor:
+        if not isinstance(output, torch.Tensor):
+            raise TypeError(
+                f"model must return token losses, got {type(output).__name__}"
+            )
+        return selection.restore(output)
+
+    handle = model.register_forward_hook(restore, prepend=True)
+    try:
+        yield
+    finally:
+        handle.remove()
+
+
+@contextmanager
+def _select_output_rows(
+    output_layer: torch.nn.Module,
+    selection: LmHeadTokenSelection,
+) -> Iterator[None]:
+    sequence_parallel = bool(getattr(output_layer, "sequence_parallel", False))
+    disable_grad_reduce = bool(getattr(output_layer, "disable_grad_reduce", False))
+    calls = 0
+
+    def select_rows(
+        module: torch.nn.Module,
+        args: tuple[Any, ...],
+        kwargs: dict[str, Any],
+    ) -> tuple[tuple[Any, ...], dict[str, Any]]:
+        nonlocal calls
+        calls += 1
+        if calls != 1 or not args or not isinstance(args[0], torch.Tensor):
+            raise RuntimeError("selective LM head expects one positional output call")
+        hidden_states = args[0]
+        if sequence_parallel:
+            hidden_states = gather_from_sequence_parallel_region(
+                hidden_states,
+                group=getattr(module, "tp_group"),
+            )
+            setattr(module, "sequence_parallel", False)
+            setattr(module, "disable_grad_reduce", True)
+        batch, sequence = selection.full_shape
+        if tuple(hidden_states.shape[:2]) != (sequence, batch):
+            raise ValueError(
+                "LM-head hidden states do not match labels: "
+                f"hidden={tuple(hidden_states.shape)} labels={selection.full_shape}"
+            )
+        selected = (
+            hidden_states.transpose(0, 1)
+            .reshape(batch * sequence, hidden_states.shape[-1])
+            .index_select(0, selection.flat_indices)
+            .unsqueeze(1)
+        )
+        return (selected, *args[1:]), kwargs
+
+    handle = output_layer.register_forward_pre_hook(select_rows, with_kwargs=True)
+    succeeded = False
+    try:
+        yield
+        succeeded = True
+    finally:
+        handle.remove()
+        if sequence_parallel:
+            setattr(output_layer, "sequence_parallel", True)
+            setattr(output_layer, "disable_grad_reduce", disable_grad_reduce)
+    if succeeded and calls != 1:
+        raise RuntimeError(f"selective LM head expected one output call, got {calls}")
+
+
+def _empty_token_losses(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
+    if labels.numel() or logits.ndim != 3 or not logits.shape[-1]:
+        raise ValueError(
+            f"expected empty labels and [B, 0, V] logits, got {tuple(logits.shape)}"
+        )
+    losses = logits[..., 0]
+    if tuple(losses.shape) == tuple(labels.shape):
+        return losses
+    losses = losses.transpose(0, 1).contiguous()
+    if tuple(losses.shape) != tuple(labels.shape):
+        raise ValueError(
+            f"empty logits={tuple(logits.shape)} labels={tuple(labels.shape)}"
+        )
+    return losses
diff --git a/src/art/megatron/service.py b/src/art/megatron/service.py
deleted file mode 100644
index 92f93c2aa..000000000
--- a/src/art/megatron/service.py
+++ /dev/null
@@ -1,1592 +0,0 @@
-import asyncio
-from dataclasses import dataclass, field
-import importlib
-import json
-import os
-from pathlib import Path
-import shutil
-import socket
-import subprocess
-import sys
-from typing import Any, AsyncIterator, Literal, TypedDict, cast
-from urllib.parse import urlparse
-import uuid
-import warnings
-
-from peft.tuners.lora.config import LoraConfig
-import torch
-
-from .. import dev, types
-from ..adapter_leases import in_flight_lora_name
-from ..dev.get_model_config import default_target_modules
-from ..dev.validate import is_dedicated_mode
-from ..preprocessing.pack import DiskPackedTensors
-from ..preprocessing.tokenize import SFTBatch
-from ..serving_capabilities import (
-    ServingCapabilities,
-    discover_serving_capabilities,
-)
-from ..types import MegatronRuntimeConfig, MegatronTopologyConfig
-from ..utils.get_model_step import get_step_from_dir
-from ..utils.lifecycle import (
-    ChildProcessSupervisor,
-    ServiceLifecycle,
-    cleanup_after_failure,
-    managed_process_cmd,
-    terminate_popen_process_group,
-)
-from ..utils.output_dirs import get_step_checkpoint_dir
-from ..vllm_runtime import (
-    ManagedVllmRuntime,
-    VllmRuntimeLaunchConfig,
-    get_external_vllm_runtime_config,
-    map_checkpoint_path_for_vllm,
-    normalize_vllm_server_url,
-    wait_for_vllm_http_runtime,
-)
-from .lora import (
-    LORA_ALPHA,
-    MEGATRON_LORA_RANK_ENV,
-    MEGATRON_LORA_TARGET_MODULES_ENV,
-    default_lora_rank_for_handler,
-)
-from .migrations import optimizer_state_path
-from .model_support.lora_disk import normalize_lora_checkpoint_to_vllm
-from .model_support.registry import (
-    UnsupportedModelArchitectureError,
-    model_uses_expert_parallel,
-)
-from .optimizer_state import (
-    MegatronResumeStep,
-    commit_optimizer_generation,
-    format_megatron_resume_message,
-    optimizer_generation_files,
-    prepare_megatron_resume_state,
-    read_optimizer_commit,
-)
-from .runtime.client import (
-    create_megatron_job_paths,
-    stream_megatron_job,
-    write_megatron_job,
-)
-from .runtime.jobs import (
-    LORA_READY_EVENT,
-    OPTIMIZER_READY_EVENT,
-    MegatronMergedTrainingJob,
-    MegatronOptimizerSaveJob,
-    MegatronSFTTrainingJob,
-    MegatronSyncJob,
-    MegatronTrainingJob,
-    MergedWeightTransferInitInfo,
-    MergedWeightTransferSpec,
-)
-from .runtime.te_cutlass_grouped_gemm import force_te_cutlass_grouped_gemm_env
-from .runtime_config import get_megatron_runtime_config
-from .training.sft_batches import materialize_sft_batches
-
-safetensors = importlib.import_module("safetensors")
-safe_open = safetensors.safe_open
-OFFLOAD_BETWEEN_JOBS_ENV = "ART_MEGATRON_OFFLOAD_BETWEEN_JOBS"
-
-
-class _RuntimeRequestKwargs(TypedDict, total=False):
-    headers: dict[str, str]
-
-
-def _lora_config_from_model_config(
-    config: dev.InternalModelConfig | dev.BackendModelConfig,
-) -> dev.LoRAConfig:
-    return cast(dev.BackendModelConfig, config).get("lora_config") or dev.LoRAConfig()
-
-
-def create_identity_lora(
-    base_model: str,
-    lora_path: str,
-    rank: int | None = None,
-    target_modules: list[str] | None = None,
-    lora_alpha: int = LORA_ALPHA,
-    random_state: int | None = None,
-    allow_unvalidated_arch: bool = False,
-) -> None:
-    """Create an identity LoRA adapter for a Megatron model.
-
-    For MoE models, this targets fused expert parameters and lets the model
-    support handler normalize the saved PEFT tensors to vLLM layout.
-
-    Args:
-        base_model: HuggingFace model identifier.
-        lora_path: Directory to save the adapter files.
-        rank: LoRA rank. Defaults to rank 1 for MoE models and rank 8 for dense models.
-        lora_alpha: LoRA alpha scaling factor.
-    """
-    from unittest.mock import patch
-
-    from accelerate import init_empty_weights
-    from peft import get_peft_model
-    from transformers import AutoConfig, AutoModelForCausalLM
-
-    from .model_support import get_model_support_handler
-
-    if random_state is not None:
-        torch.manual_seed(random_state)
-    target_modules = target_modules or default_target_modules(base_model)
-    handler = get_model_support_handler(
-        base_model,
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
-    if rank is None:
-        rank = default_lora_rank_for_handler(handler)
-    base_config = AutoConfig.from_pretrained(base_model, trust_remote_code=True)
-    model_config = handler.identity_lora_model_config(base_config)
-    with init_empty_weights():
-        model = AutoModelForCausalLM.from_config(
-            model_config, dtype=torch.bfloat16, trust_remote_code=True
-        )
-    model.name_or_path = base_model
-
-    lora_config = LoraConfig(
-        base_model_name_or_path=base_model,
-        r=rank,
-        lora_alpha=lora_alpha,
-        target_modules=[],
-        target_parameters=handler.identity_lora_target_parameters(
-            model,
-            target_modules=target_modules,
-        ),
-        bias="none",
-    )
-
-    meta = torch.device("meta")
-    orig_to = torch.nn.Module.to
-
-    def _skip_meta_to(
-        module: torch.nn.Module, *args: Any, **kwargs: Any
-    ) -> torch.nn.Module:
-        device = kwargs.get("device") or (args[0] if args else None)
-        if device == meta or str(device) == "meta":
-            return module
-        return orig_to(module, *args, **kwargs)
-
-    # PEFT does not recognize fused MoE expert modules, but our handler
-    # converts the resulting identity LoRA checkpoint into supported tensors.
-    with warnings.catch_warnings():
-        if bool(getattr(handler, "is_moe", False)):
-            warnings.filterwarnings(
-                "ignore",
-                message=(
-                    r"Unsupported layer type '.*MoeExperts.*' encountered, "
-                    r"proceed at your own risk\."
-                ),
-                category=UserWarning,
-                module=r"peft\.tuners\.tuners_utils",
-            )
-        with patch.object(torch.nn.Module, "to", _skip_meta_to):
-            peft_model = get_peft_model(model, lora_config)
-
-    os.makedirs(lora_path, exist_ok=True)
-    peft_model.save_pretrained(lora_path)
-
-    final_config = LoraConfig(
-        base_model_name_or_path=base_model,
-        r=rank,
-        lora_alpha=lora_alpha,
-        target_modules=target_modules,
-        bias="none",
-    ).to_dict()
-    normalize_lora_checkpoint_to_vllm(
-        lora_path,
-        handler=handler,
-        adapter_config=final_config,
-    )
-    del peft_model, model
-
-
-@dataclass
-class MegatronService:
-    model_name: str
-    base_model: str
-    config: dev.InternalModelConfig | dev.BackendModelConfig
-    output_dir: str
-    enable_expert_replay: bool = True
-    runtime_config: MegatronRuntimeConfig = field(
-        default_factory=get_megatron_runtime_config
-    )
-    _is_sleeping: bool = False
-    _latest_step: int = 0
-    _training_session_id: str = field(
-        default_factory=lambda: uuid.uuid4().hex,
-        init=False,
-    )
-    _resume_step: MegatronResumeStep | None = None
-    _megatron_process: subprocess.Popen[Any] | None = None
-    _megatron_log_file: Any = None
-    _megatron_log_path: str | None = None
-    _vllm_runtime: ManagedVllmRuntime = field(
-        default_factory=ManagedVllmRuntime,
-        init=False,
-        repr=False,
-    )
-    _merged_weight_transfer_init_info: MergedWeightTransferInitInfo | None = None
-    _active_megatron_topology: MegatronTopologyConfig | None = None
-    _lifecycle: ServiceLifecycle = field(
-        default_factory=ServiceLifecycle,
-        init=False,
-        repr=False,
-    )
-    _child_processes: ChildProcessSupervisor = field(init=False, repr=False)
-    _loaded_adapter_steps: set[int] = field(
-        default_factory=set,
-        init=False,
-        repr=False,
-    )
-    _loaded_exact_adapter_steps: set[int] = field(
-        default_factory=set,
-        init=False,
-        repr=False,
-    )
-    _exact_adapter_refcounts: dict[int, int] = field(
-        default_factory=dict,
-        init=False,
-        repr=False,
-    )
-    _exact_adapter_lock: asyncio.Lock = field(
-        default_factory=asyncio.Lock,
-        init=False,
-        repr=False,
-    )
-    _serving_capabilities: ServingCapabilities | None = field(
-        default=None,
-        init=False,
-        repr=False,
-    )
-
-    def __post_init__(self) -> None:
-        self._child_processes = ChildProcessSupervisor(self._on_child_process_exit)
-        self._validate_megatron_dependencies()
-
-    def _on_child_process_exit(self, error: RuntimeError) -> None:
-        self._status(f"Child process exited unexpectedly: {error}")
-        self.close()
-
-    def _raise_if_child_failed(self) -> None:
-        self._child_processes.raise_if_failed()
-
-    def _status(self, message: str) -> None:
-        print(f"[ART Megatron] {message}", flush=True)
-
-    @staticmethod
-    def _display_path(path: str | os.PathLike[str]) -> str:
-        return str(Path(path).resolve())
-
-    @property
-    def is_dedicated(self) -> bool:
-        return is_dedicated_mode(self.config)
-
-    @property
-    def rollout_weights_mode(self) -> Literal["lora", "merged"]:
-        mode = self.config.get("rollout_weights_mode", "lora")
-        assert mode in {"lora", "merged"}
-        return mode
-
-    @property
-    def rollout_weight_update_mode(self) -> Literal["step_lora", "in_flight_lora"]:
-        mode = self.config.get("rollout_weight_update_mode", "step_lora")
-        assert mode in {"step_lora", "in_flight_lora"}
-        return mode
-
-    @property
-    def _in_flight_lora_slot(self) -> str:
-        return in_flight_lora_name(self.model_name)
-
-    @property
-    def _initial_served_model_name(self) -> str:
-        if (
-            self.rollout_weights_mode == "lora"
-            and self.rollout_weight_update_mode == "in_flight_lora"
-        ):
-            return self._in_flight_lora_slot
-        return f"{self.model_name}@{self._latest_step}"
-
-    def _exact_lora_name(self, step: int) -> str:
-        if self.rollout_weight_update_mode == "in_flight_lora":
-            return f"{self.model_name}:eval@{step}"
-        return f"{self.model_name}@{step}"
-
-    @property
-    def _vllm_base_url(self) -> str:
-        if external_runtime := get_external_vllm_runtime_config(self.config):
-            return normalize_vllm_server_url(external_runtime.server_url)
-        return self._vllm_runtime.base_url
-
-    @property
-    def _vllm_host(self) -> str:
-        if external_runtime := get_external_vllm_runtime_config(self.config):
-            parsed = urlparse(normalize_vllm_server_url(external_runtime.server_url))
-            return parsed.hostname or self._vllm_runtime.host
-        return self._vllm_runtime.host
-
-    @property
-    def _vllm_port(self) -> int:
-        if external_runtime := get_external_vllm_runtime_config(self.config):
-            parsed = urlparse(normalize_vllm_server_url(external_runtime.server_url))
-            return parsed.port or (443 if parsed.scheme == "https" else 80)
-        return self._vllm_runtime.port
-
-    @_vllm_port.setter
-    def _vllm_port(self, port: int) -> None:
-        self._vllm_runtime.port = port
-
-    @property
-    def _vllm_api_key(self) -> str | None:
-        if external_runtime := get_external_vllm_runtime_config(self.config):
-            return external_runtime.api_key
-        return self._vllm_runtime.api_key
-
-    @property
-    def _vllm_nccl_so_path(self) -> str | None:
-        return self._vllm_runtime.nccl_so_path
-
-    def _megatron_random_state(self) -> int | None:
-        for config_key in ("peft_args", "init_args"):
-            random_state = self.config.get(config_key, {}).get("random_state")
-            if random_state is not None:
-                return int(random_state)
-        return None
-
-    @property
-    def _allow_unvalidated_arch(self) -> bool:
-        return bool(self.config.get("allow_unvalidated_arch", False))
-
-    def _model_uses_expert_replay(self) -> bool:
-        if not self.enable_expert_replay:
-            return False
-        try:
-            return model_uses_expert_parallel(
-                self.base_model,
-                allow_unvalidated_arch=self._allow_unvalidated_arch,
-            )
-        except UnsupportedModelArchitectureError:
-            return False
-
-    def _trainer_gpu_count(self) -> int:
-        if self.is_dedicated:
-            return len(self.config["trainer_gpu_ids"])
-        return max(int(torch.cuda.device_count()), 1)
-
-    def _data_parallel_world_size(self) -> int:
-        num_gpus = self._trainer_gpu_count()
-        topology = self.runtime_config.topology
-        tp, cp, pp = topology.tp, topology.cp, topology.pp
-        denominator = max(tp * cp * pp, 1)
-        if num_gpus % denominator != 0:
-            raise RuntimeError(
-                "Cannot resolve Megatron data-parallel world size from trainer "
-                f"GPUs/topology: num_gpus={num_gpus}, tp={tp}, cp={cp}, pp={pp}"
-            )
-        return max(num_gpus // denominator, 1)
-
-    async def resolve_global_grad_accumulation_sequences(
-        self,
-        config: types.TrainConfig,
-    ) -> int:
-        if config.grad_accumulation_sequences is not None:
-            return int(config.grad_accumulation_sequences)
-        return self._data_parallel_world_size()
-
-    def _megatron_runtime_paths(self) -> tuple[str, str, str]:
-        runtime_dir = Path(self.output_dir) / "megatron_runtime"
-        jobs_dir = runtime_dir / "jobs"
-        training_log_dir = runtime_dir / "training_logs"
-        jobs_dir.mkdir(parents=True, exist_ok=True)
-        training_log_dir.mkdir(parents=True, exist_ok=True)
-        return (
-            str(jobs_dir),
-            str(training_log_dir),
-            str(runtime_dir / "vllm_waking.lock"),
-        )
-
-    def _staging_lora_dir(self, step: int) -> str:
-        return str(
-            Path(self.output_dir) / "megatron_runtime" / "staging" / f"{step:04d}"
-        )
-
-    def _prepare_training_lora_dir(self, source_path: str, step: int) -> str:
-        staging_dir = self._staging_lora_dir(step)
-        if os.path.exists(staging_dir):
-            shutil.rmtree(staging_dir)
-        shutil.copytree(source_path, staging_dir)
-        return staging_dir
-
-    def _clear_wake_lock(self) -> None:
-        _, _, wake_lock_path = self._megatron_runtime_paths()
-        if os.path.exists(wake_lock_path):
-            os.remove(wake_lock_path)
-
-    def _allocate_master_port(self) -> int:
-        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
-            sock.bind(("", 0))
-            return int(sock.getsockname()[1])
-
-    @staticmethod
-    def _megatron_topology_env(topology: MegatronTopologyConfig) -> dict[str, str]:
-        env = {
-            "ART_MEGATRON_TENSOR_MODEL_PARALLEL_SIZE": str(topology.tp),
-            "ART_MEGATRON_CONTEXT_PARALLEL_SIZE": str(topology.cp),
-            "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE": str(topology.ep),
-            "ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE": str(topology.pp),
-            "ART_MEGATRON_EXPERT_TENSOR_PARALLEL_SIZE": str(topology.etp),
-        }
-        if topology.vpp is not None:
-            env["ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE"] = str(topology.vpp)
-        return env
-
-    @staticmethod
-    def _megatron_topology_env_names() -> tuple[str, ...]:
-        return (
-            "ART_MEGATRON_TENSOR_MODEL_PARALLEL_SIZE",
-            "ART_MEGATRON_CONTEXT_PARALLEL_SIZE",
-            "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE",
-            "ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE",
-            "ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE",
-            "ART_MEGATRON_EXPERT_TENSOR_PARALLEL_SIZE",
-        )
-
-    def _install_parent_signal_cleanup(self) -> None:
-        self._lifecycle.install_parent_cleanup(self.close)
-
-    def _restore_parent_signal_cleanup(self) -> None:
-        self._lifecycle.restore_parent_cleanup()
-
-    def _runtime_cuda_visible_devices(self) -> str:
-        if self.is_dedicated:
-            return ",".join(str(gpu_id) for gpu_id in self.config["inference_gpu_ids"])
-        if visible := os.environ.get("CUDA_VISIBLE_DEVICES"):
-            return visible
-        return ",".join(str(index) for index in range(torch.cuda.device_count()))
-
-    def _runtime_engine_args(
-        self, config: dev.OpenAIServerConfig | None
-    ) -> dict[str, object]:
-        from .model_support import get_model_support_handler
-
-        engine_args = dict(self.config.get("engine_args", {}))
-        if config and "engine_args" in config:
-            engine_args.update(dict(config["engine_args"]))
-        handler = get_model_support_handler(
-            self.base_model,
-            allow_unvalidated_arch=self._allow_unvalidated_arch,
-        )
-        for key, value in handler.vllm_engine_args(
-            rollout_weights_mode=self.rollout_weights_mode
-        ).items():
-            engine_args.setdefault(key, value)
-        engine_args.setdefault("generation_config", "vllm")
-        if self.rollout_weights_mode == "merged":
-            engine_args["weight_transfer_config"] = {"backend": "nccl"}
-            engine_args.pop("enable_lora", None)
-            engine_args.pop("max_loras", None)
-        else:
-            engine_args["enable_lora"] = True
-            engine_args.setdefault("max_loras", 2)
-        for key in ("model", "served_model_name"):
-            engine_args.pop(key, None)
-        return engine_args
-
-    def _runtime_server_args(
-        self, config: dev.OpenAIServerConfig | None
-    ) -> dict[str, object]:
-        from .model_support import get_model_support_handler
-
-        server_args: dict[str, object] = {
-            "return_tokens_as_token_ids": True,
-            "enable_auto_tool_choice": True,
-            "tool_call_parser": "hermes",
-        }
-        handler = get_model_support_handler(
-            self.base_model,
-            allow_unvalidated_arch=self._allow_unvalidated_arch,
-        )
-        server_args.update(handler.vllm_server_args())
-        if config and "server_args" in config:
-            server_args.update(dict(config["server_args"]))
-        for key in ("port", "host", "lora_modules"):
-            server_args.pop(key, None)
-        return server_args
-
-    def _runtime_headers(self) -> dict[str, str]:
-        if self._vllm_api_key is None:
-            return {}
-        return {"Authorization": f"Bearer {self._vllm_api_key}"}
-
-    def _runtime_request_kwargs(self) -> _RuntimeRequestKwargs:
-        headers = self._runtime_headers()
-        return {"headers": headers} if headers else {}
-
-    @property
-    def serving_capabilities(self) -> ServingCapabilities:
-        if self._serving_capabilities is None:
-            raise RuntimeError("vLLM serving capabilities have not been discovered")
-        return self._serving_capabilities
-
-    async def get_serving_capabilities(self) -> ServingCapabilities:
-        return self.serving_capabilities
-
-    async def _discover_serving_capabilities(self, *, external: bool) -> None:
-        self._serving_capabilities = await discover_serving_capabilities(
-            base_url=self._vllm_base_url,
-            headers=self._runtime_headers(),
-            allow_openai_compatible=external,
-        )
-
-    def _vllm_checkpoint_path(self, checkpoint_path: str) -> str:
-        return map_checkpoint_path_for_vllm(self.config, checkpoint_path)
-
-    def _sleep_mode_enabled(self) -> bool:
-        return bool(self.config.get("engine_args", {}).get("enable_sleep_mode", True))
-
-    def _get_optimizer_state_path(self) -> str:
-        path = optimizer_state_path(self.output_dir)
-        os.makedirs(path, exist_ok=True)
-        return path
-
-    def _resolve_resume_step(self) -> MegatronResumeStep:
-        if self._resume_step is not None:
-            return self._resume_step
-        info = prepare_megatron_resume_state(
-            output_dir=self.output_dir,
-            optimizer_state_path=self._get_optimizer_state_path(),
-        )
-        self._resume_step = info
-        self._status(format_megatron_resume_message(info))
-        return info
-
-    def _default_lora_adapter_config(self) -> LoraConfig:
-        from .model_support import get_model_support_handler
-
-        handler = get_model_support_handler(
-            self.base_model,
-            allow_unvalidated_arch=self._allow_unvalidated_arch,
-        )
-        lora_config = _lora_config_from_model_config(self.config)
-        rank = int(lora_config.get("rank", default_lora_rank_for_handler(handler)))
-        target_modules = lora_config.get("target_modules") or default_target_modules(
-            self.base_model
-        )
-        return LoraConfig(
-            base_model_name_or_path=self.base_model,
-            r=rank,
-            lora_alpha=LORA_ALPHA,
-            target_modules=target_modules,
-            bias="none",
-        )
-
-    def _adapter_exists_and_loads(
-        self,
-        lora_path: str,
-        *,
-        normalize_existing: bool = False,
-    ) -> bool:
-        adapter_path = os.path.join(lora_path, "adapter_model.safetensors")
-        if not os.path.exists(adapter_path):
-            return False
-        with safe_open(adapter_path, framework="pt") as adapter_file:
-            keys = list(adapter_file.keys())
-            if not keys:
-                raise RuntimeError(f"LoRA adapter contains no tensors: {adapter_path}")
-            for key in keys:
-                adapter_file.get_tensor(key)
-        if normalize_existing:
-            normalize_lora_checkpoint_to_vllm(
-                lora_path,
-                allow_unvalidated_arch=self._allow_unvalidated_arch,
-            )
-        return True
-
-    def _create_identity_lora(self, lora_path: str) -> None:
-        self._status(
-            "Preparing initial LoRA adapter "
-            f"for {self.base_model} at {self._display_path(lora_path)}"
-        )
-        lora_config = _lora_config_from_model_config(self.config)
-        rank = lora_config.get("rank")
-        create_identity_lora(
-            self.base_model,
-            lora_path,
-            rank=int(rank) if rank is not None else None,
-            target_modules=lora_config.get("target_modules"),
-            random_state=self._megatron_random_state(),
-            allow_unvalidated_arch=self._allow_unvalidated_arch,
-        )
-
-    def _ensure_identity_lora(
-        self,
-        lora_path: str,
-        *,
-        normalize_existing: bool = False,
-    ) -> None:
-        if self._adapter_exists_and_loads(
-            lora_path,
-            normalize_existing=normalize_existing,
-        ):
-            return
-        self._create_identity_lora(lora_path)
-
-    def _ensure_lora_adapter_config(
-        self, lora_path: str, *, source_path: str | None = None
-    ) -> None:
-        config_path = os.path.join(lora_path, "adapter_config.json")
-        if os.path.exists(config_path):
-            return
-        os.makedirs(lora_path, exist_ok=True)
-        if source_path is not None:
-            source_config = os.path.join(source_path, "adapter_config.json")
-            if os.path.exists(source_config):
-                shutil.copy(source_config, config_path)
-                return
-        self._default_lora_adapter_config().save_pretrained(lora_path)
-
-    def _build_merged_weight_transfer_spec(self, step: int) -> MergedWeightTransferSpec:
-        init_info = self._merged_weight_transfer_init_info
-        assert init_info is not None
-        if self._vllm_nccl_so_path is None:
-            raise RuntimeError("vLLM runtime NCCL path is not initialized")
-        return MergedWeightTransferSpec(
-            init_info=init_info,
-            vllm_base_url=self._vllm_base_url,
-            served_model_name=f"{self.model_name}@{step}",
-            api_key=self._vllm_api_key,
-            nccl_so_path=self._vllm_nccl_so_path,
-        )
-
-    def _resolve_current_lora_path(self) -> str:
-        resume_step = self._resolve_resume_step()
-        if self._latest_step < resume_step.step:
-            self._latest_step = resume_step.step
-        lora_path = get_step_checkpoint_dir(self.output_dir, self._latest_step)
-        if self._latest_step == 0 and not os.path.exists(lora_path):
-            lora_path = get_step_checkpoint_dir(self.output_dir, 0)
-        self._ensure_identity_lora(
-            lora_path,
-            normalize_existing=self._latest_step == 0,
-        )
-        self._ensure_lora_adapter_config(lora_path)
-        return lora_path
-
-    def _resolve_active_lora_path(self) -> str:
-        return self._resolve_current_lora_path()
-
-    async def _set_served_model_name(self, step: int) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self._vllm_base_url}/art/set_served_model_name",
-                json={"name": f"{self.model_name}@{step}"},
-                **self._runtime_request_kwargs(),
-                timeout=30.0,
-            )
-            response.raise_for_status()
-        self._latest_step = step
-
-    async def _init_merged_weight_transfer(self) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        if self._merged_weight_transfer_init_info is not None:
-            return
-        async with httpx.AsyncClient() as client:
-            response = await client.get(
-                f"{self._vllm_base_url}/get_world_size",
-                **self._runtime_request_kwargs(),
-                timeout=30.0,
-            )
-            response.raise_for_status()
-            inference_world_size = int(response.json()["world_size"])
-        self._merged_weight_transfer_init_info = MergedWeightTransferInitInfo(
-            master_address="127.0.0.1",
-            master_port=self._allocate_master_port(),
-            rank_offset=1,
-            world_size=inference_world_size + 1,
-        )
-
-    async def _start_vllm_subprocess(
-        self,
-        lora_path: str,
-        port: int,
-        config: dev.OpenAIServerConfig | None,
-    ) -> tuple[str, int]:
-        self._raise_if_child_failed()
-        server_args = self._runtime_server_args(config)
-        vllm_log_path = Path(self.output_dir) / "logs" / "vllm-runtime.log"
-        self._status(
-            "Starting vLLM runtime "
-            f"for {self.base_model}. Logs: {self._display_path(vllm_log_path)}"
-        )
-        location = await self._vllm_runtime.start(
-            launch_config=VllmRuntimeLaunchConfig(
-                base_model=self.base_model,
-                port=port,
-                host=self._vllm_runtime.host,
-                cuda_visible_devices=self._runtime_cuda_visible_devices(),
-                lora_path=lora_path,
-                served_model_name=self._initial_served_model_name,
-                rollout_weights_mode=self.rollout_weights_mode,
-                engine_args=self._runtime_engine_args(config),
-                server_args=server_args,
-            ),
-            output_dir=self.output_dir,
-            child_processes=self._child_processes,
-            install_parent_cleanup=self._install_parent_signal_cleanup,
-            cleanup_on_error=self._stop_vllm_subprocess,
-        )
-        self._status(f"vLLM runtime is ready at {self._vllm_base_url}")
-        return location
-
-    async def _reload_adapter(self, checkpoint_path: str, step: int) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        payload: dict[str, Any] = {
-            "lora_name": f"{self.model_name}@{step}",
-            "lora_path": self._vllm_checkpoint_path(checkpoint_path),
-        }
-        if self.serving_capabilities.inplace_lora_load:
-            payload["load_inplace"] = True
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self._vllm_base_url}/v1/load_lora_adapter",
-                json=payload,
-                **self._runtime_request_kwargs(),
-                timeout=60.0,
-            )
-            response.raise_for_status()
-        self._latest_step = step
-        self._loaded_adapter_steps.add(step)
-
-    async def _update_in_flight_adapter(self, checkpoint_path: str, step: int) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        self.serving_capabilities.require(
-            "in_flight_lora_updates", operation="In-flight LoRA updates"
-        )
-        self.serving_capabilities.require(
-            "policy_token_spans", operation="In-flight LoRA updates"
-        )
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self._vllm_base_url}/art/in_flight_lora_update",
-                json={
-                    "model_name": self._in_flight_lora_slot,
-                    "lora_slot": self._in_flight_lora_slot,
-                    "lora_path": self._vllm_checkpoint_path(checkpoint_path),
-                    "policy_version": step,
-                },
-                **self._runtime_request_kwargs(),
-                timeout=60.0,
-            )
-            response.raise_for_status()
-        self._latest_step = step
-        self._loaded_adapter_steps.add(step)
-
-    async def _load_rollout_lora_for_step(
-        self, checkpoint_path: str, step: int
-    ) -> None:
-        if self.rollout_weight_update_mode == "in_flight_lora":
-            await self._update_in_flight_adapter(checkpoint_path, step)
-        else:
-            await self._reload_adapter(checkpoint_path, step)
-
-    async def acquire_exact_adapter(self, step: int, checkpoint_path: str) -> str:
-        if self.rollout_weights_mode != "lora":
-            raise RuntimeError("Exact checkpoint eval requires LoRA rollout serving")
-        lora_name = self._exact_lora_name(step)
-        async with self._exact_adapter_lock:
-            loaded_steps = (
-                self._loaded_exact_adapter_steps
-                if self.rollout_weight_update_mode == "in_flight_lora"
-                else self._loaded_adapter_steps
-            )
-            if step in loaded_steps:
-                if self.rollout_weight_update_mode == "in_flight_lora":
-                    self._exact_adapter_refcounts[step] += 1
-                return lora_name
-            import httpx
-
-            self._raise_if_child_failed()
-            async with httpx.AsyncClient() as client:
-                response = await client.post(
-                    f"{self._vllm_base_url}/v1/load_lora_adapter",
-                    json={
-                        "lora_name": lora_name,
-                        "lora_path": self._vllm_checkpoint_path(checkpoint_path),
-                    },
-                    **self._runtime_request_kwargs(),
-                    timeout=60.0,
-                )
-                response.raise_for_status()
-            loaded_steps.add(step)
-            if self.rollout_weight_update_mode == "in_flight_lora":
-                self._exact_adapter_refcounts[step] = 1
-        return lora_name
-
-    async def release_exact_adapter(self, step: int) -> None:
-        if self.rollout_weight_update_mode != "in_flight_lora":
-            return
-        async with self._exact_adapter_lock:
-            count = self._exact_adapter_refcounts[step]
-            if count > 1:
-                self._exact_adapter_refcounts[step] = count - 1
-                return
-            await self._unload_exact_adapter(step)
-            del self._exact_adapter_refcounts[step]
-
-    async def _unload_adapter_name(self, lora_name: str) -> bool:
-        import httpx
-
-        self._raise_if_child_failed()
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self._vllm_base_url}/v1/unload_lora_adapter",
-                json={"lora_name": lora_name},
-                **self._runtime_request_kwargs(),
-                timeout=30.0,
-            )
-            if response.status_code == 404:
-                return False
-            response.raise_for_status()
-        return True
-
-    async def _unload_adapter(self, step: int) -> None:
-        await self._unload_adapter_name(f"{self.model_name}@{step}")
-        self._loaded_adapter_steps.discard(step)
-
-    async def _unload_exact_adapter(self, step: int) -> None:
-        await self._unload_adapter_name(self._exact_lora_name(step))
-        self._loaded_exact_adapter_steps.discard(step)
-
-    async def prune_loaded_adapters(self, *, retain_steps: set[int]) -> None:
-        if self.rollout_weights_mode != "lora" or self._vllm_port == 0:
-            return
-        async with self._exact_adapter_lock:
-            for step in sorted(self._loaded_exact_adapter_steps - retain_steps):
-                if self._exact_adapter_refcounts.get(step, 0) == 0:
-                    await self._unload_exact_adapter(step)
-        if self.rollout_weight_update_mode == "in_flight_lora":
-            return
-        for step in sorted(self._loaded_adapter_steps - retain_steps):
-            if step == self._latest_step:
-                continue
-            await self._unload_adapter(step)
-
-    async def _sync_dedicated_merged_weights(
-        self,
-        *,
-        lora_path: str,
-        step: int,
-    ) -> None:
-        self._raise_if_child_failed()
-        await self._ensure_megatron_running()
-        await self._init_merged_weight_transfer()
-        self._clear_pending_jobs()
-        job_path, log_path = self._create_megatron_job_paths()
-        job = MegatronSyncJob(
-            lora_path=lora_path,
-            allow_unvalidated_arch=self._allow_unvalidated_arch,
-            merged_weight_transfer=self._build_merged_weight_transfer_spec(step),
-            log_path=log_path,
-        )
-        write_megatron_job(job, job_path=job_path)
-        async for _ in stream_megatron_job(
-            job,
-            job_path=job_path,
-            process=self._megatron_process,
-            process_log_path=self._megatron_log_path,
-        ):
-            pass
-        self._latest_step = step
-
-    async def _sleep_runtime(self) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        self._status("Sleeping vLLM runtime to free GPU memory for training")
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self._vllm_base_url}/sleep",
-                params={"level": 1, "mode": "wait"},
-                **self._runtime_request_kwargs(),
-                timeout=300.0,
-            )
-            response.raise_for_status()
-        self._is_sleeping = True
-        self._status("vLLM runtime is sleeping")
-
-    async def _wake_runtime(self) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        self._status("Waking vLLM runtime")
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self._vllm_base_url}/wake_up",
-                **self._runtime_request_kwargs(),
-                timeout=300.0,
-            )
-            response.raise_for_status()
-        self._is_sleeping = False
-        self._status("vLLM runtime is awake")
-
-    async def register_lora_for_step(self, step: int, checkpoint_dir: str) -> None:
-        self._raise_if_child_failed()
-        if self.rollout_weights_mode == "merged":
-            await self._set_served_model_name(step)
-        else:
-            await self._load_rollout_lora_for_step(checkpoint_dir, step)
-        self._latest_step = step
-
-    def _validate_megatron_dependencies(self) -> None:
-        try:
-            from .hybrid_ep_setup import validate_hybrid_ep
-
-            validate_hybrid_ep()
-            importlib.import_module("deep_ep")
-            import megatron.bridge  # type: ignore
-        except ImportError as exc:
-            raise RuntimeError(
-                "Megatron dependencies are not available in the active ART environment. "
-                "Run ART's Megatron setup before starting training."
-            ) from exc
-
-    async def _ensure_megatron_running(self) -> None:
-        """Lazily start Megatron training process if not running."""
-        self._raise_if_child_failed()
-        megatron_topology = self.runtime_config.topology
-        if self._megatron_process is not None:
-            if self._megatron_process.returncode is None:
-                assert self._active_megatron_topology == megatron_topology
-                return
-            self._megatron_process = None
-            self._active_megatron_topology = None
-
-        self._validate_megatron_dependencies()
-
-        train_script = Path(__file__).parent / "train.py"
-        project_root = Path(__file__).resolve().parents[3]
-        env = os.environ.copy()
-        force_te_cutlass_grouped_gemm_env(env)
-        if self.is_dedicated:
-            trainer_gpu_ids = self.config["trainer_gpu_ids"]
-            num_gpus = len(trainer_gpu_ids)
-            env["CUDA_VISIBLE_DEVICES"] = ",".join(
-                str(gpu_id) for gpu_id in trainer_gpu_ids
-            )
-        else:
-            num_gpus = torch.cuda.device_count()
-        jobs_dir, _training_log_dir, wake_lock_path = self._megatron_runtime_paths()
-        env["MODEL_IDENTIFIER"] = self.base_model
-        if self._allow_unvalidated_arch:
-            env["ART_MEGATRON_ALLOW_UNVALIDATED_ARCH"] = "1"
-        if self._model_uses_expert_replay():
-            env["ART_MEGATRON_ENABLE_MOE_ROUTING_REPLAY"] = "1"
-        env["ART_MEGATRON_JOBS_DIR"] = jobs_dir
-        env["ART_MEGATRON_WAKE_LOCK_PATH"] = wake_lock_path
-        env[OFFLOAD_BETWEEN_JOBS_ENV] = "0" if self.is_dedicated else "1"
-        env["ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD"] = (
-            "1" if self.runtime_config.streaming_weight_offload else "0"
-        )
-        master_addr = env.get("MASTER_ADDR", "127.0.0.1")
-        master_port = str(self._allocate_master_port())
-        env["MASTER_ADDR"] = master_addr
-        env["MASTER_PORT"] = master_port
-        random_state = self._megatron_random_state()
-        if random_state is not None:
-            env["ART_MEGATRON_RANDOM_STATE"] = str(random_state)
-        lora_config = _lora_config_from_model_config(self.config)
-        if (rank := lora_config.get("rank")) is not None:
-            env[MEGATRON_LORA_RANK_ENV] = str(int(rank))
-        if target_modules := lora_config.get("target_modules"):
-            env[MEGATRON_LORA_TARGET_MODULES_ENV] = json.dumps(list(target_modules))
-        for env_name in self._megatron_topology_env_names():
-            env.pop(env_name, None)
-        env.update(self._megatron_topology_env(megatron_topology))
-
-        command = [
-            sys.executable,
-            "-m",
-            "torch.distributed.run",
-            "--master-addr",
-            master_addr,
-            "--master-port",
-            master_port,
-            "--nproc_per_node",
-            str(num_gpus),
-            str(train_script),
-        ]
-        log_dir = Path(self.output_dir) / "logs"
-        log_dir.mkdir(parents=True, exist_ok=True)
-        megatron_log_path = str(log_dir / "megatron-runtime.log")
-        self._megatron_log_path = megatron_log_path
-        self._megatron_log_file = open(
-            megatron_log_path,
-            "w",
-            buffering=1,
-        )
-        self._status(
-            f"Starting Megatron worker on {num_gpus} GPU(s). "
-            f"Logs: {self._display_path(megatron_log_path)}"
-        )
-        self._megatron_process = subprocess.Popen(
-            managed_process_cmd(command),
-            cwd=str(project_root),
-            env=env,
-            stdout=self._megatron_log_file,
-            stderr=self._megatron_log_file,
-            start_new_session=True,
-        )
-        self._install_parent_signal_cleanup()
-        self._child_processes.watch_popen(
-            "Megatron worker",
-            self._megatron_process,
-            log_path=megatron_log_path,
-        )
-        self._active_megatron_topology = megatron_topology
-        self._status("Megatron worker is initializing")
-
-    def _clear_pending_jobs(self) -> None:
-        jobs_dir, _training_log_dir, _wake_lock_path = self._megatron_runtime_paths()
-        os.makedirs(jobs_dir, exist_ok=True)
-        for job_name in os.listdir(jobs_dir):
-            if job_name.endswith(".json"):
-                os.remove(os.path.join(jobs_dir, job_name))
-
-    def _create_megatron_job_paths(self) -> tuple[str, str]:
-        jobs_dir, training_log_dir, _wake_lock_path = self._megatron_runtime_paths()
-        return create_megatron_job_paths(
-            jobs_dir=jobs_dir,
-            training_log_dir=training_log_dir,
-        )
-
-    def _resolve_training_lora_path(self) -> str:
-        return self._resolve_current_lora_path()
-
-    async def _prepare_for_training(self) -> str:
-        self._raise_if_child_failed()
-        self._validate_megatron_dependencies()
-        # Shared-GPU Megatron must start after vLLM has released GPU memory.
-        await self._sleep_runtime()
-        await self._ensure_megatron_running()
-
-        lora_path = self._resolve_training_lora_path()
-        self._clear_pending_jobs()
-        return lora_path
-
-    def _publish_staged_training_checkpoint(
-        self,
-        *,
-        staging_lora_path: str,
-        step: int,
-    ) -> str:
-        self._ensure_lora_adapter_config(staging_lora_path)
-        checkpoint_dir = get_step_checkpoint_dir(self.output_dir, step)
-        if os.path.exists(checkpoint_dir):
-            raise RuntimeError(
-                f"Refusing to publish Megatron checkpoint over existing directory: "
-                f"{checkpoint_dir}"
-            )
-        self._status(
-            f"Publishing training checkpoint {step} "
-            f"to {self._display_path(checkpoint_dir)}"
-        )
-        Path(checkpoint_dir).parent.mkdir(parents=True, exist_ok=True)
-        Path(staging_lora_path).rename(checkpoint_dir)
-        return checkpoint_dir
-
-    def _commit_optimizer_checkpoint(self, *, step: int, world_size: int) -> None:
-        checkpoint_dir = Path(get_step_checkpoint_dir(self.output_dir, step))
-        if not checkpoint_dir.is_dir():
-            raise RuntimeError(
-                f"Cannot commit optimizer step {step} before its LoRA checkpoint"
-            )
-        path = self._get_optimizer_state_path()
-        commit_optimizer_generation(
-            path,
-            step=step,
-            world_size=world_size,
-            files=optimizer_generation_files(step, world_size),
-        )
-
-    @staticmethod
-    def _optimizer_ready_world_size(
-        result: dict[str, Any], *, expected_step: int
-    ) -> int | None:
-        if result.get("event") != OPTIMIZER_READY_EVENT:
-            return None
-        step = int(result.get("step", -1))
-        world_size = int(result.get("world_size", 0))
-        if step != expected_step or world_size < 1:
-            raise RuntimeError(f"Invalid optimizer-ready event: {result!r}")
-        return world_size
-
-    async def _wake_and_reload_training_checkpoint(
-        self,
-        *,
-        checkpoint_dir: str,
-        step: int,
-    ) -> None:
-        _jobs_dir, _training_log_dir, wake_lock_path = self._megatron_runtime_paths()
-        try:
-            with open(wake_lock_path, "w") as lock_file:
-                lock_file.write("waking vllm\n")
-            await self._wake_runtime()
-        finally:
-            if os.path.exists(wake_lock_path):
-                os.remove(wake_lock_path)
-
-        await self._load_rollout_lora_for_step(checkpoint_dir, step)
-        self._status(f"Loaded checkpoint {step} into vLLM")
-
-    async def _handle_training_lora_ready(
-        self,
-        *,
-        checkpoint_dir: str | None,
-        staging_lora_path: str,
-        step: int,
-    ) -> str:
-        if checkpoint_dir is None:
-            checkpoint_dir = self._publish_staged_training_checkpoint(
-                staging_lora_path=staging_lora_path,
-                step=step,
-            )
-        if self.is_dedicated and self.rollout_weights_mode == "lora":
-            await self._load_rollout_lora_for_step(checkpoint_dir, step)
-            self._status(f"Loaded checkpoint {step} into vLLM")
-        return checkpoint_dir
-
-    async def _finish_training_checkpoint(
-        self,
-        *,
-        checkpoint_dir: str | None,
-        staging_lora_path: str,
-        step: int,
-    ) -> str:
-        if checkpoint_dir is None:
-            checkpoint_dir = self._publish_staged_training_checkpoint(
-                staging_lora_path=staging_lora_path,
-                step=step,
-            )
-        if self.rollout_weights_mode == "merged":
-            self._latest_step = step
-        elif self.is_dedicated:
-            if self._latest_step != step:
-                await self._load_rollout_lora_for_step(checkpoint_dir, step)
-                self._status(f"Loaded checkpoint {step} into vLLM")
-        else:
-            await self._wake_and_reload_training_checkpoint(
-                checkpoint_dir=checkpoint_dir,
-                step=step,
-            )
-        return checkpoint_dir
-
-    async def start_openai_server(
-        self, config: dev.OpenAIServerConfig | None
-    ) -> tuple[str, int]:
-        self._raise_if_child_failed()
-        lora_path = self._resolve_active_lora_path()
-        external_runtime = get_external_vllm_runtime_config(self.config)
-
-        if not self.is_dedicated and not self._sleep_mode_enabled():
-            raise ValueError(
-                "Shared-GPU mode requires engine_args.enable_sleep_mode=True "
-                "for the external vLLM runtime"
-            )
-
-        if external_runtime is not None:
-            if self.rollout_weights_mode != "lora":
-                raise RuntimeError(
-                    "External vLLM runtime requires LoRA rollout weights"
-                )
-            await wait_for_vllm_http_runtime(
-                base_url=self._vllm_base_url,
-                timeout=external_runtime.health_timeout_s,
-                headers=self._runtime_headers(),
-            )
-            try:
-                await self._discover_serving_capabilities(external=True)
-                await self._load_rollout_lora_for_step(lora_path, self._latest_step)
-                self._loaded_adapter_steps.add(self._latest_step)
-            except BaseException as exc:
-                await cleanup_after_failure(
-                    exc,
-                    self.aclose,
-                    message="vLLM startup and Megatron cleanup failed.",
-                )
-                raise
-            self._status(f"External vLLM runtime is ready at {self._vllm_base_url}")
-            return self._vllm_host, self._vllm_port
-
-        port = (config or {}).get("server_args", {}).get("port", 8000)
-        location = await self._start_vllm_subprocess(lora_path, port, config)
-        try:
-            await self._discover_serving_capabilities(external=False)
-            if self.rollout_weights_mode == "lora":
-                if self.rollout_weight_update_mode == "in_flight_lora":
-                    await self._update_in_flight_adapter(lora_path, self._latest_step)
-                else:
-                    self._loaded_adapter_steps.add(self._latest_step)
-            if self.rollout_weights_mode == "merged":
-                await self._sync_dedicated_merged_weights(
-                    lora_path=lora_path,
-                    step=self._latest_step,
-                )
-        except BaseException as exc:
-            await cleanup_after_failure(
-                exc,
-                self.aclose,
-                message="vLLM startup and Megatron cleanup failed.",
-            )
-            raise
-        return location
-
-    async def vllm_engine_is_sleeping(self) -> bool:
-        return self._is_sleeping
-
-    async def train(
-        self,
-        disk_packed_tensors: DiskPackedTensors,
-        config: types.TrainConfig,
-        _config: dev.TrainConfig,
-        verbose: bool = False,
-    ) -> AsyncIterator[dict[str, float]]:
-        try:
-            self._raise_if_child_failed()
-            if _config.get("moe_routing_replay_bundle") is not None:
-                raise RuntimeError(
-                    "moe_routing_replay_bundle is only supported for in-process/runtime APIs; "
-                    "MegatronService subprocess jobs must use moe_routing_replay_path."
-                )
-            if self.is_dedicated:
-                await self._ensure_megatron_running()
-                lora_path = self._resolve_active_lora_path()
-                self._clear_pending_jobs()
-                next_step = self._latest_step + 1
-                staging_lora_path = self._prepare_training_lora_dir(
-                    lora_path,
-                    next_step,
-                )
-                job_path, log_path = self._create_megatron_job_paths()
-                if self.rollout_weights_mode == "merged":
-                    await self._init_merged_weight_transfer()
-                    job: MegatronTrainingJob | MegatronMergedTrainingJob = (
-                        MegatronMergedTrainingJob(
-                            step=next_step,
-                            source_policy_step=self._latest_step,
-                            training_session_id=self._training_session_id,
-                            lora_path=staging_lora_path,
-                            allow_unvalidated_arch=self._allow_unvalidated_arch,
-                            optimizer_state_path=self._get_optimizer_state_path(),
-                            disk_packed_tensors=disk_packed_tensors,
-                            config=config,
-                            experimental_config=cast(dict[str, Any], _config),
-                            moe_routing_replay_path=_config.get(
-                                "moe_routing_replay_path"
-                            ),
-                            moe_routing_replay_strict=_config.get(
-                                "moe_routing_replay_strict",
-                                True,
-                            ),
-                            merged_weight_transfer=self._build_merged_weight_transfer_spec(
-                                next_step
-                            ),
-                            log_path=log_path,
-                        )
-                    )
-                else:
-                    job = MegatronTrainingJob(
-                        step=next_step,
-                        source_policy_step=self._latest_step,
-                        training_session_id=self._training_session_id,
-                        lora_path=staging_lora_path,
-                        allow_unvalidated_arch=self._allow_unvalidated_arch,
-                        optimizer_state_path=self._get_optimizer_state_path(),
-                        disk_packed_tensors=disk_packed_tensors,
-                        config=config,
-                        experimental_config=cast(dict[str, Any], _config),
-                        moe_routing_replay_path=_config.get("moe_routing_replay_path"),
-                        moe_routing_replay_strict=_config.get(
-                            "moe_routing_replay_strict",
-                            True,
-                        ),
-                        log_path=log_path,
-                    )
-                write_megatron_job(job, job_path=job_path)
-                checkpoint_dir: str | None = None
-                optimizer_world_size: int | None = None
-                async for result in stream_megatron_job(
-                    job,
-                    job_path=job_path,
-                    process=self._megatron_process,
-                    process_log_path=self._megatron_log_path,
-                ):
-                    if result.get("event") == LORA_READY_EVENT:
-                        checkpoint_dir = await self._handle_training_lora_ready(
-                            checkpoint_dir=checkpoint_dir,
-                            staging_lora_path=staging_lora_path,
-                            step=next_step,
-                        )
-                        continue
-                    if (
-                        world_size := self._optimizer_ready_world_size(
-                            result, expected_step=next_step
-                        )
-                    ) is not None:
-                        optimizer_world_size = world_size
-                        continue
-                    yield {key: float(value) for key, value in result.items()}
-
-                await self._finish_training_checkpoint(
-                    checkpoint_dir=checkpoint_dir,
-                    staging_lora_path=staging_lora_path,
-                    step=next_step,
-                )
-                if optimizer_world_size is not None:
-                    self._commit_optimizer_checkpoint(
-                        step=next_step, world_size=optimizer_world_size
-                    )
-                return
-
-            lora_path = await self._prepare_for_training()
-            next_step = self._latest_step + 1
-            staging_lora_path = self._prepare_training_lora_dir(
-                lora_path,
-                next_step,
-            )
-            job_path, log_path = self._create_megatron_job_paths()
-            job = MegatronTrainingJob(
-                step=next_step,
-                source_policy_step=self._latest_step,
-                training_session_id=self._training_session_id,
-                lora_path=staging_lora_path,
-                allow_unvalidated_arch=self._allow_unvalidated_arch,
-                optimizer_state_path=self._get_optimizer_state_path(),
-                disk_packed_tensors=disk_packed_tensors,
-                config=config,
-                experimental_config=cast(dict[str, Any], _config),
-                moe_routing_replay_path=_config.get("moe_routing_replay_path"),
-                moe_routing_replay_strict=_config.get(
-                    "moe_routing_replay_strict", True
-                ),
-                log_path=log_path,
-            )
-            write_megatron_job(job, job_path=job_path)
-
-            checkpoint_dir = None
-            optimizer_world_size = None
-            async for result in stream_megatron_job(
-                job,
-                job_path=job_path,
-                process=self._megatron_process,
-                process_log_path=self._megatron_log_path,
-            ):
-                if result.get("event") == LORA_READY_EVENT:
-                    checkpoint_dir = await self._handle_training_lora_ready(
-                        checkpoint_dir=checkpoint_dir,
-                        staging_lora_path=staging_lora_path,
-                        step=next_step,
-                    )
-                    continue
-                if (
-                    world_size := self._optimizer_ready_world_size(
-                        result, expected_step=next_step
-                    )
-                ) is not None:
-                    optimizer_world_size = world_size
-                    continue
-                yield {key: float(value) for key, value in result.items()}
-
-            await self._finish_training_checkpoint(
-                checkpoint_dir=checkpoint_dir,
-                staging_lora_path=staging_lora_path,
-                step=next_step,
-            )
-            if optimizer_world_size is not None:
-                self._commit_optimizer_checkpoint(
-                    step=next_step, world_size=optimizer_world_size
-                )
-        except GeneratorExit:
-            raise
-        except BaseException as exc:
-            self._status(f"Megatron train failed: {type(exc).__name__}: {exc}")
-            await cleanup_after_failure(
-                exc,
-                self.aclose,
-                message="Megatron training and cleanup failed.",
-            )
-            raise
-
-    async def train_sft(
-        self,
-        batches: list[SFTBatch],
-        config: types.TrainSFTConfig,
-        verbose: bool = False,
-    ) -> AsyncIterator[dict[str, float]]:
-        try:
-            self._raise_if_child_failed()
-            if self.is_dedicated:
-                raise NotImplementedError(
-                    "train_sft is not yet supported in dedicated mode"
-                )
-            lora_path = await self._prepare_for_training()
-            next_step = self._latest_step + 1
-            staging_lora_path = self._prepare_training_lora_dir(
-                lora_path,
-                next_step,
-            )
-            serialized_batches = materialize_sft_batches(batches)
-            job_path, log_path = self._create_megatron_job_paths()
-            grad_accumulation_sequences = (
-                config.batch_size if isinstance(config.batch_size, int) else None
-            )
-            job = MegatronSFTTrainingJob(
-                step=next_step,
-                source_policy_step=self._latest_step,
-                training_session_id=self._training_session_id,
-                lora_path=staging_lora_path,
-                allow_unvalidated_arch=self._allow_unvalidated_arch,
-                optimizer_state_path=self._get_optimizer_state_path(),
-                sft_data_dir=serialized_batches.sft_data_dir,
-                num_batches=serialized_batches.num_batches,
-                learning_rates=serialized_batches.learning_rates,
-                grad_accumulation_sequences=grad_accumulation_sequences,
-                log_path=log_path,
-            )
-            write_megatron_job(job, job_path=job_path)
-            self._status(
-                f"Starting Megatron SFT job with {serialized_batches.num_batches} "
-                f"batch(es). First batch may take a few minutes while kernels compile. "
-                f"Training log: {self._display_path(log_path)}"
-            )
-
-            optimizer_world_size = None
-            async for result in stream_megatron_job(
-                job,
-                job_path=job_path,
-                process=self._megatron_process,
-                process_log_path=self._megatron_log_path,
-            ):
-                if (
-                    world_size := self._optimizer_ready_world_size(
-                        result, expected_step=next_step
-                    )
-                ) is not None:
-                    optimizer_world_size = world_size
-                    continue
-                metrics = {
-                    "loss/train": float(result["loss"]),
-                    "loss/learning_rate": float(result["learning_rate"]),
-                    "loss/grad_norm": float(result["grad_norm"]),
-                }
-                if "tokens_per_second" in result:
-                    metrics["throughput/train_packed_tok_per_s"] = float(
-                        result["tokens_per_second"]
-                    )
-                yield metrics
-
-            new_checkpoint_dir = self._publish_staged_training_checkpoint(
-                staging_lora_path=staging_lora_path,
-                step=next_step,
-            )
-            if optimizer_world_size is None:
-                raise RuntimeError("Megatron SFT job did not persist its optimizer")
-            self._commit_optimizer_checkpoint(
-                step=next_step, world_size=optimizer_world_size
-            )
-            await self._wake_and_reload_training_checkpoint(
-                checkpoint_dir=new_checkpoint_dir,
-                step=next_step,
-            )
-        except GeneratorExit:
-            raise
-        except BaseException as exc:
-            self._status(f"Megatron SFT train failed: {type(exc).__name__}: {exc}")
-            await cleanup_after_failure(
-                exc,
-                self.aclose,
-                message="Megatron SFT training and cleanup failed.",
-            )
-            raise
-
-    async def finalize_training_session(self) -> None:
-        path = self._get_optimizer_state_path()
-        commit = read_optimizer_commit(path)
-        if self._megatron_process is None or (
-            commit is not None and commit.step == self._latest_step
-        ):
-            return
-        self._raise_if_child_failed()
-        job_path, log_path = self._create_megatron_job_paths()
-        job = MegatronOptimizerSaveJob(
-            step=self._latest_step,
-            training_session_id=self._training_session_id,
-            optimizer_state_path=path,
-            log_path=log_path,
-        )
-        write_megatron_job(job, job_path=job_path)
-        optimizer_world_size = None
-        async for result in stream_megatron_job(
-            job,
-            job_path=job_path,
-            process=self._megatron_process,
-            process_log_path=self._megatron_log_path,
-        ):
-            if (
-                world_size := self._optimizer_ready_world_size(
-                    result, expected_step=self._latest_step
-                )
-            ) is not None:
-                optimizer_world_size = world_size
-                continue
-            raise RuntimeError(f"Optimizer finalization returned data: {result!r}")
-        if optimizer_world_size is None:
-            raise RuntimeError("Megatron optimizer finalization produced no commit")
-        self._commit_optimizer_checkpoint(
-            step=self._latest_step, world_size=optimizer_world_size
-        )
-
-    async def aclose(self) -> None:
-        self.close()
-
-    def _stop_vllm_subprocess(self) -> None:
-        self._vllm_runtime.close()
-        self._merged_weight_transfer_init_info = None
-        self._loaded_adapter_steps.clear()
-        self._loaded_exact_adapter_steps.clear()
-        self._exact_adapter_refcounts.clear()
-
-    def _stop_megatron_process(self) -> None:
-        if self._megatron_process is None:
-            if self._megatron_log_file is not None:
-                self._megatron_log_file.close()
-                self._megatron_log_file = None
-            self._megatron_log_path = None
-            self._active_megatron_topology = None
-            return
-        terminate_popen_process_group(self._megatron_process)
-        self._megatron_process = None
-        self._active_megatron_topology = None
-        if self._megatron_log_file is not None:
-            self._megatron_log_file.close()
-            self._megatron_log_file = None
-        self._megatron_log_path = None
-
-    def close(self) -> None:
-        if not self._lifecycle.begin_close():
-            return
-        try:
-            self._child_processes.close()
-            self._stop_vllm_subprocess()
-            self._stop_megatron_process()
-            self._clear_wake_lock()
-        finally:
-            self._restore_parent_signal_cleanup()
diff --git a/src/art/megatron/setup.sh b/src/art/megatron/setup.sh
index d5612bc40..5b2a07e39 100755
--- a/src/art/megatron/setup.sh
+++ b/src/art/megatron/setup.sh
@@ -1,41 +1,68 @@
 #!/usr/bin/env bash
 set -euo pipefail
 
-export CUDA_HOME="${CUDA_HOME:-/usr/local/cuda-12.8}"
-# Install missing cuDNN headers, HybridEP RDMA headers, and Ninja build tools.
-missing_packages=()
-for package in libcudnn9-headers-cuda-12 libibverbs-dev ninja-build; do
-    if ! dpkg-query -W "${package}" >/dev/null 2>&1; then
-        missing_packages+=("${package}")
-    fi
+log() {
+    echo "[art-megatron-setup] $*"
+}
+
+fail() {
+    log "$*" >&2
+    exit 1
+}
+
+repo_root="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/../../.." && pwd)"
+cuda_home="${CUDA_HOME:-/usr/local/cuda}"
+[ -x "${cuda_home}/bin/nvcc" ] || fail "CUDA_HOME must contain bin/nvcc: ${cuda_home}"
+for command in gcc g++ ninja nvidia-smi uv; do
+    command -v "${command}" >/dev/null || fail "Supported trainer image is missing ${command}"
 done
 
-if [ "${#missing_packages[@]}" -gt 0 ]; then
-    if [ "$(id -u)" -eq 0 ]; then
-        apt-get update
-        apt-get install -y "${missing_packages[@]}"
-    elif command -v sudo >/dev/null 2>&1 && sudo -n true >/dev/null 2>&1; then
-        sudo apt-get update
-        sudo apt-get install -y "${missing_packages[@]}"
-    else
-        echo "Missing required packages: ${missing_packages[*]}" >&2
-        echo "Install them as root or run with passwordless sudo available." >&2
-        exit 1
-    fi
+cuda_major="$("${cuda_home}/bin/nvcc" --version | sed -n 's/.*release \([0-9][0-9]*\)\..*/\1/p' | head -1)"
+case "${cuda_major}" in
+    12)
+        root_extra=megatron
+        runtime_extra=cuda12
+        ;;
+    13)
+        root_extra=megatron-cu130
+        runtime_extra=cuda13
+        ;;
+    *)
+        fail "Unsupported CUDA major ${cuda_major:-unknown}; expected 12 or 13"
+        ;;
+esac
+
+if [ -z "${TORCH_CUDA_ARCH_LIST:-}" ]; then
+    TORCH_CUDA_ARCH_LIST="$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader,nounits | sort -u | paste -sd ';')"
 fi
+[ -n "${TORCH_CUDA_ARCH_LIST}" ] || fail "Could not determine TORCH_CUDA_ARCH_LIST"
+export CUDA_HOME="${cuda_home}"
+export CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}"
+export TORCH_CUDA_ARCH_LIST
 
-# Python dependencies are declared in pyproject.toml extras. The vLLM runtime
-# lives in its own project and venv under vllm_runtime/.
-script_dir="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
-repo_root="$(cd -- "${script_dir}/../../.." && pwd)"
 cd "${repo_root}"
-uv_bin="uv"
-if [ -x "${HOME}/.local/bin/uv" ]; then
-    uv_bin="${HOME}/.local/bin/uv"
+log "installing root=${root_extra} trainer=${runtime_extra} arch=${TORCH_CUDA_ARCH_LIST}"
+uv sync --extra "${root_extra}" --frozen --inexact
+uv sync \
+    --project megatron_runtime \
+    --extra "${runtime_extra}" \
+    --frozen \
+    --no-dev \
+    --no-install-project \
+    --python "${repo_root}/.venv/bin/python"
+uv pip install \
+    --python "${repo_root}/megatron_runtime/.venv/bin/python" \
+    --no-deps \
+    --editable "${repo_root}"
+
+multinode=0
+if [ "${INSTALL_MULTINODE:-false}" = "true" ]; then
+    multinode=1
 fi
-"${uv_bin}" sync --extra megatron --frozen --active
-"${uv_bin}" run --active --frozen --no-sync python -m art.megatron.hybrid_ep_setup
+HYBRID_EP_MULTINODE="${multinode}" USE_NIXL="${multinode}" \
+    "${repo_root}/megatron_runtime/.venv/bin/python" \
+    -m art.megatron.hybrid_ep_setup
 
 if [ "${INSTALL_VLLM_RUNTIME:-true}" = "true" ]; then
-    "${uv_bin}" sync --project vllm_runtime --frozen --no-dev
+    CUDA_HOME="${cuda_home}" bash vllm_runtime/setup.sh
 fi
diff --git a/src/art/megatron/tensor_snapshot.py b/src/art/megatron/tensor_snapshot.py
new file mode 100644
index 000000000..7b7fc9746
--- /dev/null
+++ b/src/art/megatron/tensor_snapshot.py
@@ -0,0 +1,129 @@
+from __future__ import annotations
+
+from threading import Lock
+from typing import Any, Generic, NamedTuple, TypeVar
+
+import torch
+
+_T = TypeVar("_T")
+
+
+class _CudaFence(NamedTuple):
+    device: int
+    event: torch.cuda.Event
+
+
+class PendingCpuSnapshot(Generic[_T]):
+    def __init__(
+        self,
+        payload: _T,
+        fences: tuple[_CudaFence, ...],
+        sources: tuple[torch.Tensor, ...],
+    ) -> None:
+        self.payload = payload
+        self.fences = fences
+        self._sources = sources
+
+    def resolve(self) -> _T:
+        for fence in self.fences:
+            fence.event.synchronize()
+        self._sources = ()
+        return self.payload
+
+
+class PinnedCpuSnapshotBuilder:
+    def __init__(self, stager: "PinnedCpuSnapshotStager") -> None:
+        self._stager = stager
+        self._devices: set[int] = set()
+        self._sources: list[torch.Tensor] = []
+
+    def stage(self, tensor: torch.Tensor) -> torch.Tensor:
+        source = tensor.detach()
+        if not source.is_cuda:
+            return source.to(device="cpu", copy=True)
+        source = source.contiguous()
+        device = source.device.index
+        if device is None:
+            raise RuntimeError("CUDA snapshot tensor has no device index")
+        stream = self._stager.stream(device)
+        target = self._stager.target_like(source)
+        stream.wait_stream(torch.cuda.current_stream(device))
+        with torch.cuda.stream(stream):
+            target.copy_(source, non_blocking=True)
+            source.record_stream(stream)
+        self._devices.add(device)
+        self._sources.append(source)
+        return target
+
+    def finish(self, payload: _T) -> PendingCpuSnapshot[_T]:
+        fences: list[_CudaFence] = []
+        for device in sorted(self._devices):
+            stream = self._stager.stream(device)
+            with torch.cuda.device(device), torch.cuda.stream(stream):
+                event = torch.cuda.Event(blocking=True)
+                event.record(stream)
+            fences.append(_CudaFence(device, event))
+        return PendingCpuSnapshot(payload, tuple(fences), tuple(self._sources))
+
+
+class PinnedCpuSnapshotStager:
+    def __init__(self, *, reusable: bool = False) -> None:
+        self._streams: dict[int, torch.cuda.Stream] = {}
+        self._buffers: list[torch.Tensor] | None = [] if reusable else None
+        self._next_buffer = 0
+
+    def stream(self, device: int) -> torch.cuda.Stream:
+        stream = self._streams.get(device)
+        if stream is None:
+            with torch.cuda.device(device):
+                stream = torch.cuda.Stream()
+            self._streams[device] = stream
+        return stream
+
+    def reset(self) -> None:
+        self._next_buffer = 0
+
+    def target_like(self, source: torch.Tensor) -> torch.Tensor:
+        if self._buffers is None:
+            return torch.empty_like(source, device="cpu", pin_memory=True)
+        index = self._next_buffer
+        self._next_buffer += 1
+        required = source.nbytes
+        if index == len(self._buffers):
+            self._buffers.append(
+                torch.empty(required, dtype=torch.uint8, device="cpu", pin_memory=True)
+            )
+        elif self._buffers[index].numel() < required:
+            self._buffers[index] = torch.empty(
+                required, dtype=torch.uint8, device="cpu", pin_memory=True
+            )
+        return self._buffers[index][:required].view(source.dtype).view(source.shape)
+
+    def begin(self) -> PinnedCpuSnapshotBuilder:
+        return PinnedCpuSnapshotBuilder(self)
+
+
+class SnapshotReadBarrier:
+    """Lets forward/backward overlap snapshots while fencing optimizer mutation."""
+
+    def __init__(self) -> None:
+        self._lock = Lock()
+        self._fences: list[_CudaFence] = []
+
+    def register(self, snapshot: PendingCpuSnapshot[Any]) -> None:
+        with self._lock:
+            self._fences.extend(snapshot.fences)
+
+    def wait_before_mutation(self) -> None:
+        for fence in self._take():
+            torch.cuda.current_stream(fence.device).wait_event(fence.event)
+
+    def synchronize(self) -> None:
+        for fence in self._take():
+            fence.event.synchronize()
+
+    def _take(self) -> tuple[_CudaFence, ...]:
+        with self._lock:
+            fences = tuple(self._fences)
+            self._fences.clear()
+        return fences
diff --git a/src/art/megatron/train.py b/src/art/megatron/train.py
index 284620115..87330689c 100644
--- a/src/art/megatron/train.py
+++ b/src/art/megatron/train.py
@@ -7,20 +7,24 @@
 install_art_bridge_runtime_patches()
 # isort: on
 
-"""Megatron training runtime and public worker API.
+"""Megatron training runtime and typed executor API.
 
 Public cross-repo API consumed by serverless-training:
 - build_training_runtime
-- run_megatron_worker_loop
+- execute_megatron_rl_job
+- execute_megatron_score_job
+- execute_megatron_sft_job
+- inspect_resident_lora
 """
 
-import json
+from contextlib import contextmanager
+import hashlib
 import math
 import os
 import random
-import shutil
+from threading import Event
 import time
-from typing import Any, Callable, Literal, cast
+from typing import Any, Callable, Iterator, Literal, cast
 
 from megatron.core import parallel_state as ps
 from megatron.core.distributed import DistributedDataParallelConfig
@@ -32,28 +36,27 @@
 
 from art import dev, types
 from art.loss import (
-    Loss,
+    AlignedLossInputs,
     LossInputs,
     LossOffPolicyDiagnosticsAccumulator,
     loss_fn,
     shift_tensor,
 )
 from art.megatron.context_parallel.types import (
-    DispatchedPackedTensors,
     ParallelTopology,
     PreparedMegatronBatch,
+    TrainingStepWorkload,
 )
-from art.megatron.lora import apply_lora_adapters
+from art.megatron.lora import LoRA, apply_lora_adapters
 from art.megatron.megatron_patches import install_fast_frozen_output_backward
 from art.megatron.model_support.lora_disk import (
     load_adapter_config,
     load_lora_tensors_for_megatron,
 )
 from art.megatron.optimizer_state import (
-    commit_optimizer_generation,
-    optimizer_generation_files,
-    read_optimizer_commit,
-    resolve_optimizer_shard_path,
+    ALLOW_UNPAIRED_MEGATRON_RESUME_ENV,
+    _model_runtime_sha256,
+    load_optimizer_state,
 )
 from art.megatron.provider import (
     ProviderBundle,
@@ -63,25 +66,27 @@
 from art.megatron.routing_replay import (
     MoeRoutingReplayBundle,
     MoeRoutingReplayController,
+    build_moe_routing_replay_bundle_from_packed_tensors,
+    prepare_moe_routing_replay_boundaries,
 )
-from art.megatron.runtime.jobs import (
-    DEFAULT_JOBS_DIR,
-    DEFAULT_VLLM_WAKE_LOCK_PATH,
-    LORA_READY_EVENT,
-    OPTIMIZER_READY_EVENT,
-    MegatronJob,
-    MegatronMergedTrainingJob,
-    MegatronOptimizerSaveJob,
-    MegatronSFTTrainingJob,
-    MegatronSyncJob,
-    MegatronTrainingJob,
-    MergedWeightTransferInitInfo,
-    MergedWeightTransferSpec,
-    load_megatron_job,
+from art.megatron.runtime.data_plane import SFTBatchData
+from art.megatron.runtime.specs import (
+    PackedTokenScore,
+    ResidentLoraExport,
+    ResidentLoraInspectionShard,
+    ResidentLoraInspectionSpec,
+    ResidentScoreJobSpec,
+    ResidentScoreShard,
+    SFTJobSpec,
+    TrainJobSpec,
 )
-from art.megatron.training.compile import (
-    configure_training_compile,
+from art.megatron.selective_lm_head import (
+    TokenLossOutput,
+    forward_token_logits,
+    forward_token_losses,
 )
+from art.megatron.tensor_snapshot import SnapshotReadBarrier
+from art.megatron.training.compile import configure_training_compile
 from art.megatron.training.finalize_grads import (
     finalize_model_grads_extended,
     flush_param_grads_to_main_grads,
@@ -90,12 +95,9 @@
     CpBatchLookaheadState,
     PreparedRLMicroInputs,
     PreparedSFTMicroInputs,
-    _causal_attention_state,
     _clone_packed_tensors,
     _clone_sft_tensors,
     _count_sft_trainable_tokens,
-    _count_trainable_tokens,
-    _empty_new_logprobs_from_logits,
     _local_trainable_sft_token_count_tensor,
     _local_trainable_token_count_tensor,
     _next_micro_lookahead,
@@ -108,7 +110,6 @@
     _zero_contribution_inputs,
     _zero_contribution_sft_inputs,
     build_micro_sample_indices,
-    build_micro_sample_indices_by_dp_rank,
     build_rl_hybridep_token_counts,
     build_sft_hybridep_token_counts,
     resolve_global_grad_accumulation_sequences,
@@ -121,37 +122,49 @@
     as_megatron_api_chunks,
     validate_model_chunks,
 )
-from art.megatron.training.sft_batches import load_sft_batch_from_disk
+from art.megatron.training.pipeline_schedule import (
+    MCoreScheduleAdapter,
+    PipelineMicrobatchState,
+    ScheduleMicrobatch,
+    _set_hybridep_token_count,
+    _validate_hybridep_token_counts,
+    chunk_post_process,
+    validate_pipeline_topology,
+)
 from art.megatron.training.trace import (
     attach_trace_token_uids,
     context_parallel_trace_token_uids_enabled,
-    prepare_replay_local_input_token_uids,
-)
-from art.megatron.training.weight_offload import WeightOffloadManager
-from art.megatron.weights.lora_publish import save_vllm_lora_from_model
-from art.megatron.weights.merged_weight_export import (
-    sync_merged_weights_to_vllm,
 )
 from art.metrics_taxonomy import TRAIN_GRADIENT_STEPS_KEY
-from art.preprocessing.pack import (
-    PackedTensors,
-    packed_tensors_from_dir,
-)
+from art.preprocessing.pack import PackedTensors
 
 DEFAULT_MODEL_IDENTIFIER = "Qwen/Qwen3-30B-A3B-Instruct-2507"
 _optimizer_stats_printed = False
+_INTER_FORWARD_BACKWARD_GAP_PREFIX = "time/inter_forward_backward_gap_rank_"
+_INTER_FORWARD_BACKWARD_GPU_GAP_PREFIX = "time/inter_forward_backward_gpu_gap_rank_"
+_INTER_FORWARD_BACKWARD_PHASE_PREFIX = "time/inter_forward_backward_"
 
 __all__ = [
     "DEFAULT_MODEL_IDENTIFIER",
     "TrainingRuntime",
     "build_training_runtime",
-    "run_megatron_worker_loop",
-    "run_megatron_rl_job",
-    "run_megatron_sft_job",
-    "finalize_megatron_job",
+    "execute_megatron_rl_job",
+    "execute_megatron_score_job",
+    "execute_megatron_sft_job",
+    "inspect_resident_lora",
 ]
 
 
+class _InterForwardBackwardTiming(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    metrics_group: Any | None = None
+    previous_schedule_end_s: float | None = None
+    previous_schedule_cuda_end: torch.cuda.Event | None = None
+    previous_job_complete_s: float | None = None
+    current_job_start_s: float | None = None
+
+
 class TrainingRuntime(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -160,19 +173,26 @@ class TrainingRuntime(BaseModel):
     model: ModelChunks
     optimizer: Any | None
     optimizer_config: OptimizerConfig
+    optimizer_runtime_sha256: str | None = None
     optimizer_persistent: bool = True
+    resident_run_id: str | None = None
     resident_training_session_id: str | None = None
-    resident_optimizer_state_path: str | None = None
     resident_policy_step: int | None = None
-    resident_optimizer_dirty: bool = False
+    resident_generation_id: str | None = None
     optimizer_state_loaded: bool = False
     adapter_export_dtypes: dict[str, torch.dtype] | None = None
+    adapter_export_config: dict[str, Any] | None = None
+    snapshot_pool_capacity: int = Field(default=2, ge=1, le=4)
+    optimizer_snapshot_barrier: SnapshotReadBarrier = Field(
+        default_factory=SnapshotReadBarrier
+    )
     transformer_layers_compiled: bool = False
     rank: int
     world_size: int
     moe_routing_replay_controller: MoeRoutingReplayController | None = None
-    merged_weight_transfer_group: Any | None = None
-    merged_weight_transfer_init_info: MergedWeightTransferInitInfo | None = None
+    inter_forward_backward_timing: _InterForwardBackwardTiming = Field(
+        default_factory=_InterForwardBackwardTiming
+    )
 
     @field_validator("model")
     @classmethod
@@ -203,7 +223,9 @@ class TrainStepResult(BaseModel):
     update_successful: bool
     grad_norm: float
     num_zeros_in_grad: int | None
+    workload: TrainingStepWorkload
     loss_metrics: dict[str, float] = Field(default_factory=dict)
+    pipeline_metrics: dict[str, float] = Field(default_factory=dict)
 
 
 def print0(rank: int, *values: Any) -> None:
@@ -363,9 +385,30 @@ def _enable_native_moe_routing_replay(provider: Any) -> None:
     provider.moe_enable_routing_replay = True
 
 
+def _is_bridge_hf_load_hook(hook: Any) -> bool:
+    function = hook
+    seen: set[int] = set()
+    while id(function) not in seen:
+        seen.add(id(function))
+        if getattr(function, "__name__", "") in {
+            "load_weights_hf_to_megatron",
+            "_optimized_load_weights_hf_to_megatron",
+        } or getattr(function, "__qualname__", "").endswith(
+            ".load_weights_hf_to_megatron"
+        ):
+            return True
+        function = getattr(function, "func", None) or getattr(
+            function, "__wrapped__", None
+        )
+        if function is None:
+            return False
+    return False
+
+
 def build_training_runtime(
     *,
     model_identifier: str | None = None,
+    model_initialization: Literal["pretrained", "random"] = "pretrained",
     provider_torch_dtype: torch.dtype = torch.bfloat16,
     provider_bundle_configure: Callable[[ProviderBundle], None] | None = None,
     provider_configure: Callable[[Any], None] | None = None,
@@ -377,6 +420,8 @@ def build_training_runtime(
     build_optimizer: bool = True,
     trainable_parameter_mode: Literal["lora", "base_model"] = "lora",
     allow_unvalidated_arch: bool | None = None,
+    model_support_key: str | None = None,
+    snapshot_pool_capacity: int = 2,
 ) -> TrainingRuntime:
     if random_state := os.environ.get("ART_MEGATRON_RANDOM_STATE"):
         seed = int(random_state)
@@ -389,22 +434,35 @@ def build_training_runtime(
         model_identifier
         or os.environ.get("MODEL_IDENTIFIER", DEFAULT_MODEL_IDENTIFIER),
         torch_dtype=provider_torch_dtype,
+        load_weights=model_initialization == "pretrained",
         allow_unvalidated_arch=(
             os.environ.get("ART_MEGATRON_ALLOW_UNVALIDATED_ARCH", "").strip().lower()
             in {"1", "true", "yes", "on"}
             if allow_unvalidated_arch is None
             else allow_unvalidated_arch
         ),
+        model_support_key=model_support_key,
     )
+    if model_initialization == "random":
+        hooks = list(getattr(provider_bundle.provider, "_pre_wrap_hooks", ()))
+        checkpoint_hooks = [hook for hook in hooks if _is_bridge_hf_load_hook(hook)]
+        if len(checkpoint_hooks) != len(hooks):
+            raise RuntimeError(
+                "random model initialization requires only Bridge checkpoint loaders; "
+                f"found {len(checkpoint_hooks)} loaders among {len(hooks)} hooks"
+            )
+        provider_bundle.provider._pre_wrap_hooks = []
+        provider_bundle.provider.perform_initialization = True
     if provider_bundle_configure is not None:
         provider_bundle_configure(provider_bundle)
     provider = provider_bundle.provider
     if provider_configure is not None:
         provider_configure(provider)
-    if _moe_routing_replay_requested(
+    replay_requested = _moe_routing_replay_requested(
         replay_bundle_path=moe_routing_replay_path,
         replay_bundle=moe_routing_replay_bundle,
-    ):
+    )
+    if replay_requested:
         _enable_native_moe_routing_replay(provider)
     finalize_provider_bundle(provider_bundle)
     _register_trainable_parameter_mode(
@@ -421,7 +479,7 @@ def build_training_runtime(
                 average_in_collective=False,
             ),
             data_parallel_random_init=False,
-            init_model_with_meta_device=True,
+            init_model_with_meta_device=model_initialization == "pretrained",
         ),
     )
 
@@ -431,6 +489,20 @@ def build_training_runtime(
         )
     rank = torch.distributed.get_rank()  # ty: ignore[possibly-missing-attribute]
     world_size = torch.distributed.get_world_size()  # ty: ignore[possibly-missing-attribute]
+    validate_pipeline_topology(
+        world_size=world_size,
+        tp=int(ps.get_tensor_model_parallel_world_size()),
+        cp=int(ps.get_context_parallel_world_size()),
+        pp=int(ps.get_pipeline_model_parallel_world_size()),
+        ep=int(ps.get_expert_model_parallel_world_size()),
+        etp=int(ps.get_expert_tensor_parallel_world_size()),
+        vp=int(ps.get_virtual_pipeline_model_parallel_world_size() or 1),
+        num_layers=(
+            None
+            if provider.pipeline_model_parallel_layout is not None
+            else int(provider.num_layers)
+        ),
+    )
 
     if rank == 0 and print_env:
         print("TORCHINDUCTOR_CACHE_DIR:", os.environ["TORCHINDUCTOR_CACHE_DIR"])
@@ -438,6 +510,8 @@ def build_training_runtime(
         print("TRITON_CACHE_DIR:", os.environ["TRITON_CACHE_DIR"])
 
     provider_bundle.handler.install_preprocess_patch(model)
+    if replay_requested:
+        prepare_moe_routing_replay_boundaries(model)
     transformer_layers_compiled = configure_training_compile(
         model=model,
         provider=provider,
@@ -446,6 +520,11 @@ def build_training_runtime(
 
     optimizer_config = optimizer_config or _default_optimizer_config()
     optimizer = _build_optimizer(model, optimizer_config) if build_optimizer else None
+    metrics_group = (
+        torch.distributed.new_group(backend="gloo")  # ty: ignore[possibly-missing-attribute]
+        if world_size > 1
+        else None
+    )
 
     runtime = TrainingRuntime(
         provider_bundle=provider_bundle,
@@ -456,7 +535,12 @@ def build_training_runtime(
         transformer_layers_compiled=transformer_layers_compiled,
         rank=rank,
         world_size=world_size,
+        snapshot_pool_capacity=snapshot_pool_capacity,
+        inter_forward_backward_timing=_InterForwardBackwardTiming(
+            metrics_group=metrics_group
+        ),
     )
+    _model_runtime_sha256(runtime)
     configure_moe_routing_replay(
         runtime,
         replay_bundle_path=moe_routing_replay_path,
@@ -466,103 +550,90 @@ def build_training_runtime(
     return runtime
 
 
-def _poll_next_megatron_job_path(
-    runtime: TrainingRuntime,
-    jobs_dir: str,
-) -> str | None:
-    selected_job: list[str | None] = [None]
-    if runtime.rank == 0:
-        os.makedirs(jobs_dir, exist_ok=True)
-        job_names = sorted(
-            job_name for job_name in os.listdir(jobs_dir) if job_name.endswith(".json")
-        )
-        if job_names:
-            selected_job[0] = os.path.join(jobs_dir, job_names[0])
-    torch.distributed.broadcast_object_list(selected_job, src=0)  # type: ignore[possibly-missing-attribute]
-    return selected_job[0]
-
-
-def run_megatron_worker_loop(
+def execute_megatron_rl_job(
     runtime: TrainingRuntime,
+    job: TrainJobSpec,
+    packed_tensors: PackedTensors,
     *,
-    supports_sft: bool,
-    wait_until_ready: Callable[[], None] | None = None,
-    before_job: Callable[[], None] | None = None,
-    after_job: Callable[[], None] | None = None,
-) -> None:
-    jobs_dir = os.environ.get("ART_MEGATRON_JOBS_DIR", DEFAULT_JOBS_DIR)
-    while True:
-        job_path = _poll_next_megatron_job_path(runtime, jobs_dir)
-        if job_path is None:
-            time.sleep(0.05)
-            continue
-
-        if wait_until_ready is not None:
-            wait_until_ready()
-        if before_job is not None:
-            before_job()
-
-        job = _load_megatron_job(job_path, supports_sft=supports_sft)
-        print0(runtime.rank, "Loaded job from", job_path)
-        print0(runtime.rank, "Job:", job)
-
-        job_completed = False
-        try:
-            _run_megatron_job(runtime, job)
-            job_completed = True
-        finally:
-            if job_completed and after_job is not None:
-                after_job()
-
-        finalize_megatron_job(
-            runtime,
-            job_path=job_path,
-            log_path=job.log_path,
-            cleanup_path=_job_cleanup_path(job),
-        )
-
-
-def run_megatron_rl_job(
-    runtime: TrainingRuntime,
-    job: MegatronTrainingJob | MegatronMergedTrainingJob,
-) -> None:
-    packed_tensors = None
+    progress_sink: Callable[[int, int, dict[str, float]], None],
+    adapter_ready_sink: Callable[[], None] | None,
+    snapshot_sink: Callable[
+        [TrainJobSpec, dict[str, torch.dtype], dict[str, Any], bool],
+        dict[str, float],
+    ]
+    | None = None,
+    cancelled: Event | None = None,
+    replay_bundle: MoeRoutingReplayBundle | None = None,
+) -> dict[str, float]:
+    """Execute one current RL update from an in-memory packed batch."""
+    job_prepare_started = time.perf_counter()
     adapter_dtypes = None
     template = None
     zero_template = None
     ref_logprobs_by_index = None
     cp_lookahead_state = None
+    inter_schedule_metrics: dict[str, float] = {}
     next_step_first_micro = None
     next_step_first_ref_logprobs = None
     step_result = None
     job_succeeded = False
+    final_metrics: dict[str, float] = {}
 
     try:
+        global_grad_accumulation_sequences = resolve_global_grad_accumulation_sequences(
+            job.config.grad_accumulation_sequences
+        )
+        replay_finalize_started = time.perf_counter()
+        if (
+            replay_bundle is None
+            and packed_tensors.get("moe_routing_replay") is not None
+        ):
+            replay_bundle = build_moe_routing_replay_bundle_from_packed_tensors(
+                packed_tensors=packed_tensors,
+                global_grad_accumulation_sequences=global_grad_accumulation_sequences,
+            )
         configure_moe_routing_replay(
             runtime,
-            replay_bundle_path=job.moe_routing_replay_path,
-            strict=job.moe_routing_replay_strict,
+            replay_bundle=replay_bundle,
+            strict=_moe_replay_strict(job),
         )
+        replay_finalize_s = time.perf_counter() - replay_finalize_started
         adapter_dtypes = _prepare_rl_training_state(runtime, job)
 
-        print0(
-            runtime.rank,
-            "Loading packed tensors from",
-            job.disk_packed_tensors["dir"],
-        )
-        packed_tensors = packed_tensors_from_dir(**job.disk_packed_tensors)
         template = _clone_packed_tensors(select_indexed_inputs(packed_tensors, 0))
         zero_template = _zero_contribution_inputs(template)
-        num_sequences = job.disk_packed_tensors["num_sequences"]
-        global_grad_accumulation_sequences = resolve_global_grad_accumulation_sequences(
-            job.config.grad_accumulation_sequences
-        )
+        num_sequences, packed_sequence_length = map(int, packed_tensors["tokens"].shape)
         num_steps = math.ceil(num_sequences / global_grad_accumulation_sequences)
         topology = _infer_parallel_topology(runtime.model)
+        has_local_loss_stage = any(chunk_post_process(chunk) for chunk in runtime.model)
+        hybridep_token_counts_by_step = (
+            [
+                build_rl_hybridep_token_counts(
+                    packed_tensors=packed_tensors,
+                    step_index=step_index,
+                    num_sequences=num_sequences,
+                    global_grad_accumulation_sequences=global_grad_accumulation_sequences,
+                    topology=topology,
+                    provider=runtime.provider,
+                    model_support_handler=runtime.model_support_handler,
+                )
+                for step_index in range(num_steps)
+            ]
+            if ps.get_expert_model_parallel_world_size() > 1
+            else None
+        )
         _ensure_hybridep_capacity(
             runtime,
-            packed_sequence_length=job.disk_packed_tensors["sequence_length"],
+            packed_sequence_length=packed_sequence_length,
             context_parallel_size=topology.cp,
+            required_capacity=max(
+                (
+                    count
+                    for step_counts in hybridep_token_counts_by_step or ()
+                    for count in step_counts
+                ),
+                default=0,
+            ),
         )
         ref_logprobs_by_index = _prepare_kl_reference_logprobs(
             runtime=runtime,
@@ -573,19 +644,17 @@ def run_megatron_rl_job(
             global_grad_accumulation_sequences=global_grad_accumulation_sequences,
         )
         cp_lookahead_state = CpBatchLookaheadState() if int(topology.cp) > 1 else None
+        job_prepare_s = time.perf_counter() - job_prepare_started
         for step_index in range(num_steps):
+            step_input_prepare_started = time.perf_counter()
+            if cancelled is not None and cancelled.is_set():
+                from art.megatron.runtime.trainer_run import TrainingCancelledError
+
+                raise TrainingCancelledError("train job was cancelled")
             hybridep_token_counts = (
-                build_rl_hybridep_token_counts(
-                    packed_tensors=packed_tensors,
-                    step_index=step_index,
-                    num_sequences=num_sequences,
-                    global_grad_accumulation_sequences=global_grad_accumulation_sequences,
-                    topology=topology,
-                    provider=runtime.provider,
-                    model_support_handler=runtime.model_support_handler,
-                )
-                if ps.get_expert_model_parallel_world_size() > 1
-                else None
+                None
+                if hybridep_token_counts_by_step is None
+                else hybridep_token_counts_by_step[step_index]
             )
             micro_indices = build_micro_sample_indices(
                 step_index=step_index,
@@ -603,7 +672,7 @@ def run_megatron_rl_job(
                     micro_indices,
                     zero_template,
                 )
-                if ref_logprobs_by_index is not None
+                if ref_logprobs_by_index is not None and has_local_loss_stage
                 else None
             )
             next_step_first_micro = (
@@ -627,9 +696,14 @@ def run_megatron_rl_job(
                     num_sequences=num_sequences,
                     global_grad_accumulation_sequences=global_grad_accumulation_sequences,
                 )
-                if cp_lookahead_state is not None and ref_logprobs_by_index is not None
+                if (
+                    cp_lookahead_state is not None
+                    and ref_logprobs_by_index is not None
+                    and has_local_loss_stage
+                )
                 else None
             )
+            step_input_prepare_s = time.perf_counter() - step_input_prepare_started
             train_step_started = time.perf_counter()
             step_result = run_training_step(
                 model_chunks=runtime.model,
@@ -639,7 +713,7 @@ def run_megatron_rl_job(
                 learning_rate=job.config.learning_rate,
                 inputs=micro_inputs,
                 config=job.config,
-                experimental_config=cast(dev.TrainConfig, job.experimental_config),
+                experimental_config=_experimental_train_config(job),
                 ref_logprobs=ref_logprobs,
                 step_index=step_index,
                 sample_index=micro_indices,
@@ -648,54 +722,88 @@ def run_megatron_rl_job(
                 next_step_first_micro=next_step_first_micro,
                 next_step_first_ref_logprobs=next_step_first_ref_logprobs,
                 hybridep_token_counts=hybridep_token_counts,
+                before_optimizer_step=(
+                    runtime.optimizer_snapshot_barrier.wait_before_mutation
+                ),
+                inter_forward_backward_timing=(runtime.inter_forward_backward_timing),
             )
             train_step_s = time.perf_counter() - train_step_started
-            global_packed_train_tokens = _global_packed_train_tokens(
-                packed_tensors,
-                step_index=step_index,
-                num_sequences=num_sequences,
-                global_grad_accumulation_sequences=global_grad_accumulation_sequences,
-            )
+            result_finalize_started = time.perf_counter()
             print0(
                 runtime.rank,
                 "Correlation between old and new probabilities:",
                 step_result.probs_corr,
             )
             _validate_train_step_result_finite(runtime, step_result)
-            _log_rl_step_result(
-                runtime.rank,
-                job.log_path,
+            final_metrics = _rl_step_metrics(
                 step_result,
                 num_gradient_steps=num_steps,
-                packed_train_tokens=global_packed_train_tokens,
                 train_step_s=train_step_s,
             )
+            if step_index == 0:
+                inter_schedule_metrics = {
+                    name: value
+                    for name, value in step_result.pipeline_metrics.items()
+                    if name.startswith(
+                        (
+                            _INTER_FORWARD_BACKWARD_GAP_PREFIX,
+                            _INTER_FORWARD_BACKWARD_GPU_GAP_PREFIX,
+                        )
+                    )
+                }
+            final_metrics.update(inter_schedule_metrics)
+            final_metrics["time/replay_finalize_s"] = replay_finalize_s
+            final_metrics["time/step_input_prepare_s"] = step_input_prepare_s
+            final_metrics["time/step_result_finalize_s"] = (
+                time.perf_counter() - result_finalize_started
+            )
+            if runtime.rank == 0:
+                progress_started = time.perf_counter()
+                progress_sink(step_index, num_steps, final_metrics)
+                final_metrics["time/step_progress_emit_s"] = (
+                    time.perf_counter() - progress_started
+                )
 
-        runtime.resident_optimizer_dirty = True
-        _save_lora_and_optimizer(
-            runtime,
-            adapter_dtypes=adapter_dtypes,
-            lora_path=job.lora_path,
-            optimizer_state_path=job.optimizer_state_path,
-            step=job.step,
-            optimizer_save_interval=job.config.optimizer_save_interval,
-            lora_ready_log_path=(
-                None if isinstance(job, MegatronMergedTrainingJob) else job.log_path
-            ),
-            optimizer_ready_log_path=job.log_path,
+        if cancelled is not None and cancelled.is_set():
+            from art.megatron.runtime.trainer_run import TrainingCancelledError
+
+            raise TrainingCancelledError("train job was cancelled")
+
+        if snapshot_sink is None or adapter_ready_sink is None:
+            raise RuntimeError("Typed training requires a snapshot publisher")
+        if runtime.adapter_export_config is None:
+            raise RuntimeError("Trainer has no resident adapter export config")
+        final_metrics.update(
+            snapshot_sink(
+                job,
+                adapter_dtypes,
+                runtime.adapter_export_config,
+                _should_snapshot_optimizer(
+                    runtime,
+                    step=job.step,
+                    optimizer_save_interval=job.config.optimizer_save_interval,
+                    final_training_step=job.config.final_training_step,
+                ),
+            )
         )
-        runtime.resident_training_session_id = job.training_session_id
-        runtime.resident_optimizer_state_path = os.path.realpath(
-            job.optimizer_state_path
+        final_metrics["time/job_prepare_s"] = job_prepare_s
+        adapter_ready_started = time.perf_counter()
+        adapter_ready_sink()
+        final_metrics["time/step_adapter_ready_emit_s"] = (
+            time.perf_counter() - adapter_ready_started
         )
+        runtime.resident_training_session_id = job.training_session_id
         runtime.resident_policy_step = job.step
+        runtime.resident_generation_id = job.output_generation_id
         runtime.optimizer_state_loaded = True
         job_succeeded = True
+        return final_metrics
     finally:
         if not job_succeeded:
-            _clear_resident_optimizer(runtime)
-        if packed_tensors is not None:
-            del packed_tensors
+            runtime.resident_training_session_id = None
+            runtime.resident_policy_step = None
+            runtime.resident_generation_id = None
+            runtime.optimizer_state_loaded = False
         if adapter_dtypes is not None:
             del adapter_dtypes
         if template is not None:
@@ -717,70 +825,59 @@ def run_megatron_rl_job(
             del cp_lookahead_state
 
 
-def run_megatron_sft_job(
+def execute_megatron_sft_job(
     runtime: TrainingRuntime,
-    job: MegatronSFTTrainingJob,
-) -> None:
+    job: SFTJobSpec,
+    batches: tuple[SFTBatchData, ...],
+    *,
+    progress_sink: Callable[[int, int, dict[str, float]], None],
+    adapter_ready_sink: Callable[[], None],
+    snapshot_sink: Callable[
+        [SFTJobSpec, dict[str, Any], dict[str, Any], bool], dict[str, float]
+    ]
+    | None = None,
+    cancelled: Event | None = None,
+) -> dict[str, float]:
+    """Execute SFT from in-memory batches; callers own transport and events."""
+    if len(batches) != job.num_batches:
+        raise ValueError("SFT job batch count does not match its payload")
     adapter_dtypes = None
-
+    succeeded = False
+    final_metrics: dict[str, float] = {}
     try:
         configure_moe_routing_replay(runtime)
-        adapter_dtypes = _prepare_sft_training_state(runtime, job)
-
+        adapter_dtypes = _prepare_rl_training_state(runtime, job)
+        grad_accumulation_sequences = int(job.config.batch_size)
+        grad_accumulation_sequences = resolve_global_grad_accumulation_sequences(
+            grad_accumulation_sequences
+        )
         assert runtime.optimizer is not None
         runtime.optimizer.config.clip_grad = job.max_grad_norm
         for param_group in runtime.optimizer.param_groups:
             param_group["weight_decay"] = job.weight_decay
+        topology = _infer_parallel_topology(runtime.model)
 
-        grad_accumulation_sequences = resolve_global_grad_accumulation_sequences(
-            job.grad_accumulation_sequences
-        )
-        checkpoint_interval = job.internal_checkpoint_interval
-
-        for batch_idx in range(job.num_batches):
-            batch_start_time = time.perf_counter()
-            batch_dir = os.path.join(job.sft_data_dir, f"batch_{batch_idx:06d}")
-            batch_metadata, trajectory_tensors = load_sft_batch_from_disk(batch_dir)
-            num_trajectories = int(batch_metadata["num_trajectories"])
-            if not trajectory_tensors:
-                raise RuntimeError(f"SFT batch {batch_idx} is empty")
-            if num_trajectories != len(trajectory_tensors):
-                raise RuntimeError(
-                    "SFT batch metadata does not match trajectory count: "
-                    f"{num_trajectories} != {len(trajectory_tensors)}"
-                )
+        for batch_index, batch in enumerate(batches):
+            if cancelled is not None and cancelled.is_set():
+                from art.megatron.runtime.trainer_run import TrainingCancelledError
 
-            global_tokens = max(
-                int(batch_metadata.get("num_tokens", 0)),
-                1,
-            )
-            if "num_tokens" not in batch_metadata:
-                global_tokens = max(
-                    sum(
-                        int(inputs["attention_mask"].sum().item())
-                        for inputs in trajectory_tensors
-                    ),
-                    1,
-                )
-            global_trainable_tokens = max(
-                int(batch_metadata["num_trainable_tokens"]),
-                1,
-            )
+                raise TrainingCancelledError("SFT job was cancelled")
+            started = time.perf_counter()
+            trajectory_tensors = list(batch.trajectory_tensors)
             template = _clone_sft_tensors(trajectory_tensors[0])
             zero_template = _zero_contribution_sft_inputs(template)
-            topology = _infer_parallel_topology(runtime.model)
-            _ensure_hybridep_capacity(
-                runtime,
-                packed_sequence_length=max(
-                    int(inputs["input_ids"].numel()) for inputs in trajectory_tensors
-                ),
-                context_parallel_size=topology.cp,
-            )
+            # Scheduling uses run-global sample IDs while each payload only owns one
+            # batch. Prefix aliases place this window in global index space without
+            # copying tensors, then selected IDs are rebased for local lookup.
+            sample_offset = batch_index * grad_accumulation_sequences
+            scheduled_tensors = [
+                trajectory_tensors[0]
+            ] * sample_offset + trajectory_tensors
             hybridep_token_counts = (
                 build_sft_hybridep_token_counts(
-                    trajectory_tensors=trajectory_tensors,
-                    step_index=0,
-                    global_grad_accumulation_sequences=grad_accumulation_sequences,
+                    trajectory_tensors=scheduled_tensors,
+                    step_index=batch_index,
+                    global_grad_accumulation_sequences=(grad_accumulation_sequences),
                     topology=topology,
                     provider=runtime.provider,
                     model_support_handler=runtime.model_support_handler,
@@ -788,211 +885,177 @@ def run_megatron_sft_job(
                 if ps.get_expert_model_parallel_world_size() > 1
                 else None
             )
-            micro_indices = build_micro_sample_indices(
-                step_index=0,
-                num_sequences=num_trajectories,
-                global_grad_accumulation_sequences=grad_accumulation_sequences,
+            _ensure_hybridep_capacity(
+                runtime,
+                packed_sequence_length=max(
+                    int(inputs["input_ids"].numel()) for inputs in trajectory_tensors
+                ),
+                context_parallel_size=topology.cp,
+                required_capacity=max(hybridep_token_counts or (), default=0),
             )
-            micro_inputs = select_sft_micro_inputs(
-                trajectory_tensors,
-                micro_indices,
-                zero_template,
+            scheduled_indices = build_micro_sample_indices(
+                step_index=batch_index,
+                num_sequences=len(scheduled_tensors),
+                global_grad_accumulation_sequences=grad_accumulation_sequences,
             )
+            micro_indices = [
+                None if index is None else index - sample_offset
+                for index in scheduled_indices
+            ]
             step_result = run_megatron_sft_step(
                 model_chunks=runtime.model,
                 provider=runtime.provider,
                 model_support_handler=runtime.model_support_handler,
                 optimizer=runtime.optimizer,
-                learning_rate=job.learning_rates[batch_idx],
-                inputs=micro_inputs,
-                step_index=batch_idx,
+                learning_rate=batch.learning_rate,
+                inputs=select_sft_micro_inputs(
+                    trajectory_tensors, micro_indices, zero_template
+                ),
+                step_index=batch_index,
                 sample_index=micro_indices,
-                moe_routing_replay_controller=runtime.moe_routing_replay_controller,
+                moe_routing_replay_controller=(runtime.moe_routing_replay_controller),
                 hybridep_token_counts=hybridep_token_counts,
+                before_optimizer_step=(
+                    runtime.optimizer_snapshot_barrier.wait_before_mutation
+                ),
             )
-            runtime.resident_optimizer_dirty = True
-            batch_time = time.perf_counter() - batch_start_time
-            tokens_per_second = global_tokens / batch_time if batch_time > 0 else 0.0
-            completed_batches = batch_idx + 1
-
-            if (
-                checkpoint_interval is not None
-                and completed_batches < job.num_batches
-                and completed_batches % checkpoint_interval == 0
-            ):
-                _save_lora_and_optimizer(
-                    runtime,
-                    adapter_dtypes=adapter_dtypes,
-                    lora_path=job.lora_path,
-                    optimizer_state_path=job.optimizer_state_path,
-                    step=job.step,
-                    optimizer_save_interval=1,
-                    optimizer_ready_log_path=job.log_path,
-                )
-                torch.distributed.barrier()  # type: ignore[possibly-missing-attribute]
-
+            elapsed = time.perf_counter() - started
+            final_metrics = {
+                "loss/train": float(step_result.reduced_loss.item()),
+                "loss/learning_rate": batch.learning_rate,
+                "loss/grad_norm": float(step_result.grad_norm),
+                "throughput/train_executed_tok_equiv_per_s": (
+                    batch.num_tokens / elapsed if elapsed else 0.0
+                ),
+                **step_result.pipeline_metrics,
+            }
             if runtime.rank == 0:
-                with open(job.log_path, "a+", encoding="utf-8") as log_file:
-                    log_msg = json.dumps(
-                        {
-                            "loss": step_result.reduced_loss.item(),
-                            "learning_rate": job.learning_rates[batch_idx],
-                            "grad_norm": float(step_result.grad_norm),
-                            "num_trajectories": float(num_trajectories),
-                            "num_tokens": float(global_tokens),
-                            "num_trainable_tokens": float(global_trainable_tokens),
-                            "tokens_per_second": tokens_per_second,
-                        }
-                    )
-                    print("Logging SFT", log_msg)
-                    log_file.write(log_msg + "\n")
+                progress_sink(batch_index, len(batches), final_metrics)
+            del step_result, template, zero_template
 
-        _save_lora_and_optimizer(
-            runtime,
-            adapter_dtypes=adapter_dtypes,
-            lora_path=job.lora_path,
-            optimizer_state_path=job.optimizer_state_path,
-            step=job.step,
-            optimizer_save_interval=1,
-            optimizer_ready_log_path=job.log_path,
+        if snapshot_sink is None or runtime.adapter_export_config is None:
+            raise RuntimeError("typed SFT requires an immutable snapshot publisher")
+        final_metrics.update(
+            snapshot_sink(job, adapter_dtypes, runtime.adapter_export_config, True)
         )
-        runtime.resident_policy_step = job.step
+        adapter_ready_sink()
+        runtime.resident_training_session_id = job.training_session_id
+        runtime.resident_policy_step = job.learner_version
+        runtime.resident_generation_id = job.output_generation_id
+        runtime.optimizer_state_loaded = True
+        succeeded = True
+        return final_metrics
     finally:
+        if not succeeded:
+            runtime.resident_training_session_id = None
+            runtime.resident_policy_step = None
+            runtime.resident_generation_id = None
+            runtime.optimizer_state_loaded = False
         if adapter_dtypes is not None:
             del adapter_dtypes
 
 
-def _load_megatron_job(job_path: str, *, supports_sft: bool) -> MegatronJob:
-    with open(job_path, "rb") as handle:
-        job = load_megatron_job(handle.read())
-    if isinstance(job, MegatronSFTTrainingJob) and not supports_sft:
-        raise NotImplementedError("SFT jobs are not supported in this worker loop")
-    return job
-
-
-def _run_megatron_job(runtime: TrainingRuntime, job: MegatronJob) -> None:
-    if isinstance(job, MegatronOptimizerSaveJob):
-        _save_resident_optimizer(runtime, job)
-        return
-    if isinstance(job, MegatronSyncJob):
-        adapter_model = _load_adapter_into_model(
-            runtime.model,
-            job.lora_path,
-            runtime.rank,
-            handler=runtime.model_support_handler,
-        )
-        del adapter_model
-        _sync_merged_weights_to_vllm(
-            runtime,
-            job.merged_weight_transfer,
-            lora_path=job.lora_path,
-            pause_generation=False,
-        )
-        return
-    if isinstance(job, MegatronSFTTrainingJob):
-        run_megatron_sft_job(runtime, job)
-        return
-    run_megatron_rl_job(runtime, job)
-    if isinstance(job, MegatronMergedTrainingJob):
-        _sync_merged_weights_to_vllm(
-            runtime,
-            job.merged_weight_transfer,
-            lora_path=job.lora_path,
-            pause_generation=True,
-        )
-
-
-def _job_cleanup_path(job: MegatronJob) -> str | None:
-    if isinstance(job, (MegatronOptimizerSaveJob, MegatronSyncJob)):
-        return None
-    if isinstance(job, MegatronSFTTrainingJob):
-        return job.sft_data_dir
-    return job.disk_packed_tensors["dir"]
-
-
-def _prepare_rl_training_state(
-    runtime: TrainingRuntime,
-    job: MegatronTrainingJob | MegatronMergedTrainingJob,
-) -> dict[str, torch.dtype]:
-    return _prepare_training_state(
-        runtime,
-        training_session_id=job.training_session_id,
-        source_policy_step=job.source_policy_step,
-        lora_path=job.lora_path,
-        optimizer_state_path=job.optimizer_state_path,
+def _experimental_train_config(job: TrainJobSpec) -> dev.TrainConfig:
+    return cast(
+        dev.TrainConfig,
+        job.experimental_config.model_dump(exclude_none=True),
     )
 
 
-def _prepare_sft_training_state(
-    runtime: TrainingRuntime,
-    job: MegatronSFTTrainingJob,
-) -> dict[str, torch.dtype]:
-    return _prepare_training_state(
-        runtime,
-        training_session_id=job.training_session_id,
-        source_policy_step=job.source_policy_step,
-        lora_path=job.lora_path,
-        optimizer_state_path=job.optimizer_state_path,
-    )
+def _moe_replay_strict(job: TrainJobSpec) -> bool:
+    return job.experimental_config.moe_routing_replay_strict
 
 
-def _prepare_training_state(
+def _prepare_rl_training_state(
     runtime: TrainingRuntime,
-    *,
-    training_session_id: str,
-    source_policy_step: int,
-    lora_path: str,
-    optimizer_state_path: str,
+    job: TrainJobSpec | SFTJobSpec,
 ) -> dict[str, torch.dtype]:
-    normalized_path = os.path.realpath(optimizer_state_path)
     state_is_resident = (
         runtime.optimizer_persistent
-        and runtime.resident_training_session_id == training_session_id
-        and runtime.resident_optimizer_state_path == normalized_path
-        and runtime.resident_policy_step == source_policy_step
+        and runtime.resident_training_session_id == job.training_session_id
+        and runtime.resident_policy_step == job.source_policy_step
+        and runtime.resident_generation_id == job.source.generation_id
         and runtime.optimizer_state_loaded
+        and runtime.optimizer is not None
     )
     if state_is_resident:
-        if runtime.adapter_export_dtypes is None:
-            raise RuntimeError("Resident Megatron state has no LoRA export template")
+        if (
+            runtime.adapter_export_dtypes is None
+            or runtime.adapter_export_config is None
+        ):
+            raise RuntimeError("Resident Megatron state has no LoRA export metadata")
         return runtime.adapter_export_dtypes
 
-    _commit_resident_optimizer(runtime)
-    _clear_resident_optimizer(runtime)
-    adapter_model = _load_adapter_into_model(
+    runtime.optimizer_snapshot_barrier.synchronize()
+    replacing_resident_state = runtime.resident_training_session_id is not None
+    runtime.resident_training_session_id = None
+    runtime.resident_policy_step = None
+    runtime.resident_generation_id = None
+    runtime.optimizer_state_loaded = False
+    runtime.adapter_export_config = None
+    if replacing_resident_state or not runtime.optimizer_persistent:
+        runtime.optimizer = None
+
+    _load_adapter_into_model(
         runtime.model,
-        lora_path,
+        job.source_adapter_path,
         runtime.rank,
         handler=runtime.model_support_handler,
+        optimizer=runtime.optimizer,
     )
-    runtime.optimizer = _build_optimizer(runtime.model, runtime.optimizer_config)
+    if runtime.optimizer is None:
+        runtime.optimizer = _build_optimizer(runtime.model, runtime.optimizer_config)
     assert runtime.optimizer is not None
-    optimizer_shard_path = resolve_optimizer_shard_path(
-        optimizer_state_path,
-        rank=runtime.rank,
-        world_size=runtime.world_size,
-        expected_step=source_policy_step,
+
+    _load_optimizer(
+        runtime,
+        optimizer_state_path=job.optimizer_state_path,
+        adapter_path=job.source_adapter_path,
+        adapter_step=job.source_policy_step,
+        allow_missing=(
+            job.source_policy_step == 0
+            or os.environ.get(ALLOW_UNPAIRED_MEGATRON_RESUME_ENV, "").lower()
+            in {"1", "true", "yes"}
+        ),
+    )
+
+    # Serialize the live LoRA dtype instead of perpetuating a source checkpoint's
+    # PEFT-upcast FP32 dtype.
+    runtime.adapter_export_dtypes = {}
+    runtime.adapter_export_config = load_adapter_config(job.source_adapter_path)
+    runtime.resident_training_session_id = job.training_session_id
+    runtime.resident_policy_step = job.source_policy_step
+    runtime.resident_generation_id = job.source.generation_id
+    runtime.optimizer_state_loaded = True
+    return runtime.adapter_export_dtypes
+
+
+def _load_optimizer(
+    runtime: TrainingRuntime,
+    *,
+    optimizer_state_path: str,
+    adapter_path: str,
+    adapter_step: int,
+    allow_missing: bool,
+) -> None:
+    assert runtime.optimizer is not None
+    shard_path = load_optimizer_state(
+        runtime,
+        optimizer_state_path=optimizer_state_path,
+        adapter_path=adapter_path,
+        adapter_step=adapter_step,
+        allow_missing=allow_missing,
+        initialize=_eager_initialize_optimizer_state,
     )
-    if optimizer_shard_path is None:
+    if shard_path is None:
         print0(
             runtime.rank,
-            "No optimizer state found at",
+            "No committed optimizer state found at",
             optimizer_state_path,
-            "- resetting optimizer for new run",
+            "- resetting optimizer for a new lineage",
         )
-        _eager_initialize_optimizer_state(runtime.optimizer)
-    else:
-        print0(runtime.rank, "Loading optimizer state from", optimizer_shard_path)
-        runtime.optimizer.load_state_dict(torch.load(optimizer_shard_path))
-
-    runtime.adapter_export_dtypes = {
-        key: tensor.dtype for key, tensor in adapter_model.items()
-    }
-    runtime.resident_training_session_id = training_session_id
-    runtime.resident_optimizer_state_path = normalized_path
-    runtime.resident_policy_step = source_policy_step
-    runtime.optimizer_state_loaded = True
-    return runtime.adapter_export_dtypes
+        return
+    print0(runtime.rank, "Loading optimizer state from", shard_path)
 
 
 def _load_adapter_into_model(
@@ -1014,43 +1077,6 @@ def _load_adapter_into_model(
     return adapter_model
 
 
-def _save_lora_and_optimizer(
-    runtime: TrainingRuntime,
-    *,
-    adapter_dtypes: dict[str, torch.dtype],
-    lora_path: str,
-    optimizer_state_path: str,
-    step: int,
-    optimizer_save_interval: int,
-    lora_ready_log_path: str | None = None,
-    optimizer_ready_log_path: str | None = None,
-) -> None:
-    assert runtime.optimizer is not None
-    save_vllm_lora_from_model(
-        model=runtime.model,
-        adapter_dtypes=adapter_dtypes,
-        handler=runtime.model_support_handler,
-        adapter_config=load_adapter_config(lora_path),
-        output_dir=lora_path,
-        rank=runtime.rank,
-        world_size=runtime.world_size,
-    )
-    if lora_ready_log_path is not None and runtime.rank == 0:
-        _write_job_event(lora_ready_log_path, LORA_READY_EVENT, step=step)
-    if _should_save_optimizer(
-        runtime,
-        step=step,
-        optimizer_state_path=optimizer_state_path,
-        optimizer_save_interval=optimizer_save_interval,
-    ):
-        _save_optimizer(
-            runtime,
-            optimizer_state_path=optimizer_state_path,
-            step=step,
-            ready_log_path=optimizer_ready_log_path,
-        )
-
-
 def _validate_train_step_result_finite(
     runtime: TrainingRuntime,
     step_result: TrainStepResult,
@@ -1072,205 +1098,63 @@ def _validate_train_step_result_finite(
     )
 
 
-def _should_save_optimizer(
+def _should_snapshot_optimizer(
     runtime: TrainingRuntime,
     *,
     step: int,
-    optimizer_state_path: str,
     optimizer_save_interval: int,
+    final_training_step: int | None,
 ) -> bool:
-    if not runtime.optimizer_persistent or optimizer_save_interval == 1:
-        return True
     return (
-        step <= 1
+        not runtime.optimizer_persistent
+        or optimizer_save_interval == 1
+        or step <= 1
         or step % optimizer_save_interval == 0
-        or read_optimizer_commit(optimizer_state_path) is None
+        or (final_training_step is not None and step >= final_training_step)
     )
 
 
-def _write_job_event(log_path: str, event: str, **payload: int | float | str) -> None:
-    with open(log_path, "a+", encoding="utf-8") as log_file:
-        log_file.write(json.dumps({"event": event, **payload}) + "\n")
-        log_file.flush()
-
-
-def _log_rl_step_result(
-    rank: int,
-    log_path: str,
+def _rl_step_metrics(
     step_result: TrainStepResult,
     *,
     num_gradient_steps: int,
-    packed_train_tokens: int,
     train_step_s: float,
-) -> None:
-    if rank != 0:
-        return
-    with open(log_path, "a+", encoding="utf-8") as log_file:
-        train_packed_tok_per_s = (
-            float(packed_train_tokens) / train_step_s if train_step_s > 0 else 0.0
-        )
-        metrics = {
-            "loss/train": step_result.reduced_loss.item(),
-            "loss/grad_norm": step_result.grad_norm,
-            "loss/probs_corr": step_result.probs_corr,
-            TRAIN_GRADIENT_STEPS_KEY: num_gradient_steps,
-            "data/step_executed_packed_train_tokens": packed_train_tokens,
-            "throughput/train_packed_tok_per_s": train_packed_tok_per_s,
-        }
-        if step_result.kl_policy_ref is not None:
-            metrics["loss/kl_policy_ref"] = step_result.kl_policy_ref
-        metrics.update(step_result.loss_metrics)
-        log_msg = json.dumps(metrics)
-        print("Logging", log_msg)
-        log_file.write(log_msg + "\n")
+) -> dict[str, float]:
+    workload = step_result.workload
+    metrics = {
+        "loss/train": step_result.reduced_loss.item(),
+        "loss/grad_norm": step_result.grad_norm,
+        "loss/probs_corr": step_result.probs_corr,
+        TRAIN_GRADIENT_STEPS_KEY: float(num_gradient_steps),
+        "data/gradient_step_nonpadding_logical_tokens": float(
+            workload.logical_nonpadding_tokens
+        ),
+        "data/gradient_step_loss_bearing_tokens": float(workload.loss_bearing_tokens),
+        "data/gradient_step_executed_token_equivalents": float(
+            workload.executed_token_equivalents
+        ),
+        "data/gradient_step_nominal_schedule_capacity_tokens": float(
+            workload.nominal_schedule_capacity_tokens
+        ),
+        "data/gradient_step_dummy_executed_token_equivalents": float(
+            workload.dummy_executed_token_equivalents
+        ),
+        "data/gradient_step_dummy_schedule_capacity_tokens": float(
+            workload.dummy_schedule_capacity_tokens
+        ),
+        "pipeline/gradient_step_real_microbatches": float(workload.real_microbatches),
+        "pipeline/gradient_step_dummy_microbatches": float(workload.dummy_microbatches),
+        "time/gradient_step_train_s": train_step_s,
+    }
+    if step_result.kl_policy_ref is not None:
+        metrics["loss/kl_policy_ref"] = step_result.kl_policy_ref
+    metrics.update(step_result.loss_metrics)
+    metrics.update(step_result.pipeline_metrics)
+    return metrics
 
 
-def _global_packed_train_tokens(
-    packed_tensors: PackedTensors,
-    *,
-    step_index: int,
-    num_sequences: int,
-    global_grad_accumulation_sequences: int | None,
-) -> int:
-    sample_rows = build_micro_sample_indices_by_dp_rank(
-        step_index=step_index,
-        num_sequences=num_sequences,
-        global_grad_accumulation_sequences=global_grad_accumulation_sequences,
-    )
-    sequence_length = int(packed_tensors["tokens"].shape[1])
-    if ps.get_context_parallel_world_size() <= 1:
-        return sum(len(row) for row in sample_rows) * sequence_length
-    return sum(
-        int(
-            (packed_tensors["group_ids"][0 if index is None else index] != -1)
-            .sum()
-            .item()
-        )
-        for row in sample_rows
-        for index in row
-    )
-
-
-def _save_optimizer(
-    runtime: TrainingRuntime,
-    *,
-    optimizer_state_path: str,
-    step: int,
-    ready_log_path: str | None = None,
-    commit: bool = False,
-) -> None:
-    assert runtime.optimizer is not None
-    files = optimizer_generation_files(step, runtime.world_size)
-    optimizer_shard_path = os.path.join(optimizer_state_path, files[runtime.rank])
-    temporary_path = f"{optimizer_shard_path}.tmp"
-    print("Saving optimizer shard to", optimizer_shard_path)
-    os.makedirs(optimizer_state_path, exist_ok=True)
-    with open(temporary_path, "wb") as handle:
-        torch.save(runtime.optimizer.state_dict(), handle)
-        handle.flush()
-        os.fsync(handle.fileno())
-    os.replace(temporary_path, optimizer_shard_path)
-    directory_fd = os.open(optimizer_state_path, os.O_RDONLY)
-    try:
-        os.fsync(directory_fd)
-    finally:
-        os.close(directory_fd)
-    if torch.distributed.is_initialized():  # ty:ignore[possibly-missing-attribute]
-        torch.distributed.barrier()  # ty:ignore[possibly-missing-attribute]
-    if runtime.rank == 0 and commit:
-        commit_optimizer_generation(
-            optimizer_state_path,
-            step=step,
-            world_size=runtime.world_size,
-            files=files,
-        )
-    if runtime.rank == 0 and ready_log_path is not None:
-        _write_job_event(
-            ready_log_path,
-            OPTIMIZER_READY_EVENT,
-            step=step,
-            world_size=runtime.world_size,
-        )
-    if commit and torch.distributed.is_initialized():  # ty:ignore[possibly-missing-attribute]
-        torch.distributed.barrier()  # ty:ignore[possibly-missing-attribute]
-    runtime.resident_optimizer_dirty = False
-
-
-def _commit_resident_optimizer(runtime: TrainingRuntime) -> None:
-    if not runtime.resident_optimizer_dirty:
-        return
-    if (
-        runtime.optimizer is None
-        or runtime.resident_policy_step is None
-        or runtime.resident_optimizer_state_path is None
-    ):
-        raise RuntimeError("Dirty resident optimizer has incomplete identity")
-    _save_optimizer(
-        runtime,
-        optimizer_state_path=runtime.resident_optimizer_state_path,
-        step=runtime.resident_policy_step,
-        commit=True,
-    )
-
-
-def _clear_resident_optimizer(runtime: TrainingRuntime) -> None:
-    runtime.optimizer = None
-    runtime.resident_training_session_id = None
-    runtime.resident_optimizer_state_path = None
-    runtime.resident_policy_step = None
-    runtime.resident_optimizer_dirty = False
-    runtime.optimizer_state_loaded = False
-    runtime.adapter_export_dtypes = None
-
-
-def _save_resident_optimizer(
-    runtime: TrainingRuntime,
-    job: MegatronOptimizerSaveJob,
-) -> None:
-    expected_path = os.path.realpath(job.optimizer_state_path)
-    identity = (
-        runtime.resident_training_session_id,
-        runtime.resident_optimizer_state_path,
-        runtime.resident_policy_step,
-    )
-    expected = (
-        job.training_session_id,
-        expected_path,
-        job.step,
-    )
-    if identity != expected:
-        raise RuntimeError(
-            f"Cannot finalize non-resident optimizer state: {identity!r} != {expected!r}"
-        )
-    _save_optimizer(
-        runtime,
-        optimizer_state_path=expected_path,
-        step=job.step,
-        ready_log_path=job.log_path,
-    )
-
-
-def finalize_megatron_job(
-    runtime: TrainingRuntime,
-    *,
-    job_path: str | None,
-    log_path: str,
-    cleanup_path: str | None,
-) -> None:
-    torch.distributed.barrier()  # type: ignore[possibly-missing-attribute]
-    if runtime.rank != 0:
-        return
-
-    if job_path is not None and os.path.exists(job_path):
-        os.remove(job_path)
-    if cleanup_path is not None and os.path.exists(cleanup_path):
-        shutil.rmtree(cleanup_path)
-    with open(log_path, "a+", encoding="utf-8") as log_file:
-        log_file.write("all done\n")
-
-
-def _placeholder_attention_mask(device: torch.device) -> torch.Tensor:
-    return torch.zeros((1, 1, 1, 1), dtype=torch.bool, device=device)
+def _placeholder_attention_mask(device: torch.device) -> torch.Tensor:
+    return torch.zeros((1, 1, 1, 1), dtype=torch.bool, device=device)
 
 
 def load_adapter_into_model(
@@ -1308,11 +1192,14 @@ def _optimizer_step(
     *,
     model_support_handler: Any | None = None,
     model_chunks: ModelChunks | None = None,
+    before_step: Callable[[], None] | None = None,
 ) -> tuple[bool, float, int | None]:
     for param_group in optimizer.param_groups:
         param_group["lr"] = learning_rate
     if model_support_handler is not None and model_chunks is not None:
         model_support_handler.zero_internal_padding_grads(model_chunks)
+    if before_step is not None:
+        before_step()
     update_successful, grad_norm, num_zeros_in_grad = cast(
         tuple[bool, float, int | None], optimizer.step()
     )
@@ -1322,18 +1209,32 @@ def _optimizer_step(
     return update_successful, grad_norm, num_zeros_in_grad
 
 
-def _reduce_loss(
-    loss: torch.Tensor,
-    op: Any = torch.distributed.ReduceOp.AVG,  # ty: ignore[possibly-missing-attribute]
+def _reduce_loss_sum(
+    loss_sum: torch.Tensor,
+    token_count: torch.Tensor,
     group: Any | None = None,
 ) -> torch.Tensor:
-    reduced_loss = loss.detach().clone()
+    totals = torch.stack(
+        (loss_sum.detach(), token_count.to(dtype=loss_sum.dtype)),
+    )
     torch.distributed.all_reduce(  # ty: ignore[possibly-missing-attribute]
-        reduced_loss,
-        op=op,
+        totals,
+        op=torch.distributed.ReduceOp.SUM,  # ty: ignore[possibly-missing-attribute]
         group=group,
     )
-    return reduced_loss
+    return totals[0] / totals[1].clamp_min(1.0)
+
+
+def _broadcast_from_pipeline_last(value: Any) -> Any:
+    if ps.get_pipeline_model_parallel_world_size() <= 1:
+        return value
+    objects = [value]
+    torch.distributed.broadcast_object_list(  # ty: ignore[possibly-missing-attribute]
+        objects,
+        src=ps.get_pipeline_model_parallel_last_rank(),
+        group=ps.get_pipeline_model_parallel_group(),
+    )
+    return objects[0]
 
 
 def _unwrap_model_config(model_chunks: ModelChunks) -> Any | None:
@@ -1359,9 +1260,8 @@ def _hybridep_token_capacity(
 ) -> int:
     from art.megatron.context_parallel.types import ContextParallelConfig
 
-    # HybridEP JIT keys include this capacity. A CP tree unit is at most one
-    # mean rank load and is assigned to the least-loaded rank, so twice the
-    # rounded mean is the layout-independent upper bound. Reserve it once.
+    # Reserve the normal near-balanced extent once; cost-aware CP plans can
+    # exceed it, so callers also provide their exact maximum planned extent.
     planner_chunk = ContextParallelConfig().planner_chunk_size
     mean_rank_load = (
         math.ceil(packed_sequence_length / (planner_chunk * context_parallel_size))
@@ -1375,14 +1275,16 @@ def _ensure_hybridep_capacity(
     *,
     packed_sequence_length: int,
     context_parallel_size: int,
+    required_capacity: int = 0,
 ) -> None:
     expert_parallel_size = ps.get_expert_model_parallel_world_size()
     if expert_parallel_size <= 1:
         return
     from megatron.core.transformer.moe import fused_a2a
 
-    token_capacity = _hybridep_token_capacity(
-        packed_sequence_length, context_parallel_size
+    token_capacity = max(
+        _hybridep_token_capacity(packed_sequence_length, context_parallel_size),
+        int(required_capacity),
     )
     current = fused_a2a._hybrid_ep_buffer
     if (
@@ -1408,24 +1310,6 @@ def _ensure_hybridep_capacity(
     )
 
 
-def _set_hybridep_token_count(rows: int) -> None:
-    from megatron.core.transformer.moe import fused_a2a
-
-    buffer = fused_a2a._hybrid_ep_buffer
-    if buffer is None:
-        raise RuntimeError("HybridEP buffer is not initialized")
-    buffer.set_num_tokens_per_rank(rows)
-
-
-def _validate_hybridep_token_counts(values: list[int] | None, micro_count: int) -> bool:
-    enabled = ps.get_expert_model_parallel_world_size() > 1
-    if enabled and (values is None or len(values) != micro_count):
-        raise RuntimeError(
-            "HybridEP requires one planned communication extent per microbatch"
-        )
-    return enabled
-
-
 def select_micro_ref_logprobs(
     ref_logprobs_by_index: dict[int, torch.Tensor],
     sample_indices: list[int | None],
@@ -1492,92 +1376,128 @@ def _select_next_ref_logprobs(
 def _forward_prepared_rl_micro(
     *,
     model_chunks: ModelChunks,
+    model_chunk: MegatronModule | None = None,
     model_support_handler: Any,
     prepared_micro: PreparedRLMicroInputs,
     device: torch.device,
-) -> torch.Tensor:
+) -> TokenLossOutput:
+    model = model_chunks[0] if model_chunk is None else model_chunk
     model_forward_kwargs = dict(
         input_ids=prepared_micro.model_tokens,
         position_ids=prepared_micro.model_input_pos,
         attention_mask=_placeholder_attention_mask(device),
         packed_seq_params=prepared_micro.packed_seq_params,
         **model_support_handler.get_forward_kwargs(
-            model_chunks[0],
+            model,
             attention_bias=prepared_micro.attention_state,
         ),
     )
     with attach_trace_token_uids(model_chunks, prepared_micro.local_token_uids):
-        if int(prepared_micro.model_tokens.numel()) == 0:
-            logits = model_chunks[0](**model_forward_kwargs, labels=None)
-            return _empty_new_logprobs_from_logits(logits, prepared_micro.model_labels)
-        return -model_chunks[0](
-            **model_forward_kwargs,
-            labels=prepared_micro.model_labels,
-        )
+        if chunk_post_process(model):
+            return forward_token_losses(
+                model,
+                labels=prepared_micro.model_labels,
+                selection=prepared_micro.lm_head_selection,
+                forward_kwargs=model_forward_kwargs,
+            )
+        output = model(**model_forward_kwargs, labels=None)
+        if not isinstance(output, torch.Tensor):
+            raise TypeError(
+                f"pipeline model chunk must return a tensor, got {type(output).__name__}"
+            )
+        return TokenLossOutput(token_losses=output)
+
+
+def _install_schedule_finalize(model_chunks: ModelChunks) -> None:
+    seen: set[int] = set()
+    for chunk in model_chunks:
+        config = _unwrap_model_config([chunk])
+        if config is None or id(config) in seen:
+            continue
+        seen.add(id(config))
+        config.finalize_model_grads_func = finalize_model_grads_extended
 
 
 def _zero_logprob_graph_contribution(
     new_logprobs: torch.Tensor,
-    loss_inputs: LossInputs | DispatchedPackedTensors,
+    loss_inputs: LossInputs | AlignedLossInputs,
 ) -> torch.Tensor:
     assistant_mask = loss_inputs.align_inputs().assistant_mask.to(dtype=torch.bool)
     return new_logprobs.masked_fill(~assistant_mask, 0.0).sum() * 0.0
 
 
-def _globalize_context_parallel_logprobs(
+def _globalize_context_parallel_logprob_batch(
     *,
-    local_logprobs: torch.Tensor,
-    attention_state: Any,
+    local_logprobs: list[torch.Tensor],
+    attention_states: list[Any],
     seq_len: int,
-) -> torch.Tensor:
-    rank_plan = getattr(attention_state, "rank_plan", None)
-    cp_group = getattr(attention_state, "cp_group", None)
-    if rank_plan is None or cp_group is None:
-        raise RuntimeError("Context-parallel reference logprobs require a rank plan")
-
-    global_logprobs = local_logprobs.new_zeros((1, seq_len))
-    local_values = local_logprobs.reshape(-1)
-    cursor = 0
-    for range_ in rank_plan.local_row_ranges:
-        if range_ is None:
-            continue
-        size = int(range_.size())
-        if size <= 0:
-            continue
-        global_logprobs[0, int(range_.start) : int(range_.end)] = local_values[
-            cursor : cursor + size
-        ]
-        cursor += size
+) -> list[torch.Tensor]:
+    if len(local_logprobs) != len(attention_states):
+        raise ValueError("Context-parallel logprob/state counts differ")
+    rows: list[torch.Tensor] = []
+    cp_group = None
+    for values, attention_state in zip(local_logprobs, attention_states, strict=True):
+        rank_plan = getattr(attention_state, "rank_plan", None)
+        micro_cp_group = getattr(attention_state, "cp_group", None)
+        if rank_plan is None or micro_cp_group is None:
+            raise RuntimeError(
+                "Context-parallel reference logprobs require a rank plan"
+            )
+        if cp_group is not None and micro_cp_group is not cp_group:
+            raise RuntimeError(
+                "Context-parallel microbatches use different process groups"
+            )
+        cp_group = micro_cp_group
+        row = values.new_zeros((1, seq_len))
+        local_values = values.reshape(-1)
+        cursor = 0
+        for range_ in rank_plan.local_row_ranges:
+            if range_ is None:
+                continue
+            size = int(range_.size())
+            if size <= 0:
+                continue
+            row[0, int(range_.start) : int(range_.end)] = local_values[
+                cursor : cursor + size
+            ]
+            cursor += size
+        if cursor != int(local_values.numel()):
+            raise RuntimeError(
+                "Context-parallel reference-logprob layout did not consume all values: "
+                f"consumed={cursor}, values={local_values.numel()}"
+            )
+        rows.append(row)
 
+    global_logprobs = torch.cat(rows)
     torch.distributed.all_reduce(  # ty: ignore[possibly-missing-attribute]
-        global_logprobs,
-        group=cp_group,
+        global_logprobs, group=cp_group
     )
-    return global_logprobs
+    return list(global_logprobs.split(1))
 
 
 @torch.no_grad()
-def _calculate_megatron_logprobs(
+def _calculate_megatron_logprob_batch(
     *,
     model_chunks: ModelChunks,
     provider: Any,
     model_support_handler: Any,
-    inputs: PackedTensors,
+    inputs: list[PackedTensors],
+    sample_indices: list[int | None],
     moe_routing_replay_controller: MoeRoutingReplayController | None = None,
     step_index: int | None = None,
-    sample_index: int | None = None,
-    hybridep_token_count: int | None = None,
-) -> torch.Tensor:
+    hybridep_token_counts: list[int] | None = None,
+) -> list[torch.Tensor]:
+    if not inputs or len(inputs) != len(sample_indices):
+        raise ValueError("Reference input/sample counts must match and be nonzero")
     if moe_routing_replay_controller is not None:
-        if step_index is None or sample_index is None:
-            raise ValueError(
-                "step_index and sample_index are required for routing replay"
-            )
+        if step_index is None:
+            raise ValueError("step_index is required for routing replay")
         moe_routing_replay_controller.set_step(
             step_index=step_index,
-            sample_index=sample_index,
+            sample_index=(
+                sample_indices[0] if len(sample_indices) == 1 else sample_indices
+            ),
         )
-        moe_routing_replay_controller.begin_micro(sample_index, 0)
 
     device = next(model_chunks[0].parameters()).device
     topology = _infer_parallel_topology(model_chunks)
@@ -1590,45 +1510,670 @@ def _calculate_megatron_logprobs(
         chunk.eval()
     forward_succeeded = False
     try:
-        prepared_micro, _pending_prepared_micro = _prepare_current_rl_micro(
-            inputs,
-            device=device,
-            topology=topology,
-            provider=provider,
-            model_support_handler=model_support_handler,
-            ref_logprobs=None,
-            trace_token_uids=trace_token_uids,
-            pending_prepared_micro=None,
-        )
-        prepare_replay_local_input_token_uids(
-            moe_routing_replay_controller,
-            prepared_micro.local_token_uids,
-            prepared_micro.attention_state,
+        pending_prepared_micro: PreparedMegatronBatch | None = None
+        prepared_micros: list[PreparedRLMicroInputs] = []
+        for order, micro in enumerate(inputs):
+            prepared, pending_prepared_micro = _prepare_current_rl_micro(
+                micro,
+                device=device,
+                topology=topology,
+                provider=provider,
+                model_support_handler=model_support_handler,
+                ref_logprobs=None,
+                trace_token_uids=trace_token_uids,
+                pending_prepared_micro=pending_prepared_micro,
+            )
+            prepared_micros.append(prepared)
+            pending_prepared_micro = _prepare_next_rl_cp_micro(
+                _next_micro_lookahead(inputs, order),
+                device=device,
+                topology=topology,
+                provider=provider,
+                model_support_handler=model_support_handler,
+                trace_token_uids=trace_token_uids,
+                ref_logprobs=None,
+            )
+        microbatch_state = PipelineMicrobatchState(
+            controller=moe_routing_replay_controller,
+            hybridep_token_counts=hybridep_token_counts,
+            microbatch_count=len(prepared_micros),
+            model_activator=model_support_handler.build_pipeline_microbatch_activator(
+                model_chunks
+            ),
         )
-        if _validate_hybridep_token_counts(
-            None if hybridep_token_count is None else [hybridep_token_count], 1
-        ):
-            assert hybridep_token_count is not None
-            _set_hybridep_token_count(hybridep_token_count)
-        logprobs = _forward_prepared_rl_micro(
+        if not ps.model_parallel_is_initialized():
+            # Unit/static callers do not have MCore process groups. Production
+            # reference forwards always take the common schedule path below.
+            if len(prepared_micros) != 1:
+                raise RuntimeError("Static reference forward accepts one microbatch")
+            prepared = prepared_micros[0]
+            microbatch_state.activate(
+                ScheduleMicrobatch(
+                    0, sample_indices[0], prepared, prepared.attention_state
+                ),
+                chunk_index=0,
+            )
+            token_output = forward_token_losses(
+                model_chunks[0],
+                labels=prepared.model_labels,
+                selection=prepared.lm_head_selection,
+                forward_kwargs=dict(
+                    input_ids=prepared.model_tokens,
+                    position_ids=prepared.model_input_pos,
+                    attention_mask=_placeholder_attention_mask(device),
+                    packed_seq_params=prepared.packed_seq_params,
+                    **model_support_handler.get_forward_kwargs(
+                        model_chunks[0], attention_bias=prepared.attention_state
+                    ),
+                ),
+                enabled=False,
+            )
+            forward_succeeded = True
+            return [token_output.restore(-token_output.token_losses).detach().cpu()]
+        schedule = MCoreScheduleAdapter(
             model_chunks=model_chunks,
-            model_support_handler=model_support_handler,
-            prepared_micro=prepared_micro,
-            device=device,
+            prepared_microbatches=prepared_micros,
+            sample_indices=sample_indices,
+            model_inputs=[prepared.model_tokens for prepared in prepared_micros],
+            moe_routing_replay_controller=moe_routing_replay_controller,
+            hybridep_token_counts=hybridep_token_counts,
+            model_activator=model_support_handler.build_pipeline_microbatch_activator(
+                model_chunks
+            ),
+        )
+
+        def forward_step_func(data_iterator: Any, model: MegatronModule, *_args: Any):
+            item = next(data_iterator)
+            token_output = _forward_prepared_rl_micro(
+                model_chunks=model_chunks,
+                model_chunk=model,
+                model_support_handler=model_support_handler,
+                prepared_micro=item.payload,
+                device=device,
+            )
+
+            def collect(output_tensor: torch.Tensor, **_kwargs: Any) -> dict[str, Any]:
+                return {
+                    "order": item.order,
+                    "logprobs": token_output.restore(-output_tensor).detach(),
+                }
+
+            return token_output.token_losses, collect
+
+        forward_outputs = schedule.run(
+            forward_step_func,
+            forward_only=True,
+            collect_non_loss_data=True,
         )
+        if not any(chunk_post_process(chunk) for chunk in model_chunks):
+            forward_succeeded = True
+            return []
+        outputs = cast(list[dict[str, Any]], forward_outputs)
+        if len(outputs) != len(prepared_micros):
+            raise RuntimeError(
+                "Reference pipeline did not return one result per microbatch: "
+                f"expected={len(prepared_micros)}, got={len(outputs)}"
+            )
+        outputs.sort(key=lambda output: int(output["order"]))
+        logprobs = [cast(torch.Tensor, output["logprobs"]) for output in outputs]
         if int(topology.cp) > 1:
-            logprobs = _globalize_context_parallel_logprobs(
+            logprobs = _globalize_context_parallel_logprob_batch(
                 local_logprobs=logprobs,
-                attention_state=prepared_micro.attention_state,
-                seq_len=int(inputs["tokens"].shape[1]),
+                attention_states=[
+                    prepared.attention_state for prepared in prepared_micros
+                ],
+                seq_len=int(inputs[0]["tokens"].shape[1]),
             )
+        host_logprobs = torch.cat(logprobs).detach().cpu()
         forward_succeeded = True
+        return list(host_logprobs.split(1))
     finally:
         for chunk, was_training in zip(model_chunks, previous_training_modes):
             chunk.train(was_training)
         if moe_routing_replay_controller is not None and forward_succeeded:
             moe_routing_replay_controller.finalize_step()
-    return logprobs.detach().cpu()
+
+
+def _update_fingerprint(digest: Any, value: str | bytes) -> None:
+    payload = value.encode() if isinstance(value, str) else value
+    digest.update(len(payload).to_bytes(8, "big"))
+    digest.update(payload)
+
+
+def _update_tensor_fingerprint(digest: Any, name: str, value: Any) -> None:
+    tensor = torch.as_tensor(value).detach().cpu().contiguous()
+    _update_fingerprint(digest, name)
+    _update_fingerprint(digest, str(tuple(tensor.shape)))
+    _update_fingerprint(digest, str(tensor.dtype))
+    _update_fingerprint(digest, tensor.numpy().tobytes())
+
+
+def _packed_batch_fingerprint(packed_tensors: PackedTensors) -> str:
+    digest = hashlib.sha256()
+    for name in ("tokens", "group_ids", "parent_ids", "input_pos", "assistant_mask"):
+        _update_tensor_fingerprint(digest, name, packed_tensors[name])
+    replay = packed_tensors.get("moe_routing_replay")
+    if replay is not None:
+        _update_tensor_fingerprint(digest, "moe_routing_replay", replay.expert_indices)
+    return digest.hexdigest()
+
+
+@contextmanager
+def _preserve_diagnostic_rng(device: torch.device) -> Iterator[None]:
+    python_state = random.getstate()
+    devices = (
+        [device.index if device.index is not None else torch.cuda.current_device()]
+        if device.type == "cuda"
+        else []
+    )
+    try:
+        with torch.random.fork_rng(devices=devices):
+            yield
+    finally:
+        random.setstate(python_state)
+
+
+@contextmanager
+def _temporary_resident_replay(
+    runtime: TrainingRuntime,
+    packed_tensors: PackedTensors,
+    *,
+    global_grad_accumulation_sequences: int,
+) -> Iterator[tuple[MoeRoutingReplayController | None, int]]:
+    packed_replay = packed_tensors.get("moe_routing_replay")
+    if packed_replay is None:
+        if bool(getattr(runtime.model_support_handler, "is_moe", False)):
+            raise RuntimeError("resident MoE scoring requires packed routing replay")
+        yield None, 0
+        return
+
+    bundle = build_moe_routing_replay_bundle_from_packed_tensors(
+        packed_tensors=packed_tensors,
+        global_grad_accumulation_sequences=global_grad_accumulation_sequences,
+    )
+    packed_tokens = int(packed_replay.pack_stats.packed_tokens)
+    previous = runtime.moe_routing_replay_controller
+    if previous is None:
+        configure_moe_routing_replay(runtime, replay_bundle=bundle, strict=True)
+        controller = runtime.moe_routing_replay_controller
+        assert controller is not None
+        try:
+            yield controller, packed_tokens
+        finally:
+            controller.remove_router_patches()
+            runtime.moe_routing_replay_controller = None
+        return
+
+    if getattr(previous, "_active_step_index", None) is not None:
+        raise RuntimeError("resident routing replay is active during score dispatch")
+    previous_bundle = previous.bundle
+    previous_strict = previous.strict
+    previous.update_bundle(bundle=bundle, strict=True)
+    try:
+        yield previous, packed_tokens
+    finally:
+        previous.update_bundle(bundle=previous_bundle, strict=previous_strict)
+
+
+def _vocab_parallel_token_scores(
+    local_logits: torch.Tensor,
+    labels: torch.Tensor,
+    *,
+    top_k: int,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    if local_logits.ndim != 2 or labels.shape != local_logits.shape[:1]:
+        raise ValueError(
+            "resident score logits/labels do not align: "
+            f"logits={tuple(local_logits.shape)} labels={tuple(labels.shape)}"
+        )
+    local_logits = local_logits.float()
+    tp_size = int(ps.get_tensor_model_parallel_world_size())
+    tp_rank = int(ps.get_tensor_model_parallel_rank())
+    group = ps.get_tensor_model_parallel_group(check_initialized=False)
+
+    local_max = local_logits.max(dim=-1).values
+    global_max = local_max.clone()
+    if tp_size > 1:
+        torch.distributed.all_reduce(
+            global_max,
+            op=torch.distributed.ReduceOp.MAX,  # ty: ignore[possibly-missing-attribute]
+            group=group,
+        )
+    local_exp_sum = torch.exp(local_logits - global_max.unsqueeze(1)).sum(dim=-1)
+    global_exp_sum = local_exp_sum.clone()
+    if tp_size > 1:
+        torch.distributed.all_reduce(
+            global_exp_sum,
+            op=torch.distributed.ReduceOp.SUM,  # ty: ignore[possibly-missing-attribute]
+            group=group,
+        )
+    log_z = global_max + torch.log(global_exp_sum)
+
+    local_vocab = int(local_logits.shape[1])
+    vocab_start = tp_rank * local_vocab
+    local_labels = labels - vocab_start
+    owns_target = (labels >= 0) & (local_labels >= 0) & (local_labels < local_vocab)
+    rows = torch.arange(labels.numel(), device=labels.device)
+    target_logits = local_logits[
+        rows, local_labels.clamp(0, local_vocab - 1)
+    ].masked_fill(~owns_target, 0.0)
+    if tp_size > 1:
+        torch.distributed.all_reduce(
+            target_logits,
+            op=torch.distributed.ReduceOp.SUM,  # ty: ignore[possibly-missing-attribute]
+            group=group,
+        )
+
+    local_k = min(top_k, local_vocab)
+    local_values, local_ids = torch.topk(local_logits, k=local_k, dim=-1)
+    local_ids += vocab_start
+    if tp_size > 1:
+        gathered_values = [torch.empty_like(local_values) for _ in range(tp_size)]
+        gathered_ids = [torch.empty_like(local_ids) for _ in range(tp_size)]
+        torch.distributed.all_gather(gathered_values, local_values, group=group)
+        torch.distributed.all_gather(gathered_ids, local_ids, group=group)
+        candidate_values = torch.cat(gathered_values, dim=-1)
+        candidate_ids = torch.cat(gathered_ids, dim=-1)
+    else:
+        candidate_values = local_values
+        candidate_ids = local_ids
+    if int(candidate_values.shape[1]) < top_k:
+        raise ValueError("resident score top_k exceeds the padded vocabulary")
+    top_values, top_offsets = torch.topk(candidate_values, k=top_k, dim=-1)
+    top_ids = candidate_ids.gather(1, top_offsets)
+    return target_logits - log_z, top_values - log_z.unsqueeze(1), top_ids
+
+
+def _local_packed_token_scores(
+    *,
+    local_logits: torch.Tensor,
+    prepared: PreparedRLMicroInputs,
+    sample_index: int | None,
+    top_k: int,
+) -> tuple[PackedTokenScore, ...]:
+    selection = prepared.lm_head_selection
+    labels = selection.select(prepared.model_labels).reshape(-1)
+    token_uids = prepared.local_token_uids
+    if token_uids is None:
+        raise RuntimeError("resident scoring requires packed token UIDs")
+    uid_indices = selection.flat_indices.to(device=token_uids.device)
+    selected_uids = token_uids.reshape(-1).index_select(0, uid_indices)
+    target_logprobs, top_logprobs, top_ids = _vocab_parallel_token_scores(
+        local_logits,
+        labels,
+        top_k=top_k,
+    )
+    if sample_index is None or int(ps.get_tensor_model_parallel_rank()) != 0:
+        return ()
+
+    labels_cpu = labels.detach().cpu()
+    uids_cpu = selected_uids.detach().cpu()
+    target_cpu = target_logprobs.detach().cpu()
+    top_logprobs_cpu = top_logprobs.detach().cpu()
+    top_ids_cpu = top_ids.detach().cpu()
+    scores = []
+    for index in range(int(labels_cpu.numel())):
+        target_token_id = int(labels_cpu[index].item())
+        logit_index = int(uids_cpu[index].item())
+        if target_token_id < 0 or logit_index < 0:
+            continue
+        scores.append(
+            PackedTokenScore(
+                sample_index=sample_index,
+                logit_index=logit_index,
+                target_token_id=target_token_id,
+                target_logprob=float(target_cpu[index].item()),
+                top_token_ids=tuple(
+                    int(value) for value in top_ids_cpu[index].tolist()
+                ),
+                top_logprobs=tuple(
+                    float(value) for value in top_logprobs_cpu[index].tolist()
+                ),
+            )
+        )
+    return tuple(scores)
+
+
+def _forward_prepared_score_micro(
+    *,
+    model_chunks: ModelChunks,
+    model_chunk: MegatronModule,
+    model_support_handler: Any,
+    prepared: PreparedRLMicroInputs,
+    device: torch.device,
+) -> torch.Tensor:
+    forward_kwargs = dict(
+        input_ids=prepared.model_tokens,
+        position_ids=prepared.model_input_pos,
+        attention_mask=_placeholder_attention_mask(device),
+        packed_seq_params=prepared.packed_seq_params,
+        **model_support_handler.get_forward_kwargs(
+            model_chunk,
+            attention_bias=prepared.attention_state,
+        ),
+    )
+    with attach_trace_token_uids(model_chunks, prepared.local_token_uids):
+        if chunk_post_process(model_chunk):
+            return forward_token_logits(
+                model_chunk,
+                selection=prepared.lm_head_selection,
+                forward_kwargs=forward_kwargs,
+            )
+        output = model_chunk(**forward_kwargs, labels=None)
+    if not isinstance(output, torch.Tensor):
+        raise TypeError(
+            f"pipeline model chunk must return a tensor, got {type(output).__name__}"
+        )
+    return output
+
+
+@torch.no_grad()
+def _calculate_megatron_score_batch(
+    *,
+    runtime: TrainingRuntime,
+    inputs: list[PackedTensors],
+    sample_indices: list[int | None],
+    step_index: int,
+    top_k: int,
+    controller: MoeRoutingReplayController | None,
+    hybridep_token_counts: list[int] | None,
+) -> tuple[PackedTokenScore, ...]:
+    if not ps.model_parallel_is_initialized():
+        raise RuntimeError("resident scoring requires initialized model parallelism")
+    if not inputs or len(inputs) != len(sample_indices):
+        raise ValueError("resident score input/sample counts must match and be nonzero")
+    if controller is not None:
+        controller.set_step(step_index=step_index, sample_index=sample_indices)
+
+    model_chunks = runtime.model
+    device = next(model_chunks[0].parameters()).device
+    topology = _infer_parallel_topology(model_chunks)
+    modules = {
+        id(module): module for chunk in model_chunks for module in chunk.modules()
+    }
+    previous_training_modes = {
+        module_id: module.training for module_id, module in modules.items()
+    }
+    for chunk in model_chunks:
+        chunk.eval()
+    forward_succeeded = False
+    try:
+        pending_prepared_micro: PreparedMegatronBatch | None = None
+        prepared_micros: list[PreparedRLMicroInputs] = []
+        for order, micro in enumerate(inputs):
+            prepared, pending_prepared_micro = _prepare_current_rl_micro(
+                micro,
+                device=device,
+                topology=topology,
+                provider=runtime.provider,
+                model_support_handler=runtime.model_support_handler,
+                ref_logprobs=None,
+                trace_token_uids=True,
+                pending_prepared_micro=pending_prepared_micro,
+            )
+            prepared_micros.append(prepared)
+            pending_prepared_micro = _prepare_next_rl_cp_micro(
+                _next_micro_lookahead(inputs, order),
+                device=device,
+                topology=topology,
+                provider=runtime.provider,
+                model_support_handler=runtime.model_support_handler,
+                trace_token_uids=True,
+            )
+        schedule = MCoreScheduleAdapter(
+            model_chunks=model_chunks,
+            prepared_microbatches=prepared_micros,
+            sample_indices=sample_indices,
+            model_inputs=[prepared.model_tokens for prepared in prepared_micros],
+            moe_routing_replay_controller=controller,
+            hybridep_token_counts=hybridep_token_counts,
+            model_activator=runtime.model_support_handler.build_pipeline_microbatch_activator(
+                model_chunks
+            ),
+        )
+
+        def forward_step_func(data_iterator: Any, model: MegatronModule, *_args: Any):
+            item = next(data_iterator)
+            output = _forward_prepared_score_micro(
+                model_chunks=model_chunks,
+                model_chunk=model,
+                model_support_handler=runtime.model_support_handler,
+                prepared=item.payload,
+                device=device,
+            )
+
+            def collect(output_tensor: torch.Tensor, **_kwargs: Any) -> dict[str, Any]:
+                return {
+                    "order": item.order,
+                    "scores": _local_packed_token_scores(
+                        local_logits=output_tensor,
+                        prepared=item.payload,
+                        sample_index=item.sample_index,
+                        top_k=top_k,
+                    ),
+                }
+
+            return output, collect
+
+        forward_outputs = schedule.run(
+            forward_step_func,
+            forward_only=True,
+            collect_non_loss_data=True,
+        )
+        if not any(chunk_post_process(chunk) for chunk in model_chunks):
+            forward_succeeded = True
+            return ()
+        outputs = cast(list[dict[str, Any]], forward_outputs)
+        if len(outputs) != len(prepared_micros):
+            raise RuntimeError(
+                "resident score pipeline did not return every microbatch: "
+                f"expected={len(prepared_micros)}, got={len(outputs)}"
+            )
+        outputs.sort(key=lambda output: int(output["order"]))
+        forward_succeeded = True
+        return tuple(
+            score
+            for output in outputs
+            for score in cast(tuple[PackedTokenScore, ...], output["scores"])
+        )
+    finally:
+        for module_id, module in modules.items():
+            module.training = previous_training_modes[module_id]
+        if controller is not None and forward_succeeded:
+            controller.finalize_step()
+
+
+def execute_megatron_score_job(
+    runtime: TrainingRuntime,
+    job: ResidentScoreJobSpec,
+    packed_tensors: PackedTensors,
+) -> ResidentScoreShard:
+    """Score one packed batch against the exact resident learner without mutation."""
+    global_accumulation = resolve_global_grad_accumulation_sequences(
+        job.global_grad_accumulation_sequences
+    )
+    num_sequences, packed_sequence_length = map(int, packed_tensors["tokens"].shape)
+    num_steps = math.ceil(num_sequences / global_accumulation)
+    topology = _infer_parallel_topology(runtime.model)
+    template = _clone_packed_tensors(select_indexed_inputs(packed_tensors, 0))
+    zero_template = _zero_contribution_inputs(template)
+    hybridep_token_counts_by_step = (
+        [
+            build_rl_hybridep_token_counts(
+                packed_tensors=packed_tensors,
+                step_index=step_index,
+                num_sequences=num_sequences,
+                global_grad_accumulation_sequences=global_accumulation,
+                topology=topology,
+                provider=runtime.provider,
+                model_support_handler=runtime.model_support_handler,
+            )
+            for step_index in range(num_steps)
+        ]
+        if ps.get_expert_model_parallel_world_size() > 1
+        else None
+    )
+    _ensure_hybridep_capacity(
+        runtime,
+        packed_sequence_length=packed_sequence_length,
+        context_parallel_size=topology.cp,
+        required_capacity=max(
+            (
+                count
+                for step_counts in hybridep_token_counts_by_step or ()
+                for count in step_counts
+            ),
+            default=0,
+        ),
+    )
+    device = next(runtime.model[0].parameters()).device
+    scores: list[PackedTokenScore] = []
+    with (
+        runtime.model_support_handler.preserve_pipeline_microbatch_activation(
+            runtime.model
+        ),
+        _preserve_diagnostic_rng(device),
+        _temporary_resident_replay(
+            runtime,
+            packed_tensors,
+            global_grad_accumulation_sequences=global_accumulation,
+        ) as (controller, replay_tokens),
+    ):
+        for step_index in range(num_steps):
+            micro_indices = build_micro_sample_indices(
+                step_index=step_index,
+                num_sequences=num_sequences,
+                global_grad_accumulation_sequences=global_accumulation,
+            )
+            scores.extend(
+                _calculate_megatron_score_batch(
+                    runtime=runtime,
+                    inputs=select_micro_inputs(
+                        packed_tensors, micro_indices, zero_template
+                    ),
+                    sample_indices=micro_indices,
+                    step_index=step_index,
+                    top_k=job.top_k,
+                    controller=controller,
+                    hybridep_token_counts=(
+                        None
+                        if hybridep_token_counts_by_step is None
+                        else hybridep_token_counts_by_step[step_index]
+                    ),
+                )
+            )
+    scores.sort(key=lambda score: (score.sample_index, score.logit_index))
+    expected_score_count = int(packed_tensors["assistant_mask"][:, 1:].sum().item())
+    if expected_score_count < 1:
+        raise ValueError("resident scoring requires at least one assistant target")
+    return ResidentScoreShard(
+        rank=runtime.rank,
+        job_id=job.job_id,
+        run_id=job.run_id,
+        learner=job.learner,
+        batch_id=job.batch.batch_id,
+        batch_fingerprint=_packed_batch_fingerprint(packed_tensors),
+        top_k=job.top_k,
+        expected_score_count=expected_score_count,
+        routing_replay_packed_tokens=replay_tokens,
+        scores=tuple(scores),
+    )
+
+
+@torch.no_grad()
+def inspect_resident_lora(
+    runtime: TrainingRuntime,
+    request: ResidentLoraInspectionSpec,
+) -> ResidentLoraInspectionShard:
+    """Inspect resident LoRA wrappers and export coverage without changing state."""
+    modules: dict[int, LoRA] = {}
+    prefixes: set[str] = set()
+    lora_parameter_ids: set[int] = set()
+    for chunk in runtime.model:
+        for module in chunk.modules():
+            if not isinstance(module, LoRA) or id(module) in modules:
+                continue
+            modules[id(module)] = module
+            prefixes.add(module.adapter_model_prefix)
+            lora_parameter_ids.update(
+                id(parameter) for parameter in module.parameters()
+            )
+
+    trainable_lora_names: set[str] = set()
+    unexpected_trainable_names: set[str] = set()
+    trainable_numel = 0
+    seen_parameters: set[int] = set()
+    for chunk_index, chunk in enumerate(runtime.model):
+        for name, parameter in chunk.named_parameters():
+            parameter_id = id(parameter)
+            if not parameter.requires_grad or parameter_id in seen_parameters:
+                continue
+            seen_parameters.add(parameter_id)
+            qualified_name = f"chunk_{chunk_index}.{name}"
+            trainable_numel += int(parameter.numel())
+            if parameter_id in lora_parameter_ids:
+                trainable_lora_names.add(qualified_name)
+            else:
+                unexpected_trainable_names.add(qualified_name)
+
+    device = next(runtime.model[0].parameters()).device
+    with _preserve_diagnostic_rng(device):
+        exported = runtime.model_support_handler.build_adapter_weights_by_base(
+            runtime.model
+        )
+    exports = tuple(
+        ResidentLoraExport(
+            base_name=base_name,
+            adapter_keys=tuple(
+                sorted(
+                    {getattr(weight, "adapter_key", None) for weight in weights},
+                    key=lambda value: "" if value is None else value,
+                )
+            ),
+        )
+        for base_name, weights in sorted(exported.items())
+    )
+    return ResidentLoraInspectionShard(
+        rank=runtime.rank,
+        request_id=request.request_id,
+        run_id=request.run_id,
+        learner=request.learner,
+        target_modules=request.target_modules,
+        module_count=len(modules),
+        wrapped_adapter_prefixes=tuple(sorted(prefixes)),
+        exports=exports,
+        trainable_lora_parameter_names=tuple(sorted(trainable_lora_names)),
+        unexpected_trainable_parameter_names=tuple(sorted(unexpected_trainable_names)),
+        trainable_numel=trainable_numel,
+    )
+
+
+def _calculate_megatron_logprobs(
+    *,
+    model_chunks: ModelChunks,
+    provider: Any,
+    model_support_handler: Any,
+    inputs: PackedTensors,
+    moe_routing_replay_controller: MoeRoutingReplayController | None = None,
+    step_index: int | None = None,
+    sample_index: int | None = None,
+    hybridep_token_count: int | None = None,
+) -> torch.Tensor:
+    results = _calculate_megatron_logprob_batch(
+        model_chunks=model_chunks,
+        provider=provider,
+        model_support_handler=model_support_handler,
+        inputs=[inputs],
+        sample_indices=[sample_index],
+        moe_routing_replay_controller=moe_routing_replay_controller,
+        step_index=step_index,
+        hybridep_token_counts=(
+            None if hybridep_token_count is None else [hybridep_token_count]
+        ),
+    )
+    if len(results) != 1:
+        raise RuntimeError("Single reference forward did not run on the loss stage")
+    return results[0]
 
 
 def _precompute_reference_logprobs(
@@ -1644,40 +2189,69 @@ def _precompute_reference_logprobs(
         len(sample_step_indices),
         "local sequences",
     )
-    hybridep_by_step: dict[int, list[int]] = {}
     hybridep_enabled = ps.get_expert_model_parallel_world_size() > 1
     topology = _infer_parallel_topology(runtime.model) if hybridep_enabled else None
     results: dict[int, torch.Tensor] = {}
-    for sample_index, step_index in sorted(sample_step_indices.items()):
-        hybridep_token_count = None
+    if not ps.model_parallel_is_initialized():
+        for sample_index, step_index in sorted(sample_step_indices.items()):
+            results[sample_index] = _calculate_megatron_logprobs(
+                model_chunks=runtime.model,
+                provider=runtime.provider,
+                model_support_handler=runtime.model_support_handler,
+                inputs=select_indexed_inputs(packed_tensors, sample_index),
+                moe_routing_replay_controller=runtime.moe_routing_replay_controller,
+                step_index=step_index,
+                sample_index=sample_index,
+                hybridep_token_count=None,
+            )
+        return results
+
+    num_sequences = int(packed_tensors["tokens"].shape[0])
+    zero_template = _zero_contribution_inputs(
+        _clone_packed_tensors(select_indexed_inputs(packed_tensors, 0))
+    )
+    for step_index in sorted(set(sample_step_indices.values())):
+        micro_indices = build_micro_sample_indices(
+            step_index=step_index,
+            num_sequences=num_sequences,
+            global_grad_accumulation_sequences=global_grad_accumulation_sequences,
+        )
+        hybridep_token_counts = None
         if hybridep_enabled:
             assert topology is not None
-            counts = hybridep_by_step.get(step_index)
-            if counts is None:
-                counts = build_rl_hybridep_token_counts(
-                    packed_tensors=packed_tensors,
-                    step_index=step_index,
-                    num_sequences=int(packed_tensors["tokens"].shape[0]),
-                    global_grad_accumulation_sequences=global_grad_accumulation_sequences,
-                    topology=topology,
-                    provider=runtime.provider,
-                    model_support_handler=runtime.model_support_handler,
-                )
-                hybridep_by_step[step_index] = counts
-            micro_order = (
-                sample_index - step_index * global_grad_accumulation_sequences
-            ) // ps.get_data_parallel_world_size()
-            hybridep_token_count = counts[micro_order]
-        results[sample_index] = _calculate_megatron_logprobs(
+            hybridep_token_counts = build_rl_hybridep_token_counts(
+                packed_tensors=packed_tensors,
+                step_index=step_index,
+                num_sequences=num_sequences,
+                global_grad_accumulation_sequences=global_grad_accumulation_sequences,
+                topology=topology,
+                provider=runtime.provider,
+                model_support_handler=runtime.model_support_handler,
+            )
+        outputs = _calculate_megatron_logprob_batch(
             model_chunks=runtime.model,
             provider=runtime.provider,
             model_support_handler=runtime.model_support_handler,
-            inputs=select_indexed_inputs(packed_tensors, sample_index),
+            inputs=select_micro_inputs(packed_tensors, micro_indices, zero_template),
+            sample_indices=micro_indices,
             moe_routing_replay_controller=runtime.moe_routing_replay_controller,
             step_index=step_index,
-            sample_index=sample_index,
-            hybridep_token_count=hybridep_token_count,
+            hybridep_token_counts=hybridep_token_counts,
         )
+        if not outputs:
+            continue
+        for sample_index, output in zip(micro_indices, outputs, strict=True):
+            if sample_index is not None:
+                if sample_step_indices.get(sample_index) != step_index:
+                    raise RuntimeError(
+                        "Reference microbatch does not match its planned training step: "
+                        f"sample={sample_index}, step={step_index}"
+                    )
+                results[sample_index] = output
+    if any(chunk_post_process(chunk) for chunk in runtime.model) and set(
+        results
+    ) != set(sample_step_indices):
+        raise RuntimeError("Reference forward did not materialize every local sample")
     return results
 
 
@@ -1702,7 +2276,7 @@ def _reference_sample_step_indices(
 def _prepare_kl_reference_logprobs(
     *,
     runtime: TrainingRuntime,
-    job: MegatronTrainingJob | MegatronMergedTrainingJob,
+    job: TrainJobSpec,
     packed_tensors: PackedTensors,
     num_sequences: int,
     num_steps: int,
@@ -1711,9 +2285,7 @@ def _prepare_kl_reference_logprobs(
     if job.config.kl_penalty_coef <= 0.0:
         return None
 
-    ref_adapter_path = cast(dev.TrainConfig, job.experimental_config).get(
-        "kl_ref_adapter_path"
-    )
+    ref_adapter_path = _experimental_train_config(job).get("kl_ref_adapter_path")
     if ref_adapter_path is None:
         raise RuntimeError(
             "KL penalty is enabled but no kl_ref_adapter_path was provided. "
@@ -1722,17 +2294,15 @@ def _prepare_kl_reference_logprobs(
             "provide kl_ref_adapter_path."
         )
 
+    current_adapter_path = job.source_adapter_path
     adapter_swapped = os.path.abspath(ref_adapter_path) != os.path.abspath(
-        job.lora_path
+        current_adapter_path
     )
     loaded_ref_adapter = False
-    restore_adapter = None
+    restore_parameters: list[tuple[torch.Tensor, torch.Tensor]] | None = None
     try:
         if adapter_swapped:
-            restore_adapter = load_lora_tensors_for_megatron(
-                job.lora_path,
-                handler=runtime.model_support_handler,
-            )
+            restore_parameters = _snapshot_trainable_parameters(runtime.model)
             _load_adapter_into_model(
                 runtime.model,
                 ref_adapter_path,
@@ -1753,13 +2323,28 @@ def _prepare_kl_reference_logprobs(
     finally:
         if loaded_ref_adapter:
             assert runtime.optimizer is not None
-            assert restore_adapter is not None
-            load_adapter_into_model(
-                runtime.model,
-                restore_adapter,
-                runtime.optimizer,
-                model_support_handler=runtime.model_support_handler,
-            )
+            assert restore_parameters is not None
+            with torch.no_grad():
+                for parameter, value in restore_parameters:
+                    parameter.copy_(value)
+                runtime.model_support_handler.zero_internal_padding_params(
+                    runtime.model
+                )
+            runtime.optimizer_snapshot_barrier.synchronize()
+            runtime.optimizer.reload_model_params()
+
+
+def _snapshot_trainable_parameters(
+    model_chunks: ModelChunks,
+) -> list[tuple[torch.Tensor, torch.Tensor]]:
+    seen: set[int] = set()
+    snapshot: list[tuple[torch.Tensor, torch.Tensor]] = []
+    for chunk in model_chunks:
+        for parameter in chunk.parameters():
+            if parameter.requires_grad and id(parameter) not in seen:
+                seen.add(id(parameter))
+                snapshot.append((parameter, parameter.detach().clone()))
+    return snapshot
 
 
 def run_megatron_sft_step(
@@ -1774,6 +2359,7 @@ def run_megatron_sft_step(
     sample_index: int | list[int | None],
     moe_routing_replay_controller: MoeRoutingReplayController | None = None,
     hybridep_token_counts: list[int] | None = None,
+    before_optimizer_step: Callable[[], None] | None = None,
 ) -> TrainStepResult:
     micro_inputs = inputs if isinstance(inputs, list) else [inputs]
     if not micro_inputs:
@@ -1807,20 +2393,11 @@ def run_megatron_sft_step(
     )
 
     _zero_grad_buffers(model_chunks)
+    _install_schedule_finalize(model_chunks)
 
-    raw_loss_sum: torch.Tensor | None = None
-    loss_inputs_for_count: list[dict[str, torch.Tensor] | PreparedSFTMicroInputs] = []
     pending_prepared_micro: PreparedMegatronBatch | None = None
-    hybridep_enabled = _validate_hybridep_token_counts(
-        hybridep_token_counts, len(micro_inputs)
-    )
-
+    prepared_micros: list[PreparedSFTMicroInputs] = []
     for micro_order, micro in enumerate(micro_inputs):
-        if moe_routing_replay_controller is not None:
-            moe_routing_replay_controller.begin_micro(
-                micro_sample_indices[micro_order],
-                micro_order,
-            )
         prepared_micro, pending_prepared_micro = _prepare_current_sft_micro(
             micro,
             device=device,
@@ -1830,30 +2407,7 @@ def run_megatron_sft_step(
             trace_token_uids=trace_token_uids,
             pending_prepared_micro=pending_prepared_micro,
         )
-        prepare_replay_local_input_token_uids(
-            moe_routing_replay_controller,
-            prepared_micro.local_token_uids,
-            prepared_micro.attention_state,
-        )
-        if hybridep_enabled:
-            assert hybridep_token_counts is not None
-            _set_hybridep_token_count(hybridep_token_counts[micro_order])
-        with attach_trace_token_uids(model_chunks, prepared_micro.local_token_uids):
-            per_token_loss: torch.Tensor = model_chunks[0](
-                input_ids=prepared_micro.input_ids,
-                position_ids=prepared_micro.position_ids,
-                attention_mask=_placeholder_attention_mask(device),
-                labels=prepared_micro.labels,
-                packed_seq_params=prepared_micro.packed_seq_params,
-                **model_support_handler.get_forward_kwargs(
-                    model_chunks[0],
-                    attention_bias=prepared_micro.attention_state,
-                ),
-            )
-        masked_loss = (
-            per_token_loss[prepared_micro.loss_mask].sum() + per_token_loss.sum() * 0.0
-        )
-        masked_loss.backward()
+        prepared_micros.append(prepared_micro)
         pending_prepared_micro = _prepare_next_sft_cp_micro(
             _next_micro_lookahead(micro_inputs, micro_order),
             device=device,
@@ -1862,40 +2416,80 @@ def run_megatron_sft_step(
             model_support_handler=model_support_handler,
             trace_token_uids=trace_token_uids,
         )
-        detached_micro_loss = masked_loss.detach()
-        if raw_loss_sum is None:
-            raw_loss_sum = detached_micro_loss
-        else:
-            raw_loss_sum = raw_loss_sum + detached_micro_loss
-        loss_inputs_for_count.append(prepared_micro)
+    schedule = MCoreScheduleAdapter(
+        model_chunks=model_chunks,
+        prepared_microbatches=prepared_micros,
+        sample_indices=micro_sample_indices,
+        model_inputs=[prepared.input_ids for prepared in prepared_micros],
+        moe_routing_replay_controller=moe_routing_replay_controller,
+        hybridep_token_counts=hybridep_token_counts,
+        model_activator=model_support_handler.build_pipeline_microbatch_activator(
+            model_chunks
+        ),
+    )
 
-    if raw_loss_sum is None:
-        raise RuntimeError("run_megatron_sft_step did not produce outputs")
+    def forward_step_func(data_iterator: Any, model: MegatronModule, *_args: Any):
+        item = next(data_iterator)
+        prepared = item.payload
+        kwargs = dict(
+            input_ids=prepared.input_ids,
+            position_ids=prepared.position_ids,
+            attention_mask=_placeholder_attention_mask(device),
+            packed_seq_params=prepared.packed_seq_params,
+            **model_support_handler.get_forward_kwargs(
+                model, attention_bias=prepared.attention_state
+            ),
+        )
+        with attach_trace_token_uids(model_chunks, prepared.local_token_uids):
+            if chunk_post_process(model):
+                token_output = forward_token_losses(
+                    model,
+                    labels=prepared.labels,
+                    selection=prepared.lm_head_selection,
+                    forward_kwargs=kwargs,
+                )
+                output = token_output.token_losses
+            else:
+                output = model(**kwargs, labels=None)
+                token_output = None
+
+        def reduce_loss(output_tensor: torch.Tensor):
+            assert token_output is not None
+            masked_loss = token_output.masked_sum(prepared.loss_mask)
+            num_tokens = _local_trainable_sft_token_count_tensor(
+                [prepared], device=device
+            )
+            return masked_loss, num_tokens, {"raw_loss_sum": masked_loss.detach()}
 
-    num_tokens = _local_trainable_sft_token_count_tensor(
-        loss_inputs_for_count,
-        device=device,
+        return output, reduce_loss
+
+    forward_data_store = schedule.run(forward_step_func, forward_only=False)
+    if moe_routing_replay_controller is not None:
+        moe_routing_replay_controller.finalize_step(expect_recompute=True)
+    forward_data_store = cast(
+        list[dict[str, Any]], _broadcast_from_pipeline_last(forward_data_store)
     )
-    flush_param_grads_to_main_grads(model_chunks)
-    finalize_model_grads_extended(
-        as_megatron_api_chunks(model_chunks), num_tokens=num_tokens
+    raw_loss_sum = sum(
+        (
+            cast(torch.Tensor, data["raw_loss_sum"]).to(device)
+            for data in forward_data_store
+        ),
+        torch.zeros([], device=device, dtype=torch.float32),
     )
     update_successful, grad_norm, num_zeros_in_grad = _optimizer_step(
         optimizer,
         learning_rate,
         model_support_handler=model_support_handler,
         model_chunks=model_chunks,
+        before_step=before_optimizer_step,
     )
-    global_num_tokens = max(num_tokens.item(), 1.0)
-    reduced_loss = _reduce_loss(
-        raw_loss_sum / global_num_tokens,
-        op=torch.distributed.ReduceOp.SUM,  # ty: ignore[possibly-missing-attribute]
+    num_tokens = _local_trainable_sft_token_count_tensor(prepared_micros, device=device)
+    reduced_loss = _reduce_loss_sum(
+        raw_loss_sum,
+        num_tokens,
         group=ps.get_data_parallel_group(with_context_parallel=True),
     )
 
-    if moe_routing_replay_controller is not None:
-        moe_routing_replay_controller.finalize_step()
-
     return TrainStepResult(
         reduced_loss=reduced_loss,
         probs_corr=1.0,
@@ -1903,7 +2497,112 @@ def run_megatron_sft_step(
         update_successful=update_successful,
         grad_norm=grad_norm,
         num_zeros_in_grad=num_zeros_in_grad,
+        workload=schedule.training_workload(),
+        pipeline_metrics=schedule.telemetry.metrics(),
+    )
+
+
+def _run_training_schedule(
+    schedule: MCoreScheduleAdapter[Any],
+    forward_step_func: Callable[..., Any],
+    timing: _InterForwardBackwardTiming | None,
+) -> tuple[list[Any], Callable[[], dict[str, float]]]:
+    started_s = time.monotonic()
+    gap_s = (
+        None
+        if timing is None or timing.previous_schedule_end_s is None
+        else started_s - timing.previous_schedule_end_s
+    )
+    phase_s = (
+        None
+        if timing is None
+        or timing.previous_schedule_end_s is None
+        or timing.previous_job_complete_s is None
+        or timing.current_job_start_s is None
+        or not (
+            timing.previous_schedule_end_s
+            <= timing.previous_job_complete_s
+            <= timing.current_job_start_s
+            <= started_s
+        )
+        else (
+            timing.previous_job_complete_s - timing.previous_schedule_end_s,
+            timing.current_job_start_s - timing.previous_job_complete_s,
+            started_s - timing.current_job_start_s,
+        )
+    )
+    previous_cuda_end = (
+        timing.previous_schedule_cuda_end if timing is not None else None
+    )
+    outputs = schedule.run(forward_step_func, forward_only=False)
+    ended_s = time.monotonic()
+    if timing is None:
+        return outputs, lambda: {}
+    cuda_span = schedule.telemetry.cuda_span()
+    timing.previous_schedule_end_s = ended_s
+    timing.previous_schedule_cuda_end = cuda_span[1] if cuda_span is not None else None
+    if gap_s is None:
+        return outputs, lambda: {}
+    world_size = torch.distributed.get_world_size()  # ty: ignore[possibly-missing-attribute]
+    local_timing = torch.tensor(
+        (gap_s, *(phase_s or (math.nan, math.nan, math.nan))), dtype=torch.float64
     )
+    rank_timings = [torch.empty_like(local_timing) for _ in range(world_size)]
+    work = None
+    if world_size == 1:
+        rank_timings[0].copy_(local_timing)
+    else:
+        if timing.metrics_group is None:
+            raise RuntimeError("Multi-rank schedule timing requires a Gloo group")
+        work = torch.distributed.all_gather(  # ty: ignore[possibly-missing-attribute]
+            rank_timings,
+            local_timing,
+            group=timing.metrics_group,
+            async_op=True,
+        )
+
+    def metrics() -> dict[str, float]:
+        if work is not None:
+            work.wait()
+        values = {
+            f"{_INTER_FORWARD_BACKWARD_GAP_PREFIX}{rank}_s": float(parts[0].item())
+            for rank, parts in enumerate(rank_timings)
+        }
+        phase_names = ("previous_job_tail", "worker_idle", "current_job_prepare")
+        values.update(
+            {
+                f"{_INTER_FORWARD_BACKWARD_PHASE_PREFIX}{name}_rank_{rank}_s": float(
+                    parts[index].item()
+                )
+                for rank, parts in enumerate(rank_timings)
+                for index, name in enumerate(phase_names, start=1)
+                if not torch.isnan(parts[index])
+            }
+        )
+        if previous_cuda_end is None or cuda_span is None:
+            return values
+        local_gpu_gap = torch.tensor(
+            previous_cuda_end.elapsed_time(cuda_span[0]) / 1e3,
+            dtype=torch.float64,
+        )
+        gpu_gaps = [torch.empty_like(local_gpu_gap) for _ in range(world_size)]
+        if world_size == 1:
+            gpu_gaps[0].copy_(local_gpu_gap)
+        else:
+            torch.distributed.all_gather(  # ty: ignore[possibly-missing-attribute]
+                gpu_gaps,
+                local_gpu_gap,
+                group=timing.metrics_group,
+            )
+        values.update(
+            {
+                f"{_INTER_FORWARD_BACKWARD_GPU_GAP_PREFIX}{rank}_s": float(gap.item())
+                for rank, gap in enumerate(gpu_gaps)
+            }
+        )
+        return values
+
+    return outputs, metrics
 
 
 def run_training_step(
@@ -1924,7 +2623,10 @@ def run_training_step(
     next_step_first_micro: PackedTensors | None = None,
     next_step_first_ref_logprobs: torch.Tensor | None = None,
     hybridep_token_counts: list[int] | None = None,
+    before_optimizer_step: Callable[[], None] | None = None,
+    inter_forward_backward_timing: _InterForwardBackwardTiming | None = None,
 ) -> TrainStepResult:
+    schedule_prepare_started = time.perf_counter()
     micro_inputs = inputs if isinstance(inputs, list) else [inputs]
     if not micro_inputs:
         raise ValueError("run_training_step requires at least one packed sequence")
@@ -1964,28 +2666,11 @@ def run_training_step(
         cp_lookahead_state.pending_prepared_micro = None
 
     _zero_grad_buffers(model_chunks)
+    _install_schedule_finalize(model_chunks)
 
     micro_count = len(micro_inputs)
-    hybridep_enabled = _validate_hybridep_token_counts(
-        hybridep_token_counts, micro_count
-    )
-    raw_loss_sum: torch.Tensor | None = None
-    loss_inputs_for_count: list[LossInputs | DispatchedPackedTensors] = []
-    probs_corr_total: torch.Tensor | None = None
-    kl_policy_ref_sum = 0.0
-    kl_policy_ref_count = 0
-    loss_diagnostics = LossOffPolicyDiagnosticsAccumulator()
-    new_logprobs_gpu: list[torch.Tensor] = []
-
-    def begin_micro(micro_order: int) -> None:
-        if moe_routing_replay_controller is not None:
-            moe_routing_replay_controller.begin_micro(
-                micro_sample_indices[micro_order],
-                micro_order,
-            )
-
+    prepared_micros: list[PreparedRLMicroInputs] = []
     for micro_order in range(micro_count):
-        begin_micro(micro_order)
         micro_ref_logprobs = _select_ref_logprobs(ref_logprobs, micro_order)
         if micro_ref_logprobs is not None and int(topology.cp) <= 1:
             micro_ref_logprobs = micro_ref_logprobs.to(device)
@@ -1999,57 +2684,7 @@ def begin_micro(micro_order: int) -> None:
             trace_token_uids=trace_token_uids,
             pending_prepared_micro=pending_prepared_micro,
         )
-        prepare_replay_local_input_token_uids(
-            moe_routing_replay_controller,
-            prepared_micro.local_token_uids,
-            prepared_micro.attention_state,
-        )
-        if hybridep_enabled:
-            assert hybridep_token_counts is not None
-            _set_hybridep_token_count(hybridep_token_counts[micro_order])
-
-        new_logprobs = _forward_prepared_rl_micro(
-            model_chunks=model_chunks,
-            model_support_handler=model_support_handler,
-            prepared_micro=prepared_micro,
-            device=device,
-        )
-
-        loss_info = loss_fn(
-            prepared_micro.loss_inputs,
-            new_logprobs=new_logprobs,
-            ref_logprobs=prepared_micro.ref_logprobs,
-            entropies=None,
-            experimental_config=experimental_config,
-            reduction="sum",
-        )
-        micro_loss = loss_info.policy_loss + _zero_logprob_graph_contribution(
-            new_logprobs,
-            prepared_micro.loss_inputs,
-        )
-        if not micro_loss.requires_grad:
-            assistant_tokens = _count_trainable_tokens(prepared_micro.loss_inputs)
-            nonzero_weights = int(
-                torch.count_nonzero(
-                    prepared_micro.loss_inputs.align_inputs().weights
-                ).item()
-            )
-            nonzero_advantages = int(
-                torch.count_nonzero(
-                    prepared_micro.loss_inputs.align_inputs().advantages
-                ).item()
-            )
-            raise RuntimeError(
-                "RL micro_loss is detached before backward: "
-                f"new_logprobs.requires_grad={new_logprobs.requires_grad}, "
-                f"policy_loss_sum_requires_grad={loss_info.policy_loss_sum.requires_grad}, "
-                f"assistant_tokens={assistant_tokens}, "
-                f"nonzero_weights={nonzero_weights}, "
-                f"nonzero_advantages={nonzero_advantages}"
-            )
-        micro_loss.backward()
-        loss_inputs_for_count.append(prepared_micro.loss_inputs)
-        del prepared_micro
+        prepared_micros.append(prepared_micro)
         pending_prepared_micro = _prepare_next_rl_cp_micro(
             _next_micro_lookahead(
                 micro_inputs,
@@ -2068,166 +2703,174 @@ def begin_micro(micro_order: int) -> None:
                 next_step_first_ref_logprobs=next_step_first_ref_logprobs,
             ),
         )
-        detached_probs_corr = loss_info.probs_corr.detach()
-        if probs_corr_total is None:
-            probs_corr_total = detached_probs_corr
-        else:
-            probs_corr_total = probs_corr_total + detached_probs_corr
-        if loss_info.kl_policy_ref is not None:
-            kl_policy_ref_sum += float(loss_info.kl_policy_ref.item())
-            kl_policy_ref_count += 1
-        loss_diagnostics.add(loss_info.offpolicy_diagnostics)
-        detached_micro_loss = micro_loss.detach()
-        if raw_loss_sum is None:
-            raw_loss_sum = detached_micro_loss
-        else:
-            raw_loss_sum = raw_loss_sum + detached_micro_loss
-        del loss_info
-        del micro_loss
-        new_logprobs_gpu.append(new_logprobs.detach())
-        del new_logprobs
-
-    if raw_loss_sum is None:
-        raise RuntimeError("run_training_step did not produce outputs")
-    if probs_corr_total is None:
-        raise RuntimeError("run_training_step did not accumulate probs_corr")
     if cp_lookahead_state is not None:
         cp_lookahead_state.pending_prepared_micro = pending_prepared_micro
 
+    schedule = MCoreScheduleAdapter(
+        model_chunks=model_chunks,
+        prepared_microbatches=prepared_micros,
+        sample_indices=micro_sample_indices,
+        model_inputs=[prepared.model_tokens for prepared in prepared_micros],
+        moe_routing_replay_controller=moe_routing_replay_controller,
+        hybridep_token_counts=hybridep_token_counts,
+        model_activator=model_support_handler.build_pipeline_microbatch_activator(
+            model_chunks
+        ),
+    )
+
+    def forward_step_func(data_iterator: Any, model: MegatronModule, *_args: Any):
+        item = next(data_iterator)
+        prepared = item.payload
+        token_output = _forward_prepared_rl_micro(
+            model_chunks=model_chunks,
+            model_chunk=model,
+            model_support_handler=model_support_handler,
+            prepared_micro=prepared,
+            device=device,
+        )
+
+        def reduce_loss(output_tensor: torch.Tensor):
+            new_logprobs = -output_tensor
+            compact_loss_inputs = token_output.compact_loss_inputs(prepared.loss_inputs)
+            loss_info = loss_fn(
+                compact_loss_inputs,
+                new_logprobs=new_logprobs,
+                ref_logprobs=token_output.select_optional(prepared.ref_logprobs),
+                entropies=None,
+                experimental_config=experimental_config,
+                reduction="sum",
+            )
+            micro_loss = loss_info.policy_loss + _zero_logprob_graph_contribution(
+                new_logprobs, compact_loss_inputs
+            )
+            if not micro_loss.requires_grad:
+                raise RuntimeError(
+                    "RL micro_loss is detached before pipeline backward: "
+                    f"micro={item.order}, sample={item.sample_index}"
+                )
+            num_tokens = _local_trainable_token_count_tensor(
+                [prepared.loss_inputs], device=device
+            )
+            return (
+                micro_loss,
+                num_tokens,
+                {
+                    "order": item.order,
+                    "raw_loss_sum": micro_loss.detach(),
+                    "probs_corr": loss_info.probs_corr.detach(),
+                    "kl_policy_ref": (
+                        None
+                        if loss_info.kl_policy_ref is None
+                        else float(loss_info.kl_policy_ref.item())
+                    ),
+                    "offpolicy_diagnostics": loss_info.offpolicy_diagnostics,
+                    "new_logprobs": token_output.restore(new_logprobs.detach()).to(
+                        "cpu"
+                    ),
+                },
+            )
+
+        return token_output.token_losses, reduce_loss
+
+    schedule_prepare_s = time.perf_counter() - schedule_prepare_started
+    forward_data_store, collect_inter_schedule_metrics = _run_training_schedule(
+        schedule, forward_step_func, inter_forward_backward_timing
+    )
+    replay_finalize_started = time.perf_counter()
+    if moe_routing_replay_controller is not None:
+        moe_routing_replay_controller.finalize_step(expect_recompute=True)
+    replay_finalize_s = time.perf_counter() - replay_finalize_started
+    result_collect_started = time.perf_counter()
+    pipeline_results = cast(
+        list[dict[str, Any]],
+        _broadcast_from_pipeline_last(forward_data_store),
+    )
+    if len(pipeline_results) != micro_count:
+        raise RuntimeError(
+            "MCore schedule did not return one final-stage result per microbatch: "
+            f"expected={micro_count}, got={len(pipeline_results)}"
+        )
+    pipeline_results.sort(key=lambda data: int(data["order"]))
+    raw_loss_sum = sum(
+        (
+            cast(torch.Tensor, data["raw_loss_sum"]).to(device)
+            for data in pipeline_results
+        ),
+        torch.zeros([], device=device, dtype=torch.float32),
+    )
+    probs_corr_total = sum(
+        (
+            cast(torch.Tensor, data["probs_corr"]).to(device)
+            for data in pipeline_results
+        ),
+        torch.zeros([], device=device, dtype=torch.float32),
+    )
+    kl_values = [
+        float(value)
+        for data in pipeline_results
+        if (value := data["kl_policy_ref"]) is not None
+    ]
+    loss_diagnostics = LossOffPolicyDiagnosticsAccumulator()
+    for data in pipeline_results:
+        loss_diagnostics.add(data["offpolicy_diagnostics"])
+
     token_count = _local_trainable_token_count_tensor(
-        loss_inputs_for_count,
+        [prepared.loss_inputs for prepared in prepared_micros],
         device=device,
     )
-    finalize_model_grads_extended(
-        as_megatron_api_chunks(model_chunks),
-        num_tokens=token_count,
-    )
+    result_collect_s = time.perf_counter() - result_collect_started
+    optimizer_started = time.perf_counter()
     update_successful, grad_norm, num_zeros_in_grad = _optimizer_step(
         optimizer,
         learning_rate,
         model_support_handler=model_support_handler,
         model_chunks=model_chunks,
+        before_step=before_optimizer_step,
     )
-    global_num_tokens = max(token_count.item(), 1.0)
-    reduced_loss = _reduce_loss(
-        raw_loss_sum / global_num_tokens,
-        op=torch.distributed.ReduceOp.SUM,  # ty: ignore[possibly-missing-attribute]
+    optimizer_s = time.perf_counter() - optimizer_started
+    loss_reduce_started = time.perf_counter()
+    reduced_loss = _reduce_loss_sum(
+        raw_loss_sum,
+        token_count,
         group=ps.get_data_parallel_group(with_context_parallel=True),
     )
-
-    if moe_routing_replay_controller is not None:
-        moe_routing_replay_controller.finalize_step()
-
-    return TrainStepResult(
+    loss_reduce_s = time.perf_counter() - loss_reduce_started
+    loss_metrics_started = time.perf_counter()
+    loss_metrics = loss_diagnostics.to_metrics(
+        group=ps.get_data_parallel_group(with_context_parallel=True),
+    )
+    loss_metrics_s = time.perf_counter() - loss_metrics_started
+    inter_metrics_started = time.perf_counter()
+    inter_metrics = collect_inter_schedule_metrics()
+    inter_metrics_s = time.perf_counter() - inter_metrics_started
+
+    result_build_started = time.perf_counter()
+    pipeline_metrics = {
+        **schedule.telemetry.metrics(),
+        **inter_metrics,
+        "time/schedule_prepare_s": schedule_prepare_s,
+        "time/post_schedule_replay_finalize_s": replay_finalize_s,
+        "time/post_schedule_result_collect_s": result_collect_s,
+        "time/post_schedule_optimizer_s": optimizer_s,
+        "time/post_schedule_loss_reduce_s": loss_reduce_s,
+        "time/post_schedule_loss_metrics_s": loss_metrics_s,
+        "time/post_schedule_inter_metrics_s": inter_metrics_s,
+    }
+    result = TrainStepResult(
         reduced_loss=reduced_loss,
         probs_corr=float((probs_corr_total / micro_count).item()),
-        kl_policy_ref=(
-            kl_policy_ref_sum / kl_policy_ref_count if kl_policy_ref_count > 0 else None
-        ),
+        kl_policy_ref=(sum(kl_values) / len(kl_values) if kl_values else None),
         new_logprobs=[
-            tensor.to(device="cpu", non_blocking=True) for tensor in new_logprobs_gpu
+            cast(torch.Tensor, data["new_logprobs"]) for data in pipeline_results
         ],
         update_successful=update_successful,
         grad_norm=grad_norm,
         num_zeros_in_grad=num_zeros_in_grad,
-        loss_metrics=loss_diagnostics.to_metrics(
-            group=ps.get_data_parallel_group(with_context_parallel=True),
-        ),
-    )
-
-
-def _sync_merged_weights_to_vllm(
-    runtime: TrainingRuntime,
-    spec: MergedWeightTransferSpec,
-    *,
-    lora_path: str,
-    pause_generation: bool,
-) -> None:
-    adapter_model = load_lora_tensors_for_megatron(
-        lora_path,
-        handler=runtime.model_support_handler,
-    )
-    (
-        runtime.merged_weight_transfer_group,
-        runtime.merged_weight_transfer_init_info,
-    ) = sync_merged_weights_to_vllm(
-        bridge=runtime.bridge,
-        model=runtime.model,
-        model_support_handler=runtime.model_support_handler,
-        adapter_model=adapter_model,
-        adapter_config=load_adapter_config(lora_path),
-        rank=runtime.rank,
-        world_size=runtime.world_size,
-        merged_weight_transfer_group=runtime.merged_weight_transfer_group,
-        merged_weight_transfer_init_info=runtime.merged_weight_transfer_init_info,
-        spec=spec,
-        pause_generation=pause_generation,
-    )
-
-
-def _close_merged_weight_transfer_group(
-    runtime: TrainingRuntime, *, abort: bool = False
-) -> None:
-    weight_transfer_group = runtime.merged_weight_transfer_group
-    runtime.merged_weight_transfer_group = None
-    runtime.merged_weight_transfer_init_info = None
-    if weight_transfer_group is None:
-        return
-    shutdown = getattr(weight_transfer_group, "abort" if abort else "close", None)
-    if shutdown is None and abort:
-        shutdown = getattr(weight_transfer_group, "close", None)
-    if shutdown is not None:
-        shutdown()
-
-
-def _run_service_loop(runtime: TrainingRuntime) -> None:
-    weight_offload = WeightOffloadManager.from_env(
-        model=runtime.model,
-        rank=runtime.rank,
-        compile_enabled=runtime.transformer_layers_compiled,
-    )
-    runtime.optimizer_persistent = not weight_offload.offload_between_jobs
-    weight_offload.install()
-    wake_lock_path = os.environ.get(
-        "ART_MEGATRON_WAKE_LOCK_PATH", DEFAULT_VLLM_WAKE_LOCK_PATH
+        workload=schedule.training_workload(),
+        loss_metrics=loss_metrics,
+        pipeline_metrics=pipeline_metrics,
     )
-
-    def wait_until_ready() -> None:
-        while os.path.exists(wake_lock_path):
-            time.sleep(0.2)
-
-    def before_job() -> None:
-        weight_offload.before_job()
-
-    def after_job() -> None:
-        if not runtime.optimizer_persistent:
-            _clear_resident_optimizer(runtime)
-        weight_offload.after_job()
-
-    worker_error = False
-    try:
-        after_job()
-        run_megatron_worker_loop(
-            runtime,
-            supports_sft=True,
-            wait_until_ready=wait_until_ready,
-            before_job=before_job,
-            after_job=after_job,
-        )
-    except BaseException:
-        worker_error = True
-        raise
-    finally:
-        _close_merged_weight_transfer_group(runtime, abort=worker_error)
-
-
-def main() -> None:
-    runtime = build_training_runtime(
-        model_identifier=os.environ.get("MODEL_IDENTIFIER", DEFAULT_MODEL_IDENTIFIER),
-        build_optimizer=False,
+    result.pipeline_metrics["time/post_schedule_result_build_s"] = (
+        time.perf_counter() - result_build_started
     )
-    _run_service_loop(runtime)
-
-
-if __name__ == "__main__":
-    main()
+    return result
diff --git a/src/art/megatron/training/finalize_grads.py b/src/art/megatron/training/finalize_grads.py
index e00cd8218..a842e60df 100644
--- a/src/art/megatron/training/finalize_grads.py
+++ b/src/art/megatron/training/finalize_grads.py
@@ -127,6 +127,9 @@ def flush_param_grads_to_main_grads(model_chunks: Iterable[torch.nn.Module]) ->
 def finalize_model_grads_extended(
     model: list[MegatronModule],
     num_tokens: torch.Tensor | None = None,
+    *,
+    pg_collection: Any | None = None,
+    force_all_reduce: bool = False,
 ) -> None:
     """Run Megatron finalize, then apply extra LoRA grad-sync reductions.
 
@@ -139,6 +142,8 @@ def finalize_model_grads_extended(
     finalize_model_grads(
         cast(list[torch.nn.Module], model),
         num_tokens=num_tokens,
+        pg_collection=pg_collection,
+        force_all_reduce=force_all_reduce,
     )
 
     buckets: dict[
diff --git a/src/art/megatron/training/microbatches.py b/src/art/megatron/training/microbatches.py
index 307a599aa..e4b864ed9 100644
--- a/src/art/megatron/training/microbatches.py
+++ b/src/art/megatron/training/microbatches.py
@@ -11,6 +11,7 @@
 from art.megatron.context_parallel.runtime import (
     context_parallel_rank_model_token_counts,
     prepare_cp_micro,
+    preplan_megatron_context_parallel_state,
 )
 from art.megatron.context_parallel.types import (
     ContextParallelConfig,
@@ -18,9 +19,12 @@
     DispatchedPackedTensors,
     ParallelTopology,
     PreparedMegatronBatch,
+    TrainingMicrobatchWorkload,
 )
 from art.megatron.flex_attn.compiled import flash_sparse_block_size_for_head_dim
+from art.megatron.prefix_tree import parse_prefix_tree
 from art.megatron.prefix_tree_state import create_prefix_tree_state
+from art.megatron.selective_lm_head import LmHeadTokenSelection
 from art.megatron.training.trace import (
     packed_sequence_token_uids,
     sft_sequence_token_uids,
@@ -34,6 +38,74 @@ class CpBatchLookaheadState(BaseModel):
     pending_prepared_micro: PreparedMegatronBatch | None = None
 
 
+class CpBatchPreplanner(BaseModel):
+    """One trainer rank's immutable CPU planning context."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+    topology: ParallelTopology
+    config: ContextParallelConfig
+    cp_rank: int
+    build_gdn_execution_spec: bool
+    gdn_planner_config: Any | None = None
+
+    @classmethod
+    def from_runtime(
+        cls, runtime: Any, *, device: torch.device
+    ) -> "CpBatchPreplanner | None":
+        from art.megatron.train import _infer_parallel_topology
+
+        topology = _infer_parallel_topology(runtime.model)
+        if int(topology.cp) <= 1:
+            return None
+        handler = runtime.model_support_handler
+        return cls(
+            topology=topology,
+            config=_context_parallel_config_for_provider(
+                runtime.provider, device, handler
+            ),
+            cp_rank=int(ps.get_context_parallel_rank()),
+            build_gdn_execution_spec=bool(
+                getattr(handler, "build_gdn_execution_spec", False)
+            ),
+            gdn_planner_config=_gdn_planner_config_for_provider(
+                runtime.provider, handler
+            ),
+        )
+
+    def preplan(
+        self,
+        packed_tensors: PackedTensors,
+        *,
+        global_grad_accumulation_sequences: int | None,
+    ) -> int:
+        num_sequences, sequence_length = map(int, packed_tensors["tokens"].shape)
+        accumulation = resolve_global_grad_accumulation_sequences(
+            global_grad_accumulation_sequences
+        )
+        sample_indices = {
+            0 if index is None else index
+            for step_index in range((num_sequences + accumulation - 1) // accumulation)
+            for index in build_micro_sample_indices(
+                step_index,
+                num_sequences,
+                accumulation,
+            )
+        }
+        for index in sorted(sample_indices):
+            preplan_megatron_context_parallel_state(
+                group_ids=packed_tensors["group_ids"][index : index + 1],
+                parent_ids=packed_tensors["parent_ids"][index : index + 1],
+                original_seq_len=sequence_length,
+                topology=self.topology,
+                config=self.config,
+                cp_rank=self.cp_rank,
+                build_gdn_execution_spec=self.build_gdn_execution_spec,
+                gdn_planner_config=self.gdn_planner_config,
+            )
+        return len(sample_indices)
+
+
 class PreparedRLMicroInputs(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
@@ -43,8 +115,10 @@ class PreparedRLMicroInputs(BaseModel):
     attention_state: Any
     packed_seq_params: Any | None = None
     loss_inputs: LossInputs | DispatchedPackedTensors
+    lm_head_selection: LmHeadTokenSelection
     ref_logprobs: torch.Tensor | None = None
     local_token_uids: torch.Tensor | None = None
+    workload: TrainingMicrobatchWorkload
 
 
 class PreparedSFTMicroInputs(BaseModel):
@@ -54,9 +128,11 @@ class PreparedSFTMicroInputs(BaseModel):
     position_ids: torch.Tensor
     labels: torch.Tensor
     loss_mask: torch.Tensor
+    lm_head_selection: LmHeadTokenSelection
     attention_state: Any
     packed_seq_params: Any | None = None
     local_token_uids: torch.Tensor | None = None
+    workload: TrainingMicrobatchWorkload
 
 
 def _map_packed_tensors(
@@ -213,7 +289,9 @@ def build_rl_hybridep_token_counts(
         return [sequence_length for _ in sample_rows]
 
     config = _context_parallel_config_for_provider(
-        provider, torch.device("cuda", torch.cuda.current_device())
+        provider,
+        torch.device("cuda", torch.cuda.current_device()),
+        model_support_handler,
     )
     build_gdn = bool(getattr(model_support_handler, "build_gdn_execution_spec", False))
     gdn_planner_config = _gdn_planner_config_for_provider(
@@ -263,7 +341,9 @@ def sample(sample_index: int | None) -> dict[str, torch.Tensor]:
         ]
 
     config = _context_parallel_config_for_provider(
-        provider, torch.device("cuda", torch.cuda.current_device())
+        provider,
+        torch.device("cuda", torch.cuda.current_device()),
+        model_support_handler,
     )
     build_gdn = bool(getattr(model_support_handler, "build_gdn_execution_spec", False))
     gdn_planner_config = _gdn_planner_config_for_provider(
@@ -358,7 +438,7 @@ def _local_trainable_token_count_tensor(
     device: torch.device,
 ) -> torch.Tensor:
     local_token_total = sum(_count_trainable_tokens(micro) for micro in micro_inputs)
-    return torch.tensor([local_token_total], device=device, dtype=torch.float32)
+    return torch.tensor(local_token_total, device=device, dtype=torch.int)
 
 
 def _art_flex_sliding_windows(provider: Any) -> tuple[int, ...]:
@@ -414,16 +494,24 @@ def _art_flex_cp_block_mask_variants(
 def _context_parallel_config_for_provider(
     provider: Any,
     device: torch.device,
+    model_support_handler: Any,
 ) -> ContextParallelConfig:
     head_dim = getattr(provider, "kv_channels", None)
     if head_dim is None:
-        return ContextParallelConfig()
+        return ContextParallelConfig(
+            workload_profile=model_support_handler.context_parallel_workload_profile(
+                provider
+            )
+        )
     return ContextParallelConfig(
         attention_sparse_block_size=flash_sparse_block_size_for_head_dim(
             head_dim=int(head_dim),
             head_dim_v=int(head_dim),
             device=device,
-        )
+        ),
+        workload_profile=model_support_handler.context_parallel_workload_profile(
+            provider
+        ),
     )
 
 
@@ -503,7 +591,6 @@ def _prepare_dense_rl_micro(
             model_support_handler,
         ),
     )
-    _move_inputs_to_device(micro, device)
     shifted_labels = shift_tensor(micro["tokens"], -100)
     shifted_assistant_mask = shift_tensor(micro["assistant_mask"], False)
     shifted_labels = torch.where(
@@ -511,14 +598,33 @@ def _prepare_dense_rl_micro(
         shifted_labels,
         torch.full_like(shifted_labels, -100),
     )
+    lm_head_selection = LmHeadTokenSelection.from_labels(
+        shifted_labels,
+        target_device=device,
+    )
+    workload = TrainingMicrobatchWorkload(
+        logical_nonpadding_tokens=sum(
+            row.valid_tokens
+            for row in parse_prefix_tree(
+                group_ids=micro["group_ids"], parent_ids=micro["parent_ids"]
+            )
+        ),
+        loss_bearing_tokens=int(shifted_assistant_mask.sum().item()),
+        executed_token_equivalents=int(micro["tokens"].numel()),
+        nominal_schedule_capacity_tokens=int(micro["tokens"].numel()),
+    )
+    shifted_labels = shifted_labels.to(device)
+    _move_inputs_to_device(micro, device)
     return PreparedRLMicroInputs(
         model_tokens=micro["tokens"],
         model_input_pos=micro["input_pos"],
         model_labels=shifted_labels,
         attention_state=attention_state,
         loss_inputs=LossInputs(inputs=micro),
+        lm_head_selection=lm_head_selection,
         ref_logprobs=ref_logprobs,
         local_token_uids=packed_sequence_token_uids(micro, device=device),
+        workload=workload,
     )
 
 
@@ -541,7 +647,9 @@ def _prepare_rl_cp_micro_full(
     return prepare_cp_micro(
         micro=micro,
         topology=topology,
-        config=_context_parallel_config_for_provider(provider, device),
+        config=_context_parallel_config_for_provider(
+            provider, device, model_support_handler
+        ),
         cp_group=ps.get_context_parallel_group(check_initialized=False),
         cp_rank=ps.get_context_parallel_rank(),
         build_gdn_execution_spec=bool(
@@ -555,6 +663,9 @@ def _prepare_rl_cp_micro_full(
         block_mask_variants=_art_flex_cp_block_mask_variants(provider, device),
         target_device=device,
         ref_logprobs=ref_logprobs,
+        model_support_handler=model_support_handler,
+        attention_head_dim=getattr(provider, "kv_channels", None),
+        attention_value_head_dim=getattr(provider, "kv_channels", None),
     )
 
 
@@ -570,34 +681,15 @@ def _prepared_rl_micro_from_cp_batch(
         attention_state=prepared.attention_state,
         packed_seq_params=prepared.packed_seq_params,
         loss_inputs=prepared.tensors,
+        lm_head_selection=prepared.tensors.lm_head_selection,
         ref_logprobs=(
             prepared.tensors.ref_logprobs if ref_logprobs is not None else None
         ),
         local_token_uids=prepared.tensors.token_uids,
+        workload=prepared.workload,
     )
 
 
-def _empty_new_logprobs_from_logits(
-    logits: torch.Tensor, labels: torch.Tensor
-) -> torch.Tensor:
-    if int(labels.numel()) != 0:
-        raise ValueError("empty-logprob path requires empty local labels")
-    if logits.ndim < 3 or int(logits.shape[-1]) == 0:
-        raise ValueError(
-            f"expected empty local logits [B, S, V], got {tuple(logits.shape)}"
-        )
-    candidate = logits[..., 0]
-    if tuple(candidate.shape) == tuple(labels.shape):
-        return candidate
-    candidate = candidate.transpose(0, 1).contiguous()
-    if tuple(candidate.shape) != tuple(labels.shape):
-        raise ValueError(
-            "empty local logits shape must match labels after removing vocab dim, "
-            f"got logits={tuple(logits.shape)} labels={tuple(labels.shape)}"
-        )
-    return candidate
-
-
 def _prepare_current_rl_micro(
     micro: PackedTensors,
     *,
@@ -676,7 +768,7 @@ def _local_trainable_sft_token_count_tensor(
     local_token_total = sum(
         _count_sft_trainable_tokens(micro) for micro in micro_inputs
     )
-    return torch.tensor([local_token_total], device=device, dtype=torch.float32)
+    return torch.tensor(local_token_total, device=device, dtype=torch.int)
 
 
 def _prepare_dense_sft_micro(
@@ -689,15 +781,28 @@ def _prepare_dense_sft_micro(
     attention_mask = micro["attention_mask"].reshape(-1)
     seq_len = max(int(attention_mask.sum().item()), 1)
     input_ids = micro["input_ids"].reshape(-1)[:seq_len].unsqueeze(0).to(device)
-    labels = micro["labels"].reshape(-1)[:seq_len].unsqueeze(0).to(device)
+    labels = micro["labels"].reshape(-1)[:seq_len].unsqueeze(0)
     position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
     shifted_labels = shift_tensor(labels, -100)
     loss_mask = shifted_labels != -100
+    workload = TrainingMicrobatchWorkload(
+        logical_nonpadding_tokens=int(attention_mask.sum().item()),
+        loss_bearing_tokens=int(loss_mask.sum().item()),
+        executed_token_equivalents=seq_len,
+        nominal_schedule_capacity_tokens=int(micro["input_ids"].numel()),
+    )
+    lm_head_selection = LmHeadTokenSelection.from_labels(
+        shifted_labels,
+        target_device=device,
+    )
+    shifted_labels = shifted_labels.to(device)
+    loss_mask = loss_mask.to(device)
     return PreparedSFTMicroInputs(
         input_ids=input_ids,
         position_ids=position_ids,
         labels=shifted_labels,
         loss_mask=loss_mask,
+        lm_head_selection=lm_head_selection,
         attention_state=_causal_attention_state(
             seq_len,
             device,
@@ -713,6 +818,7 @@ def _prepare_dense_sft_micro(
         local_token_uids=sft_sequence_token_uids(micro, device=device)[
             :, : int(input_ids.shape[1])
         ],
+        workload=workload,
     )
 
 
@@ -777,7 +883,9 @@ def _prepare_sft_cp_micro_full(
     return prepare_cp_micro(
         micro=sparse_micro,
         topology=topology,
-        config=_context_parallel_config_for_provider(provider, device),
+        config=_context_parallel_config_for_provider(
+            provider, device, model_support_handler
+        ),
         cp_group=ps.get_context_parallel_group(check_initialized=False),
         cp_rank=ps.get_context_parallel_rank(),
         build_gdn_execution_spec=bool(
@@ -790,6 +898,9 @@ def _prepare_sft_cp_micro_full(
         trace_token_uids=trace_token_uids,
         block_mask_variants=_art_flex_cp_block_mask_variants(provider, device),
         target_device=device,
+        model_support_handler=model_support_handler,
+        attention_head_dim=getattr(provider, "kv_channels", None),
+        attention_value_head_dim=getattr(provider, "kv_channels", None),
     )
 
 
@@ -802,9 +913,11 @@ def _prepared_sft_micro_from_cp_batch(
         position_ids=prepared.tensors.input_pos,
         labels=prepared.tensors.labels.masked_fill(~loss_mask, -100),
         loss_mask=loss_mask,
+        lm_head_selection=prepared.tensors.lm_head_selection,
         attention_state=prepared.attention_state,
         packed_seq_params=prepared.packed_seq_params,
         local_token_uids=prepared.tensors.token_uids,
+        workload=prepared.workload,
     )
 
 
diff --git a/src/art/megatron/training/pipeline_schedule.py b/src/art/megatron/training/pipeline_schedule.py
new file mode 100644
index 000000000..28d6a101a
--- /dev/null
+++ b/src/art/megatron/training/pipeline_schedule.py
@@ -0,0 +1,995 @@
+from __future__ import annotations
+
+from collections.abc import Callable, Iterator, Sequence
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+import time
+from typing import Any, Generic, Protocol, TypeVar, cast
+
+from megatron.core import parallel_state as ps
+from megatron.core.pipeline_parallel.p2p_communication import P2PCommunicator
+from megatron.core.pipeline_parallel.schedules import (
+    get_forward_backward_func,
+    get_schedule_table,
+)
+from megatron.core.process_groups_config import ProcessGroupCollection
+from megatron.core.timers import DummyTimer
+from megatron.core.utils import get_model_config
+import torch
+
+from art.megatron.context_parallel.types import (
+    TrainingMicrobatchWorkload,
+    TrainingStepWorkload,
+)
+from art.megatron.routing_replay import MoeRoutingReplayController
+from art.megatron.training.model_chunks import ModelChunks
+from art.megatron.training.trace import prepare_replay_local_input_token_uids
+
+
+class _PreparedMicrobatch(Protocol):
+    attention_state: Any
+    local_token_uids: torch.Tensor | None
+    workload: TrainingMicrobatchWorkload
+
+
+_T = TypeVar("_T", bound=_PreparedMicrobatch)
+
+
+@dataclass(frozen=True)
+class ScheduleMicrobatch(Generic[_T]):
+    order: int
+    sample_index: int | None
+    payload: _T
+    recompute_state: object | None = None
+
+
+def _local_training_workload_values(
+    microbatches: Sequence[ScheduleMicrobatch[Any]], cp_rank: int
+) -> list[int]:
+    real = tuple(item for item in microbatches if item.sample_index is not None)
+    dummy = tuple(item for item in microbatches if item.sample_index is None)
+    return [
+        sum(item.payload.workload.logical_nonpadding_tokens for item in real),
+        sum(item.payload.workload.loss_bearing_tokens for item in real),
+        sum(item.payload.workload.executed_token_equivalents for item in microbatches),
+        (
+            sum(
+                item.payload.workload.nominal_schedule_capacity_tokens
+                for item in microbatches
+            )
+            if cp_rank == 0
+            else 0
+        ),
+        sum(item.payload.workload.executed_token_equivalents for item in dummy),
+        (
+            sum(
+                item.payload.workload.nominal_schedule_capacity_tokens for item in dummy
+            )
+            if cp_rank == 0
+            else 0
+        ),
+        len(real) if cp_rank == 0 else 0,
+        len(microbatches) - len(real) if cp_rank == 0 else 0,
+    ]
+
+
+def _set_hybridep_token_count(rows: int) -> None:
+    from megatron.core.transformer.moe import fused_a2a
+
+    buffer = fused_a2a._hybrid_ep_buffer
+    if buffer is None:
+        raise RuntimeError("HybridEP buffer is not initialized")
+    buffer.set_num_tokens_per_rank(rows)
+
+
+def _validate_hybridep_token_counts(
+    values: Sequence[int] | None, microbatch_count: int
+) -> bool:
+    enabled = ps.get_expert_model_parallel_world_size() > 1
+    if enabled and (values is None or len(values) != microbatch_count):
+        raise RuntimeError(
+            "HybridEP requires one planned communication extent per microbatch"
+        )
+    return enabled
+
+
+class PipelineMicrobatchState(Generic[_T]):
+    def __init__(
+        self,
+        *,
+        controller: MoeRoutingReplayController | None,
+        hybridep_token_counts: Sequence[int] | None,
+        microbatch_count: int,
+        model_activator: Callable[[_T, int], None] | None,
+    ) -> None:
+        hybridep_enabled = _validate_hybridep_token_counts(
+            hybridep_token_counts, microbatch_count
+        )
+        self._controller = controller
+        self._model_activator = model_activator
+        self._hybridep_token_counts = (
+            tuple(cast(Sequence[int], hybridep_token_counts))
+            if hybridep_enabled
+            else None
+        )
+
+    @property
+    def enabled(self) -> bool:
+        return (
+            self._controller is not None
+            or self._hybridep_token_counts is not None
+            or self._model_activator is not None
+        )
+
+    def activate(self, item: ScheduleMicrobatch[_T], chunk_index: int) -> None:
+        prepared = item.payload
+        if self._controller is not None:
+            self._controller.begin_micro(
+                item.sample_index,
+                item.order,
+                chunk_index=chunk_index,
+            )
+            prepare_replay_local_input_token_uids(
+                self._controller,
+                prepared.local_token_uids,
+                prepared.attention_state,
+            )
+        if self._hybridep_token_counts is not None:
+            _set_hybridep_token_count(self._hybridep_token_counts[item.order])
+        if self._model_activator is not None:
+            self._model_activator(prepared, chunk_index)
+
+
+@dataclass
+class PipelineScheduleTelemetry:
+    pp_rank: int
+    pp_size: int
+    vp_size: int
+    num_microbatches: int
+    real_microbatches: int
+    dummy_microbatches: int
+    micro_batch_size: int
+    seq_length: int
+    microbatch_group_size: int
+    forward_compute_s_by_chunk: dict[int, float] = field(default_factory=dict)
+    backward_compute_s_by_chunk: dict[int, float] = field(default_factory=dict)
+    forward_host_s_by_chunk: dict[int, float] = field(default_factory=dict)
+    forward_calls_by_chunk: dict[int, int] = field(default_factory=dict)
+    p2p_s: float = 0.0
+    p2p_calls: int = 0
+    p2p_wait_s: float = 0.0
+    p2p_wait_calls: int = 0
+    schedule_wall_s: float = 0.0
+    schedule_gpu_s: float = 0.0
+    memory_allocated_start_bytes: int = 0
+    peak_memory_bytes: int = 0
+    _cuda_timers: _DeferredCudaTimers | None = field(default=None, repr=False)
+    _metrics_cache: dict[str, float] | None = field(default=None, repr=False)
+
+    def cuda_span(self) -> tuple[torch.cuda.Event, torch.cuda.Event] | None:
+        timers = self._cuda_timers
+        return None if timers is None else timers.span("forward-backward")
+
+    def metrics(self) -> dict[str, float]:
+        if self._metrics_cache is not None:
+            return dict(self._metrics_cache)
+        self._resolve_cuda_timers()
+        bubble_fraction = pipeline_bubble_fraction(
+            pp_size=self.pp_size,
+            vp_size=self.vp_size,
+            num_microbatches=self.num_microbatches,
+        )
+        metrics = {
+            "pipeline/pp_rank": float(self.pp_rank),
+            "pipeline/pp_size": float(self.pp_size),
+            "pipeline/vp_size": float(self.vp_size),
+            "pipeline/microbatches_per_dp_rank": float(self.num_microbatches),
+            "pipeline/real_microbatches_per_dp_rank": float(self.real_microbatches),
+            "pipeline/dummy_microbatches_per_dp_rank": float(self.dummy_microbatches),
+            "pipeline/micro_batch_size": float(self.micro_batch_size),
+            "pipeline/packed_sequence_length": float(self.seq_length),
+            "pipeline/microbatch_group_size_per_vp_stage": float(
+                self.microbatch_group_size
+            ),
+            "pipeline/schedule_wall_s": self.schedule_wall_s,
+            "pipeline/schedule_gpu_s": self.schedule_gpu_s,
+            "pipeline/p2p_s": self.p2p_s,
+            "pipeline/p2p_call_host_s": self.p2p_s,
+            "pipeline/p2p_calls": float(self.p2p_calls),
+            "pipeline/p2p_wait_host_s": self.p2p_wait_s,
+            "pipeline/p2p_wait_calls": float(self.p2p_wait_calls),
+            "pipeline/memory_allocated_start_bytes": float(
+                self.memory_allocated_start_bytes
+            ),
+            "pipeline/peak_memory_bytes": float(self.peak_memory_bytes),
+            "pipeline/ideal_bubble_fraction": bubble_fraction,
+        }
+        for chunk, forward_compute in sorted(self.forward_compute_s_by_chunk.items()):
+            backward_compute = self.backward_compute_s_by_chunk.get(chunk, 0.0)
+            metrics[f"pipeline/chunk_{chunk}/compute_s"] = (
+                forward_compute + backward_compute
+            )
+            metrics[f"pipeline/chunk_{chunk}/forward_compute_s"] = forward_compute
+            metrics[f"pipeline/chunk_{chunk}/backward_compute_s"] = backward_compute
+            metrics[f"pipeline/chunk_{chunk}/forward_host_s"] = (
+                self.forward_host_s_by_chunk.get(chunk, 0.0)
+            )
+            metrics[f"pipeline/chunk_{chunk}/forward_calls"] = float(
+                self.forward_calls_by_chunk[chunk]
+            )
+        metrics.update(self._stage_metrics())
+        self._metrics_cache = metrics
+        return dict(metrics)
+
+    def _resolve_cuda_timers(self) -> None:
+        timers = self._cuda_timers
+        if timers is None:
+            return
+        timers.synchronize()
+        self.schedule_gpu_s = timers.total("forward-backward")
+        self.forward_compute_s_by_chunk = timers.by_chunk("forward-compute")
+        self.backward_compute_s_by_chunk = timers.by_chunk("backward-compute")
+
+    def _stage_metrics(self) -> dict[str, float]:
+        local = [
+            self.schedule_gpu_s,
+            sum(self.forward_compute_s_by_chunk.values()),
+            sum(self.backward_compute_s_by_chunk.values()),
+            self.p2p_s,
+            self.p2p_wait_s,
+            float(self.peak_memory_bytes),
+            *(
+                values.get(chunk, 0.0)
+                for values in (
+                    self.forward_compute_s_by_chunk,
+                    self.backward_compute_s_by_chunk,
+                )
+                for chunk in range(self.vp_size)
+            ),
+        ]
+        rows = [local]
+        if self.pp_size > 1:
+            value = torch.tensor(
+                local, device=torch.cuda.current_device(), dtype=torch.float64
+            )
+            gathered = torch.empty(
+                self.pp_size * value.numel(), device=value.device, dtype=value.dtype
+            )
+            torch.distributed.all_gather_into_tensor(  # ty: ignore[possibly-missing-attribute]
+                gathered,
+                value,
+                group=ps.get_pipeline_model_parallel_group(),
+            )
+            rows = gathered.view(self.pp_size, -1).cpu().tolist()
+
+        metrics: dict[str, float] = {}
+        for stage, row in enumerate(rows):
+            prefix = f"pipeline/stage_{stage}"
+            metrics[f"{prefix}/schedule_gpu_s"] = row[0]
+            metrics[f"{prefix}/forward_compute_s"] = row[1]
+            metrics[f"{prefix}/backward_compute_s"] = row[2]
+            metrics[f"{prefix}/p2p_call_host_s"] = row[3]
+            metrics[f"{prefix}/p2p_wait_host_s"] = row[4]
+            metrics[f"{prefix}/peak_memory_bytes"] = row[5]
+            for chunk in range(self.vp_size):
+                metrics[f"{prefix}/chunk_{chunk}/forward_compute_s"] = row[6 + chunk]
+                metrics[f"{prefix}/chunk_{chunk}/backward_compute_s"] = row[
+                    6 + self.vp_size + chunk
+                ]
+        stage_compute = [row[1] + row[2] for row in rows]
+        max_compute = max(stage_compute, default=0.0)
+        metrics["pipeline/stage_compute_imbalance_fraction"] = (
+            (max_compute - min(stage_compute)) / max_compute if max_compute > 0 else 0.0
+        )
+        return metrics
+
+
+def pipeline_bubble_fraction(
+    *, pp_size: int, vp_size: int, num_microbatches: int
+) -> float:
+    if pp_size <= 1:
+        return 0.0
+    useful = max(1, num_microbatches * max(1, vp_size))
+    bubbles = max(0, pp_size - 1)
+    return bubbles / (useful + bubbles)
+
+
+def validate_pipeline_topology(
+    *,
+    world_size: int,
+    tp: int,
+    cp: int,
+    pp: int,
+    ep: int,
+    etp: int,
+    vp: int,
+    num_layers: int | None = None,
+) -> None:
+    values = {
+        "world_size": world_size,
+        "tp": tp,
+        "cp": cp,
+        "pp": pp,
+        "ep": ep,
+        "etp": etp,
+        "vp": vp,
+    }
+    invalid = {name: value for name, value in values.items() if value < 1}
+    if invalid:
+        raise ValueError(f"Megatron topology sizes must be positive: {invalid}")
+    dense = tp * cp * pp
+    expert = etp * ep * pp
+    if world_size % dense:
+        raise ValueError(
+            f"world_size={world_size} must be divisible by TP*CP*PP={dense}"
+        )
+    if world_size % expert:
+        raise ValueError(
+            f"world_size={world_size} must be divisible by ETP*EP*PP={expert}"
+        )
+    if vp > 1 and pp <= 1:
+        raise ValueError("VPP requires pipeline_model_parallel_size > 1")
+    if num_layers is not None and num_layers % (pp * vp):
+        raise ValueError(
+            f"num_layers={num_layers} must be divisible by PP*VPP={pp * vp}"
+        )
+
+
+def validate_microbatch_shapes(
+    shapes: Sequence[tuple[int, int]],
+) -> tuple[int, int, bool]:
+    if not shapes:
+        raise ValueError("MCore schedule requires at least one microbatch")
+    invalid = [
+        (index, shape)
+        for index, shape in enumerate(shapes)
+        if shape[0] != 1 or shape[1] < 1
+    ]
+    if invalid:
+        raise ValueError(
+            "ART pipeline microbatches must have [batch=1, sequence>0] shapes; "
+            f"invalid={invalid}"
+        )
+    sequence_lengths = {shape[1] for shape in shapes}
+    return 1, max(sequence_lengths), len(sequence_lengths) > 1
+
+
+def chunk_pre_process(model: torch.nn.Module) -> bool:
+    return bool(_chunk_attr(model, "pre_process"))
+
+
+def chunk_post_process(model: torch.nn.Module) -> bool:
+    return bool(_chunk_attr(model, "post_process"))
+
+
+def _chunk_attr(model: torch.nn.Module, name: str) -> Any:
+    current: Any = model
+    seen: set[int] = set()
+    while id(current) not in seen:
+        seen.add(id(current))
+        if hasattr(current, name):
+            return getattr(current, name)
+        for wrapper_name in ("module", "_orig_mod", "language_model"):
+            wrapped = getattr(current, wrapper_name, None)
+            if isinstance(wrapped, torch.nn.Module):
+                current = wrapped
+                break
+        else:
+            return None
+    return None
+
+
+class _DeferredCudaTimer:
+    def __init__(self, owner: _DeferredCudaTimers, name: str) -> None:
+        self._owner = owner
+        self._name = name
+        self._start: torch.cuda.Event | None = None
+
+    def start(self, barrier: bool = False) -> None:
+        if self._start is not None:
+            raise RuntimeError(f"CUDA timer {self._name!r} is already running")
+        if barrier:
+            torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
+        self._start = torch.cuda.Event(enable_timing=True)
+        self._start.record()
+
+    def stop(self, barrier: bool = False) -> None:
+        if self._start is None:
+            raise RuntimeError(f"CUDA timer {self._name!r} is not running")
+        if barrier:
+            torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
+        end = torch.cuda.Event(enable_timing=True)
+        end.record()
+        self._owner._spans.setdefault(self._name, []).append((self._start, end))
+        self._start = None
+
+
+class _DeferredCudaTimers:
+    _TIMED_NAMES = {"forward-backward", "forward-compute", "backward-compute"}
+
+    def __init__(
+        self, *, forward_chunks: Sequence[int], backward_chunks: Sequence[int]
+    ):
+        self._chunk_sequences = {
+            "forward-compute": tuple(forward_chunks),
+            "backward-compute": tuple(backward_chunks),
+        }
+        self._timers = {
+            name: _DeferredCudaTimer(self, name) for name in self._TIMED_NAMES
+        }
+        self._null_timer = DummyTimer()
+        self._spans: dict[str, list[tuple[torch.cuda.Event, torch.cuda.Event]]] = {}
+
+    def __call__(self, name: str, **_kwargs: Any) -> _DeferredCudaTimer | DummyTimer:
+        return self._timers.get(name, self._null_timer)
+
+    def validate_counts(self, *, forward_only: bool) -> None:
+        names = ("forward-compute",) + (() if forward_only else ("backward-compute",))
+        for name in names:
+            actual = len(self._spans.get(name, ()))
+            expected = len(self._chunk_sequences[name])
+            if actual != expected:
+                raise RuntimeError(
+                    f"MCore {name} count differs from the schedule table: "
+                    f"expected={expected}, got={actual}"
+                )
+
+    def synchronize(self) -> None:
+        schedule = self._spans.get("forward-backward", ())
+        if schedule:
+            schedule[-1][1].synchronize()
+
+    def span(self, name: str) -> tuple[torch.cuda.Event, torch.cuda.Event] | None:
+        spans = self._spans.get(name, ())
+        return (spans[0][0], spans[-1][1]) if spans else None
+
+    def total(self, name: str) -> float:
+        return (
+            sum(start.elapsed_time(end) for start, end in self._spans.get(name, ()))
+            / 1e3
+        )
+
+    def by_chunk(self, name: str) -> dict[int, float]:
+        spans = self._spans.get(name, ())
+        if not spans:
+            return {}
+        chunks = self._chunk_sequences[name]
+        if len(spans) != len(chunks):
+            raise RuntimeError(
+                f"Cannot resolve {name}: spans={len(spans)}, chunks={len(chunks)}"
+            )
+        values: dict[int, float] = {}
+        for chunk, (start, end) in zip(chunks, spans, strict=True):
+            values[chunk] = values.get(chunk, 0.0) + start.elapsed_time(end) / 1e3
+        return values
+
+
+class _TimedWork:
+    def __init__(self, work: Any, telemetry: PipelineScheduleTelemetry) -> None:
+        self._work = work
+        self._telemetry = telemetry
+
+    def __getattr__(self, name: str) -> Any:
+        return getattr(self._work, name)
+
+    def wait(self, *args: Any, **kwargs: Any) -> Any:
+        start = time.perf_counter()
+        try:
+            return self._work.wait(*args, **kwargs)
+        finally:
+            self._telemetry.p2p_wait_s += time.perf_counter() - start
+            self._telemetry.p2p_wait_calls += 1
+
+
+def _wrap_p2p_work(value: Any, telemetry: PipelineScheduleTelemetry) -> Any:
+    if isinstance(value, torch.distributed.Work):  # ty: ignore[possibly-missing-attribute]
+        return _TimedWork(value, telemetry)
+    if isinstance(value, dict):
+        return {key: _wrap_p2p_work(item, telemetry) for key, item in value.items()}
+    if isinstance(value, list):
+        return [_wrap_p2p_work(item, telemetry) for item in value]
+    if isinstance(value, tuple):
+        return tuple(_wrap_p2p_work(item, telemetry) for item in value)
+    return value
+
+
+class _TimedP2PCommunicator:
+    def __init__(
+        self, communicator: P2PCommunicator, telemetry: PipelineScheduleTelemetry
+    ):
+        self._communicator = communicator
+        self._telemetry = telemetry
+
+    def __getattr__(self, name: str) -> Any:
+        value = getattr(self._communicator, name)
+        if not callable(value) or not (
+            name.startswith("send") or name.startswith("recv")
+        ):
+            return value
+
+        def timed(*args: Any, **kwargs: Any) -> Any:
+            start = time.perf_counter()
+            try:
+                result = value(*args, **kwargs)
+            finally:
+                self._telemetry.p2p_s += time.perf_counter() - start
+                self._telemetry.p2p_calls += 1
+            return _wrap_p2p_work(result, self._telemetry)
+
+        return timed
+
+
+class _ArtP2PCommunicator(P2PCommunicator):
+    def _communicate(self, *, tensor_shape: Any, **kwargs: Any) -> Any:
+        multiplier = getattr(self.config, "art_pipeline_activation_multiplier", None)
+        if tensor_shape is not None and multiplier is not None:
+            tensor_shape = torch.Size(
+                (*tensor_shape[:-1], multiplier, tensor_shape[-1])
+            )
+        return super()._communicate(tensor_shape=tensor_shape, **kwargs)
+
+
+class MCoreScheduleAdapter(Generic[_T]):
+    """Small ART boundary around MCore's PP1, PP and VPP schedules."""
+
+    def __init__(
+        self,
+        *,
+        model_chunks: ModelChunks,
+        prepared_microbatches: Sequence[_T],
+        sample_indices: Sequence[int | None],
+        model_inputs: Sequence[torch.Tensor],
+        moe_routing_replay_controller: MoeRoutingReplayController | None = None,
+        hybridep_token_counts: Sequence[int] | None = None,
+        model_activator: Callable[[_T, int], None] | None = None,
+    ) -> None:
+        if not model_chunks:
+            raise ValueError("MCore schedule requires at least one model chunk")
+        if not (len(prepared_microbatches) == len(sample_indices) == len(model_inputs)):
+            raise ValueError("microbatch payload/sample/input counts differ")
+        self.model_chunks = model_chunks
+        self.microbatches = tuple(
+            ScheduleMicrobatch(order, sample_index, prepared, prepared.attention_state)
+            for order, (sample_index, prepared) in enumerate(
+                zip(sample_indices, prepared_microbatches, strict=True)
+            )
+        )
+        self._microbatch_state = PipelineMicrobatchState(
+            controller=moe_routing_replay_controller,
+            hybridep_token_counts=hybridep_token_counts,
+            microbatch_count=len(self.microbatches),
+            model_activator=model_activator,
+        )
+        self._active_activation_key: tuple[int, int] | None = None
+        self.pp_size = int(ps.get_pipeline_model_parallel_world_size())
+        (
+            self.micro_batch_size,
+            local_seq_length,
+            self.variable_seq_lengths,
+        ) = validate_microbatch_shapes(
+            [(int(value.shape[0]), int(value.shape[1])) for value in model_inputs]
+        )
+        self.seq_length = local_seq_length * (
+            int(ps.get_context_parallel_world_size()) if self.pp_size > 1 else 1
+        )
+        self.pp_rank = int(ps.get_pipeline_model_parallel_rank())
+        self.vp_size = int(ps.get_virtual_pipeline_model_parallel_world_size() or 1)
+        self.microbatch_group_size = len(self.microbatches)
+        if self.vp_size != len(model_chunks):
+            raise ValueError(
+                "Local model chunk count must equal VPP size: "
+                f"chunks={len(model_chunks)}, vpp={self.vp_size}"
+            )
+        self._validate_stage_ownership()
+        self._configure()
+        self.telemetry = PipelineScheduleTelemetry(
+            pp_rank=self.pp_rank,
+            pp_size=self.pp_size,
+            vp_size=self.vp_size,
+            num_microbatches=len(self.microbatches),
+            real_microbatches=sum(
+                microbatch.sample_index is not None for microbatch in self.microbatches
+            ),
+            dummy_microbatches=sum(
+                microbatch.sample_index is None for microbatch in self.microbatches
+            ),
+            micro_batch_size=self.micro_batch_size,
+            seq_length=self.seq_length,
+            microbatch_group_size=self.microbatch_group_size,
+        )
+        forward_chunks = [0] * len(self.microbatches)
+        if self.vp_size > 1:
+            table = get_schedule_table(
+                len(self.microbatches), self.vp_size, self.microbatch_group_size
+            )
+            forward_chunks = [int(chunk) for _, chunk in table]
+        backward_chunks = [self.vp_size - chunk - 1 for chunk in forward_chunks]
+        if torch.cuda.is_available():
+            self.telemetry._cuda_timers = _DeferredCudaTimers(
+                forward_chunks=forward_chunks,
+                backward_chunks=backward_chunks,
+            )
+        self._chunk_by_id = {
+            id(chunk): index for index, chunk in enumerate(model_chunks)
+        }
+
+    def _validate_stage_ownership(self) -> None:
+        for chunk_index, chunk in enumerate(self.model_chunks):
+            expected_pre = self.pp_rank == 0 and chunk_index == 0
+            expected_post = (
+                self.pp_rank == self.pp_size - 1
+                and chunk_index == len(self.model_chunks) - 1
+            )
+            actual_pre = chunk_pre_process(chunk)
+            actual_post = chunk_post_process(chunk)
+            if (actual_pre, actual_post) != (expected_pre, expected_post):
+                raise RuntimeError(
+                    "Megatron model chunk pipeline ownership is inconsistent: "
+                    f"pp_rank={self.pp_rank}, chunk={chunk_index}, "
+                    f"pre_process={actual_pre} (expected {expected_pre}), "
+                    f"post_process={actual_post} (expected {expected_post})"
+                )
+
+    def _configure(self) -> None:
+        vpp_group: int | None = None
+        for config in _model_configs(self.model_chunks):
+            config.variable_seq_lengths = self.pp_size > 1 and self.variable_seq_lengths
+            if self.vp_size > 1:
+                group = int(
+                    getattr(config, "microbatch_group_size_per_vp_stage", 0)
+                    or self.pp_size
+                )
+                if vpp_group is not None and group != vpp_group:
+                    raise ValueError(
+                        "All VPP model chunks must use one microbatch group size: "
+                        f"expected={vpp_group}, got={group}"
+                    )
+                vpp_group = group
+                validate_vpp_microbatch_group(
+                    num_microbatches=len(self.microbatches),
+                    pp_size=self.pp_size,
+                    group_size=group,
+                )
+                config.microbatch_group_size_per_vp_stage = group
+                self.microbatch_group_size = group
+                config.overlap_p2p_comm = True
+                config.batch_p2p_comm = False
+            elif self.pp_size > 1:
+                config.overlap_p2p_comm = False
+                config.batch_p2p_comm = True
+                # PyTorch 2.11 does not need MCore's legacy batch-P2P device sync.
+                config.batch_p2p_sync = False
+
+    def activate(self, microbatch: ScheduleMicrobatch[_T], chunk_index: int) -> None:
+        if not self._microbatch_state.enabled:
+            return
+        activation_key = (microbatch.order, chunk_index)
+        if activation_key == self._active_activation_key:
+            return
+        self._microbatch_state.activate(microbatch, chunk_index)
+        self._active_activation_key = activation_key
+
+    def training_workload(self) -> TrainingStepWorkload:
+        values = torch.tensor(
+            _local_training_workload_values(
+                self.microbatches, int(ps.get_context_parallel_rank())
+            ),
+            device=torch.cuda.current_device(),
+            dtype=torch.int64,
+        )
+        if torch.distributed.is_initialized():
+            torch.distributed.all_reduce(
+                values,
+                group=ps.get_data_parallel_group(with_context_parallel=True),
+            )
+        (
+            logical,
+            loss_bearing,
+            executed,
+            nominal,
+            dummy_executed,
+            dummy_nominal,
+            real_microbatches,
+            dummy_microbatches,
+        ) = values.cpu().tolist()
+        return TrainingStepWorkload(
+            logical_nonpadding_tokens=logical,
+            loss_bearing_tokens=loss_bearing,
+            executed_token_equivalents=executed,
+            nominal_schedule_capacity_tokens=nominal,
+            dummy_executed_token_equivalents=dummy_executed,
+            dummy_schedule_capacity_tokens=dummy_nominal,
+            real_microbatches=real_microbatches,
+            dummy_microbatches=dummy_microbatches,
+        )
+
+    @contextmanager
+    def _recompute_activation_hooks(self, *, enabled: bool) -> Iterator[None]:
+        config = get_model_config(self.model_chunks[0])
+        if (
+            not enabled
+            or self.pp_size <= 1
+            or not self._microbatch_state.enabled
+            or not _stateful_recompute_enabled(config)
+        ):
+            yield
+            return
+        _validate_stateful_recompute_mode(config)
+
+        by_state_id: dict[int, ScheduleMicrobatch[_T]] = {}
+        for microbatch in self.microbatches:
+            state = microbatch.recompute_state
+            if state is None:
+                raise RuntimeError(
+                    "Stateful PP recomputation requires recompute_state on every microbatch"
+                )
+            previous = by_state_id.setdefault(id(state), microbatch)
+            if previous is not microbatch:
+                raise RuntimeError(
+                    "recompute_state must identify one logical microbatch"
+                )
+
+        def restore(
+            _module: Any,
+            args: tuple[Any, ...],
+            kwargs: dict[str, Any],
+            *,
+            chunk_index: int,
+        ) -> None:
+            microbatch = _find_bound_microbatch(by_state_id, (*args, kwargs))
+            self.activate(microbatch, chunk_index)
+
+        handles = []
+        for chunk_index, chunk in enumerate(self.model_chunks):
+            for layer in _transformer_layer_callers([chunk]):
+
+                def restore_chunk(
+                    module: Any,
+                    args: tuple[Any, ...],
+                    kwargs: dict[str, Any],
+                    *,
+                    _chunk_index: int = chunk_index,
+                ) -> None:
+                    restore(
+                        module,
+                        args,
+                        kwargs,
+                        chunk_index=_chunk_index,
+                    )
+
+                handles.append(
+                    layer.register_forward_pre_hook(
+                        torch.compiler.disable(restore_chunk),
+                        with_kwargs=True,
+                    )
+                )
+        if not handles:
+            raise RuntimeError(
+                "Stateful PP recomputation could not find TransformerLayer call sites"
+            )
+        try:
+            yield
+        finally:
+            for handle in handles:
+                handle.remove()
+
+    def independent_iterators(self) -> list[Iterator[ScheduleMicrobatch[_T]]]:
+        def activate(
+            chunk_index: int,
+        ) -> Iterator[ScheduleMicrobatch[_T]]:
+            for microbatch in self.microbatches:
+                self.activate(microbatch, chunk_index)
+                yield microbatch
+
+        return [activate(index) for index in range(len(self.model_chunks))]
+
+    @contextmanager
+    def _telemetry_timer_context(self) -> Iterator[None]:
+        timers = self.telemetry._cuda_timers
+        if timers is None:
+            yield
+            return
+        configs = _model_configs(self.model_chunks)
+        previous = [config.timers for config in configs]
+        for config in configs:
+            config.timers = timers
+        try:
+            yield
+        finally:
+            for config, prior in zip(configs, previous, strict=True):
+                config.timers = prior
+
+    def run(
+        self,
+        forward_step_func: Callable[
+            ..., tuple[torch.Tensor, Callable[..., Any] | None]
+        ],
+        *,
+        forward_only: bool,
+        collect_non_loss_data: bool = False,
+    ) -> list[Any]:
+        def timed_forward(data_iterator: Any, model: Any, *args: Any) -> Any:
+            chunk = self._chunk_by_id.get(id(model))
+            if chunk is None:
+                raise RuntimeError("MCore schedule passed an unknown local model chunk")
+            start = time.perf_counter()
+            try:
+                result = forward_step_func(data_iterator, model, *args)
+                output = result[0]
+                if (
+                    self.pp_size > 1
+                    and isinstance(output, torch.Tensor)
+                    and output._base is not None
+                ):
+                    result = (output.clone(), *result[1:])
+                return result
+            finally:
+                elapsed = time.perf_counter() - start
+                self.telemetry.forward_host_s_by_chunk[chunk] = (
+                    self.telemetry.forward_host_s_by_chunk.get(chunk, 0.0) + elapsed
+                )
+                self.telemetry.forward_calls_by_chunk[chunk] = (
+                    self.telemetry.forward_calls_by_chunk.get(chunk, 0) + 1
+                )
+
+        config = get_model_config(self.model_chunks[0])
+        if not forward_only and bool(config.overlap_moe_expert_parallel_comm):
+            raise RuntimeError(
+                "ART's forward-step contract does not support MCore's combined "
+                "EP-overlap schedule; disable overlap_moe_expert_parallel_comm"
+            )
+        communicator: Any | None = None
+        pg_collection: ProcessGroupCollection | None = None
+        if self.pp_size > 1:
+            communicator = _TimedP2PCommunicator(
+                _ArtP2PCommunicator(
+                    pp_group=ps.get_pipeline_model_parallel_group(), config=config
+                ),
+                self.telemetry,
+            )
+            pg_collection = _process_group_collection()
+        start = time.perf_counter()
+        self._active_activation_key = None
+        if torch.cuda.is_available():
+            torch.cuda.reset_peak_memory_stats()
+            self.telemetry.memory_allocated_start_bytes = int(
+                torch.cuda.memory_allocated()
+            )
+        with (
+            self._telemetry_timer_context(),
+            self._recompute_activation_hooks(enabled=not forward_only),
+        ):
+            outputs = get_forward_backward_func(
+                pp_size=self.pp_size,
+                vp_size=None if self.vp_size == 1 else self.vp_size,
+            )(
+                forward_step_func=timed_forward,
+                data_iterator=self.independent_iterators(),
+                model=self.model_chunks,
+                num_microbatches=len(self.microbatches),
+                seq_length=self.seq_length,
+                micro_batch_size=self.micro_batch_size,
+                forward_only=forward_only,
+                collect_non_loss_data=collect_non_loss_data,
+                p2p_communicator=cast(Any, communicator),
+                pg_collection=pg_collection,
+            )
+        self.telemetry.schedule_wall_s = time.perf_counter() - start
+        expected_calls = len(self.microbatches)
+        invalid_calls = {
+            chunk: self.telemetry.forward_calls_by_chunk.get(chunk, 0)
+            for chunk in range(self.vp_size)
+            if self.telemetry.forward_calls_by_chunk.get(chunk, 0) != expected_calls
+        }
+        if invalid_calls:
+            raise RuntimeError(
+                "MCore schedule did not run every local chunk once per microbatch: "
+                f"expected={expected_calls}, got={invalid_calls}"
+            )
+        if self.telemetry._cuda_timers is not None:
+            self.telemetry._cuda_timers.validate_counts(forward_only=forward_only)
+        if torch.cuda.is_available():
+            self.telemetry.peak_memory_bytes = int(torch.cuda.max_memory_allocated())
+        return cast(list[Any], outputs)
+
+
+def validate_vpp_microbatch_group(
+    *, num_microbatches: int, pp_size: int, group_size: int
+) -> None:
+    if not (pp_size <= group_size <= num_microbatches):
+        raise ValueError(
+            "VPP microbatch group must be in [PP, num_microbatches]: "
+            f"pp={pp_size}, group={group_size}, num_microbatches={num_microbatches}"
+        )
+    remainder = num_microbatches % group_size
+    if 0 < remainder < pp_size:
+        raise ValueError(
+            "VPP final microbatch group must be empty or contain at least PP "
+            f"microbatches: remainder={remainder}, pp={pp_size}"
+        )
+
+
+def _stateful_recompute_enabled(config: Any) -> bool:
+    granularity = getattr(config, "recompute_granularity", None)
+    if granularity == "full":
+        return True
+    modules = set(getattr(config, "recompute_modules", None) or ())
+    return granularity == "selective" and bool(modules & {"mlp", "moe"})
+
+
+def _validate_stateful_recompute_mode(config: Any) -> None:
+    if getattr(config, "recompute_granularity", None) != "full":
+        raise RuntimeError(
+            "HybridEP/MoE replay requires full-layer activation recomputation under "
+            "PP; selective MLP/MoE checkpoints do not retain ART's exact microbatch "
+            "state"
+        )
+
+
+def _transformer_layer_callers(
+    model_chunks: Sequence[torch.nn.Module],
+) -> list[torch.nn.Module]:
+    from megatron.core.transformer.transformer_layer import TransformerLayer
+
+    callers: dict[int, torch.nn.Module] = {}
+    for chunk in model_chunks:
+        for module in chunk.modules():
+            original = getattr(module, "_orig_mod", None)
+            if isinstance(original, TransformerLayer):
+                callers[id(original)] = module
+            elif isinstance(module, TransformerLayer):
+                callers.setdefault(id(module), module)
+    return list(callers.values())
+
+
+def _find_bound_microbatch(
+    by_state_id: dict[int, ScheduleMicrobatch[_T]],
+    values: Sequence[Any],
+) -> ScheduleMicrobatch[_T]:
+    pending = list(values)
+    seen: set[int] = set()
+    match: ScheduleMicrobatch[_T] | None = None
+    while pending:
+        value = pending.pop()
+        value_id = id(value)
+        if value_id in seen:
+            continue
+        seen.add(value_id)
+        microbatch = by_state_id.get(value_id)
+        if microbatch is not None and microbatch.recompute_state is value:
+            if match is not None and match is not microbatch:
+                raise RuntimeError(
+                    "Stateful PP recomputation received multiple microbatch states: "
+                    f"orders={[match.order, microbatch.order]}"
+                )
+            match = microbatch
+        if isinstance(value, dict):
+            pending.extend(value.values())
+        elif isinstance(value, list | tuple):
+            pending.extend(value)
+    if match is None:
+        raise RuntimeError(
+            "Stateful PP recomputation did not receive its exact microbatch state"
+        )
+    return match
+
+
+def _process_group_collection() -> ProcessGroupCollection:
+    groups = ProcessGroupCollection()
+    groups.tp = ps.get_tensor_model_parallel_group()
+    groups.pp = ps.get_pipeline_model_parallel_group()
+    groups.cp = ps.get_context_parallel_group()
+    groups.embd = ps.get_embedding_group(check_initialized=False)
+    groups.pos_embd = ps.get_position_embedding_group(check_initialized=False)
+    groups.dp_cp = ps.get_data_parallel_group(
+        with_context_parallel=True, partial_data_parallel=False
+    )
+    return groups
+
+
+def _model_configs(model_chunks: Sequence[torch.nn.Module]) -> list[Any]:
+    configs: dict[int, Any] = {}
+    for chunk in model_chunks:
+        config = get_model_config(chunk)
+        configs.setdefault(id(config), config)
+    return list(configs.values())
diff --git a/src/art/megatron/training/sft_batches.py b/src/art/megatron/training/sft_batches.py
deleted file mode 100644
index 9c20640f2..000000000
--- a/src/art/megatron/training/sft_batches.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from dataclasses import dataclass
-import importlib
-import json
-import os
-from typing import TYPE_CHECKING, Any, Iterable
-import uuid
-
-import torch
-
-safetensors_torch = importlib.import_module("safetensors.torch")
-load_file = safetensors_torch.load_file
-save_file = safetensors_torch.save_file
-
-if TYPE_CHECKING:
-    from ...preprocessing.tokenize import SFTBatch
-
-
-DEFAULT_SFT_DATA_DIR = "/tmp/megatron_sft_data"
-
-
-@dataclass(frozen=True)
-class SerializedSFTBatches:
-    sft_data_dir: str
-    num_batches: int
-    learning_rates: list[float]
-
-
-def serialize_sft_batch_to_disk(batch: "SFTBatch", batch_dir: str) -> None:
-    os.makedirs(batch_dir, exist_ok=True)
-    metadata = {
-        "learning_rate": batch.learning_rate,
-        "num_trajectories": batch.num_trajectories,
-        "num_tokens": batch.num_tokens,
-        "num_trainable_tokens": batch.num_trainable_tokens,
-        "num_dropped_trajectories": batch.num_dropped_trajectories,
-        "num_trajectory_tensors": len(batch.trajectory_tensors),
-    }
-    with open(os.path.join(batch_dir, "metadata.json"), "w", encoding="utf-8") as f:
-        json.dump(metadata, f)
-    for index, trajectory_tensors in enumerate(batch.trajectory_tensors):
-        save_file(
-            {
-                key: value.squeeze(0) if value.dim() > 0 else value
-                for key, value in trajectory_tensors.items()
-            },
-            os.path.join(batch_dir, f"trajectory_{index}.safetensors"),
-        )
-
-
-def materialize_sft_batches(
-    batches: Iterable["SFTBatch"],
-    *,
-    sft_data_dir: str | None = None,
-) -> SerializedSFTBatches:
-    if sft_data_dir is None:
-        sft_data_dir = os.path.join(DEFAULT_SFT_DATA_DIR, uuid.uuid4().hex)
-
-    learning_rates: list[float] = []
-    num_batches = 0
-    for batch_index, batch in enumerate(batches):
-        batch_dir = os.path.join(sft_data_dir, f"batch_{batch_index:06d}")
-        serialize_sft_batch_to_disk(batch, batch_dir)
-        learning_rates.append(batch.learning_rate)
-        num_batches += 1
-
-    return SerializedSFTBatches(
-        sft_data_dir=sft_data_dir,
-        num_batches=num_batches,
-        learning_rates=learning_rates,
-    )
-
-
-def load_sft_batch_from_disk(
-    batch_dir: str,
-) -> tuple[dict[str, Any], list[dict[str, torch.Tensor]]]:
-    with open(os.path.join(batch_dir, "metadata.json"), encoding="utf-8") as f:
-        metadata = json.load(f)
-
-    trajectory_tensors = []
-    for index in range(metadata["num_trajectory_tensors"]):
-        trajectory_tensors.append(
-            load_file(os.path.join(batch_dir, f"trajectory_{index}.safetensors"))
-        )
-    return metadata, trajectory_tensors
diff --git a/src/art/megatron/training/trace.py b/src/art/megatron/training/trace.py
index b3461c164..bb2f19fe9 100644
--- a/src/art/megatron/training/trace.py
+++ b/src/art/megatron/training/trace.py
@@ -156,12 +156,14 @@ def attach_trace_token_uids(
     token_uids: torch.Tensor | None,
 ) -> Iterator[None]:
     attach_module_token_uids = trace_token_uids_enabled()
-    _set_root_output_trace_token_uids(model_chunks[0], token_uids)
+    for chunk in model_chunks:
+        _set_root_output_trace_token_uids(chunk, token_uids)
     if attach_module_token_uids:
         _set_module_trace_token_uids(model_chunks, token_uids)
     try:
         yield
     finally:
-        _set_root_output_trace_token_uids(model_chunks[0], None)
+        for chunk in model_chunks:
+            _set_root_output_trace_token_uids(chunk, None)
         if attach_module_token_uids:
             _set_module_trace_token_uids(model_chunks, None)
diff --git a/src/art/megatron/weights/adapter_export.py b/src/art/megatron/weights/adapter_export.py
index bb96b51c7..13f3196cc 100644
--- a/src/art/megatron/weights/adapter_export.py
+++ b/src/art/megatron/weights/adapter_export.py
@@ -158,13 +158,14 @@ def _set_expert_adapter_weights(
     lora: LoRA,
     build_weight: Callable[[int], AdapterWeight],
 ) -> None:
-    for local_expert_idx in range(lora.num_local_experts):
-        global_expert_idx = local_expert_idx + lora._expert_offset
+    for local_expert_idx, logical_expert_idx in enumerate(lora.expert_ids):
+        if logical_expert_idx is None:
+            continue
         _set_adapter_weights(
             out,
             base_prefix,
             build_weight(local_expert_idx),
-            weight_suffix=f".weight{global_expert_idx}",
+            weight_suffix=f".weight{local_expert_idx + lora._expert_offset}",
         )
 
 
diff --git a/src/art/megatron/weights/conversion_tasks.py b/src/art/megatron/weights/conversion_tasks.py
new file mode 100644
index 000000000..bfaba35b8
--- /dev/null
+++ b/src/art/megatron/weights/conversion_tasks.py
@@ -0,0 +1,110 @@
+from itertools import chain
+from typing import Any, cast
+
+from megatron.bridge import AutoBridge
+from megatron.bridge.models.conversion.param_mapping import (
+    extract_expert_number_from_param,
+)
+import torch
+
+from art.megatron.expert_parallel import get_expert_parallel_layout
+from art.megatron.runtime.bridge_runtime import (
+    _logical_hf_param,
+)
+from art.megatron.training.model_chunks import ModelChunks, as_megatron_api_chunks
+from art.megatron.weights.param_name_canonicalization import (
+    canonical_art_param_name,
+    is_art_adapter_param_name,
+)
+
+
+def _hf_param_names(hf_param: Any) -> list[str]:
+    if isinstance(hf_param, str):
+        return [hf_param]
+    return list(hf_param.values())
+
+
+def _checkpoint_hf_param_names(mapping: Any, model_config: Any) -> list[str]:
+    layout = get_expert_parallel_layout(model_config)
+    if layout is None or not bool(getattr(mapping, "is_expert", False)):
+        return _hf_param_names(mapping.hf_param)
+    physical_expert = extract_expert_number_from_param(mapping.megatron_param)
+    logical_expert = layout.logical_expert(physical_expert)
+    if logical_expert is None:
+        return []
+    return _hf_param_names(
+        _logical_hf_param(
+            mapping.hf_param,
+            physical_expert=physical_expert,
+            logical_expert=logical_expert,
+        )
+    )
+
+
+def build_art_conversion_tasks(*, bridge: AutoBridge, model: ModelChunks) -> list[Any]:
+    from megatron.bridge.models.conversion.model_bridge import (
+        WeightConversionTask,
+        _megatron_local_name_to_global,
+    )
+    from megatron.bridge.models.conversion.utils import (
+        get_module_and_param_from_name,
+        persistent_buffers,
+    )
+
+    mapping_registry = bridge._model_bridge.mapping_registry()
+    hf_source = bridge.hf_pretrained.state.source
+    hf_keys = set(hf_source.get_all_keys())
+    megatron_models = as_megatron_api_chunks(model)
+    model_config = getattr(model[0], "config")
+    tasks: list[Any] = []
+    for vp_stage, chunk in enumerate(model):
+        for local_name, _ in chain(
+            chunk.named_parameters(),
+            persistent_buffers(chunk),
+        ):
+            if "_extra_state" in local_name or is_art_adapter_param_name(local_name):
+                continue
+            global_name = _megatron_local_name_to_global(
+                megatron_models,
+                model_config,
+                canonical_art_param_name(local_name),
+                vp_stage,
+            )
+            mapping = mapping_registry.megatron_to_hf_lookup(global_name)
+            if mapping is None:
+                raise RuntimeError(
+                    f"Missing HF conversion mapping for Megatron param {global_name}"
+                )
+            hf_params = _checkpoint_hf_param_names(mapping, model_config)
+            missing_hf_params = sorted(set(hf_params) - hf_keys)
+            if missing_hf_params and not getattr(
+                mapping,
+                "allow_hf_name_mismatch",
+                False,
+            ):
+                raise RuntimeError(
+                    f"Missing HF checkpoint weights for Megatron param {global_name}: "
+                    f"{missing_hf_params}"
+                )
+            local_module, local_weights = cast(
+                tuple[Any, torch.Tensor],
+                get_module_and_param_from_name(
+                    megatron_models,
+                    local_name,
+                    vp_stage,
+                ),
+            )
+            if local_module is not None and not hasattr(local_module, "config"):
+                setattr(local_module, "config", model_config)
+            tasks.append(
+                WeightConversionTask(
+                    pp_rank=0,
+                    vp_stage=vp_stage,
+                    param_name=local_name,
+                    global_param_name=global_name,
+                    megatron_module=local_module,
+                    param_weight=local_weights,
+                    mapping=mapping,
+                )
+            )
+    return tasks
diff --git a/src/art/megatron/weights/lora_publish.py b/src/art/megatron/weights/lora_publish.py
index e9d8b4b08..63044560e 100644
--- a/src/art/megatron/weights/lora_publish.py
+++ b/src/art/megatron/weights/lora_publish.py
@@ -1,11 +1,11 @@
 from collections.abc import Iterable, Sequence
 from typing import Any, NamedTuple
 
+from pydantic import BaseModel, ConfigDict
 import torch
 
 from art.megatron.lora import (
     LoRA,
-    LoRAPublishPlanner,
     LoraShardMeta,
     LoRASlotRef,
     _block_for_key,
@@ -16,7 +16,13 @@
 )
 from art.megatron.model_support.lora_disk import save_vllm_lora_tensors
 from art.megatron.model_support.spec import ExpertPackedLoraGroup, ExpertPackedLoraSlot
+from art.megatron.tensor_snapshot import (
+    PendingCpuSnapshot,
+    PinnedCpuSnapshotBuilder,
+    PinnedCpuSnapshotStager,
+)
 from art.megatron.training.model_chunks import ModelChunks
+from art.utils.safetensors import PreparedSafetensors
 
 
 class PackedExpertShardMeta(NamedTuple):
@@ -37,32 +43,11 @@ def numel(self) -> int:
         return total
 
 
-class _PinnedCpuStager:
-    def __init__(self) -> None:
-        self._events: list[torch.cuda.Event] = []
-        self._stream = torch.cuda.Stream() if torch.cuda.is_available() else None
-
-    def stage(self, tensor: torch.Tensor) -> torch.Tensor:
-        source = tensor.detach()
-        if self._stream is None or not source.is_cuda:
-            return source.cpu()
-
-        source = source.contiguous()
-        target = torch.empty_like(source, device="cpu", pin_memory=True)
-        source_stream = torch.cuda.current_stream(source.device)
-        self._stream.wait_stream(source_stream)
-        with torch.cuda.stream(self._stream):
-            target.copy_(source, non_blocking=True)
-            source.record_stream(self._stream)
-            event = torch.cuda.Event()
-            event.record(self._stream)
-        self._events.append(event)
-        return target
+class LoraSnapshot(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
 
-    def finish(self) -> None:
-        for event in self._events:
-            event.synchronize()
-        self._events.clear()
+    tensors: dict[str, torch.Tensor]
+    adapter_config: dict[str, Any]
 
 
 def iter_lora_modules(model_chunks: ModelChunks) -> Iterable[LoRA]:
@@ -161,8 +146,11 @@ def collect_local_packed_expert_entries(
     for module in iter_lora_modules(model_chunks):
         if not _uses_packed_expert_publish(module, packed_expert_groups, slot_ref):
             continue
-        expert_start = int(module._expert_offset)
-        expert_count = int(module.num_local_experts)
+        expert_ids = tuple(expert for expert in module.expert_ids if expert is not None)
+        if not expert_ids:
+            continue
+        expert_start = expert_ids[0]
+        expert_count = len(expert_ids)
         for suffix, param in module._lora_params(slot_ref):
             slot_match = _packed_expert_slot(
                 module.adapter_model_prefix,
@@ -173,7 +161,7 @@ def collect_local_packed_expert_entries(
                 continue
             group_prefix, slot = slot_match
             key = f"{group_prefix}.{slot.output_suffix}"
-            tensor = param.data.transpose(1, 2).contiguous()
+            tensor = param.data[:expert_count].transpose(1, 2).contiguous()
             source_keys = module._expected_weight_keys(suffix.removesuffix(".weight"))
             target_dtype = (
                 adapter_dtypes[source_keys[0]]
@@ -199,91 +187,6 @@ def collect_local_packed_expert_entries(
     return local_tensors, metadata
 
 
-def _global_packed_expert_metadata(
-    planner: LoRAPublishPlanner,
-    adapter_dtypes: dict[str, torch.dtype],
-    packed_expert_groups: Sequence[ExpertPackedLoraGroup],
-) -> list[PackedExpertShardMeta]:
-    metadata: list[PackedExpertShardMeta] = []
-    for template in planner.templates:
-        if int(template.num_local_experts) <= 1:
-            continue
-        slot_match = _packed_expert_slot(
-            template.adapter_model_prefix,
-            template.suffix,
-            packed_expert_groups,
-        )
-        if slot_match is None:
-            continue
-        group_prefix, slot = slot_match
-        shard_ranks = range(template.shard_world_size) if template.sharded else (0,)
-        ep_world_size = 1
-        if _distributed_ready():
-            from megatron.core import parallel_state as ps
-
-            ep_world_size = ps.get_expert_model_parallel_world_size()
-        for ep_rank in range(ep_world_size):
-            expert_start = ep_rank * template.num_local_experts
-            expert_key = (
-                f"{template.adapter_model_prefix.format(expert=expert_start)}."
-                f"{template.suffix}"
-            )
-            for shard_rank in shard_ranks:
-                owner_rank = planner._expert_owner_rank(ep_rank, shard_rank)
-                per_expert_meta = planner._make_metadata(
-                    template,
-                    key=expert_key,
-                    owner_rank=owner_rank,
-                    shard_rank=shard_rank,
-                    adapter_dtypes=adapter_dtypes,
-                )
-                metadata.append(
-                    PackedExpertShardMeta(
-                        key=f"{group_prefix}.{slot.output_suffix}",
-                        owner_rank=owner_rank,
-                        shape=(template.num_local_experts, *per_expert_meta.shape),
-                        dtype_name=per_expert_meta.dtype_name,
-                        manifest=per_expert_meta.manifest,
-                        expert_start=expert_start,
-                        expert_count=template.num_local_experts,
-                        pack_layout=slot.pack_layout,
-                    )
-                )
-    return metadata
-
-
-def _global_regular_metadata(
-    planner: LoRAPublishPlanner,
-    adapter_dtypes: dict[str, torch.dtype],
-    packed_expert_groups: Sequence[ExpertPackedLoraGroup],
-) -> list[LoraShardMeta]:
-    if not packed_expert_groups:
-        return planner.global_metadata(adapter_dtypes)
-    if _distributed_ready():
-        from megatron.core import parallel_state as ps
-
-        pp_world_size = ps.get_pipeline_model_parallel_world_size()
-        if pp_world_size != 1:
-            raise RuntimeError(
-                "LoRA publish planner requires pipeline_model_parallel_size=1; "
-                f"got {pp_world_size}. Rank-local modules cannot describe remote "
-                "pipeline stages without exchanging templates."
-            )
-    metadata: list[LoraShardMeta] = []
-    for template in planner.templates:
-        if (
-            _packed_expert_slot(
-                template.adapter_model_prefix,
-                template.suffix,
-                packed_expert_groups,
-            )
-            is not None
-        ):
-            continue
-        metadata.extend(planner._metadata_for_template(template, adapter_dtypes))
-    return metadata
-
-
 def _merge_sharded_tensor(
     key: str,
     *,
@@ -394,6 +297,37 @@ def _metadata_by_owner_dtype(
     }
 
 
+def _canonical_global_metadata(local_metadata: list[Any]) -> list[Any]:
+    """Gather stage-local manifests; select one canonical DP/CP replica per shard."""
+    if not _distributed_ready():
+        return local_metadata
+    world_size = torch.distributed.get_world_size()  # type: ignore[possibly-missing-attribute]
+    gathered: list[list[Any] | None] = [None] * world_size
+    torch.distributed.all_gather_object(gathered, local_metadata)  # type: ignore[possibly-missing-attribute]
+    canonical: dict[tuple[Any, ...], Any] = {}
+    for rank_entries in gathered:
+        if rank_entries is None:
+            raise RuntimeError("LoRA manifest gather returned a missing rank")
+        for meta in rank_entries:
+            manifest = meta.manifest
+            identity = (
+                meta.key,
+                int(manifest.get("shard_rank", 0)),
+                int(getattr(meta, "expert_start", -1)),
+            )
+            current = canonical.get(identity)
+            if current is None or meta.owner_rank < current.owner_rank:
+                canonical[identity] = meta
+    return sorted(
+        canonical.values(),
+        key=lambda meta: (
+            meta.key,
+            int(getattr(meta, "expert_start", -1)),
+            int(meta.manifest.get("shard_rank", 0)),
+        ),
+    )
+
+
 def _pack_metadata_tensors(
     metadata: Sequence[Any],
     tensors: dict[str, torch.Tensor],
@@ -584,15 +518,56 @@ def merge_packed_expert_adapter_entries(
 
 def _stage_published_tensors(
     tensors: dict[str, torch.Tensor],
-    stager: _PinnedCpuStager,
+    stager: PinnedCpuSnapshotBuilder,
 ) -> dict[str, torch.Tensor]:
-    grouped: dict[tuple[str, int | None, str], list[tuple[str, torch.Tensor]]] = {}
+    aliases: dict[
+        tuple[str, int | None, torch.dtype, int], list[tuple[str, torch.Tensor]]
+    ] = {}
+    regular: list[tuple[str, torch.Tensor]] = []
     for key, tensor in tensors.items():
+        if not tensor.numel() or not tensor.is_contiguous():
+            regular.append((key, tensor))
+            continue
+        storage = tensor.untyped_storage()
+        aliases.setdefault(
+            (
+                tensor.device.type,
+                tensor.device.index,
+                tensor.dtype,
+                storage.data_ptr(),
+            ),
+            [],
+        ).append((key, tensor))
+
+    staged: dict[str, torch.Tensor] = {}
+    for group in aliases.values():
+        storage = group[0][1].untyped_storage()
+        if len(group) == 1 or storage.nbytes() > sum(
+            tensor.nbytes for _key, tensor in group
+        ):
+            regular.extend(group)
+            continue
+        representative = group[0][1]
+        flat = representative.new_empty(0).set_(
+            storage,
+            0,
+            (storage.nbytes() // representative.element_size(),),
+            (1,),
+        )
+        staged_flat = stager.stage(flat)
+        for key, tensor in group:
+            staged[key] = staged_flat.as_strided(
+                tensor.shape,
+                tensor.stride(),
+                tensor.storage_offset(),
+            )
+
+    grouped: dict[tuple[str, int | None, str], list[tuple[str, torch.Tensor]]] = {}
+    for key, tensor in regular:
         dtype_name = _dtype_name(tensor.dtype)
         group_key = (tensor.device.type, tensor.device.index, dtype_name)
         grouped.setdefault(group_key, []).append((key, tensor))
 
-    staged: dict[str, torch.Tensor] = {}
     for _group_key, group in sorted(grouped.items()):
         flat = torch.cat(
             [tensor.detach().contiguous().view(-1) for _key, tensor in sorted(group)]
@@ -610,33 +585,7 @@ def _stage_published_tensors(
     return staged
 
 
-def _save_rank0_vllm_lora(
-    *,
-    metadata: list[LoraShardMeta],
-    tensors_by_owner_key: dict[tuple[int, str], torch.Tensor],
-    packed_expert_metadata: list[PackedExpertShardMeta] | None = None,
-    packed_expert_tensors_by_owner_key: (
-        dict[tuple[int, str], torch.Tensor] | None
-    ) = None,
-    handler: Any,
-    adapter_config: dict[str, Any],
-    output_dir: str,
-) -> None:
-    vllm_tensors, published_config = _rank0_vllm_lora_tensors(
-        metadata=metadata,
-        tensors_by_owner_key=tensors_by_owner_key,
-        packed_expert_metadata=packed_expert_metadata,
-        packed_expert_tensors_by_owner_key=packed_expert_tensors_by_owner_key,
-        handler=handler,
-        adapter_config=adapter_config,
-    )
-    stager = _PinnedCpuStager()
-    published_tensors = _stage_published_tensors(vllm_tensors, stager)
-    stager.finish()
-    save_vllm_lora_tensors(output_dir, published_tensors, published_config)
-
-
-def _rank0_vllm_lora_tensors(
+def _rank0_merged_lora_tensors(
     *,
     metadata: list[LoraShardMeta],
     tensors_by_owner_key: dict[tuple[int, str], torch.Tensor],
@@ -644,9 +593,7 @@ def _rank0_vllm_lora_tensors(
     packed_expert_tensors_by_owner_key: (
         dict[tuple[int, str], torch.Tensor] | None
     ) = None,
-    handler: Any,
-    adapter_config: dict[str, Any],
-) -> tuple[dict[str, torch.Tensor], dict[str, Any]]:
+) -> dict[str, torch.Tensor]:
     merged_tensors = merge_sharded_adapter_entries(
         _entries_by_key(metadata, tensors_by_owner_key)
     )
@@ -661,22 +608,44 @@ def _rank0_vllm_lora_tensors(
             if key in merged_tensors:
                 raise RuntimeError(f"Duplicate LoRA tensor after packed publish: {key}")
             merged_tensors[key] = tensor
+    return merged_tensors
+
+
+def build_vllm_lora_tensors_from_model(
+    *,
+    model: ModelChunks,
+    adapter_dtypes: dict[str, torch.dtype],
+    handler: Any,
+    adapter_config: dict[str, Any],
+    rank: int,
+    world_size: int,
+    slot_ref: LoRASlotRef | None = None,
+) -> tuple[dict[str, torch.Tensor], dict[str, Any]] | None:
+    merged_tensors = _build_merged_lora_tensors_from_model(
+        model=model,
+        adapter_dtypes=adapter_dtypes,
+        handler=handler,
+        rank=rank,
+        world_size=world_size,
+        slot_ref=slot_ref,
+    )
+    if merged_tensors is None:
+        return None
     return handler.to_vllm_lora_tensors(
         merged_tensors,
         adapter_config=dict(adapter_config),
     )
 
 
-def build_vllm_lora_tensors_from_model(
+def _build_merged_lora_tensors_from_model(
     *,
     model: ModelChunks,
     adapter_dtypes: dict[str, torch.dtype],
     handler: Any,
-    adapter_config: dict[str, Any],
     rank: int,
     world_size: int,
     slot_ref: LoRASlotRef | None = None,
-) -> tuple[dict[str, torch.Tensor], dict[str, Any]] | None:
+) -> dict[str, torch.Tensor] | None:
     actual_rank, device = _rank_and_device()
     if _distributed_ready():
         actual_world_size = torch.distributed.get_world_size()  # type: ignore[possibly-missing-attribute]
@@ -693,7 +662,6 @@ def build_vllm_lora_tensors_from_model(
             )
         rank = 0
     packed_expert_groups = tuple(handler.expert_packed_lora_groups())
-    planner = LoRAPublishPlanner(model, slot_ref)
     local_tensors, local_metadata = collect_local_lora_entries(
         model,
         adapter_dtypes,
@@ -708,19 +676,8 @@ def build_vllm_lora_tensors_from_model(
         packed_expert_groups=packed_expert_groups,
         slot_ref=slot_ref,
     )
-    all_packed_metadata = (
-        _global_packed_expert_metadata(planner, adapter_dtypes, packed_expert_groups)
-        if rank == 0
-        else local_packed_metadata
-    )
-    if rank == 0:
-        all_metadata = _global_regular_metadata(
-            planner,
-            adapter_dtypes,
-            packed_expert_groups if all_packed_metadata else (),
-        )
-    else:
-        all_metadata = local_metadata
+    all_packed_metadata = _canonical_global_metadata(local_packed_metadata)
+    all_metadata = _canonical_global_metadata(local_metadata)
     exchanged_tensors = _exchange_batched_tensors(
         all_metadata,
         local_tensors=local_tensors,
@@ -737,13 +694,11 @@ def build_vllm_lora_tensors_from_model(
     if rank != 0:
         return None
 
-    return _rank0_vllm_lora_tensors(
+    return _rank0_merged_lora_tensors(
         metadata=all_metadata,
         tensors_by_owner_key=exchanged_tensors,
         packed_expert_metadata=all_packed_metadata,
         packed_expert_tensors_by_owner_key=exchanged_packed_tensors,
-        handler=handler,
-        adapter_config=adapter_config,
     )
 
 
@@ -758,7 +713,7 @@ def save_vllm_lora_from_model(
     world_size: int,
     slot_ref: LoRASlotRef | None = None,
 ) -> None:
-    result = build_vllm_lora_tensors_from_model(
+    snapshot = snapshot_vllm_lora_from_model(
         model=model,
         adapter_dtypes=adapter_dtypes,
         handler=handler,
@@ -767,10 +722,85 @@ def save_vllm_lora_from_model(
         world_size=world_size,
         slot_ref=slot_ref,
     )
-    if result is None:
+    if snapshot is None:
         return
-    vllm_tensors, published_config = result
-    stager = _PinnedCpuStager()
-    published_tensors = _stage_published_tensors(vllm_tensors, stager)
-    stager.finish()
-    save_vllm_lora_tensors(output_dir, published_tensors, published_config)
+    save_vllm_lora_snapshot(snapshot, output_dir)
+
+
+def snapshot_vllm_lora_from_model(
+    *,
+    model: ModelChunks,
+    adapter_dtypes: dict[str, torch.dtype],
+    handler: Any,
+    adapter_config: dict[str, Any],
+    rank: int,
+    world_size: int,
+    slot_ref: LoRASlotRef | None = None,
+) -> LoraSnapshot | None:
+    pending = stage_vllm_lora_snapshot_from_model(
+        model=model,
+        adapter_dtypes=adapter_dtypes,
+        handler=handler,
+        adapter_config=adapter_config,
+        rank=rank,
+        world_size=world_size,
+        slot_ref=slot_ref,
+        stager=PinnedCpuSnapshotStager(),
+    )
+    return None if pending is None else pending.resolve()
+
+
+def stage_vllm_lora_snapshot_from_model(
+    *,
+    model: ModelChunks,
+    adapter_dtypes: dict[str, torch.dtype],
+    handler: Any,
+    adapter_config: dict[str, Any],
+    rank: int,
+    world_size: int,
+    stager: PinnedCpuSnapshotStager,
+    slot_ref: LoRASlotRef | None = None,
+) -> PendingCpuSnapshot[LoraSnapshot] | None:
+    merged_tensors = _build_merged_lora_tensors_from_model(
+        model=model,
+        adapter_dtypes=adapter_dtypes,
+        handler=handler,
+        rank=rank,
+        world_size=world_size,
+        slot_ref=slot_ref,
+    )
+    if merged_tensors is None:
+        return None
+    builder = stager.begin()
+    if handler.vllm_lora_conversion_is_view_only():
+        merged_tensors = _stage_published_tensors(merged_tensors, builder)
+        vllm_tensors, published_config = handler.to_vllm_lora_tensors(
+            merged_tensors,
+            adapter_config=dict(adapter_config),
+        )
+    else:
+        vllm_tensors, published_config = handler.to_vllm_lora_tensors(
+            merged_tensors,
+            adapter_config=dict(adapter_config),
+        )
+        vllm_tensors = _stage_published_tensors(vllm_tensors, builder)
+    return builder.finish(
+        LoraSnapshot(
+            tensors=vllm_tensors,
+            adapter_config=published_config,
+        )
+    )
+
+
+def save_vllm_lora_snapshot(
+    snapshot: LoraSnapshot,
+    output_dir: str,
+    *,
+    prepared_tensors: PreparedSafetensors | None = None,
+) -> None:
+    save_vllm_lora_tensors(
+        output_dir,
+        snapshot.tensors,
+        snapshot.adapter_config,
+        prepared_tensors=prepared_tensors,
+    )
diff --git a/src/art/megatron/weights/merged_weight_export.py b/src/art/megatron/weights/merged_weight_export.py
deleted file mode 100644
index 2c6287be2..000000000
--- a/src/art/megatron/weights/merged_weight_export.py
+++ /dev/null
@@ -1,523 +0,0 @@
-from concurrent.futures import ThreadPoolExecutor
-from itertools import chain
-import time
-from typing import Any, Iterator, cast
-
-from megatron.bridge import AutoBridge
-from pydantic import BaseModel, ConfigDict
-import torch
-
-from art.megatron.model_support.spec import ModelSupportHandler
-from art.megatron.runtime.jobs import (
-    MergedWeightTransferInitInfo,
-    MergedWeightTransferSpec,
-)
-from art.megatron.training.model_chunks import ModelChunks, as_megatron_api_chunks
-from art.megatron.weights.lora_publish import build_vllm_lora_tensors_from_model
-from art.megatron.weights.param_name_canonicalization import (
-    canonical_art_param_name,
-    is_art_adapter_param_name,
-)
-from art.weight_transfer import (
-    DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-    DEFAULT_PACKED_NUM_BUFFERS,
-    TrainerNcclCommunicator,
-    trainer_init,
-    trainer_send_weights,
-)
-
-
-class MergedWeightExport(BaseModel):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    bridge: AutoBridge
-    model: ModelChunks
-    model_config_value: Any
-    conversion_tasks: list[Any]
-    adapter_weights_by_base: dict[str, list[Any]]
-
-
-def _hf_param_names(hf_param: Any) -> list[str]:
-    if isinstance(hf_param, str):
-        return [hf_param]
-    return list(hf_param.values())
-
-
-def build_art_conversion_tasks(*, bridge: AutoBridge, model: ModelChunks) -> list[Any]:
-    from megatron.bridge.models.conversion.model_bridge import (
-        WeightConversionTask,
-        _megatron_local_name_to_global,
-    )
-    from megatron.bridge.models.conversion.utils import (
-        get_module_and_param_from_name,
-        persistent_buffers,
-    )
-
-    mapping_registry = bridge._model_bridge.mapping_registry()
-    hf_source = bridge.hf_pretrained.state.source
-    hf_keys = set(hf_source.get_all_keys())
-    megatron_models = as_megatron_api_chunks(model)
-    model_config = getattr(model[0], "config")
-    tasks: list[Any] = []
-    for vp_stage, chunk in enumerate(model):
-        for local_name, _ in chain(
-            chunk.named_parameters(),
-            persistent_buffers(chunk),
-        ):
-            if "_extra_state" in local_name or is_art_adapter_param_name(local_name):
-                continue
-            global_name = _megatron_local_name_to_global(
-                megatron_models,
-                model_config,
-                canonical_art_param_name(local_name),
-                vp_stage,
-            )
-            mapping = mapping_registry.megatron_to_hf_lookup(global_name)
-            if mapping is None:
-                raise RuntimeError(
-                    f"Missing HF conversion mapping for Megatron param {global_name}"
-                )
-            hf_params = _hf_param_names(mapping.hf_param)
-            missing_hf_params = sorted(set(hf_params) - hf_keys)
-            if missing_hf_params and not getattr(
-                mapping,
-                "allow_hf_name_mismatch",
-                False,
-            ):
-                raise RuntimeError(
-                    f"Missing HF checkpoint weights for Megatron param {global_name}: "
-                    f"{missing_hf_params}"
-                )
-            local_module, local_weights = cast(
-                tuple[Any, torch.Tensor],
-                get_module_and_param_from_name(
-                    megatron_models,
-                    local_name,
-                    vp_stage,
-                ),
-            )
-            if local_module is not None and not hasattr(local_module, "config"):
-                setattr(local_module, "config", model_config)
-            tasks.append(
-                WeightConversionTask(
-                    pp_rank=0,
-                    vp_stage=vp_stage,
-                    param_name=local_name,
-                    global_param_name=global_name,
-                    megatron_module=local_module,
-                    param_weight=local_weights,
-                    mapping=mapping,
-                )
-            )
-    return tasks
-
-
-def build_merged_weight_export(
-    *,
-    bridge: AutoBridge,
-    model: ModelChunks,
-    model_support_handler: ModelSupportHandler,
-) -> MergedWeightExport:
-    return MergedWeightExport(
-        bridge=bridge,
-        model=model,
-        model_config_value=getattr(model[0], "config"),
-        conversion_tasks=build_art_conversion_tasks(
-            bridge=bridge,
-            model=model,
-        ),
-        adapter_weights_by_base=model_support_handler.build_adapter_weights_by_base(
-            model
-        ),
-    )
-
-
-def iter_merged_vllm_weights(
-    weight_export: MergedWeightExport,
-) -> Iterator[tuple[str, torch.Tensor]]:
-    bridge = weight_export.bridge
-    model_bridge = bridge._model_bridge
-    hf_state_dict = bridge.hf_pretrained.state
-    grouped_buffers: dict[str, dict[int, torch.Tensor]] = {}
-    for task in weight_export.conversion_tasks:
-        converted_weights_dict = task.mapping.megatron_to_hf(
-            task.param_weight,
-            task.megatron_module,
-        )
-        adapter_weights = weight_export.adapter_weights_by_base.get(
-            task.global_param_name
-        )
-        if adapter_weights is not None:
-            try:
-                converted_weights_dict = model_bridge._merge_lora_adapter_weights(
-                    weight_export.model,
-                    converted_weights_dict,
-                    adapter_weights,
-                )
-            except Exception as exc:
-                converted_shapes = {
-                    key: tuple(value.shape)
-                    for key, value in converted_weights_dict.items()
-                }
-                adapter_summaries = [
-                    {
-                        "base_prefix": adapter_weight.global_base_prefix,
-                        "adapter_key": adapter_weight.adapter_key,
-                        "linear_in": tuple(
-                            adapter_weight.linear_in_weight.weight.shape
-                        ),
-                        "linear_out": tuple(
-                            adapter_weight.linear_out_weight.weight.shape
-                        ),
-                    }
-                    for adapter_weight in adapter_weights
-                ]
-                raise RuntimeError(
-                    "Failed merged LoRA export for "
-                    f"{task.global_param_name}: converted={converted_shapes} "
-                    f"adapter_weights={adapter_summaries}"
-                ) from exc
-        if getattr(task.mapping, "is_grouped_export", False):
-            merged_result = model_bridge._accumulate_grouped_export(
-                task,
-                converted_weights_dict,
-                weight_export.model_config_value,
-                grouped_buffers,
-                hf_state_dict,
-            )
-            if merged_result is None:
-                continue
-            converted_weights_dict = merged_result
-        else:
-            converted_weights_dict = model_bridge.maybe_modify_converted_hf_weight(
-                task,
-                converted_weights_dict,
-                hf_state_dict,
-            )
-        yield from converted_weights_dict.items()
-
-
-def _is_sender_rank(rank: int) -> bool:
-    return rank == 0
-
-
-def _maybe_distributed_barrier(world_size: int) -> None:
-    if world_size <= 1:
-        return
-    dist = cast(Any, torch.distributed)
-    if not dist.is_available() or not dist.is_initialized():
-        return
-    dist.barrier()
-
-
-def _runtime_headers(spec: MergedWeightTransferSpec) -> dict[str, str]:
-    if spec.api_key is None:
-        return {}
-    return {"Authorization": f"Bearer {spec.api_key}"}
-
-
-def _post_with_retry(
-    post: Any,
-    url: str,
-    *,
-    phase: str,
-    retry_seconds: float = 10.0,
-    **kwargs: Any,
-) -> Any:
-    if kwargs.get("headers") == {}:
-        kwargs = {key: value for key, value in kwargs.items() if key != "headers"}
-    deadline = time.monotonic() + retry_seconds
-    while True:
-        try:
-            response = post(url, **kwargs)
-            response.raise_for_status()
-            return response
-        except Exception as exc:
-            if time.monotonic() >= deadline:
-                raise RuntimeError(
-                    f"{phase} failed after retrying for {retry_seconds:g}s"
-                ) from exc
-            time.sleep(0.5)
-
-
-def _sync_rank_zero_status(
-    *,
-    rank: int,
-    world_size: int,
-    phase: str,
-    error: BaseException | None,
-) -> None:
-    dist = cast(Any, torch.distributed)
-    if world_size <= 1 or not (dist.is_available() and dist.is_initialized()):
-        if error is not None:
-            raise RuntimeError(f"{phase} failed on rank 0") from error
-        return
-    payload = [
-        f"{type(error).__name__}: {error}"
-        if _is_sender_rank(rank) and error is not None
-        else None
-    ]
-    dist.broadcast_object_list(payload, src=0)
-    if payload[0] is None:
-        return
-    if _is_sender_rank(rank):
-        raise RuntimeError(f"{phase} failed on rank 0: {payload[0]}") from error
-    raise RuntimeError(f"{phase} failed on rank 0: {payload[0]}")
-
-
-def _drain_merged_vllm_weights(
-    weight_export: MergedWeightExport,
-    *,
-    names: list[str] | None = None,
-    dtype_names: list[str] | None = None,
-    shapes: list[list[int]] | None = None,
-) -> None:
-    for name, tensor in iter_merged_vllm_weights(weight_export):
-        if names is not None:
-            assert dtype_names is not None
-            assert shapes is not None
-            names.append(name)
-            dtype_names.append(str(tensor.dtype).removeprefix("torch."))
-            shapes.append(list(tensor.shape))
-
-
-def ensure_merged_weight_transfer_group(
-    *,
-    rank: int,
-    world_size: int,
-    merged_weight_transfer_group: TrainerNcclCommunicator | None,
-    merged_weight_transfer_init_info: MergedWeightTransferInitInfo | None,
-    spec: MergedWeightTransferSpec,
-) -> tuple[TrainerNcclCommunicator | None, MergedWeightTransferInitInfo]:
-    if merged_weight_transfer_init_info == spec.init_info:
-        if _is_sender_rank(rank):
-            assert merged_weight_transfer_group is not None
-        assert merged_weight_transfer_init_info is not None
-        _maybe_distributed_barrier(world_size)
-        return merged_weight_transfer_group, merged_weight_transfer_init_info
-
-    import httpx
-
-    error: BaseException | None = None
-    if _is_sender_rank(rank):
-        init_kwargs: dict[str, object] = {
-            "master_address": spec.init_info.master_address,
-            "master_port": spec.init_info.master_port,
-            "world_size": spec.init_info.world_size,
-            "nccl_so_path": spec.nccl_so_path,
-        }
-        executor = ThreadPoolExecutor(max_workers=1)
-        try:
-            trainer_future = executor.submit(trainer_init, init_kwargs)
-            _post_with_retry(
-                httpx.post,
-                f"{spec.vllm_base_url}/init_weight_transfer_engine",
-                phase="initialize merged weight transfer",
-                json={"init_info": spec.init_info.model_dump()},
-                headers=_runtime_headers(spec),
-                timeout=300.0,
-            )
-            merged_weight_transfer_group = trainer_future.result()
-        except BaseException as exc:
-            error = exc
-        finally:
-            executor.shutdown(wait=error is None, cancel_futures=error is not None)
-    _sync_rank_zero_status(
-        rank=rank,
-        world_size=world_size,
-        phase="initialize merged weight transfer",
-        error=error,
-    )
-    return merged_weight_transfer_group, spec.init_info
-
-
-def sync_merged_weights_to_vllm(
-    *,
-    bridge: Any,
-    model: ModelChunks,
-    model_support_handler: Any,
-    adapter_model: dict[str, torch.Tensor],
-    adapter_config: dict[str, Any],
-    rank: int,
-    world_size: int,
-    merged_weight_transfer_group: TrainerNcclCommunicator | None,
-    merged_weight_transfer_init_info: MergedWeightTransferInitInfo | None,
-    spec: MergedWeightTransferSpec,
-    pause_generation: bool,
-) -> tuple[TrainerNcclCommunicator | None, MergedWeightTransferInitInfo]:
-    import httpx
-
-    (
-        merged_weight_transfer_group,
-        merged_weight_transfer_init_info,
-    ) = ensure_merged_weight_transfer_group(
-        rank=rank,
-        world_size=world_size,
-        merged_weight_transfer_group=merged_weight_transfer_group,
-        merged_weight_transfer_init_info=merged_weight_transfer_init_info,
-        spec=spec,
-    )
-    _ = bridge
-    lora_result = build_vllm_lora_tensors_from_model(
-        model=model,
-        adapter_dtypes={key: tensor.dtype for key, tensor in adapter_model.items()},
-        handler=model_support_handler,
-        adapter_config=adapter_config,
-        rank=rank,
-        world_size=world_size,
-    )
-    lora_weights: list[tuple[str, torch.Tensor]] = []
-    published_config: dict[str, Any] = {}
-    if _is_sender_rank(rank):
-        assert lora_result is not None
-        vllm_lora_tensors, published_config = lora_result
-        lora_weights = sorted(vllm_lora_tensors.items())
-
-    def _send_weights() -> None:
-        assert merged_weight_transfer_group is not None
-        trainer_send_weights(
-            iter(lora_weights),
-            {
-                "group": merged_weight_transfer_group,
-                "packed": True,
-                "packed_buffer_size_bytes": DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-                "packed_num_buffers": DEFAULT_PACKED_NUM_BUFFERS,
-            },
-        )
-
-    torch.cuda.synchronize()
-    names = [name for name, _tensor in lora_weights]
-    dtype_names = [
-        str(tensor.dtype).removeprefix("torch.") for _name, tensor in lora_weights
-    ]
-    shapes = [list(tensor.shape) for _name, tensor in lora_weights]
-    _maybe_distributed_barrier(world_size)
-
-    pause_error: BaseException | None = None
-    update_error: BaseException | None = None
-    resume_error: BaseException | None = None
-
-    if _is_sender_rank(rank):
-        with httpx.Client() as client:
-            if pause_generation:
-                try:
-                    _post_with_retry(
-                        client.post,
-                        f"{spec.vllm_base_url}/pause",
-                        phase="pause generation",
-                        params={"mode": "wait"},
-                        headers=_runtime_headers(spec),
-                        timeout=300.0,
-                    )
-                except BaseException as exc:
-                    pause_error = exc
-
-            _sync_rank_zero_status(
-                rank=rank,
-                world_size=world_size,
-                phase="pause generation",
-                error=pause_error,
-            )
-            try:
-                _post_with_retry(
-                    client.post,
-                    f"{spec.vllm_base_url}/start_weight_update",
-                    phase="start merged weight update",
-                    json={"is_checkpoint_format": False},
-                    headers=_runtime_headers(spec),
-                    timeout=300.0,
-                )
-                with ThreadPoolExecutor(max_workers=1) as executor:
-                    send_future = executor.submit(_send_weights)
-                    _post_with_retry(
-                        client.post,
-                        f"{spec.vllm_base_url}/update_weights",
-                        phase="update merged weights",
-                        json={
-                            "update_info": {
-                                "art_weight_update_kind": "lora_delta",
-                                "art_lora_config": published_config,
-                                "names": names,
-                                "dtype_names": dtype_names,
-                                "shapes": shapes,
-                                "packed": True,
-                                "packed_buffer_size_bytes": DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-                                "packed_num_buffers": DEFAULT_PACKED_NUM_BUFFERS,
-                            }
-                        },
-                        headers=_runtime_headers(spec),
-                        timeout=600.0,
-                    )
-                    send_future.result()
-                _post_with_retry(
-                    client.post,
-                    f"{spec.vllm_base_url}/finish_weight_update",
-                    phase="finish merged weight update",
-                    headers=_runtime_headers(spec),
-                    timeout=600.0,
-                )
-                _post_with_retry(
-                    client.post,
-                    f"{spec.vllm_base_url}/art/set_served_model_name",
-                    phase="set served model name",
-                    json={"name": spec.served_model_name},
-                    headers=_runtime_headers(spec),
-                    timeout=30.0,
-                )
-                torch.cuda.synchronize()
-            except BaseException as exc:
-                update_error = exc
-            finally:
-                if pause_generation:
-                    try:
-                        _post_with_retry(
-                            client.post,
-                            f"{spec.vllm_base_url}/resume",
-                            phase="resume generation",
-                            headers=_runtime_headers(spec),
-                            timeout=30.0,
-                        )
-                    except BaseException as exc:
-                        resume_error = exc
-                _sync_rank_zero_status(
-                    rank=rank,
-                    world_size=world_size,
-                    phase="update merged weights",
-                    error=update_error,
-                )
-                _sync_rank_zero_status(
-                    rank=rank,
-                    world_size=world_size,
-                    phase="resume generation",
-                    error=resume_error,
-                )
-    else:
-        _sync_rank_zero_status(
-            rank=rank,
-            world_size=world_size,
-            phase="pause generation",
-            error=None,
-        )
-        _sync_rank_zero_status(
-            rank=rank,
-            world_size=world_size,
-            phase="update merged weights",
-            error=None,
-        )
-        _sync_rank_zero_status(
-            rank=rank,
-            world_size=world_size,
-            phase="resume generation",
-            error=None,
-        )
-    return merged_weight_transfer_group, merged_weight_transfer_init_info
-
-
-__all__ = [
-    "MergedWeightExport",
-    "build_art_conversion_tasks",
-    "build_merged_weight_export",
-    "ensure_merged_weight_transfer_group",
-    "iter_merged_vllm_weights",
-    "sync_merged_weights_to_vllm",
-]
diff --git a/src/art/metrics.py b/src/art/metrics.py
index f7d8ccdb5..df4f2107a 100644
--- a/src/art/metrics.py
+++ b/src/art/metrics.py
@@ -103,39 +103,109 @@ class MetricDefinition(pydantic.BaseModel):
         score_component=True,
     ),
     MetricDefinition(
-        key="data/step_padding_ratio",
-        title="Padding ratio",
-        description=(
-            "unused packed-token slots, including dummy data-parallel rows, "
-            "divided by executed packed-token capacity for this step"
-        ),
-        kind="ratio",
-        higher_is_better=False,
-        dashboard_default=True,
+        key="data/step_nonpadding_logical_tokens",
+        title="Non-padding logical train tokens",
+        description="actual non-padding tokens in real training microbatches",
+        kind="counter",
+        unit="tokens",
+        higher_is_better=None,
     ),
     MetricDefinition(
-        key="data/step_executed_packed_train_tokens",
-        title="Megatron executed packed train tokens",
+        key="data/step_loss_bearing_tokens",
+        title="Loss-bearing train tokens",
+        description="actual shifted token positions contributing to the loss",
+        kind="counter",
+        unit="tokens",
+        higher_is_better=None,
+    ),
+    MetricDefinition(
+        key="data/step_executed_token_equivalents",
+        title="Executed token-equivalents",
         description=(
-            "packed token rows included in Megatron throughput; CP excludes "
-            "configured packed-row padding that is not dispatched"
+            "materialized per-rank token extents summed over DP and CP, including "
+            "padding and dummy microbatches"
         ),
         kind="counter",
         unit="tokens",
         higher_is_better=None,
     ),
     MetricDefinition(
-        key="throughput/train_packed_tok_per_s",
-        title="Megatron packed train tokens per second",
+        key="data/step_nominal_schedule_capacity_tokens",
+        title="Nominal schedule capacity",
+        description="configured packed-row capacity before CP pruning",
+        kind="counter",
+        unit="tokens",
+        higher_is_better=None,
+    ),
+    MetricDefinition(
+        key="data/step_dummy_executed_token_equivalents",
+        title="Executed dummy token-equivalents",
+        description="runtime-plan token-equivalents executed by PP dummy microbatches",
+        kind="counter",
+        unit="tokens",
+        higher_is_better=False,
+    ),
+    MetricDefinition(
+        key="data/step_dummy_schedule_capacity_tokens",
+        title="Dummy schedule capacity",
+        description="nominal packed-token capacity assigned to PP dummy microbatches",
+        kind="counter",
+        unit="tokens",
+        higher_is_better=False,
+    ),
+    MetricDefinition(
+        key="data/step_unused_packed_capacity_tokens",
+        title="Unused packed capacity",
+        description="nominal real-microbatch capacity not occupied by logical tokens",
+        kind="counter",
+        unit="tokens",
+        higher_is_better=False,
+    ),
+    MetricDefinition(
+        key="data/step_unused_and_dummy_ratio",
+        title="Unused and dummy capacity ratio",
         description=(
-            "physical training-token throughput reported by the Megatron worker; "
-            "CP excludes configured packed-row padding that is not dispatched"
+            "unused real packed-token capacity plus PP dummy schedule capacity, "
+            "divided by nominal schedule capacity"
         ),
+        kind="ratio",
+        higher_is_better=False,
+        dashboard_default=True,
+    ),
+    MetricDefinition(
+        key="throughput/train_nonpadding_logical_tok_per_s",
+        title="Logical train tokens per second",
+        description="actual non-padding logical tokens divided by training time",
+        kind="rate",
+        unit="tok/s",
+        higher_is_better=True,
+        dashboard_default=True,
+    ),
+    MetricDefinition(
+        key="throughput/train_loss_bearing_tok_per_s",
+        title="Loss-bearing train tokens per second",
+        description="actual loss-bearing tokens divided by training time",
+        kind="rate",
+        unit="tok/s",
+        higher_is_better=True,
+    ),
+    MetricDefinition(
+        key="throughput/train_executed_tok_equiv_per_s",
+        title="Executed token-equivalents per second",
+        description="executed materialized token-equivalents divided by training time",
         kind="rate",
         unit="tok/s",
         higher_is_better=True,
         dashboard_default=True,
     ),
+    MetricDefinition(
+        key="throughput/train_nominal_capacity_tok_per_s",
+        title="Nominal schedule capacity per second",
+        description="configured packed-row capacity divided by training time",
+        kind="rate",
+        unit="tok/s",
+        higher_is_better=True,
+    ),
     MetricDefinition(
         key="loss/importance_ratio_mean",
         title="Importance ratio mean",
@@ -499,15 +569,36 @@ async def flush(self) -> dict[str, float]:
             }
             result.update(self._compute_rollups(cost_metrics))
 
+            cum_state = self._shared_state.cum_state
+            unused_and_dummy_ratio = "data/step_unused_and_dummy_ratio"
             for key, value in list(result.items()):
                 section = key.split("/", 1)[0]
-                if section not in _HIERARCHICAL_SECTIONS:
+                if (
+                    section not in _HIERARCHICAL_SECTIONS
+                    or key == unused_and_dummy_ratio
+                ):
                     continue
                 cum_key = to_cumulative_metric_key(key)
-                next_value = self._shared_state.cum_state.get(cum_key, 0.0) + value
-                self._shared_state.cum_state[cum_key] = next_value
+                next_value = cum_state.get(cum_key, 0.0) + value
+                cum_state[cum_key] = next_value
                 result[cum_key] = next_value
 
+            if unused_and_dummy_ratio in result:
+                cum_key = to_cumulative_metric_key(unused_and_dummy_ratio)
+                nominal = cum_state.get(
+                    "data/cum/nominal_schedule_capacity_tokens", 0.0
+                )
+                unused_and_dummy = sum(
+                    cum_state.get(key, 0.0)
+                    for key in (
+                        "data/cum/unused_packed_capacity_tokens",
+                        "data/cum/dummy_schedule_capacity_tokens",
+                    )
+                )
+                ratio = unused_and_dummy / nominal if nominal else 0.0
+                cum_state[cum_key] = ratio
+                result[cum_key] = ratio
+
             if pending_scenario_ids:
                 self._shared_state.unique_scenario_ids.update(pending_scenario_ids)
                 result["data/cum/num_unique_scenarios"] = float(
@@ -519,6 +610,16 @@ async def flush(self) -> dict[str, float]:
             pending_state.pending_scenario_ids.clear()
             return result
 
+    async def drain_pending(self) -> dict[str, float]:
+        """Move raw step deltas across an execution boundary without rollups."""
+
+        async with self._shared_state.lock:
+            pending_state = self._pending_state()
+            result = dict(pending_state.step_buffer)
+            pending_state.step_buffer.clear()
+            pending_state.pending_scenario_ids.clear()
+            return result
+
     def activate(self) -> Token["MetricsBuilder"]:
         return _active_builder.set(self)
 
diff --git a/src/art/model.py b/src/art/model.py
index 2930de8ae..ee187f3a0 100644
--- a/src/art/model.py
+++ b/src/art/model.py
@@ -1,4 +1,3 @@
-import asyncio
 from contextlib import contextmanager, nullcontext
 from contextvars import Token
 from datetime import datetime
@@ -31,7 +30,6 @@
     SFT_GRADIENT_STEP_KEY,
     SFT_METRIC_PREFIX,
     SFT_WANDB_GRADIENT_STEP_KEY,
-    TRAIN_GRADIENT_STEPS_KEY,
     average_metric_samples,
     build_data_metrics_from_summary,
     summarize_trajectory_groups,
@@ -305,6 +303,13 @@ def with_options(self, *args: Any, **kwargs: Any) -> "_OpenAIClientProxy":
             self._suppress_weave_trace,
         )
 
+    async def __aenter__(self) -> "_OpenAIClientProxy":
+        await self._client.__aenter__()
+        return self
+
+    async def __aexit__(self, *args: Any) -> Any:
+        return await self._client.__aexit__(*args)
+
     def __getattr__(self, name: str) -> Any:
         return getattr(self._client, name)
 
@@ -339,11 +344,19 @@ def __getattr__(self, name: str) -> Any:
         "offpolicy/token_weighted_policy_age_steps",
         "offpolicy/token_weighted_policy_age_p95_steps",
         "throughput/accepted_train_tok_per_s",
-        "throughput/train_packed_tok_per_s",
-        "data/step_executed_packed_train_tokens",
+        "throughput/train_nonpadding_logical_tok_per_s",
+        "throughput/train_loss_bearing_tok_per_s",
+        "throughput/train_executed_tok_equiv_per_s",
+        "throughput/train_nominal_capacity_tok_per_s",
         "data/step_trainable_assistant_tokens",
-        "data/step_non_padding_train_tokens",
-        "data/step_padding_ratio",
+        "data/step_nonpadding_logical_tokens",
+        "data/step_loss_bearing_tokens",
+        "data/step_executed_token_equivalents",
+        "data/step_nominal_schedule_capacity_tokens",
+        "data/step_dummy_executed_token_equivalents",
+        "data/step_dummy_schedule_capacity_tokens",
+        "data/step_unused_packed_capacity_tokens",
+        "data/step_unused_and_dummy_ratio",
         "data/cum/num_unique_scenarios",
         "data/cum/num_scenarios",
         "data/cum/num_gradient_steps",
@@ -1299,9 +1312,22 @@ async def log(
 
         # 1. Write parquet
         file_name = f"{step:04d}.parquet"
-        write_trajectory_groups_parquet(
-            trajectory_groups, f"{trajectories_dir}/{file_name}"
-        )
+        trajectory_path = f"{trajectories_dir}/{file_name}"
+        prepared_paths = {
+            group._prepared_log_path
+            for group in trajectory_groups
+            if group._prepared_log_path is not None
+        }
+        if prepared_paths:
+            if len(prepared_paths) != 1 or any(
+                group._prepared_log_path is None for group in trajectory_groups
+            ):
+                raise RuntimeError("trajectory batch has inconsistent prepared logs")
+            os.replace(prepared_paths.pop(), trajectory_path)
+            for group in trajectory_groups:
+                group._prepared_log_path = None
+        else:
+            write_trajectory_groups_parquet(trajectory_groups, trajectory_path)
 
         # 2. Calculate aggregate metrics (excluding additive costs)
         reward_key = "reward"
diff --git a/src/art/pipeline_trainer/checkpoint_retention.py b/src/art/pipeline_trainer/checkpoint_retention.py
index 776045f7b..e7e8a10c7 100644
--- a/src/art/pipeline_trainer/checkpoint_retention.py
+++ b/src/art/pipeline_trainer/checkpoint_retention.py
@@ -32,7 +32,7 @@ def keep_recent_and_top(
     *,
     recent: int = 5,
     top: int = 2,
-    metric: str = "val/reward",
+    metric: str = "reward/val",
 ) -> CheckpointRetentionStrategy:
     """Keep the most recent eligible checkpoints and top metric checkpoints."""
     if recent < 0:
diff --git a/src/art/pipeline_trainer/status.py b/src/art/pipeline_trainer/status.py
index cb58bdb3e..22432575b 100644
--- a/src/art/pipeline_trainer/status.py
+++ b/src/art/pipeline_trainer/status.py
@@ -110,11 +110,11 @@ def note_rollout_finished(self, *, errored: bool) -> None:
             self._errored += 1
         self._refresh_status()
 
-    def note_group_enqueued(self, _group: TrajectoryGroup) -> None:
+    def note_group_enqueued(self) -> None:
         self._queued += 1
         self._refresh_status()
 
-    def note_group_dequeued(self, _group: TrajectoryGroup) -> None:
+    def note_group_dequeued(self) -> None:
         if self._queued > 0:
             self._queued -= 1
         self._refresh_status()
diff --git a/src/art/pipeline_trainer/trainer.py b/src/art/pipeline_trainer/trainer.py
index 3215f93d3..df5a57e9c 100644
--- a/src/art/pipeline_trainer/trainer.py
+++ b/src/art/pipeline_trainer/trainer.py
@@ -18,19 +18,26 @@
     AsyncIterator,
     Generic,
     Iterable,
-    Mapping,
     Sequence,
     TypeVar,
     cast,
 )
 import warnings
 
+from openai.types.chat.chat_completion import Choice
+from pydantic import BaseModel, ConfigDict, Field
 from typing_extensions import TypeIs
 
 T = TypeVar("T")
 
 import art
 from art import TrajectoryGroup
+from art.distributed.rollout import (
+    DistributedTrajectoryQueue,
+    LocalRolloutExecutor,
+    RolloutExecutor,
+)
+from art.distributed.trajectory_store import TrajectoryGroupRef
 from art.errors import LocalServingUnavailableError
 from art.pipeline_tuner import (
     PackedGroupObservation,
@@ -42,6 +49,7 @@
     PipelineTuneSettings,
     RolloutWorkerController,
 )
+from art.preprocessing.policy_spans import PolicyTokenSpan
 
 from .checkpoint_retention import (
     CHECKPOINT_CREATED_AT_METRIC,
@@ -56,12 +64,55 @@
 from .types import ConfigT, EvalFn, RolloutFn, ScenarioT, SingleRolloutFn  # noqa: F401
 
 PIPELINE_STATE_KEY = "_pipeline_trainer"
+_ROLLOUT_WALL_TIME_KEY = "_art_rollout_wall_s"
+_ACTOR_IDLE_TIME_KEY = "_art_actor_idle_s"
+_QUEUE_WAIT_TIME_KEY = "_art_queue_wait_s"
 _SCORE_FRESHNESS_TAU_STEPS = 8.0
 # Rollout critical batch size from the best current GRPO/RLVR evidence. This is
 # grounded in reported experiments, not a well-validated universal constant.
 _SCORE_CRITICAL_ROLLOUT_BATCH_SIZE = 300.0
 
 
+class _ResizableAsyncQueue(asyncio.Queue[T]):
+    def resize(self, maxsize: int) -> None:
+        if maxsize < 1:
+            raise ValueError("queue maxsize must be positive")
+        grew = maxsize > self.maxsize
+        internals = cast(Any, self)
+        internals._maxsize = maxsize
+        if grew:
+            for _ in range(min(maxsize - self.qsize(), len(internals._putters))):
+                internals._wakeup_next(internals._putters)
+
+
+class _PreparedPipelineItem(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+    batch: list[TrajectoryGroup]
+    discarded: int = Field(ge=0)
+    zero_variance_discarded: int = Field(ge=0)
+    saw_sentinel: bool
+    packing_policy_step: int = Field(ge=0)
+    selection_s: float = Field(ge=0)
+    preparation_s: float = Field(ge=0)
+    preparation_metrics: dict[str, float]
+    handoff: asyncio.Event = Field(default_factory=asyncio.Event, exclude=True)
+
+
+class _PostTrainItem(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+
+    batch: list[TrajectoryGroup]
+    result: Any
+    current_step: int = Field(ge=1)
+    training_policy_step: int = Field(ge=0)
+    should_eval_step: bool
+    step_seconds: float = Field(ge=0)
+    step_completed_s: float = Field(ge=0)
+    policy_age_metrics: dict[str, float]
+    metrics: dict[str, float]
+
+
 def _is_eval_mapping(
     result: Sequence[art.Trajectory | art.TrajectoryGroup]
     | Mapping[str, Sequence[art.Trajectory | art.TrajectoryGroup]],
@@ -157,6 +208,7 @@ def __init__(
         loss_fn: str = "cispo",
         loss_fn_config: dict | None = None,
         normalize_advantages: bool = True,
+        grad_accumulation_sequences: int | None = None,
         adam_params: object | None = None,
         kl_penalty_coef: float = 0.0,
         kl_penalty_step_lag: int | None = None,
@@ -180,6 +232,7 @@ def __init__(
         checkpoint_retention_interval: int = 1,
         # Resumption
         resume: bool = True,
+        rollout_executor: RolloutExecutor | None = None,
     ) -> None:
         autotune = autotune or PipelineAutotuneConfig()
         pipeline_aliases = {
@@ -192,6 +245,9 @@ def __init__(
             }.items()
             if value is not None
         }
+        rollout_workers_explicit = num_rollout_workers is not None or (
+            pipeline is not None and "num_rollout_workers" in pipeline.model_fields_set
+        )
         if autotune.mode != "off" and (pipeline is not None or pipeline_aliases):
             raise ValueError(
                 "Pipeline runtime config cannot be provided when pipeline autotuning "
@@ -229,9 +285,27 @@ def __init__(
             raise ValueError("optimizer_save_interval must be > 0")
         if kl_penalty_step_lag is not None and kl_penalty_step_lag < 1:
             raise ValueError("kl_penalty_step_lag must be >= 1")
+        if grad_accumulation_sequences is not None and grad_accumulation_sequences < 1:
+            raise ValueError("grad_accumulation_sequences must be >= 1")
         self.model = model
         self.backend = backend
         self.rollout_fn = rollout_fn
+        if rollout_executor is None:
+            rollout_executor = LocalRolloutExecutor()
+        self._rollout_executor = rollout_executor
+        self.rollout_worker_capacity = rollout_executor.max_workers
+        if self.rollout_worker_capacity is not None:
+            if self.rollout_worker_capacity < 1:
+                raise ValueError("rollout executor capacity must be >= 1")
+            if pipeline.num_rollout_workers > self.rollout_worker_capacity:
+                if autotune.mode == "off" and rollout_workers_explicit:
+                    raise ValueError(
+                        f"num_rollout_workers={pipeline.num_rollout_workers} exceeds "
+                        f"rollout executor capacity {self.rollout_worker_capacity}"
+                    )
+                pipeline = pipeline.model_copy(
+                    update={"num_rollout_workers": self.rollout_worker_capacity}
+                )
         self.config = config
         self.eval_fn = eval_fn
         self.pipeline = pipeline
@@ -251,6 +325,7 @@ def __init__(
         self.loss_fn = loss_fn
         self.loss_fn_config = loss_fn_config
         self.normalize_advantages = normalize_advantages
+        self.grad_accumulation_sequences = grad_accumulation_sequences
         self.adam_params = adam_params
         self.kl_penalty_coef = kl_penalty_coef
         self.kl_penalty_step_lag = kl_penalty_step_lag
@@ -280,6 +355,9 @@ def __init__(
         self._checkpoint_lease_counts: Counter[int] = Counter()
         self._scheduled_eval_steps: set[int] = set()
         self._scheduled_eval_leases: dict[int, AsyncExitStack] = {}
+        self._checkpoint_log_tasks: set[asyncio.Task[None]] = set()
+        self._checkpoint_log_failure: BaseException | None = None
+        self._post_train_tasks: set[asyncio.Task[None]] = set()
 
         self.state = PipelineState()
         self._stop_event = asyncio.Event()
@@ -288,13 +366,18 @@ def __init__(
             scenarios
         )
         self._scenario_source_exhausted = False
-        self._output_queue: asyncio.Queue[TrajectoryGroup | None] | None = None
+        self._output_queue: (
+            asyncio.Queue[TrajectoryGroup | None] | DistributedTrajectoryQueue | None
+        ) = None
         self._producer_rollout_timings = (0.0, 0.0, 0.0)
         self._reported_producer_rollout_timings = (0.0, 0.0, 0.0)
+        self._packed_queue: asyncio.Queue[_PreparedPipelineItem | None] | None = None
+        self._accept_prepared_batches = True
         self._eval_queue: asyncio.Queue[int] | None = None
         self._rollout_worker_controller = RolloutWorkerController(
             self, self.num_rollout_workers
         )
+        self._rollout_executor.set_target(self.num_rollout_workers)
         self._attachments: list[PipelineAutotunerAttachment] = []
         if self.autotune.mode != "off":
             self._attachments.append(PipelineAutotunerAttachment(self.autotune))
@@ -366,7 +449,28 @@ async def train(self, *, handle_signals: bool = True) -> None:
             if self.queue_maxsize is not None
             else max(1, self._freshness_queue_window() * self.target_groups_per_step)
         )
-        self._output_queue = asyncio.Queue(maxsize=queue_maxsize)
+        result_queue_factory = getattr(
+            self._rollout_executor, "create_result_queue", None
+        )
+        local_data_plane = isinstance(self._rollout_executor, LocalRolloutExecutor)
+        supports_preparation = callable(
+            getattr(self.backend, "prepare_pipeline_batch", None)
+        )
+        packing_support = getattr(self.backend, "supports_async_pipeline_packing", None)
+        if supports_preparation and callable(packing_support):
+            supports_preparation = bool(packing_support(self.model))
+        if callable(result_queue_factory) and (
+            supports_preparation or not local_data_plane
+        ):
+            self._output_queue = result_queue_factory(queue_maxsize)
+            await self._output_queue.start()
+        else:
+            self._output_queue = _ResizableAsyncQueue(maxsize=queue_maxsize)
+        if (
+            isinstance(self._output_queue, DistributedTrajectoryQueue)
+            and supports_preparation
+        ):
+            self._packed_queue = asyncio.Queue(maxsize=1)
         self._eval_queue = asyncio.Queue()
 
         loop = asyncio.get_running_loop()
@@ -408,6 +512,8 @@ def _sync_signal_handler(signum: int, _frame: object | None) -> None:
             try:
                 async with asyncio.TaskGroup() as tg:
                     tg.create_task(self._rollout_stage(), name="rollout_stage")
+                    if self._packed_queue is not None:
+                        tg.create_task(self._packing_stage(), name="packing_stage")
                     tg.create_task(self._training_stage(), name="training_stage")
                     tg.create_task(self._eval_stage(), name="eval_stage")
                     tg.create_task(self._status_loop(), name="status_loop")
@@ -435,11 +541,29 @@ def _sync_signal_handler(signum: int, _frame: object | None) -> None:
                     except (ValueError, RuntimeError):
                         pass
             cleanup_failures: list[BaseException] = []
+            self._accept_prepared_batches = False
+            try:
+                await self._discard_pending_prepared_batches()
+            except BaseException as exc:
+                cleanup_failures.append(exc)
+            if self._post_train_tasks:
+                results = await asyncio.gather(
+                    *tuple(self._post_train_tasks), return_exceptions=True
+                )
+                self._post_train_tasks.clear()
+                cleanup_failures.extend(
+                    result for result in results if isinstance(result, BaseException)
+                )
             if not training_failed:
                 try:
                     await self._finalize_backend_training()
                 except BaseException as exc:
                     cleanup_failures.append(exc)
+            if isinstance(self._output_queue, DistributedTrajectoryQueue):
+                try:
+                    await self._output_queue.close()
+                except BaseException as exc:
+                    cleanup_failures.append(exc)
             try:
                 await self._stop_attachments(training_failed=training_failed)
             except BaseException as exc:
@@ -453,6 +577,12 @@ def _sync_signal_handler(signum: int, _frame: object | None) -> None:
                 await self._release_all_scheduled_eval_leases()
             except BaseException as exc:
                 cleanup_failures.append(exc)
+            if self._checkpoint_log_tasks:
+                await asyncio.gather(
+                    *tuple(self._checkpoint_log_tasks), return_exceptions=True
+                )
+            if self._checkpoint_log_failure is not None:
+                cleanup_failures.append(self._checkpoint_log_failure)
             if cleanup_failures:
                 if primary_failure is not None:
                     raise BaseExceptionGroup(
@@ -467,9 +597,30 @@ def _sync_signal_handler(signum: int, _frame: object | None) -> None:
 
     def request_stop(self) -> None:
         """Request a clean shutdown of the pipeline stages."""
+        if self.state.done:
+            return
         self.state.done = True
         self._stop_event.set()
 
+        async def _notify_policy() -> None:
+            async with self.state.policy_updated:
+                self.state.policy_updated.notify_all()
+
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            return
+        loop.create_task(_notify_policy())
+        if self._output_queue is None:
+            return
+        if isinstance(self._output_queue, DistributedTrajectoryQueue):
+            loop.create_task(self._output_queue.finish())
+            return
+        try:
+            self._output_queue.put_nowait(None)
+        except asyncio.QueueFull:
+            loop.create_task(self._output_queue.put(None))
+
     async def _await_or_stop(self, awaitable: Awaitable[T]) -> tuple[bool, T | None]:
         operation = asyncio.ensure_future(awaitable)
         stop_wait = asyncio.create_task(self._stop_event.wait())
@@ -490,9 +641,21 @@ async def _finalize_backend_training(self) -> None:
             return
         finalize = getattr(self.backend, "finalize_training_session", None)
         if finalize is not None:
-            await finalize(self.model)
+            metrics = await finalize(self.model)
+            if isinstance(metrics, Mapping):
+                await self._emit_pipeline_metrics(
+                    metrics, step=self.state.next_training_step
+                )
 
     def apply_pipeline_settings(self, settings: PipelineTuneSettings) -> None:
+        if (
+            self.rollout_worker_capacity is not None
+            and settings.num_rollout_workers > self.rollout_worker_capacity
+        ):
+            raise ValueError(
+                f"num_rollout_workers={settings.num_rollout_workers} exceeds rollout "
+                f"executor capacity {self.rollout_worker_capacity}"
+            )
         self.num_rollout_workers = settings.num_rollout_workers
         self.min_batch_size = settings.min_batch_size
         self.max_batch_size = settings.max_batch_size
@@ -500,8 +663,14 @@ def apply_pipeline_settings(self, settings: PipelineTuneSettings) -> None:
         self.queue_maxsize = settings.queue_maxsize
         self._discard_queue_limit = self.discard_queue_multiplier * self.min_batch_size
         self._rollout_worker_controller.set_target(self.num_rollout_workers)
+        self._rollout_executor.set_target(self.num_rollout_workers)
         if self._output_queue is not None:
-            cast(Any, self._output_queue)._maxsize = self.queue_maxsize
+            if isinstance(self._output_queue, DistributedTrajectoryQueue):
+                self._output_queue.set_maxsize(self.queue_maxsize)
+            else:
+                cast(
+                    _ResizableAsyncQueue[TrajectoryGroup | None], self._output_queue
+                ).resize(self.queue_maxsize)
         self._status._num_workers = self.num_rollout_workers
 
     async def _start_attachments(self) -> None:
@@ -524,6 +693,7 @@ async def _emit_pipeline_metric(
         value: float,
         *,
         step: int | None,
+        t_s: float | None = None,
         tags: dict[str, str] | None = None,
     ) -> None:
         if not self._attachments:
@@ -532,18 +702,22 @@ async def _emit_pipeline_metric(
             name=name,
             value=float(value),
             step=step,
-            t_s=time.monotonic(),
+            t_s=time.monotonic() if t_s is None else t_s,
             tags=tags or {},
         )
         for attachment in self._attachments:
             await attachment.on_metric(metric)
 
     async def _emit_pipeline_metrics(
-        self, metrics: Mapping[str, float], *, step: int | None
+        self,
+        metrics: Mapping[str, float],
+        *,
+        step: int | None,
+        t_s: float | None = None,
     ) -> None:
         for name, value in metrics.items():
             if isinstance(value, (int, float)):
-                await self._emit_pipeline_metric(name, float(value), step=step)
+                await self._emit_pipeline_metric(name, float(value), step=step, t_s=t_s)
 
     def _collect_attachment_train_step_metrics(self) -> tuple[dict[str, float], bool]:
         metrics: dict[str, float] = {}
@@ -603,7 +777,6 @@ async def _emit_packed_group_observations(
                 await attachment.on_packed_group(observation)
 
     def _validate_backend_support(self) -> None:
-        from art.dev.validate import is_dedicated_mode
         from art.local.backend import LocalBackend
 
         if self.eval_fn is not None and not callable(
@@ -616,8 +789,7 @@ def _validate_backend_support(self) -> None:
         if not isinstance(self.backend, LocalBackend):
             return
 
-        model_config = self.model._internal_config or art.dev.InternalModelConfig()
-        if not is_dedicated_mode(model_config):
+        if not self.backend._supports_concurrent_training_and_inference(self.model):
             raise ValueError(
                 "PipelineTrainer only supports LocalBackend in dedicated mode. "
                 "Shared LocalBackend pauses inference during training and is not "
@@ -625,15 +797,6 @@ def _validate_backend_support(self) -> None:
                 "trainer_gpu_ids and inference_gpu_ids on the TrainableModel "
                 "_internal_config to use LocalBackend with PipelineTrainer."
             )
-        if (
-            self.eval_fn is not None
-            and model_config.get("tinker_args") is None
-            and model_config.get("rollout_weights_mode", "lora") != "lora"
-        ):
-            raise ValueError(
-                "PipelineTrainer eval requires rollout_weights_mode='lora' so "
-                "the requested checkpoint can remain immutable during eval."
-            )
         if self.loss_fn not in {"cispo", "ppo"}:
             raise ValueError(
                 "PipelineTrainer + LocalBackend(dedicated) only supports "
@@ -817,25 +980,48 @@ async def _rollout_worker(self, worker_id: int) -> None:
                 rollout_started = time.monotonic()
                 try:
                     async with self._adapter_lease(initial_version):
-                        group = await self.rollout_fn(self.model, scenario, self.config)
+                        group = await self._rollout_executor.run(
+                            worker_id,
+                            self.rollout_fn,
+                            self.model,
+                            scenario,
+                            self.config,
+                        )
                 finally:
                     token.var.reset(token)
                 rollout_wall_s = time.monotonic() - rollout_started
-                if not isinstance(group, TrajectoryGroup):
+                if not isinstance(group, TrajectoryGroup | TrajectoryGroupRef):
                     errored = True
                     continue
-                self._apply_scenario_metadata(group, scenario)
-                self._apply_policy_versions(
-                    group,
-                    initial_version=initial_version,
-                    final_version=self.state.policy_version,
-                )
+                scenario_metadata = self._scenario_metadata(scenario)
+                if isinstance(group, TrajectoryGroup):
+                    group.metadata.update(scenario_metadata)
+                    self._apply_policy_versions(
+                        group,
+                        initial_version=initial_version,
+                        final_version=self.state.policy_version,
+                    )
                 if self.state.done:
+                    if isinstance(
+                        self._output_queue, DistributedTrajectoryQueue
+                    ) and isinstance(group, TrajectoryGroupRef):
+                        await self._output_queue.discard(group)
                     break
-                queue_wait_s = await self._put_output_group(group)
+                queue_wait_s = await self._put_output_group(
+                    group,
+                    metadata=scenario_metadata,
+                    initial_policy_version=initial_version,
+                    final_policy_version=self.state.policy_version,
+                    rollout_wall_s=rollout_wall_s,
+                    actor_idle_s=actor_idle_s,
+                )
                 self._record_producer_rollout_timings(
                     rollout_wall_s, actor_idle_s + queue_wait_s, queue_wait_s
                 )
+                if isinstance(group, TrajectoryGroup):
+                    group.metadata[_ROLLOUT_WALL_TIME_KEY] = rollout_wall_s
+                    group.metadata[_QUEUE_WAIT_TIME_KEY] = queue_wait_s
+                    group.metadata[_ACTOR_IDLE_TIME_KEY] = actor_idle_s + queue_wait_s
             except asyncio.CancelledError:
                 raise
             except LocalServingUnavailableError:
@@ -858,8 +1044,160 @@ async def _rollout_stage(self) -> None:
             and self._output_queue is not None
         ):
             print("Scenario source exhausted; draining completed rollouts.")
+            if isinstance(self._output_queue, DistributedTrajectoryQueue):
+                await self._output_queue.finish()
+                return
             await self._await_or_stop(self._output_queue.put(None))
 
+    async def _packing_stage(self) -> None:
+        assert self._packed_queue is not None
+        prepare = getattr(self.backend, "prepare_pipeline_batch")
+        while True:
+            packing_policy_step = self.state.next_training_step
+            started = time.monotonic()
+            zero_variance_before = self.state.discarded_zero_variance_groups
+            batch, discarded, saw_sentinel = await self._collect_batch(
+                packing_policy_step
+            )
+            zero_variance_discarded = (
+                self.state.discarded_zero_variance_groups - zero_variance_before
+            )
+            selection_s = time.monotonic() - started
+            if not self._accept_prepared_batches:
+                for group in batch:
+                    await self._discard_collected_group(group)
+                return
+            if not batch:
+                await self._packed_queue.put(None)
+                return
+            if self.autotune.mode != "off":
+                for group in batch:
+                    group._collect_packing_shape = True
+            started = time.monotonic()
+            preparation_metrics = await prepare(
+                self.model,
+                batch,
+                normalize_advantages=self.normalize_advantages,
+                grad_accumulation_sequences=self.grad_accumulation_sequences,
+            )
+            preparation_s = time.monotonic() - started
+            if preparation_metrics is None:
+                if saw_sentinel:
+                    await self._packed_queue.put(None)
+                    return
+                continue
+            item = _PreparedPipelineItem(
+                batch=batch,
+                discarded=discarded,
+                zero_variance_discarded=zero_variance_discarded,
+                saw_sentinel=saw_sentinel,
+                packing_policy_step=packing_policy_step,
+                selection_s=selection_s,
+                preparation_s=preparation_s,
+                preparation_metrics=preparation_metrics,
+            )
+            if not self._accept_prepared_batches:
+                await getattr(self.backend, "discard_pipeline_batch")(batch)
+                return
+            await self._packed_queue.put(item)
+            await item.handoff.wait()
+            if not self._accept_prepared_batches:
+                return
+            if saw_sentinel:
+                return
+
+    async def _finalize_post_train(
+        self, item: _PostTrainItem, next_train_dispatched: asyncio.Event
+    ) -> None:
+        # Controller-only work must not delay a ready next trainer job.
+        dispatch_wait_started = time.monotonic()
+        await next_train_dispatched.wait()
+        dispatch_wait_s = time.monotonic() - dispatch_wait_started
+        async with self.state.policy_updated:
+            self.state.policy_updated.notify_all()
+
+        phases: dict[str, float] = {}
+        started = time.monotonic()
+        await self._log_checkpoint_saved(item.result)
+        await self._prune_model_adapters(item.current_step)
+        await self._run_checkpoint_retention(item.current_step)
+        phases["housekeeping"] = time.monotonic() - started
+
+        started = time.monotonic()
+        metrics = dict(item.metrics)
+        metrics["time/step_post_train_dispatch_wait_s"] = dispatch_wait_s
+        metrics.update(item.result.metrics)
+        attachment_metrics, attachment_owns_vllm_metrics = (
+            self._collect_attachment_train_step_metrics()
+        )
+        metrics.update(attachment_metrics)
+        vllm_metrics_collector = getattr(
+            self.backend, "collect_train_step_vllm_metrics", None
+        )
+        if (
+            callable(vllm_metrics_collector)
+            and not attachment_owns_vllm_metrics
+            and self.model._serving_capabilities is not None
+            and self.model._serving_capabilities.fast_metrics
+        ):
+            maybe_metrics = vllm_metrics_collector(self.model)
+            if inspect.isawaitable(maybe_metrics):
+                metrics.update(await maybe_metrics)
+        metrics.update(
+            self._score_metrics(
+                item.training_policy_step,
+                item.batch,
+                step_seconds=item.step_seconds,
+                result_metrics=metrics,
+                age_metrics=item.policy_age_metrics,
+            )
+        )
+        phases["metrics"] = time.monotonic() - started
+
+        started = time.monotonic()
+        metrics.update(await self._queue_freshness_metrics(item.current_step))
+        metrics.update(self._pipeline_settings_metrics())
+        phases["queue_snapshot"] = time.monotonic() - started
+
+        started = time.monotonic()
+        await self._emit_packed_group_observations(
+            metrics, batch=item.batch, step=item.current_step
+        )
+        await self._emit_pipeline_metrics(
+            metrics, step=item.current_step, t_s=item.step_completed_s
+        )
+        phases["autotuner"] = time.monotonic() - started
+
+        started = time.monotonic()
+        await self.model.log(
+            item.batch,
+            split="train",
+            step=item.current_step,
+            metrics=metrics,
+        )
+        phases["history"] = time.monotonic() - started
+
+        started = time.monotonic()
+        await self._log_zero_variance_groups(item.current_step)
+        if self.eval_fn is not None and item.should_eval_step:
+            await self._schedule_eval_step(item.current_step)
+        self._persist_state(item.current_step)
+        phases["persistence"] = time.monotonic() - started
+
+        if os.getenv("ART_TRAIN_STEP_LOG"):
+            summary = " ".join(
+                f"{name}={duration * 1e3:.1f}ms" for name, duration in phases.items()
+            )
+            print(f"[train] step {item.current_step} controller {summary}")
+
+    async def _await_post_train(self, task: asyncio.Task[None] | None) -> None:
+        if task is None:
+            return
+        try:
+            await task
+        finally:
+            self._post_train_tasks.discard(task)
+
     async def _training_stage(self) -> None:
         if self._output_queue is None:
             return
@@ -873,6 +1211,11 @@ async def _training_stage(self) -> None:
             self.request_stop()
             return
         stop_after_batch = False
+        pending_stale_groups = 0
+        pending_zero_variance_groups = 0
+        pending_dequeued_groups = 0
+        post_train_task: asyncio.Task[None] | None = None
+        post_train_dispatch: asyncio.Event | None = None
 
         while True:
             if stop_at_step is not None and current_step >= stop_at_step:
@@ -880,26 +1223,90 @@ async def _training_stage(self) -> None:
             step_start = time.monotonic()
             collect_started = time.monotonic()
             zero_variance_before = self.state.discarded_zero_variance_groups
-            batch, discarded, saw_sentinel = await self._collect_batch(current_step)
+            selection_s = 0.0
+            preparation_s = 0.0
+            packed_queue_depth = 0
+            preparation_metrics: dict[str, float] = {}
+            packing_policy_step = current_step
+            if self._packed_queue is None:
+                if post_train_dispatch is not None:
+                    post_train_dispatch.set()
+                batch, discarded, saw_sentinel = await self._collect_batch(current_step)
+            else:
+                packed_queue_depth = self._packed_queue.qsize()
+                if packed_queue_depth == 0 and post_train_dispatch is not None:
+                    post_train_dispatch.set()
+                prepared = await self._packed_queue.get()
+                if prepared is None:
+                    if post_train_dispatch is not None:
+                        post_train_dispatch.set()
+                    break
+                batch = prepared.batch
+                discarded = prepared.discarded
+                saw_sentinel = prepared.saw_sentinel
+                selection_s = prepared.selection_s
+                preparation_s = prepared.preparation_s
+                preparation_metrics = prepared.preparation_metrics
+                packing_policy_step = prepared.packing_policy_step
             trainer_idle_s = time.monotonic() - collect_started
             zero_variance_discarded = (
-                self.state.discarded_zero_variance_groups - zero_variance_before
+                prepared.zero_variance_discarded
+                if self._packed_queue is not None
+                else self.state.discarded_zero_variance_groups - zero_variance_before
             )
             dequeued_groups = len(batch) + discarded + zero_variance_discarded
+            if self._packed_queue is not None and any(
+                self._is_group_stale(group, current_step) for group in batch
+            ):
+                discard = getattr(self.backend, "discard_pipeline_batch")
+                await discard(batch)
+                if post_train_dispatch is not None:
+                    post_train_dispatch.set()
+                try:
+                    await self._await_post_train(post_train_task)
+                finally:
+                    prepared.handoff.set()
+                post_train_task = None
+                post_train_dispatch = None
+                discarded += len(batch)
+                self.state.discarded_stale_groups += discarded
+                self._status.note_stale(discarded)
+                pending_stale_groups += discarded
+                pending_zero_variance_groups += zero_variance_discarded
+                pending_dequeued_groups += dequeued_groups
+                if saw_sentinel:
+                    break
+                continue
             self.state.discarded_stale_groups += discarded
             if discarded:
                 self._status.note_stale(discarded)
             if not batch:
                 break
+            step_stale_groups = pending_stale_groups + discarded
+            step_zero_variance_groups = (
+                pending_zero_variance_groups + zero_variance_discarded
+            )
+            step_dequeued_groups = pending_dequeued_groups + dequeued_groups
+            pending_stale_groups = 0
+            pending_zero_variance_groups = 0
+            pending_dequeued_groups = 0
 
             training_policy_step = current_step
+            policy_age_metrics = self._batch_policy_age_metrics(
+                training_policy_step, batch
+            )
             expected_step = current_step + 1
             should_eval_step = self._should_eval_step(expected_step)
             should_checkpoint = self.save_checkpoint and should_eval_step
 
-            async with self.state.policy_updated:
-                self.state.next_training_step = expected_step
-                self.state.policy_updated.notify_all()
+            self.state.next_training_step = expected_step
+            if self._packed_queue is not None:
+                if post_train_task is None:
+                    prepared.handoff.set()
+                else:
+                    post_train_task.add_done_callback(
+                        lambda _task, event=prepared.handoff: event.set()
+                    )
 
             self._status.note_training_start(len(batch))
             train_call_start = time.monotonic()
@@ -915,6 +1322,10 @@ async def _training_stage(self) -> None:
                     "adam_params": self.adam_params,
                     "optimizer_save_interval": self.optimizer_save_interval,
                 }
+                if self.grad_accumulation_sequences is not None:
+                    train_kwargs["grad_accumulation_sequences"] = (
+                        self.grad_accumulation_sequences
+                    )
                 if self.kl_penalty_coef > 0.0:
                     kl_penalty_reference_step = self._kl_penalty_reference_step(
                         current_step
@@ -927,6 +1338,15 @@ async def _training_stage(self) -> None:
                 if self.autotune.mode != "off":
                     for group in batch:
                         group._collect_packing_shape = True
+                if post_train_dispatch is not None:
+                    if getattr(
+                        self.backend, "supports_pipeline_train_dispatch_fence", False
+                    ):
+                        train_kwargs["_pipeline_train_dispatch_event"] = (
+                            post_train_dispatch
+                        )
+                    else:
+                        post_train_dispatch.set()
                 result = await self.backend.train(
                     self.model,
                     batch,
@@ -934,10 +1354,14 @@ async def _training_stage(self) -> None:
                 )
                 self._backend_training_completed = True
             except Exception:
+                if post_train_dispatch is not None:
+                    post_train_dispatch.set()
                 for group in batch:
                     group._collect_packing_shape = False
                     group._packed_group_shape = None
+                    await self._discard_collected_group(group)
                 self._status.note_training_end()
+                await self._await_post_train(post_train_task)
                 raise
             finally:
                 train_call_elapsed = time.monotonic() - train_call_start
@@ -947,110 +1371,121 @@ async def _training_stage(self) -> None:
                         f"{train_call_elapsed:.1f}s"
                     )
 
-            try:
-                current_step = result.step
-                self.state.policy_version = current_step
-                self.state.next_training_step = current_step
-                await self._log_checkpoint_saved(result)
-                await self._prune_model_adapters(current_step)
-                await self._run_checkpoint_retention(current_step)
-
-                step_seconds = time.monotonic() - step_start
-                actor_wall_s, actor_idle_s, queue_wait_s = (
-                    self._consume_producer_rollout_timings()
-                )
-                self._status.note_training_batch(
-                    batch, step=current_step, step_seconds=step_seconds
+            self._status.note_training_end()
+            if post_train_dispatch is not None and not post_train_dispatch.is_set():
+                post_train_dispatch.set()
+                raise RuntimeError(
+                    "backend completed without signaling trainer dispatch"
                 )
+            post_train_wait_started = time.monotonic()
+            await self._await_post_train(post_train_task)
+            post_train_wait_s = time.monotonic() - post_train_wait_started
+            post_train_task = None
+            post_train_dispatch = None
+
+            current_step = int(result.step)
+            self.state.policy_version = current_step
+            self.state.next_training_step = current_step
+            step_completed_s = time.monotonic()
+            step_seconds = step_completed_s - step_start
+            actor_wall_s, actor_idle_s, queue_wait_s = (
+                self._consume_producer_rollout_timings()
+            )
+            self._status.note_training_batch(
+                batch, step=current_step, step_seconds=step_seconds
+            )
 
-                stale_groups = float(self.state.discarded_stale_groups)
-                zero_variance_groups = float(self.state.discarded_zero_variance_groups)
-                self.state.accepted_trainable_groups += len(batch)
-                generated_groups_cum = (
-                    float(self.state.accepted_trainable_groups)
-                    + stale_groups
-                    + zero_variance_groups
-                )
-                metrics = {
-                    "discarded/cum/stale_groups": stale_groups,
-                    "discarded/cum/zero_variance_groups": zero_variance_groups,
-                    "discarded/step/stale_groups": float(discarded),
-                    "discarded/step/zero_variance_groups": float(
-                        zero_variance_discarded
-                    ),
-                    "discarded/rate/stale_groups": stale_groups
-                    / max(generated_groups_cum, 1.0),
-                    "discarded/rate/zero_variance_groups": zero_variance_groups
-                    / max(generated_groups_cum, 1.0),
-                    "time/step_wall_s": step_seconds,
-                    "time/step_collect_batch_s": trainer_idle_s,
-                    "time/step_trainer_idle_s": trainer_idle_s,
-                    "time/step_rollout_s": actor_wall_s,
-                    "time/step_rollout_idle_s": actor_idle_s,
-                    "queue/put_wait_s": queue_wait_s,
-                    "queue/put_wait_frac": queue_wait_s
-                    / max(queue_wait_s + actor_wall_s, 1e-9),
-                    "queue/actual_stale_fraction": discarded / max(dequeued_groups, 1),
-                }
-                metrics.setdefault("time/step_backend_train_s", train_call_elapsed)
-                metrics.update(result.metrics)
-                attachment_metrics, attachment_owns_vllm_metrics = (
-                    self._collect_attachment_train_step_metrics()
-                )
-                metrics.update(attachment_metrics)
-                vllm_metrics_collector = getattr(
-                    self.backend, "collect_train_step_vllm_metrics", None
-                )
-                if (
-                    callable(vllm_metrics_collector)
-                    and not attachment_owns_vllm_metrics
-                    and self.model._serving_capabilities is not None
-                    and self.model._serving_capabilities.fast_metrics
-                ):
-                    maybe_metrics = vllm_metrics_collector(self.model)
-                    if inspect.isawaitable(maybe_metrics):
-                        metrics.update(await maybe_metrics)
+            stale_groups = float(self.state.discarded_stale_groups)
+            zero_variance_groups = float(self.state.discarded_zero_variance_groups)
+            self.state.accepted_trainable_groups += len(batch)
+            generated_groups_cum = (
+                float(self.state.accepted_trainable_groups)
+                + stale_groups
+                + zero_variance_groups
+            )
+            metrics = {
+                "discarded/cum/stale_groups": stale_groups,
+                "discarded/cum/zero_variance_groups": zero_variance_groups,
+                "discarded/step/stale_groups": float(step_stale_groups),
+                "discarded/step/zero_variance_groups": float(step_zero_variance_groups),
+                "discarded/rate/stale_groups": stale_groups
+                / max(generated_groups_cum, 1.0),
+                "discarded/rate/zero_variance_groups": zero_variance_groups
+                / max(generated_groups_cum, 1.0),
+                "time/step_wall_s": step_seconds,
+                "time/step_collect_batch_s": trainer_idle_s,
+                "time/step_trainer_idle_s": trainer_idle_s,
+                "time/step_rollout_s": actor_wall_s,
+                "time/step_rollout_idle_s": actor_idle_s,
+                "time/step_backend_train_s": train_call_elapsed,
+                "time/step_post_train_backpressure_s": post_train_wait_s,
+                "queue/put_wait_s": queue_wait_s,
+                "queue/put_wait_frac": queue_wait_s
+                / max(queue_wait_s + actor_wall_s, 1e-9),
+                "queue/actual_stale_fraction": step_stale_groups
+                / max(step_dequeued_groups, 1),
+            }
+            if self._packed_queue is not None:
                 metrics.update(
-                    self._score_metrics(
-                        training_policy_step,
-                        batch,
-                        step_seconds=step_seconds,
-                        result_metrics=metrics,
-                    )
-                )
-                metrics.update(self._queue_freshness_metrics(current_step))
-                metrics.update(self._pipeline_settings_metrics())
-
-                await self._emit_packed_group_observations(
-                    metrics, batch=batch, step=current_step
-                )
-                await self._emit_pipeline_metrics(metrics, step=current_step)
-                await self.model.log(
-                    batch,
-                    split="train",
-                    step=current_step,
-                    metrics=metrics,
+                    {
+                        "time/step_batch_selection_s": selection_s,
+                        "time/step_batch_prepare_s": preparation_s,
+                        "queue/packed_get_wait_s": trainer_idle_s,
+                        "queue/packed_queue_depth": float(packed_queue_depth),
+                        "queue/packed_queue_occupancy": packed_queue_depth
+                        / self._packed_queue.maxsize,
+                        "queue/packing_policy_lag_steps": float(
+                            current_step - packing_policy_step
+                        ),
+                        **preparation_metrics,
+                    }
                 )
-                await self._log_zero_variance_groups(current_step)
-
-                if self.eval_fn is not None and should_eval_step:
-                    await self._schedule_eval_step(current_step)
-
-                self._persist_state(current_step)
-            finally:
-                self._status.note_training_end()
-
-            async with self.state.policy_updated:
-                self.state.policy_updated.notify_all()
+            post_train_dispatch = asyncio.Event()
+            post_train_task = asyncio.create_task(
+                self._finalize_post_train(
+                    _PostTrainItem(
+                        batch=batch,
+                        result=result,
+                        current_step=current_step,
+                        training_policy_step=training_policy_step,
+                        should_eval_step=should_eval_step,
+                        step_seconds=step_seconds,
+                        step_completed_s=step_completed_s,
+                        policy_age_metrics=policy_age_metrics,
+                        metrics=metrics,
+                    ),
+                    post_train_dispatch,
+                ),
+                name=f"post_train_step_{current_step}",
+            )
+            self._post_train_tasks.add(post_train_task)
 
             if saw_sentinel:
                 stop_after_batch = True
             if stop_after_batch:
                 break
 
+        if post_train_dispatch is not None:
+            post_train_dispatch.set()
+        await self._await_post_train(post_train_task)
+        self.state.done = True
+        self._accept_prepared_batches = False
+        if isinstance(self._output_queue, DistributedTrajectoryQueue):
+            await self._output_queue.finish()
+        await self._discard_pending_prepared_batches()
         self._persist_state(current_step)
         self.request_stop()
 
+    async def _discard_pending_prepared_batches(self) -> None:
+        if self._packed_queue is None:
+            return
+        discard = getattr(self.backend, "discard_pipeline_batch")
+        while not self._packed_queue.empty():
+            pending = self._packed_queue.get_nowait()
+            if pending is not None:
+                await discard(pending.batch)
+                pending.handoff.set()
+
     async def _collect_batch(
         self, current_step: int
     ) -> tuple[list[TrajectoryGroup], int, bool]:
@@ -1059,46 +1494,56 @@ async def _collect_batch(
         discarded = 0
         saw_sentinel = False
 
-        while len(batch) < self.min_batch_size:
-            completed, item = await self._await_or_stop(self._output_queue.get())
-            if not completed:
-                saw_sentinel = True
-                break
-            if item is None:
-                saw_sentinel = True
-                break
-            self._status.note_group_dequeued(item)
-            self._check_all_failed(item)
-            if self._is_group_stale(item, current_step):
-                discarded += 1
-                continue
-            if self._group_zero_variance(item):
-                if self._record_zero_variance(item):
-                    return [], discarded, saw_sentinel
-                continue
-            batch.append(item)
-
         while not saw_sentinel and len(batch) < self.max_batch_size:
-            try:
-                item = self._output_queue.get_nowait()
-            except asyncio.QueueEmpty:
-                break
-            if item is None:
-                saw_sentinel = True
-                break
-            self._status.note_group_dequeued(item)
-            self._check_all_failed(item)
-            if self._is_group_stale(item, current_step):
-                discarded += 1
-                continue
-            if self._group_zero_variance(item):
-                if self._record_zero_variance(item):
-                    return [], discarded, saw_sentinel
-                continue
-            batch.append(item)
+            wait = len(batch) < self.min_batch_size
+            count = (self.min_batch_size if wait else self.max_batch_size) - len(batch)
+            if isinstance(self._output_queue, DistributedTrajectoryQueue):
+                items, saw_sentinel = await self._output_queue.get_many(
+                    count, wait=wait
+                )
+                if not items:
+                    break
+            elif wait:
+                item = await self._output_queue.get()
+                if item is None:
+                    saw_sentinel = True
+                    break
+                items = [item]
+            else:
+                try:
+                    item = self._output_queue.get_nowait()
+                except asyncio.QueueEmpty:
+                    break
+                if item is None:
+                    saw_sentinel = True
+                    break
+                items = [item]
+            for item in items:
+                self._status.note_group_dequeued()
+                try:
+                    self._check_all_failed(item)
+                except BaseException:
+                    await self._discard_collected_group(item)
+                    raise
+                if self._is_group_stale(item, current_step):
+                    discarded += 1
+                    await self._discard_collected_group(item)
+                    continue
+                if self._group_zero_variance(item):
+                    if self._record_zero_variance(item):
+                        await self._discard_collected_group(item)
+                        return [], discarded, saw_sentinel
+                    await self._discard_collected_group(item)
+                    continue
+                batch.append(item)
 
         return batch, discarded, saw_sentinel
 
+    async def _discard_collected_group(self, group: TrajectoryGroup) -> None:
+        if isinstance(self._output_queue, DistributedTrajectoryQueue):
+            await self._output_queue.discard_group(group)
+            group._distributed_lease = None
+
     def _check_all_failed(self, group: TrajectoryGroup) -> None:
         """Raise if all rollouts in a group failed with exceptions."""
         if not group.trajectories and group.exceptions:
@@ -1234,21 +1679,46 @@ def _validate_eval_policy_spans(
     ) -> None:
         for trajectory in trajectories:
             for item in cls._trajectory_messages_and_choices(trajectory):
-                extra = getattr(item, "model_extra", None)
-                if not isinstance(extra, Mapping) or "policy_token_spans" not in extra:
+                is_completion = isinstance(item, Choice) or (
+                    isinstance(item, Mapping) and item.get("role") == "assistant"
+                )
+                if not is_completion:
                     continue
-                spans = extra["policy_token_spans"]
-                if not isinstance(spans, list):
-                    raise RuntimeError("Eval policy_token_spans must be a list")
+                spans = cls._validated_policy_spans(item, required=True)
+                assert spans is not None
                 for span in spans:
-                    if not isinstance(span, Mapping) or "policy_version" not in span:
-                        raise RuntimeError("Eval policy token span is malformed")
-                    policy_version = int(span["policy_version"])
-                    if policy_version != step:
+                    if span.policy_version != step:
                         raise RuntimeError(
-                            f"Eval at step {step} returned policy-{policy_version} tokens"
+                            f"Eval at step {step} returned "
+                            f"policy-{span.policy_version} tokens"
                         )
 
+    @staticmethod
+    def _validated_policy_spans(
+        item: Any, *, required: bool
+    ) -> list[PolicyTokenSpan] | None:
+        extra = (
+            item if isinstance(item, Mapping) else getattr(item, "model_extra", None)
+        )
+        raw = extra.get("policy_token_spans") if isinstance(extra, Mapping) else None
+        if raw is None:
+            if required:
+                raise RuntimeError(
+                    "Exact policy provenance is missing policy_token_spans"
+                )
+            return None
+        if not isinstance(raw, list) or not raw:
+            raise RuntimeError("policy_token_spans must be a non-empty list")
+        spans = [PolicyTokenSpan.model_validate(span) for span in raw]
+        cursor = 0
+        for span in spans:
+            if span.start_token != cursor:
+                raise RuntimeError(
+                    "policy_token_spans must be a contiguous completion partition"
+                )
+            cursor = span.end_token
+        return spans
+
     def _apply_policy_versions(
         self,
         group: TrajectoryGroup,
@@ -1262,22 +1732,24 @@ def _apply_policy_versions(
             if trajectory.final_policy_version is None:
                 trajectory.final_policy_version = final_version
 
-    def _apply_scenario_metadata(
-        self, group: TrajectoryGroup, scenario: ScenarioT
-    ) -> None:
+    def _scenario_metadata(
+        self, scenario: ScenarioT
+    ) -> dict[str, float | int | str | bool | None]:
         metadata = scenario.get("metadata") if isinstance(scenario, dict) else None
         if metadata is None or not isinstance(metadata, dict):
-            return
+            return {}
 
+        result: dict[str, float | int | str | bool | None] = {}
         for key, value in metadata.items():
             if not isinstance(key, str):
                 continue
             if not self._is_scalar_metadata(value):
                 continue
             if key == "scenario_id":
-                group.metadata["scenario_id"] = value
+                result["scenario_id"] = value
                 continue
-            group.metadata[f"scenario_{key}"] = value
+            result[f"scenario_{key}"] = value
+        return result
 
     @staticmethod
     def _scenario_error_context(scenario: ScenarioT) -> str:
@@ -1385,15 +1857,9 @@ def _freshness_queue_window(self) -> int:
             return math.ceil(self.limit_mean_steps_off_policy)
         return 1
 
-    def _queue_freshness_metrics(self, current_step: int) -> dict[str, float]:
+    async def _queue_freshness_metrics(self, current_step: int) -> dict[str, float]:
         if self._output_queue is None:
             return {}
-        output_queue = cast(Any, self._output_queue)
-        queued = [
-            group
-            for group in list(output_queue._queue)
-            if isinstance(group, TrajectoryGroup)
-        ]
         limit_raw = (
             self.limit_mean_steps_off_policy
             if self.limit_mean_steps_off_policy is not None
@@ -1403,23 +1869,86 @@ def _queue_freshness_metrics(self, current_step: int) -> dict[str, float]:
             limit_raw = 1.0
         limit = max(float(limit_raw), 1e-9)
         ages: list[float] = []
-        for group in queued:
-            if self.limit_mean_steps_off_policy is not None:
-                age = self._group_mean_steps_off_policy(current_step, group)
-            else:
-                initial = self._group_initial_version(group)
-                age = None if initial is None else float(current_step - initial)
-            if age is not None:
+        capacity_metrics: dict[str, float] = {}
+        if isinstance(self._output_queue, DistributedTrajectoryQueue):
+            snapshot = await self._output_queue.snapshot()
+            for item in snapshot.items:
+                descriptor = item.ref.descriptor
+                if self.limit_mean_steps_off_policy is not None:
+                    if descriptor.policy_token_counts:
+                        weight = sum(descriptor.policy_token_counts.values())
+                        age = (
+                            sum(
+                                (current_step - version) * count
+                                for version, count in descriptor.policy_token_counts.items()
+                            )
+                            / weight
+                        )
+                    else:
+                        versions = descriptor.initial_policy_versions or (
+                            item.annotations.initial_policy_version,
+                        )
+                        weights = descriptor.completion_tokens
+                        if len(weights) != len(versions) or sum(weights) <= 0:
+                            weights = (1.0,) * len(versions)
+                        age = sum(
+                            (current_step - version) * weight
+                            for version, weight in zip(versions, weights, strict=True)
+                        ) / sum(weights)
+                else:
+                    initial = min(
+                        descriptor.initial_policy_versions
+                        or (item.annotations.initial_policy_version,)
+                    )
+                    age = float(current_step - initial)
                 ages.append(float(age))
-        ready = float(len(queued))
+            ready = float(snapshot.ready_groups)
+            depth = float(len(snapshot.items))
+            maxsize = float(snapshot.max_ready_groups)
+            put_waiting = float(self._output_queue.put_waiters)
+            capacity_metrics = {
+                "queue/data_plane_records": float(snapshot.used_records),
+                "queue/data_plane_bytes": float(snapshot.used_bytes),
+                "queue/data_plane_record_occupancy": snapshot.used_records
+                / snapshot.capacity_records,
+                "queue/data_plane_byte_occupancy": snapshot.used_bytes
+                / snapshot.capacity_bytes,
+                "queue/leased_groups": float(snapshot.leased_groups),
+                "queue/packing_groups": float(snapshot.packing_groups),
+                "queue/packed_groups": float(snapshot.packed_groups),
+                "queue/data_plane_packed_group_occupancy": snapshot.packed_groups
+                / snapshot.max_ready_groups,
+                "queue/lease_lifetime_mean_s": snapshot.lease_lifetime_s
+                / max(snapshot.released_leases, 1),
+                "queue/lease_lifetime_max_s": snapshot.max_lease_lifetime_s,
+            }
+        else:
+            output_queue = cast(Any, self._output_queue)
+            queued = [
+                group
+                for group in list(output_queue._queue)
+                if isinstance(group, TrajectoryGroup)
+            ]
+            for group in queued:
+                if self.limit_mean_steps_off_policy is not None:
+                    age = self._group_mean_steps_off_policy(current_step, group)
+                else:
+                    initial = self._group_initial_version(group)
+                    age = None if initial is None else float(current_step - initial)
+                if age is not None:
+                    ages.append(float(age))
+            ready = float(len(queued))
+            depth = ready
+            maxsize = float(self._output_queue.maxsize)
+            put_waiting = 0.0
         stale = sum(1 for age in ages if age > limit)
         return {
             "queue/ready_groups_est": ready,
-            "queue/completed_backlog_groups": ready,
-            "queue/put_waiting_groups": 0.0,
-            "queue/groups_depth": ready,
-            "queue/groups_depth_max": float(self._output_queue.maxsize),
-            "queue/occupancy": ready / max(float(self._output_queue.maxsize), 1.0),
+            "queue/completed_backlog_groups": depth,
+            "queue/put_waiting_groups": put_waiting,
+            "queue/groups_depth": depth,
+            "queue/groups_depth_max": maxsize,
+            "queue/occupancy": depth / max(maxsize, 1.0),
             "queue/predicted_policy_age_mean_steps": sum(ages) / len(ages)
             if ages
             else 0.0,
@@ -1432,6 +1961,7 @@ def _queue_freshness_metrics(self, current_step: int) -> dict[str, float]:
             if ages
             else 0.0,
             "queue/predicted_stale_fraction": stale / len(ages) if ages else 0.0,
+            **capacity_metrics,
         }
 
     def _pipeline_settings_metrics(self) -> dict[str, float]:
@@ -1477,6 +2007,7 @@ def _score_metrics(
         *,
         step_seconds: float,
         result_metrics: dict[str, float],
+        age_metrics: dict[str, float] | None = None,
     ) -> dict[str, float]:
         metrics: dict[str, float] = {}
         accepted_groups = float(len(batch))
@@ -1488,7 +2019,11 @@ def _score_metrics(
         )
         metrics["sample_efficiency/batch_factor"] = batch_factor
 
-        age_metrics = self._batch_policy_age_metrics(current_step, batch)
+        age_metrics = (
+            self._batch_policy_age_metrics(current_step, batch)
+            if age_metrics is None
+            else dict(age_metrics)
+        )
         age_exp_moment = age_metrics.pop("_policy_age_exp_tau8", None)
         metrics.update(age_metrics)
         mean_age = age_metrics.get("offpolicy/token_weighted_policy_age_steps")
@@ -1580,38 +2115,51 @@ def _trajectory_policy_age_stats(
         self, current_step: int, trajectory: art.Trajectory
     ) -> tuple[float, float, float] | None:
         span_stats = self._trajectory_policy_span_age_stats(current_step, trajectory)
+        if self._requires_exact_policy_spans():
+            if span_stats is None:
+                raise RuntimeError(
+                    "In-flight LoRA trajectory is missing exact policy token spans"
+                )
+            completion_tokens = self._trajectory_completion_weight(trajectory)
+            if span_stats[1] != completion_tokens:
+                raise RuntimeError(
+                    "In-flight LoRA policy spans do not cover every completion token: "
+                    f"covered={span_stats[1]:g}, completion_tokens="
+                    f"{completion_tokens:g}"
+                )
+            return span_stats
         if span_stats is not None:
             return span_stats
         if trajectory.initial_policy_version is None:
             return None
         weight = self._trajectory_completion_weight(trajectory)
-        age = float(current_step - trajectory.initial_policy_version)
+        age = self._policy_age(current_step, trajectory.initial_policy_version)
         return age * weight, weight, _policy_age_exp(age) * weight
 
     def _trajectory_policy_span_age_stats(
         self, current_step: int, trajectory: art.Trajectory
     ) -> tuple[float, float, float] | None:
+        if trajectory._policy_token_counts is not None:
+            age_sum = sum(
+                self._policy_age(current_step, version) * count
+                for version, count in trajectory._policy_token_counts.items()
+            )
+            age_exp_sum = sum(
+                _policy_age_exp(self._policy_age(current_step, version)) * count
+                for version, count in trajectory._policy_token_counts.items()
+            )
+            weight = float(sum(trajectory._policy_token_counts.values()))
+            return (float(age_sum), weight, age_exp_sum) if weight > 0 else None
         age_sum = 0.0
         age_exp_sum = 0.0
         weight_sum = 0.0
         for item in self._trajectory_messages_and_choices(trajectory):
-            extra = getattr(item, "model_extra", None)
-            if not isinstance(extra, Mapping):
-                continue
-            spans = extra.get("policy_token_spans")
-            if not isinstance(spans, list):
+            spans = self._validated_policy_spans(item, required=False)
+            if spans is None:
                 continue
             for span in spans:
-                if not isinstance(span, Mapping):
-                    continue
-                try:
-                    policy_version = int(span["policy_version"])
-                    weight = int(span["end_token"]) - int(span["start_token"])
-                except (KeyError, TypeError, ValueError):
-                    continue
-                if weight <= 0:
-                    continue
-                age = float(current_step - policy_version)
+                weight = span.end_token - span.start_token
+                age = self._policy_age(current_step, span.policy_version)
                 age_sum += age * weight
                 age_exp_sum += _policy_age_exp(age) * weight
                 weight_sum += float(weight)
@@ -1619,6 +2167,20 @@ def _trajectory_policy_span_age_stats(
             return None
         return age_sum, weight_sum, age_exp_sum
 
+    def _requires_exact_policy_spans(self) -> bool:
+        return (self.model._internal_config or {}).get(
+            "rollout_weight_update_mode"
+        ) == "in_flight_lora"
+
+    @staticmethod
+    def _policy_age(current_step: int, policy_version: int) -> float:
+        if policy_version > current_step:
+            raise RuntimeError(
+                "Trajectory tokens came from a future policy: "
+                f"policy={policy_version}, trainer={current_step}"
+            )
+        return float(current_step - policy_version)
+
     @staticmethod
     def _trajectory_messages_and_choices(trajectory: art.Trajectory) -> Iterable[Any]:
         for exchange in trajectory.exchanges.chat_completions:
@@ -1691,6 +2253,27 @@ async def _log_checkpoint_saved(self, result: Any) -> None:
             if isinstance(checkpoint_path, str) and checkpoint_path
             else Path(self.model._get_output_dir()) / "checkpoints" / f"{step:04d}"
         )
+        ready = getattr(result, "checkpoint_ready", None)
+        if ready is not None:
+            task = asyncio.create_task(
+                self._log_checkpoint_when_ready(step, path, ready)
+            )
+            self._checkpoint_log_tasks.add(task)
+            task.add_done_callback(self._checkpoint_log_done)
+            return
+        self._record_checkpoint_saved(step, path)
+
+    async def _log_checkpoint_when_ready(
+        self, step: int, path: Path, ready: Awaitable[None]
+    ) -> None:
+        await ready
+        if not path.is_dir():
+            raise RuntimeError(
+                f"checkpoint {step} materialized without directory {path}"
+            )
+        self._record_checkpoint_saved(step, path)
+
+    def _record_checkpoint_saved(self, step: int, path: Path) -> None:
         if not path.exists():
             return
         self._log_checkpoint_history(
@@ -1701,6 +2284,15 @@ async def _log_checkpoint_saved(self, result: Any) -> None:
             },
         )
 
+    def _checkpoint_log_done(self, task: asyncio.Task[None]) -> None:
+        self._checkpoint_log_tasks.discard(task)
+        if task.cancelled():
+            return
+        error = task.exception()
+        if error is not None and self._checkpoint_log_failure is None:
+            self._checkpoint_log_failure = error
+            self.request_stop()
+
     async def _log_checkpoint_eval_completed(self, step: int) -> None:
         self._log_checkpoint_history(
             step,
@@ -1824,12 +2416,37 @@ async def _run_checkpoint_retention(self, current_step: int) -> None:
     def _is_scalar_metadata(value: object) -> bool:
         return value is None or isinstance(value, (str, int, float, bool))
 
-    async def _put_output_group(self, group: TrajectoryGroup) -> float:
+    async def _put_output_group(
+        self,
+        group: TrajectoryGroup | TrajectoryGroupRef,
+        *,
+        metadata: dict[str, float | int | str | bool | None],
+        initial_policy_version: int,
+        final_policy_version: int,
+        rollout_wall_s: float,
+        actor_idle_s: float,
+    ) -> float:
         assert self._output_queue is not None
         queue_wait_started = time.monotonic()
+        if isinstance(self._output_queue, DistributedTrajectoryQueue):
+            if not isinstance(group, TrajectoryGroupRef):
+                raise RuntimeError("distributed result queue requires a stored group")
+            accepted, wait_s = await self._output_queue.put(
+                group,
+                metadata=metadata,
+                initial_policy_version=initial_policy_version,
+                final_policy_version=final_policy_version,
+                rollout_wall_s=rollout_wall_s,
+                actor_idle_s=actor_idle_s,
+            )
+            if accepted:
+                self._status.note_group_enqueued()
+            return wait_s
+        if not isinstance(group, TrajectoryGroup):
+            raise RuntimeError("local result queue requires a trajectory group")
         completed, _ = await self._await_or_stop(self._output_queue.put(group))
         if completed:
-            self._status.note_group_enqueued(group)
+            self._status.note_group_enqueued()
         return time.monotonic() - queue_wait_started
 
     def _record_producer_rollout_timings(
diff --git a/src/art/pipeline_tuner/attachment.py b/src/art/pipeline_tuner/attachment.py
index 8a1985009..b4538b5db 100644
--- a/src/art/pipeline_tuner/attachment.py
+++ b/src/art/pipeline_tuner/attachment.py
@@ -3,15 +3,24 @@
 import asyncio
 import inspect
 import math
+from queue import Empty, SimpleQueue
+import threading
 import time
-from typing import Any
+from typing import Any, Literal, NamedTuple
 import warnings
 
 import pydantic
 
 from art.errors import ArtVllmMetricsTimeoutError
 
-from .autotune import PipelineAutotuner, build_initial_settings, recommended_queue_size
+from .autotune import (
+    PipelineAutotuner,
+    _vllm_sample_intervals,
+    _vllm_sample_max_age_s,
+    build_initial_settings,
+    freshness_worker_limit,
+    recommended_queue_size,
+)
 from .config import (
     PackedGroupObservation,
     PipelineAutotuneConfig,
@@ -47,6 +56,27 @@
 class VllmMetricPollHealth(pydantic.BaseModel):
     t_s: float
     timed_out: bool = False
+    scheduled_s: float | None = None
+    request_start_s: float | None = None
+    outcome: Literal["success", "timeout", "error"] = "success"
+    skipped_polls: int = 0
+
+
+class _VllmMetricPollResult(NamedTuple):
+    scheduled_s: float
+    request_start_s: float
+    completion_s: float
+    outcome: Literal["success", "timeout", "error"]
+    skipped_polls: int
+    metrics: dict[str, float] | None = None
+    error: BaseException | None = None
+
+
+def _p99(values: list[float]) -> float:
+    if not values:
+        return 0.0
+    ordered = sorted(values)
+    return ordered[max(0, math.ceil(0.99 * len(ordered)) - 1)]
 
 
 class PipelineAutotunerAttachment:
@@ -56,9 +86,11 @@ def __init__(self, config: PipelineAutotuneConfig) -> None:
         self.store: PipelineTunerProfileStore | None = None
         self.tuner: PipelineAutotuner | None = None
         self.profile_name = config.output_name
-        self._sampler_task: asyncio.Task[None] | None = None
+        self._sampler_thread: threading.Thread | None = None
+        self._sampler_stop = threading.Event()
+        self._sampler_results: SimpleQueue[_VllmMetricPollResult] = SimpleQueue()
         self._poll_health: list[VllmMetricPollHealth] = []
-        self._train_step_vllm_metrics: list[PipelineMetric] = []
+        self._train_step_vllm_metrics: dict[str, tuple[float, int]] = {}
         self._sampler_error: BaseException | None = None
         self._started = False
 
@@ -68,52 +100,70 @@ async def on_start(self, trainer: Any) -> None:
         self.trainer = trainer
         self.store = PipelineTunerProfileStore.for_model(trainer.model)
         self._validate_weight_update_mode(trainer)
-        packed_sequence_length = self._discover_packed_sequence_length()
-        target_packed_sequences = await self._discover_target_packed_sequences(trainer)
-        inference_gpu_count = await self._discover_inference_gpu_count(trainer)
-        policy_age_limit_steps = self._policy_age_limit_steps(trainer)
-        loaded = self._load_profile_if_requested(
-            packed_sequence_length, target_packed_sequences, policy_age_limit_steps
-        )
-        if loaded is not None:
-            settings = self._settings_with_current_queue(
-                loaded.settings, policy_age_limit_steps
-            )
-            self.profile_name = self.config.profile or self.config.output_name
-            trainer._pipeline_tuner_profile = self.store.resolve(
-                self.config.profile
-            ).stem
-        else:
-            settings = build_initial_settings(
-                config=self.config,
-                inference_gpu_count=inference_gpu_count,
-                target_packed_sequences=target_packed_sequences,
-                policy_age_limit_steps=policy_age_limit_steps,
+        initial_poll: _VllmMetricPollResult | None = None
+        try:
+            if self.config.mode == "online":
+                self._start_metric_sampler()
+                initial_poll = await self._wait_for_initial_serving_metrics()
+            packed_sequence_length = self._discover_packed_sequence_length()
+            target_packed_sequences = await self._discover_target_packed_sequences(
+                trainer
             )
-        trainer.apply_pipeline_settings(settings)
-        if self.config.mode == "online":
-            self.tuner = PipelineAutotuner(
-                config=self.config,
-                settings=settings,
-                model_name=trainer.model.run_name,
-                backend_name=type(trainer.backend).__name__,
-                packed_sequence_length=packed_sequence_length,
-                target_packed_sequences=target_packed_sequences,
-                inference_gpu_count=inference_gpu_count,
-                policy_age_limit_steps=policy_age_limit_steps,
-                starting_step=trainer.state.next_training_step,
+            inference_gpu_count = await self._discover_inference_gpu_count(
+                trainer,
+                None if initial_poll is None else initial_poll.metrics,
             )
-            await self._wait_for_initial_serving_metrics()
-            self._sampler_task = asyncio.create_task(
-                self._sample_serving_metrics(),
-                name="art_pipeline_autotuner_vllm_sampler",
+            rollout_worker_capacity = trainer.rollout_worker_capacity
+            policy_age_limit_steps = self._policy_age_limit_steps(trainer)
+            loaded = self._load_profile_if_requested(
+                packed_sequence_length,
+                target_packed_sequences,
+                policy_age_limit_steps,
+                rollout_worker_capacity,
             )
-            self._save_profile()
-        self._started = True
+            if loaded is not None:
+                settings = self._settings_with_current_queue(
+                    loaded.settings, policy_age_limit_steps
+                )
+                self.profile_name = self.config.profile or self.config.output_name
+                trainer._pipeline_tuner_profile = self.store.resolve(
+                    self.config.profile
+                ).stem
+            else:
+                settings = build_initial_settings(
+                    config=self.config,
+                    inference_gpu_count=inference_gpu_count,
+                    target_packed_sequences=target_packed_sequences,
+                    policy_age_limit_steps=policy_age_limit_steps,
+                    rollout_worker_capacity=rollout_worker_capacity,
+                )
+            trainer.apply_pipeline_settings(settings)
+            if self.config.mode == "online":
+                self.tuner = PipelineAutotuner(
+                    config=self.config,
+                    settings=settings,
+                    model_name=trainer.model.run_name,
+                    backend_name=type(trainer.backend).__name__,
+                    packed_sequence_length=packed_sequence_length,
+                    target_packed_sequences=target_packed_sequences,
+                    inference_gpu_count=inference_gpu_count,
+                    policy_age_limit_steps=policy_age_limit_steps,
+                    starting_step=trainer.state.next_training_step,
+                    rollout_worker_capacity=rollout_worker_capacity,
+                )
+                assert initial_poll is not None
+                self._consume_poll(initial_poll, record_train_step=False)
+                self._drain_metric_polls()
+                self._save_profile()
+            self._started = True
+        except BaseException:
+            await self._stop_metric_sampler()
+            raise
 
     async def on_metric(self, metric: PipelineMetric) -> None:
         if self.tuner is None:
             return
+        self._drain_metric_polls()
         self._raise_sampler_error()
         decision = self.tuner.on_metric(metric)
         if decision is None:
@@ -131,61 +181,170 @@ def owns_train_step_vllm_metrics(self) -> bool:
         return self.config.mode == "online"
 
     async def on_stop(self, *, training_failed: bool = False) -> None:
-        if self._sampler_task is not None:
-            self._sampler_task.cancel()
-            await asyncio.gather(self._sampler_task, return_exceptions=True)
-            self._sampler_task = None
+        await self._stop_metric_sampler()
         if self._started and self.tuner is not None:
             self._save_profile()
         if not training_failed:
             self._raise_sampler_error()
 
-    async def _wait_for_initial_serving_metrics(self) -> None:
+    def _start_metric_sampler(self) -> None:
+        if self._sampler_thread is not None:
+            raise RuntimeError("ART vLLM metrics sampler is already running")
+        self._sampler_stop.clear()
+        self._sampler_thread = threading.Thread(
+            target=self._metric_sampler_thread_main,
+            name="art_pipeline_autotuner_vllm_sampler",
+            daemon=True,
+        )
+        self._sampler_thread.start()
+
+    async def _stop_metric_sampler(self) -> None:
+        thread = self._sampler_thread
+        if thread is None:
+            return
+        self._sampler_stop.set()
+        await asyncio.to_thread(
+            thread.join, max(2.0, 2.0 * self.config.vllm_metric_interval_s)
+        )
+        if thread.is_alive() and self._sampler_error is None:
+            self._sampler_error = RuntimeError(
+                "ART vLLM metrics sampler did not stop after its request timeout"
+            )
+        if not thread.is_alive():
+            self._sampler_thread = None
+        self._drain_metric_polls()
+
+    def _metric_sampler_thread_main(self) -> None:
+        try:
+            asyncio.run(self._sample_serving_metrics())
+        except BaseException as error:
+            now = time.monotonic()
+            self._sampler_results.put(
+                _VllmMetricPollResult(now, now, now, "error", 0, error=error)
+            )
+
+    async def _wait_for_initial_serving_metrics(self) -> _VllmMetricPollResult:
         deadline = time.monotonic() + max(5.0, 2.0 * self.config.vllm_metric_interval_s)
         while True:
             try:
-                metrics = await self._collect_required_serving_metrics()
-            except ArtVllmMetricsTimeoutError as exc:
-                self._record_poll_timeout()
-                remaining = deadline - time.monotonic()
-                if remaining <= 0.0:
+                result = self._sampler_results.get_nowait()
+            except Empty:
+                if time.monotonic() >= deadline:
                     raise RuntimeError(
                         "Pipeline autotuning could not collect an initial ART vLLM "
                         "metrics sample before startup timeout."
-                    ) from exc
-                await asyncio.sleep(min(self.config.vllm_metric_interval_s, remaining))
+                    )
+                await asyncio.sleep(min(0.01, self.config.vllm_metric_interval_s))
                 continue
-            self._record_poll_success()
-            await self._emit_metrics(metrics, step=None, record_train_step=False)
-            return
+            if result.outcome == "success":
+                return result
+            self._consume_poll(result, record_train_step=False)
+            if result.outcome == "error":
+                self._raise_sampler_error()
+            if time.monotonic() >= deadline:
+                raise RuntimeError(
+                    "Pipeline autotuning could not collect an initial ART vLLM "
+                    "metrics sample before startup timeout."
+                ) from result.error
 
     async def _sample_serving_metrics(self) -> None:
         assert self.trainer is not None
-        while not self.trainer.state.done:
-            try:
-                metrics = await self._collect_required_serving_metrics()
-                self._record_poll_success()
-                await self._emit_metrics(metrics, step=None)
-            except asyncio.CancelledError:
-                raise
-            except ArtVllmMetricsTimeoutError:
-                self._record_poll_timeout()
-            except Exception as exc:
-                self._sampler_error = exc
-                self.trainer.request_stop()
-                return
-            await asyncio.sleep(self.config.vllm_metric_interval_s)
-
-    async def _collect_required_serving_metrics(self) -> dict[str, float]:
+        trainer = self.trainer
+        backend = trainer.backend
+        factory = getattr(backend, "create_train_step_vllm_metrics_collector", None)
+        session = factory(trainer.model) if callable(factory) else None
+        if session is not None:
+            collector = getattr(session, "collect", None)
+        else:
+            backend_collector = getattr(
+                backend, "collect_train_step_vllm_metrics", None
+            )
+            collector = (
+                None
+                if not callable(backend_collector)
+                else lambda: backend_collector(trainer.model)
+            )
+        if not callable(collector):
+            raise RuntimeError(
+                "Pipeline autotuning requires ART vLLM metrics collection."
+            )
+        next_s = time.monotonic()
+        try:
+            while not self._sampler_stop.is_set():
+                while not self._sampler_stop.is_set():
+                    delay_s = next_s - time.monotonic()
+                    if delay_s <= 0.0:
+                        break
+                    await asyncio.sleep(min(delay_s, 0.05))
+                if self._sampler_stop.is_set():
+                    break
+                scheduled_s = next_s
+                request_start_s = time.monotonic()
+                metrics: dict[str, float] | None = None
+                error: BaseException | None = None
+                outcome: Literal["success", "timeout", "error"] = "success"
+                try:
+                    metrics = await self._collect_required_serving_metrics(collector)
+                except ArtVllmMetricsTimeoutError as exc:
+                    outcome = "timeout"
+                    error = exc
+                except Exception as exc:
+                    outcome = "error"
+                    error = exc
+                completion_s = time.monotonic()
+                next_s = scheduled_s + self.config.vllm_metric_interval_s
+                skipped_polls = 0
+                if next_s <= completion_s:
+                    skipped_polls = (
+                        math.floor(
+                            (completion_s - next_s) / self.config.vllm_metric_interval_s
+                        )
+                        + 1
+                    )
+                    next_s += skipped_polls * self.config.vllm_metric_interval_s
+                self._sampler_results.put(
+                    _VllmMetricPollResult(
+                        scheduled_s,
+                        request_start_s,
+                        completion_s,
+                        outcome,
+                        skipped_polls,
+                        metrics,
+                        error,
+                    )
+                )
+                if outcome == "error":
+                    return
+        finally:
+            close = (
+                getattr(session, "aclose", None)
+                if session is not None
+                else getattr(backend, "close_train_step_vllm_metrics", None)
+            )
+            if callable(close):
+                maybe_close = close()
+                if inspect.isawaitable(maybe_close):
+                    await maybe_close
+
+    async def _collect_required_serving_metrics(
+        self, collector: Any | None = None
+    ) -> dict[str, float]:
         assert self.trainer is not None
-        collector = getattr(
-            self.trainer.backend, "collect_train_step_vllm_metrics", None
-        )
+        trainer = self.trainer
+        if collector is None:
+            backend_collector = getattr(
+                trainer.backend, "collect_train_step_vllm_metrics", None
+            )
+            collector = (
+                None
+                if not callable(backend_collector)
+                else lambda: backend_collector(trainer.model)
+            )
         if not callable(collector):
             raise RuntimeError(
                 "Pipeline autotuning requires ART vLLM metrics collection."
             )
-        maybe_metrics = collector(self.trainer.model)
+        maybe_metrics = collector()
         metrics = (
             await maybe_metrics if inspect.isawaitable(maybe_metrics) else maybe_metrics
         )
@@ -205,13 +364,53 @@ async def _collect_required_serving_metrics(self) -> dict[str, float]:
                 )
         return metrics
 
-    def _record_poll_success(self) -> None:
-        self._poll_health.append(VllmMetricPollHealth(t_s=time.monotonic()))
-
-    def _record_poll_timeout(self) -> None:
+    def _consume_poll(
+        self, result: _VllmMetricPollResult, *, record_train_step: bool
+    ) -> None:
         self._poll_health.append(
-            VllmMetricPollHealth(t_s=time.monotonic(), timed_out=True)
+            VllmMetricPollHealth(
+                t_s=result.completion_s,
+                timed_out=result.outcome == "timeout",
+                scheduled_s=result.scheduled_s,
+                request_start_s=result.request_start_s,
+                outcome=result.outcome,
+                skipped_polls=result.skipped_polls,
+            )
         )
+        if result.outcome == "error":
+            self._sampler_error = result.error or RuntimeError(
+                "ART vLLM metrics sampler failed without an error"
+            )
+            if self.trainer is not None:
+                self.trainer.request_stop()
+            return
+        if result.metrics is None:
+            return
+        if record_train_step:
+            for name in _TRAIN_STEP_VLLM_METRICS.intersection(result.metrics):
+                value = result.metrics[name]
+                if isinstance(value, (int, float)):
+                    total, count = self._train_step_vllm_metrics.get(name, (0.0, 0))
+                    self._train_step_vllm_metrics[name] = (
+                        total + float(value),
+                        count + 1,
+                    )
+        if self.tuner is not None:
+            self.tuner.on_vllm_pressure_sample(
+                t_s=result.completion_s,
+                running=float(result.metrics["vllm/num_requests_running"]),
+                waiting_capacity=float(
+                    result.metrics["vllm/num_requests_waiting_capacity"]
+                ),
+            )
+
+    def _drain_metric_polls(self) -> None:
+        while True:
+            try:
+                result = self._sampler_results.get_nowait()
+            except Empty:
+                return
+            self._consume_poll(result, record_train_step=True)
 
     def _raise_if_unhealthy_metric_window(self, decision: TunerDecision) -> None:
         stats = decision.stats
@@ -223,18 +422,68 @@ def _raise_if_unhealthy_metric_window(self, decision: TunerDecision) -> None:
             for poll in self._poll_health
             if stats.window_start_s <= poll.t_s <= end_s
         ]
-        if not polls:
+        timeouts = sum(poll.timed_out for poll in polls)
+        errors = sum(poll.outcome == "error" for poll in polls)
+        skipped = sum(poll.skipped_polls for poll in polls)
+        poll_slots = len(polls) + skipped
+        failed_frac = (timeouts + errors + skipped) / max(poll_slots, 1)
+        intervals = _vllm_sample_intervals(
+            [
+                poll.t_s
+                for poll in self._poll_health
+                if not poll.timed_out and poll.outcome == "success"
+            ],
+            window_start_s=stats.window_start_s,
+            window_end_s=end_s,
+            metric_interval_s=self.config.vllm_metric_interval_s,
+        )
+        coverage = sum(duration_s for _, duration_s in intervals) / (
+            end_s - stats.window_start_s
+        )
+        min_coverage = 1.0 - self.config.vllm_metric_timeout_window_frac
+        decision.stats = stats.model_copy(
+            update={
+                "vllm_poll_samples": len(polls),
+                "vllm_poll_successes": sum(
+                    not poll.timed_out and poll.outcome == "success" for poll in polls
+                ),
+                "vllm_poll_timeouts": timeouts,
+                "vllm_poll_errors": errors,
+                "vllm_poll_skipped": skipped,
+                "vllm_poll_coverage": coverage,
+                "vllm_poll_schedule_lag_p99_s": _p99(
+                    [
+                        max(0.0, poll.request_start_s - poll.scheduled_s)
+                        for poll in polls
+                        if poll.scheduled_s is not None
+                        and poll.request_start_s is not None
+                    ]
+                ),
+                "vllm_poll_request_latency_p99_s": _p99(
+                    [
+                        max(0.0, poll.t_s - poll.request_start_s)
+                        for poll in polls
+                        if poll.request_start_s is not None
+                    ]
+                ),
+            }
+        )
+        if failed_frac > self.config.vllm_metric_timeout_window_frac:
             raise RuntimeError(
-                "Pipeline autotuning did not collect any ART vLLM metrics polls "
-                f"during decision window steps {stats.start_step}-{stats.end_step}."
+                "Pipeline autotuning cannot rely on ART vLLM metrics: "
+                f"{failed_frac:.1%} of metric polls timed out, failed, or were "
+                f"skipped during decision window steps "
+                f"{stats.start_step}-{stats.end_step}."
             )
-        timeout_frac = sum(poll.timed_out for poll in polls) / len(polls)
-        if timeout_frac > self.config.vllm_metric_timeout_window_frac:
+        if coverage + 1e-9 < min_coverage:
             raise RuntimeError(
-                "Pipeline autotuning cannot rely on ART vLLM metrics: "
-                f"{timeout_frac:.1%} of metric polls timed out during decision "
-                f"window steps {stats.start_step}-{stats.end_step}."
+                "Pipeline autotuning cannot rely on ART vLLM metrics: successful "
+                f"telemetry covered {coverage:.1%} of decision window steps "
+                f"{stats.start_step}-{stats.end_step}; requires at least "
+                f"{min_coverage:.1%}."
             )
+        cutoff_s = end_s - _vllm_sample_max_age_s(self.config.vllm_metric_interval_s)
+        self._poll_health = [poll for poll in self._poll_health if poll.t_s >= cutoff_s]
 
     def _raise_sampler_error(self) -> None:
         if self._sampler_error is not None:
@@ -243,34 +492,14 @@ def _raise_sampler_error(self) -> None:
             ) from self._sampler_error
 
     def collect_train_step_metrics(self) -> dict[str, float]:
+        self._drain_metric_polls()
+        self._raise_sampler_error()
         samples = self._train_step_vllm_metrics
-        self._train_step_vllm_metrics = []
-        by_name: dict[str, list[float]] = {}
-        for metric in samples:
-            by_name.setdefault(metric.name, []).append(metric.value)
+        self._train_step_vllm_metrics = {}
         return {
-            name: sum(values) / len(values)
-            for name, values in by_name.items()
-            if values
+            name: total / count for name, (total, count) in samples.items() if count > 0
         }
 
-    async def _emit_metrics(
-        self,
-        metrics: dict[str, float],
-        step: int | None,
-        *,
-        record_train_step: bool = True,
-    ) -> None:
-        now = time.monotonic()
-        for name, value in metrics.items():
-            if isinstance(value, (int, float)):
-                metric = PipelineMetric(
-                    name=name, value=float(value), step=step, t_s=now
-                )
-                if record_train_step and name in _TRAIN_STEP_VLLM_METRICS:
-                    self._train_step_vllm_metrics.append(metric)
-                await self.on_metric(metric)
-
     def _save_profile(self) -> None:
         if self.tuner is None or self.store is None:
             return
@@ -283,6 +512,7 @@ def _load_profile_if_requested(
         active_packed_sequence_length: int,
         target_packed_sequences: int,
         policy_age_limit_steps: float,
+        rollout_worker_capacity: int | None,
     ) -> PipelineAutotunerProfile | None:
         if self.config.mode == "online" and not self.config.profile:
             return None
@@ -295,6 +525,15 @@ def _load_profile_if_requested(
                 "exceeds the active max_rollout_workers="
                 f"{self.config.max_rollout_workers}."
             )
+        if (
+            rollout_worker_capacity is not None
+            and profile.settings.num_rollout_workers > rollout_worker_capacity
+        ):
+            raise ValueError(
+                "Autotuner profile requests "
+                f"num_rollout_workers={profile.settings.num_rollout_workers}, above "
+                f"current rollout executor capacity {rollout_worker_capacity}."
+            )
         if (
             profile.packed_sequence_length is not None
             and profile.packed_sequence_length != active_packed_sequence_length
@@ -324,8 +563,8 @@ def _load_profile_if_requested(
             warnings.warn(
                 "Autotuner profile was produced with policy_age_limit_steps="
                 f"{profile.policy_age_limit_steps}, but active config uses "
-                f"{policy_age_limit_steps}. Recomputing queue size for the "
-                "active limit.",
+                f"{policy_age_limit_steps}. Recomputing the active worker target for "
+                "the active limit.",
                 stacklevel=2,
             )
         return profile
@@ -333,6 +572,17 @@ def _load_profile_if_requested(
     def _settings_with_current_queue(
         self, settings: PipelineTuneSettings, policy_age_limit_steps: float
     ) -> PipelineTuneSettings:
+        worker_limit = freshness_worker_limit(
+            target_groups_per_step=settings.target_groups_per_step,
+            limit_steps_off_policy=policy_age_limit_steps,
+            running_reserve_fraction=self.config.queue_running_reserve_fraction,
+            worker_step=self.config.worker_step,
+        )
+        workers = (
+            settings.num_rollout_workers
+            if worker_limit is None
+            else min(settings.num_rollout_workers, worker_limit)
+        )
         return settings.model_copy(
             update={
                 "min_batch_size": max(
@@ -342,11 +592,9 @@ def _settings_with_current_queue(
                         * self.config.freshness_min_batch_floor_fraction
                     ),
                 ),
+                "num_rollout_workers": workers,
                 "queue_maxsize": recommended_queue_size(
                     target_groups_per_step=settings.target_groups_per_step,
-                    limit_steps_off_policy=policy_age_limit_steps,
-                    num_rollout_workers=settings.num_rollout_workers,
-                    running_reserve_fraction=self.config.queue_running_reserve_fraction,
                 ),
             }
         )
@@ -371,20 +619,29 @@ def _validate_weight_update_mode(trainer: Any) -> None:
             )
 
     @staticmethod
-    async def _discover_inference_gpu_count(trainer: Any) -> int:
+    async def _discover_inference_gpu_count(
+        trainer: Any, serving_metrics: dict[str, float] | None = None
+    ) -> int:
         internal_config = trainer.model._internal_config or {}
         inference_gpu_ids = internal_config.get("inference_gpu_ids")
         if inference_gpu_ids:
             return len(inference_gpu_ids)
-        collector = getattr(trainer.backend, "collect_train_step_vllm_metrics", None)
-        if not callable(collector):
-            raise ValueError(
-                "Pipeline autotuning requires inference_gpu_ids or ART vLLM metrics."
+        metrics = serving_metrics
+        if metrics is None:
+            collector = getattr(
+                trainer.backend, "collect_train_step_vllm_metrics", None
+            )
+            if not callable(collector):
+                raise ValueError(
+                    "Pipeline autotuning requires inference_gpu_ids or ART vLLM "
+                    "metrics."
+                )
+            maybe_metrics = collector(trainer.model)
+            metrics = (
+                await maybe_metrics
+                if inspect.isawaitable(maybe_metrics)
+                else maybe_metrics
             )
-        maybe_metrics = collector(trainer.model)
-        metrics = (
-            await maybe_metrics if inspect.isawaitable(maybe_metrics) else maybe_metrics
-        )
         world_size = metrics.get("vllm/world_size")
         if not isinstance(world_size, (int, float)) or world_size < 1:
             raise ValueError(
@@ -402,7 +659,10 @@ async def _discover_target_packed_sequences(trainer: Any) -> int:
         resolver = getattr(backend, "_resolve_grad_accumulation_sequences", None)
         if callable(get_service) and callable(resolver):
             service = await get_service(trainer.model)
-            return max(1, int(await resolver(service, TrainConfig())))
+            config = TrainConfig(
+                grad_accumulation_sequences=trainer.grad_accumulation_sequences
+            )
+            return max(1, int(await resolver(service, config)))
         raise ValueError(
             "Pipeline autotuning requires a backend that can resolve global "
             "grad_accumulation_sequences before training starts."
diff --git a/src/art/pipeline_tuner/autotune.py b/src/art/pipeline_tuner/autotune.py
index 5429478be..4bf6fb633 100644
--- a/src/art/pipeline_tuner/autotune.py
+++ b/src/art/pipeline_tuner/autotune.py
@@ -43,7 +43,59 @@ def _ceil_to_multiple(value: float, multiple: int, *, minimum: int = 1) -> int:
     return max(minimum, int(math.ceil(value / multiple)) * multiple)
 
 
+def _round_to_multiple(value: float, multiple: int, *, minimum: int = 1) -> int:
+    return max(minimum, int(math.floor(value / multiple + 0.5)) * multiple)
+
+
 _VLLM_SCRAPE_GROUP_TOLERANCE_S = 0.05
+_TRAINER_CAPACITY_EPSILON = 1e-9
+
+
+def _vllm_sample_max_age_s(metric_interval_s: float) -> float:
+    # Preserve one delayed poll without allowing an unbounded zero-order hold.
+    return 2.0 * metric_interval_s
+
+
+def _vllm_sample_intervals(
+    sample_times: Sequence[float],
+    *,
+    window_start_s: float,
+    window_end_s: float,
+    metric_interval_s: float,
+) -> list[tuple[float, float]]:
+    times = sorted(set(sample_times))
+    max_age_s = _vllm_sample_max_age_s(metric_interval_s)
+    intervals: list[tuple[float, float]] = []
+    for index, t_s in enumerate(times):
+        next_t_s = times[index + 1] if index + 1 < len(times) else math.inf
+        start_s = max(t_s, window_start_s)
+        end_s = min(next_t_s, t_s + max_age_s, window_end_s)
+        if end_s > start_s:
+            intervals.append((t_s, end_s - start_s))
+    return intervals
+
+
+def _packing_group_candidates(
+    *, current: int, available: int, radius: int, min_change_fraction: float
+) -> list[int]:
+    # Half-hysteresis spacing brackets each actionable target change.
+    step = max(1, math.ceil(current * min_change_fraction / 2.0))
+    min_change = max(1, math.ceil(current * min_change_fraction))
+    lower = max(1, min(available, current - radius))
+    upper = min(available, current + radius)
+    candidates = {lower, min(current, available), upper}
+    candidates.update(
+        groups
+        for groups in (current - min_change, current + min_change)
+        if lower <= groups <= upper
+    )
+    for offset in range(step, radius, step):
+        candidates.update(
+            groups
+            for groups in (current - offset, current + offset)
+            if lower <= groups <= upper
+        )
+    return sorted(candidates)
 
 
 class PackingProjection(pydantic.BaseModel):
@@ -57,6 +109,16 @@ class PackingOutcome(pydantic.BaseModel):
     packed_sequences: int = pydantic.Field(ge=1)
 
 
+def _trainer_underfeed_score(
+    *, idle_frac: float, unused_and_dummy_ratio: float
+) -> float:
+    denominator = max(
+        _TRAINER_CAPACITY_EPSILON,
+        1.0 + _TRAINER_CAPACITY_EPSILON - max(0.0, min(1.0, unused_and_dummy_ratio)),
+    )
+    return max(0.0, idle_frac) / denominator
+
+
 class PipelineAutotuner:
     def __init__(
         self,
@@ -70,7 +132,15 @@ def __init__(
         inference_gpu_count: int,
         policy_age_limit_steps: float,
         starting_step: int = 0,
+        rollout_worker_capacity: int | None = None,
     ) -> None:
+        if rollout_worker_capacity is not None and rollout_worker_capacity < 1:
+            raise ValueError("rollout_worker_capacity must be >= 1")
+        if (
+            rollout_worker_capacity is not None
+            and settings.num_rollout_workers > rollout_worker_capacity
+        ):
+            raise ValueError("initial settings exceed rollout worker capacity")
         self.config = config
         self.settings = settings
         self.model_name = model_name
@@ -79,7 +149,9 @@ def __init__(
         self.target_packed_sequences = max(1, int(target_packed_sequences))
         self.inference_gpu_count = inference_gpu_count
         self.policy_age_limit_steps = policy_age_limit_steps
+        self.rollout_worker_capacity = rollout_worker_capacity
         self.metrics: list[PipelineMetric] = []
+        self.vllm_pressure_samples: list[tuple[float, float, float]] = []
         self.packed_groups: list[PackedGroupObservation] = []
         self._packing_outcomes: list[PackingOutcome] = []
         self._packing_outcome_steps: set[int] = set()
@@ -88,6 +160,8 @@ def __init__(
         self._last_decision_step = self._warmup_end_step
         self._target_candidate: int | None = None
         self._target_candidate_count = 0
+        self._worker_load_candidate_direction: int | None = None
+        self._worker_load_candidate_count = 0
         self._stale_backlog_active = False
         self._min_batch_trial_baseline_collect_s: float | None = None
         self._min_batch_trial_batch_size: int | None = None
@@ -100,6 +174,11 @@ def on_metric(self, rec: PipelineMetric) -> TunerDecision | None:
             return None
         return self.maybe_decide(int(rec.step))
 
+    def on_vllm_pressure_sample(
+        self, *, t_s: float, running: float, waiting_capacity: float
+    ) -> None:
+        self.vllm_pressure_samples.append((t_s, running, waiting_capacity))
+
     def on_packed_group(self, rec: PackedGroupObservation) -> None:
         if self.packed_groups and rec.step > self.packed_groups[-1].step:
             cutoff_step = rec.step - self.config.packing_history_steps + 1
@@ -124,8 +203,23 @@ def maybe_decide(self, step: int) -> TunerDecision | None:
         self._emit_stable_recommendations(decision)
         if decision.previous != decision.updated:
             self.settings = decision.updated
+        self._prune_metrics(stats)
         return decision
 
+    def _prune_metrics(self, stats: TunerWindowStats) -> None:
+        raw_cutoff = stats.window_end_s - _vllm_sample_max_age_s(
+            self.config.vllm_metric_interval_s
+        )
+        self.metrics = [
+            rec
+            for rec in self.metrics
+            if (rec.step is None and rec.t_s >= raw_cutoff)
+            or (rec.step is not None and int(rec.step) >= stats.end_step)
+        ]
+        self.vllm_pressure_samples = [
+            sample for sample in self.vllm_pressure_samples if sample[0] >= raw_cutoff
+        ]
+
     def window_stats(self) -> TunerWindowStats | None:
         by_step: dict[int, dict[str, PipelineMetric]] = defaultdict(dict)
         for rec in self.metrics:
@@ -140,7 +234,18 @@ def window_stats(self) -> TunerWindowStats | None:
         if len(steps) < self.config.window_steps:
             return None
         window_steps = steps[-self.config.window_steps :]
-        t0 = min(by_step[step]["objective/score"].t_s for step in window_steps)
+        preceding_objective_times = [
+            rec.t_s
+            for rec in self.metrics
+            if rec.name == "objective/score"
+            and rec.step is not None
+            and int(rec.step) < window_steps[0]
+        ]
+        t0 = (
+            max(preceding_objective_times)
+            if preceding_objective_times
+            else min(by_step[step]["objective/score"].t_s for step in window_steps)
+        )
         t1 = max(rec.t_s for step in window_steps for rec in by_step[step].values())
 
         def step_values(name: str) -> list[float]:
@@ -171,16 +276,26 @@ def step_values(name: str) -> list[float]:
         queue_put_wait_s = sum(
             _required_step_values(by_step, window_steps, "queue/put_wait_s")
         )
-        train_capacity_tokens = _required_step_values(
-            by_step, window_steps, "data/step_packed_train_tokens"
+        nominal_capacity_tokens = _required_step_values(
+            by_step, window_steps, "data/step_nominal_schedule_capacity_tokens"
         )
         non_padding_tokens = _required_step_values(
-            by_step, window_steps, "data/step_non_padding_train_tokens"
+            by_step, window_steps, "data/step_nonpadding_logical_tokens"
         )
         vllm_metrics = [
             rec
             for rec in self.metrics
-            if rec.step is None and t0 <= rec.t_s <= max(t1, t0 + 1e-6)
+            if rec.step is None
+            and t0 - _vllm_sample_max_age_s(self.config.vllm_metric_interval_s)
+            <= rec.t_s
+            <= max(t1, t0 + 1e-6)
+        ]
+        vllm_pressure_samples = [
+            sample
+            for sample in self.vllm_pressure_samples
+            if t0 - _vllm_sample_max_age_s(self.config.vllm_metric_interval_s)
+            <= sample[0]
+            <= max(t1, t0 + 1e-6)
         ]
         window_step_set = set(window_steps)
         packed_group_counts: dict[int, int] = defaultdict(int)
@@ -197,35 +312,53 @@ def step_values(name: str) -> list[float]:
                 "Pipeline autotuner requires packed-group observations in every "
                 f"trainable decision-window step; missing steps {missing_packed_steps}."
             )
-        padding_ratios = []
+        unused_and_dummy_ratios = []
         for capacity, non_padding in zip(
-            train_capacity_tokens, non_padding_tokens, strict=True
+            nominal_capacity_tokens, non_padding_tokens, strict=True
         ):
             if capacity <= 0:
                 continue
-            padding_ratios.append(max(0.0, (capacity - non_padding) / capacity))
+            unused_and_dummy_ratios.append(
+                max(0.0, (capacity - non_padding) / capacity)
+            )
         trainer_idle_frac = (collect / wall) if wall > 0 else 0.0
-        padding_ratio_mean = _mean(padding_ratios)
+        unused_and_dummy_ratio_mean = _mean(unused_and_dummy_ratios)
         self._record_packing_outcomes(
             by_step=by_step,
             window_steps=window_steps,
         )
+        if not vllm_pressure_samples:
+            vllm_pressure_samples = _vllm_samples_from_metrics(vllm_metrics)
+        waiting_capacity_request_s, running_request_s = (
+            _vllm_request_seconds_from_samples(
+                vllm_pressure_samples,
+                window_start_s=t0,
+                window_end_s=t1,
+                metric_interval_s=self.config.vllm_metric_interval_s,
+                min_coverage=1.0 - self.config.vllm_metric_timeout_window_frac,
+            )
+        )
         return TunerWindowStats(
             start_step=window_steps[0],
             end_step=window_steps[-1],
             window_start_s=t0,
             window_end_s=t1,
             collect_batch_s=collect / len(window_steps),
-            trainer_underfeed_score=max(0.0, trainer_idle_frac),
-            vllm_pressure=_vllm_pressure(
-                vllm_metrics, window_start_s=t0, window_end_s=t1
+            trainer_underfeed_score=_trainer_underfeed_score(
+                idle_frac=trainer_idle_frac,
+                unused_and_dummy_ratio=unused_and_dummy_ratio_mean,
             ),
+            vllm_pressure=_vllm_pressure_ratio(
+                waiting_capacity_request_s, running_request_s
+            ),
+            vllm_waiting_capacity_request_s=waiting_capacity_request_s,
+            vllm_running_request_s=running_request_s,
             queue_put_wait_frac=queue_put_wait_s
             / max(queue_put_wait_s + rollout_s, 1e-9),
             predicted_stale_frac=_mean(step_values("queue/predicted_stale_fraction")),
             actual_stale_frac=sum(stale_groups)
             / max(sum(groups) + sum(stale_groups) + sum(zero_variance_groups), 1.0),
-            padding_ratio_mean=padding_ratio_mean,
+            unused_and_dummy_ratio_mean=unused_and_dummy_ratio_mean,
         )
 
     def _record_packing_outcomes(
@@ -308,11 +441,14 @@ def _decide(self, stats: TunerWindowStats) -> TunerDecision:
         )
         if target_changed:
             self._clear_min_batch_trial()
+            self._clear_worker_load_candidate()
         stale_backlog_active = self._update_stale_backlog_state(stats)
         action = "hold"
+        pending_worker_action = "hold"
         reason = "inside hysteresis band or already balanced"
 
         if stale_backlog_active and updated.min_batch_size < updated.max_batch_size:
+            self._clear_worker_load_candidate()
             updated = updated.model_copy(
                 update={
                     "min_batch_size": min(
@@ -327,6 +463,7 @@ def _decide(self, stats: TunerWindowStats) -> TunerDecision:
             action = "raise_min_batch_size"
             reason = "stale backlog requires dense batches before reducing workers"
         elif stale_backlog_active:
+            self._clear_worker_load_candidate()
             updated = updated.model_copy(
                 update={
                     "num_rollout_workers": self._move_workers(
@@ -336,40 +473,87 @@ def _decide(self, stats: TunerWindowStats) -> TunerDecision:
             )
             action = "decrease_workers"
             reason = "predicted or actual stale backlog exceeds the freshness target"
+        elif target_changed:
+            reason = "batch geometry changed; worker load evidence was reset"
+        elif (
+            state == "inference_over_train_over"
+            and stats.queue_put_wait_frac >= self.config.queue_put_severe_frac
+        ):
+            pending_worker_action = "decrease_workers"
+            if self._worker_load_change_ready(-1):
+                updated = updated.model_copy(
+                    update={
+                        "num_rollout_workers": self._move_workers(
+                            updated.num_rollout_workers, -1
+                        )
+                    }
+                )
+                action = pending_worker_action
+                reason = (
+                    "sustained vLLM pressure plus queue backpressure indicates "
+                    "excess workers"
+                )
+            else:
+                reason = self._pending_worker_load_reason("decrease")
         elif stats.queue_put_wait_frac >= self.config.queue_put_severe_frac:
+            self._clear_worker_load_candidate()
             reason = "completed-group queue backpressure is active"
         elif state in {
             "inference_under_train_under",
             "inference_balanced_train_under",
         }:
-            updated = updated.model_copy(
-                update={
-                    "num_rollout_workers": self._move_workers(
-                        updated.num_rollout_workers, +1
-                    )
-                }
-            )
-            action = "increase_workers"
-            reason = "vLLM pressure is low and trainer is underfed"
+            pending_worker_action = "increase_workers"
+            if self._worker_load_change_ready(+1):
+                updated = updated.model_copy(
+                    update={
+                        "num_rollout_workers": self._move_workers(
+                            updated.num_rollout_workers, +1
+                        )
+                    }
+                )
+                action = pending_worker_action
+                reason = "sustained vLLM pressure is low and trainer is underfed"
+            else:
+                reason = self._pending_worker_load_reason("increase")
         elif state == "inference_over_train_over":
+            self._clear_worker_load_candidate()
             reason = "both sides are loaded; no throughput-safe online change"
+        else:
+            self._clear_worker_load_candidate()
 
         if not target_changed and not stale_backlog_active:
             min_update = self._min_batch_adjustment(
                 updated,
                 stats,
-                action,
+                pending_worker_action
+                if pending_worker_action == "increase_workers"
+                else action,
                 inference_over=inference_over,
             )
             if min_update is not None:
+                self._clear_worker_load_candidate()
                 updated, action, reason = min_update
 
         updated = self._settings_with_recomputed_queue(
             updated, stats, adapt_target=False
         )
-        if action == "hold" and updated != previous:
+        worker_limit = freshness_worker_limit(
+            target_groups_per_step=updated.target_groups_per_step,
+            limit_steps_off_policy=self.policy_age_limit_steps,
+            running_reserve_fraction=self.config.queue_running_reserve_fraction,
+            worker_step=self.config.worker_step,
+        )
+        if (
+            worker_limit is not None
+            and previous.num_rollout_workers > worker_limit
+            and updated.num_rollout_workers <= worker_limit
+        ):
+            self._clear_worker_load_candidate()
+            action = "decrease_workers"
+            reason = "running rollout reserve exceeded the policy-age budget"
+        elif action == "hold" and updated != previous:
             action = "resize_batch_queue"
-            reason = "recomputed target batch size and freshness-bounded queue"
+            reason = "recomputed target batch size and one-batch queue capacity"
         return TunerDecision(
             step=stats.end_step,
             state=state,
@@ -380,6 +564,28 @@ def _decide(self, stats: TunerWindowStats) -> TunerDecision:
             stats=stats,
         )
 
+    def _worker_load_change_ready(self, direction: int) -> bool:
+        if self._worker_load_candidate_direction == direction:
+            self._worker_load_candidate_count += 1
+        else:
+            self._worker_load_candidate_direction = direction
+            self._worker_load_candidate_count = 1
+        if self._worker_load_candidate_count < self.config.worker_load_change_windows:
+            return False
+        self._clear_worker_load_candidate()
+        return True
+
+    def _pending_worker_load_reason(self, direction: str) -> str:
+        return (
+            f"worker {direction} awaits sustained evidence "
+            f"({self._worker_load_candidate_count}/"
+            f"{self.config.worker_load_change_windows})"
+        )
+
+    def _clear_worker_load_candidate(self) -> None:
+        self._worker_load_candidate_direction = None
+        self._worker_load_candidate_count = 0
+
     def _update_stale_backlog_state(self, stats: TunerWindowStats) -> bool:
         stale_fractions = (stats.predicted_stale_frac, stats.actual_stale_frac)
         if self._stale_backlog_active:
@@ -552,16 +758,17 @@ def _recommendation_candidates(
                 )
             )
         if (
-            stats.padding_ratio_mean >= self.config.padding_high_frac
+            stats.unused_and_dummy_ratio_mean >= self.config.unused_and_dummy_high_frac
             and trainer_saturated
             and vllm_saturated
         ):
             recommendations.append(
                 (
                     "decrease_packed_sequence_length",
-                    "Pipeline autotuner observes high padding while Megatron and vLLM "
+                    "Pipeline autotuner observes high unused or dummy capacity while "
+                    "Megatron and vLLM "
                     "are both saturated; decrease packed_sequence_length to reduce "
-                    "padding waste.",
+                    "schedule waste.",
                 )
             )
         return recommendations
@@ -569,14 +776,20 @@ def _recommendation_candidates(
     def _move_workers(self, current: int, direction: int) -> int:
         raw = max(
             self.config.worker_step,
-            _ceil_to_multiple(
+            _round_to_multiple(
                 current * self.config.worker_move_fraction, self.config.worker_step
             ),
         )
         cap = _ceil_to_multiple(self.config.max_worker_move, self.config.worker_step)
+        floor = min(
+            self.config.worker_step,
+            self.rollout_worker_capacity or self.config.worker_step,
+        )
+        moved = max(floor, current + direction * min(cap, raw))
         return min(
+            moved,
             self.config.max_rollout_workers,
-            max(self.config.worker_step, current + direction * min(cap, raw)),
+            self.rollout_worker_capacity or moved,
         )
 
     def _settings_with_recomputed_queue(
@@ -598,14 +811,23 @@ def _settings_with_recomputed_queue(
             min_batch = max(floor, min(target, max(1, round(target * ratio))))
         # Packed sequence length is the user's cap on target/max batch size. If a
         # run should never use larger train batches, lower packed_sequence_length.
-        queue = recommended_queue_size(
+        worker_limit = freshness_worker_limit(
             target_groups_per_step=target,
             limit_steps_off_policy=self.policy_age_limit_steps,
-            num_rollout_workers=settings.num_rollout_workers,
             running_reserve_fraction=self.config.queue_running_reserve_fraction,
+            worker_step=self.config.worker_step,
+        )
+        workers = (
+            settings.num_rollout_workers
+            if worker_limit is None
+            else min(settings.num_rollout_workers, worker_limit)
+        )
+        queue = recommended_queue_size(
+            target_groups_per_step=target,
         )
         return settings.model_copy(
             update={
+                "num_rollout_workers": workers,
                 "target_groups_per_step": target,
                 "min_batch_size": min_batch,
                 "max_batch_size": target,
@@ -690,62 +912,88 @@ def _packing_projections(
             ]
         )
         current = max(1, settings.target_groups_per_step)
-        increase = max(
+        # Search only target changes that the controller can apply in one window.
+        radius = max(
             1,
             min(
                 self.config.target_group_max_increase,
                 math.ceil(current * self.config.target_group_increase_fraction),
             ),
         )
-        lo = max(1, current // 2)
-        hi = min(len(reservoir), current + increase)
-        history_risks = self._packing_history_risks(range(lo, hi + 1))
+        candidates = _packing_group_candidates(
+            current=current,
+            available=len(reservoir),
+            radius=radius,
+            min_change_fraction=self.config.target_group_min_relative_change,
+        )
+        history_risks = self._packing_history_risks(candidates)
         projections: dict[int, PackingProjection] = {}
 
         def project(groups: int) -> PackingProjection:
             existing = projections.get(groups)
             if existing is not None:
                 return existing
+            history_risk = history_risks[groups]
+            if history_risk > self.config.target_spill_probability:
+                projection = PackingProjection(
+                    groups=groups, spill_probability=history_risk
+                )
+                projections[groups] = projection
+                return projection
             rng = random.Random((stats.end_step << 32) ^ groups)
             spills = 0.0
+            trials = 0.0
             for _ in range(self.config.packing_trials):
                 selected = rng.sample(range(len(reservoir)), groups)
                 after = pool.estimate(selected, seq_len=self.packed_sequence_length)
-                spills += float(after.packed_sequences > self.target_packed_sequences)
-            trials = float(self.config.packing_trials)
+                trials += 1.0
+                if after.packed_sequences > self.target_packed_sequences:
+                    spills += 1.0
+                    best_case_risk = self._packing_probability_upper(
+                        events=spills, trials=float(self.config.packing_trials)
+                    )
+                    if best_case_risk > self.config.target_spill_probability:
+                        break
+                if (
+                    self._packing_probability_upper(events=spills, trials=trials)
+                    <= self.config.target_spill_probability
+                ):
+                    break
             counterfactual_risk = self._packing_probability_upper(
                 events=spills,
                 trials=trials,
             )
             projection = PackingProjection(
                 groups=groups,
-                spill_probability=max(counterfactual_risk, history_risks[groups]),
+                spill_probability=max(counterfactual_risk, history_risk),
             )
             projections[groups] = projection
             return projection
 
-        best = lo - 1
-        left, right = lo, hi
-        while left <= right:
-            groups = (left + right) // 2
-            if (
-                project(groups).spill_probability
-                <= self.config.target_spill_probability
-            ):
-                best = groups
-                left = groups + 1
-            else:
-                right = groups - 1
-        for groups in range(max(lo, best - 2), min(hi, best + 2) + 1):
-            project(groups)
+        upper_index = len(candidates) - 1
+        if (
+            project(candidates[upper_index]).spill_probability
+            > self.config.target_spill_probability
+        ):
+            left, right = 0, upper_index - 1
+            while left <= right:
+                index = (left + right) // 2
+                if (
+                    project(candidates[index]).spill_probability
+                    <= self.config.target_spill_probability
+                ):
+                    left = index + 1
+                else:
+                    right = index - 1
         monotone_risk = 0.0
         for groups in sorted(projections):
             projection = projections[groups]
-            monotone_risk = max(monotone_risk, projection.spill_probability)
             if projection.spill_probability < monotone_risk:
                 projections[groups] = projection.model_copy(
                     update={"spill_probability": monotone_risk}
                 )
+            else:
+                monotone_risk = projection.spill_probability
         return [projections[groups] for groups in sorted(projections)]
 
     def _packing_reservoir(
@@ -784,9 +1032,13 @@ def _packing_reservoir(
                 break
         return selected
 
-    def _packing_history_risks(self, groups_range: range) -> dict[int, float]:
-        risks: dict[int, float] = {}
-        for groups in groups_range:
+    def _packing_history_risks(self, groups_range: Sequence[int]) -> dict[int, float]:
+        exact_risks: dict[int, float] = {}
+        for groups in {
+            outcome.groups
+            for outcome in self._packing_outcomes
+            if outcome.groups <= max(groups_range)
+        }:
             outcomes = [
                 outcome
                 for outcome in self._packing_outcomes
@@ -803,16 +1055,21 @@ def _packing_history_risks(self, groups_range: range) -> dict[int, float]:
             # Zero-spill samples are useful diagnostics but should not block exploration:
             # a beta upper bound with sparse clean samples would make target batches
             # sticky. Actual spills are the hard signal we carry across the horizon.
-            risks[groups] = (
+            exact_risks[groups] = (
                 self._packing_probability_upper(events=spills, trials=trials)
                 if spills > 0.0
                 else 0.0
             )
+        risks: dict[int, float] = {}
         inherited_spill_probability = 0.0
-        for groups in sorted(risks):
-            inherited_spill_probability = max(
-                inherited_spill_probability, risks[groups]
-            )
+        history_groups = iter(sorted(exact_risks.items()))
+        next_history = next(history_groups, None)
+        for groups in sorted(groups_range):
+            while next_history is not None and next_history[0] <= groups:
+                inherited_spill_probability = max(
+                    inherited_spill_probability, next_history[1]
+                )
+                next_history = next(history_groups, None)
             risks[groups] = inherited_spill_probability
         return risks
 
@@ -838,13 +1095,15 @@ def profile(self) -> PipelineAutotunerProfile:
             packed_sequence_length=self.packed_sequence_length,
             target_packed_sequences=self.target_packed_sequences,
             inference_gpu_count=self.inference_gpu_count,
+            rollout_worker_capacity=self.rollout_worker_capacity,
             policy_age_limit_steps=self.policy_age_limit_steps,
             settings=self.settings,
             config=self.config,
             decisions=self.decisions,
             notes=[
                 "The first warmup_ignore_steps are excluded from throughput decisions.",
-                "queue_maxsize is bounded so queue_size / target_groups_per_step <= the policy-age limit.",
+                "queue_maxsize bounds ready, packing, and packed groups to one target "
+                "batch; active rollouts add at most one worker wave.",
                 *self._profile_recommendations(),
             ],
         )
@@ -867,7 +1126,14 @@ def build_initial_settings(
     inference_gpu_count: int,
     target_packed_sequences: int,
     policy_age_limit_steps: float,
+    rollout_worker_capacity: int | None,
 ) -> PipelineTuneSettings:
+    target_slots = max(1, int(target_packed_sequences))
+    max_batch = int(config.initial_max_groups_per_packed_sequence) * target_slots
+    min_batch = min(
+        int(config.initial_min_groups_per_packed_sequence) * target_slots,
+        max_batch,
+    )
     workers = min(
         config.max_rollout_workers,
         _ceil_to_multiple(
@@ -876,21 +1142,22 @@ def build_initial_settings(
             minimum=config.worker_step,
         ),
     )
-    target_slots = max(1, int(target_packed_sequences))
-    max_batch = int(config.initial_max_groups_per_packed_sequence) * target_slots
-    min_batch = min(
-        int(config.initial_min_groups_per_packed_sequence) * target_slots,
-        max_batch,
+    worker_limit = freshness_worker_limit(
+        target_groups_per_step=max_batch,
+        limit_steps_off_policy=policy_age_limit_steps,
+        running_reserve_fraction=config.queue_running_reserve_fraction,
+        worker_step=config.worker_step,
     )
+    if worker_limit is not None:
+        workers = min(workers, worker_limit)
+    if rollout_worker_capacity is not None:
+        workers = min(workers, rollout_worker_capacity)
     min_batch = max(
         min_batch,
         math.ceil(max_batch * config.freshness_min_batch_floor_fraction),
     )
     queue = recommended_queue_size(
         target_groups_per_step=max_batch,
-        limit_steps_off_policy=policy_age_limit_steps,
-        num_rollout_workers=workers,
-        running_reserve_fraction=config.queue_running_reserve_fraction,
     )
     return PipelineTuneSettings(
         num_rollout_workers=workers,
@@ -901,26 +1168,50 @@ def build_initial_settings(
     )
 
 
-def recommended_queue_size(
+def freshness_worker_limit(
     *,
     target_groups_per_step: int,
     limit_steps_off_policy: float,
-    num_rollout_workers: int,
     running_reserve_fraction: float,
-) -> int:
+    worker_step: int,
+) -> int | None:
+    """Leave one queued batch inside the completed-work freshness budget."""
+
+    if running_reserve_fraction <= 0.0:
+        return None
     target = max(1, int(target_groups_per_step))
-    limit = max(1.0, float(limit_steps_off_policy))
-    max_completed = max(1, int(math.floor(target * limit)))
-    running_reserve = int(
-        math.ceil(max(0, num_rollout_workers) * running_reserve_fraction)
-    )
-    lower = target
-    return max(lower, min(max_completed, max_completed - running_reserve))
+    max_completed = int(math.floor(target * max(1.0, limit_steps_off_policy)))
+    raw_limit = int(math.floor((max_completed - target) / running_reserve_fraction))
+    return max(1, (raw_limit // max(1, worker_step)) * max(1, worker_step))
+
+
+def recommended_queue_size(
+    *,
+    target_groups_per_step: int,
+) -> int:
+    return max(1, int(target_groups_per_step))
 
 
 def _vllm_pressure(
-    metrics: list[PipelineMetric], *, window_start_s: float, window_end_s: float
+    metrics: list[PipelineMetric],
+    *,
+    window_start_s: float,
+    window_end_s: float,
+    metric_interval_s: float,
+    min_coverage: float,
 ) -> float:
+    return _vllm_pressure_from_samples(
+        _vllm_samples_from_metrics(metrics),
+        window_start_s=window_start_s,
+        window_end_s=window_end_s,
+        metric_interval_s=metric_interval_s,
+        min_coverage=min_coverage,
+    )
+
+
+def _vllm_samples_from_metrics(
+    metrics: list[PipelineMetric],
+) -> list[tuple[float, float, float]]:
     wanted = {"vllm/num_requests_running", "vllm/num_requests_waiting_capacity"}
     rows: list[tuple[float, str, float]] = []
     for rec in metrics:
@@ -929,37 +1220,93 @@ def _vllm_pressure(
     if not rows:
         raise RuntimeError("Pipeline autotuning requires vLLM runtime metric samples.")
     by_time = _group_vllm_metric_rows(rows)
-    times = sorted(t_s for t_s, values in by_time.items() if wanted <= values.keys())
-    if not times:
+    samples = [
+        (
+            t_s,
+            values["vllm/num_requests_running"],
+            values["vllm/num_requests_waiting_capacity"],
+        )
+        for t_s, values in by_time.items()
+        if wanted <= values.keys()
+    ]
+    if not samples:
         raise RuntimeError(
             "Pipeline autotuning requires complete vLLM running/capacity samples."
         )
-    capacity_wait_request_s = 0.0
+    return samples
+
+
+def _vllm_pressure_from_samples(
+    samples: Sequence[tuple[float, float, float]],
+    *,
+    window_start_s: float,
+    window_end_s: float,
+    metric_interval_s: float,
+    min_coverage: float,
+) -> float:
+    return _vllm_pressure_ratio(
+        *_vllm_request_seconds_from_samples(
+            samples,
+            window_start_s=window_start_s,
+            window_end_s=window_end_s,
+            metric_interval_s=metric_interval_s,
+            min_coverage=min_coverage,
+        )
+    )
+
+
+def _vllm_request_seconds_from_samples(
+    samples: Sequence[tuple[float, float, float]],
+    *,
+    window_start_s: float,
+    window_end_s: float,
+    metric_interval_s: float,
+    min_coverage: float,
+) -> tuple[float, float]:
+    by_time = {
+        t_s: (running, waiting_capacity)
+        for t_s, running, waiting_capacity in samples
+        if math.isfinite(running) and math.isfinite(waiting_capacity)
+    }
+    times = sorted(by_time)
+    if not times:
+        raise RuntimeError("Pipeline autotuning requires vLLM pressure samples.")
+    window_s = window_end_s - window_start_s
+    if window_s <= 0.0:
+        raise RuntimeError(
+            "Pipeline autotuning requires a positive vLLM sample window."
+        )
+    intervals = _vllm_sample_intervals(
+        times,
+        window_start_s=window_start_s,
+        window_end_s=window_end_s,
+        metric_interval_s=metric_interval_s,
+    )
+    total_s = sum(duration_s for _, duration_s in intervals)
+    coverage = total_s / window_s
+    if coverage + 1e-9 < min_coverage:
+        raise RuntimeError(
+            "Pipeline autotuning cannot rely on vLLM pressure: successful telemetry "
+            f"covered {coverage:.1%} of the decision window; requires at least "
+            f"{min_coverage:.1%}."
+        )
+    waiting_capacity_request_s = 0.0
     running_request_s = 0.0
-    total_s = 0.0
-    for idx, t_s in enumerate(times):
-        values = by_time[t_s]
-        if not {
-            "vllm/num_requests_running",
-            "vllm/num_requests_waiting_capacity",
-        }.issubset(values):
-            continue
-        next_t_s = times[idx + 1] if idx + 1 < len(times) else window_end_s
-        start_s = max(t_s, window_start_s)
-        end_s = min(next_t_s, window_end_s)
-        if end_s <= start_s:
-            continue
-        duration_s = end_s - start_s
-        total_s += duration_s
-        capacity_wait_request_s += (
-            max(0.0, values["vllm/num_requests_waiting_capacity"]) * duration_s
-        )
-        running_request_s += max(0.0, values["vllm/num_requests_running"]) * duration_s
+    for t_s, duration_s in intervals:
+        running, waiting_capacity = by_time[t_s]
+        waiting_capacity_request_s += max(0.0, waiting_capacity) * duration_s
+        running_request_s += max(0.0, running) * duration_s
     if total_s <= 0.0:
         raise RuntimeError("Pipeline autotuning requires nonzero vLLM sample duration.")
+    return waiting_capacity_request_s, running_request_s
+
+
+def _vllm_pressure_ratio(
+    waiting_capacity_request_s: float, running_request_s: float
+) -> float:
     if running_request_s > 0.0:
-        return capacity_wait_request_s / running_request_s
-    return math.inf if capacity_wait_request_s > 0.0 else 0.0
+        return waiting_capacity_request_s / running_request_s
+    return math.inf if waiting_capacity_request_s > 0.0 else 0.0
 
 
 def _group_vllm_metric_rows(
diff --git a/src/art/pipeline_tuner/config.py b/src/art/pipeline_tuner/config.py
index e17eabba6..b56bb7d4a 100644
--- a/src/art/pipeline_tuner/config.py
+++ b/src/art/pipeline_tuner/config.py
@@ -35,14 +35,15 @@ class PipelineAutotuneConfig(pydantic.BaseModel):
     window_steps: int = pydantic.Field(default=4, ge=1)
     warmup_ignore_steps: int = pydantic.Field(default=3, ge=0)
     target_spill_probability: float = pydantic.Field(default=0.03, ge=0.0, le=1.0)
-    worker_step: int = pydantic.Field(default=4, ge=1)
+    worker_step: int = pydantic.Field(default=2, ge=1)
     worker_move_fraction: float = pydantic.Field(default=0.10, gt=0.0, le=1.0)
+    worker_load_change_windows: int = pydantic.Field(default=2, ge=1)
     max_worker_move: int = pydantic.Field(default=16, ge=4)
     max_rollout_workers: int = pydantic.Field(default=1024, ge=1)
     initial_model_calls_per_inference_gpu: int = pydantic.Field(default=8, ge=1)
     initial_min_groups_per_packed_sequence: int = pydantic.Field(default=8, ge=1)
     initial_max_groups_per_packed_sequence: int = pydantic.Field(default=8, ge=1)
-    packing_trials: int = pydantic.Field(default=64, ge=16)
+    packing_trials: int = pydantic.Field(default=48, ge=16)
     packing_reservoir_multiplier: int = pydantic.Field(default=2, ge=2)
     packing_reservoir_min_groups: int = pydantic.Field(default=32, ge=16)
     packing_history_steps: int = pydantic.Field(default=64, ge=1)
@@ -58,7 +59,7 @@ class PipelineAutotuneConfig(pydantic.BaseModel):
     queue_put_severe_frac: float = pydantic.Field(default=1.0 / 3.0, ge=0.0, le=1.0)
     stale_high_frac: float = pydantic.Field(default=0.20, ge=0.0, le=1.0)
     stale_clear_frac: float = pydantic.Field(default=0.10, ge=0.0, le=1.0)
-    padding_high_frac: float = pydantic.Field(default=0.25, ge=0.0, le=1.0)
+    unused_and_dummy_high_frac: float = pydantic.Field(default=0.25, ge=0.0, le=1.0)
     trainer_min_batch_lower_score: float = pydantic.Field(default=0.15, ge=0.0)
     trainer_min_batch_raise_score: float = pydantic.Field(default=0.10, ge=0.0)
     min_batch_collect_improvement_ratio: float = pydantic.Field(
@@ -71,7 +72,7 @@ class PipelineAutotuneConfig(pydantic.BaseModel):
         default=0.85, gt=0.0, le=1.0
     )
     target_group_change_windows: int = pydantic.Field(default=1, ge=1)
-    target_group_increase_fraction: float = pydantic.Field(default=0.25, gt=0.0, le=1.0)
+    target_group_increase_fraction: float = pydantic.Field(default=0.20, gt=0.0, le=1.0)
     target_group_max_increase: int = pydantic.Field(default=64, ge=1)
     target_group_min_relative_change: float = pydantic.Field(
         default=0.10, ge=0.0, le=1.0
@@ -142,10 +143,20 @@ class TunerWindowStats(pydantic.BaseModel):
     collect_batch_s: float = 0.0
     trainer_underfeed_score: float = 0.0
     vllm_pressure: float = 0.0
+    vllm_waiting_capacity_request_s: float = pydantic.Field(default=0.0, ge=0.0)
+    vllm_running_request_s: float = pydantic.Field(default=0.0, ge=0.0)
     queue_put_wait_frac: float = 0.0
     predicted_stale_frac: float = 0.0
     actual_stale_frac: float = 0.0
-    padding_ratio_mean: float = 0.0
+    unused_and_dummy_ratio_mean: float = 0.0
+    vllm_poll_samples: int = 0
+    vllm_poll_successes: int = 0
+    vllm_poll_timeouts: int = 0
+    vllm_poll_errors: int = 0
+    vllm_poll_skipped: int = 0
+    vllm_poll_coverage: float = 0.0
+    vllm_poll_schedule_lag_p99_s: float = 0.0
+    vllm_poll_request_latency_p99_s: float = 0.0
 
 
 class TunerDecision(pydantic.BaseModel):
@@ -166,6 +177,7 @@ class PipelineAutotunerProfile(pydantic.BaseModel):
     packed_sequence_length: int | None = None
     target_packed_sequences: int | None = None
     inference_gpu_count: int | None = None
+    rollout_worker_capacity: int | None = pydantic.Field(default=None, ge=1)
     policy_age_limit_steps: float | None = None
     settings: PipelineTuneSettings
     config: PipelineAutotuneConfig
diff --git a/src/art/pipeline_tuner/worker_controller.py b/src/art/pipeline_tuner/worker_controller.py
index 24d3fb7cf..39e3746b4 100644
--- a/src/art/pipeline_tuner/worker_controller.py
+++ b/src/art/pipeline_tuner/worker_controller.py
@@ -49,6 +49,8 @@ def _reconcile(self) -> None:
             )
             self._tasks[worker_id] = task
             active.append(worker_id)
+        # Retiring workers keep their endpoint until their acquired scenario is done.
+        self.trainer._rollout_executor.set_workers(tuple(self._tasks))
 
     async def _raise_finished_errors(self) -> None:
         errors: list[BaseException] = []
diff --git a/src/art/preprocessing/moe_routing.py b/src/art/preprocessing/moe_routing.py
index e3244934d..f07278679 100644
--- a/src/art/preprocessing/moe_routing.py
+++ b/src/art/preprocessing/moe_routing.py
@@ -2,7 +2,7 @@
 
 import os
 import time
-from typing import Any
+from typing import Any, cast
 
 import numpy as np
 from openai.types.chat.chat_completion import Choice
@@ -13,9 +13,36 @@
 PROMPT_TOKEN_IDS_KEY = "prompt_token_ids"
 COMPLETION_TOKEN_IDS_KEY = "completion_token_ids"
 ROUTED_EXPERTS_KEY = "routed_experts"
+NUM_EXPERTS_KEY = "num_experts"
 
-MoeRouteArray = np.ndarray
-MISSING_EXPERT_ID = -1
+
+class MoeRouteArray(np.ndarray):
+    num_experts: int
+
+    def __new__(
+        cls,
+        array: np.ndarray,
+        *,
+        num_experts: int,
+        validate: bool = True,
+    ) -> "MoeRouteArray":
+        result = np.asarray(array).view(cls)
+        result.num_experts = int(num_experts)
+        if validate:
+            _validate_route_array(result, field_name=ROUTED_EXPERTS_KEY)
+        result.flags.writeable = False
+        return result
+
+    def __array_finalize__(self, source: np.ndarray | None) -> None:
+        self.num_experts = int(getattr(source, "num_experts", 0))
+
+
+def moe_route_dtype(num_experts: int) -> np.dtype[Any]:
+    if not 1 <= num_experts <= 65_536:
+        raise RuntimeError(
+            f"MoE routing requires num_experts in [1, 65536], got {num_experts}"
+        )
+    return np.dtype(np.uint8 if num_experts <= 256 else np.uint16)
 
 
 class MoeRoutingAlignmentStats(BaseModel):
@@ -36,6 +63,22 @@ class MoeRouteSegments(BaseModel):
 
     segments: tuple[MoeRouteArray, ...]
 
+    @model_validator(mode="after")
+    def _validate(self) -> "MoeRouteSegments":
+        if not self.segments:
+            raise RuntimeError("MoE route segments cannot be empty")
+        contract = {
+            (segment.num_experts, segment.dtype, *segment.shape[1:])
+            for segment in self.segments
+        }
+        if len(contract) != 1:
+            raise RuntimeError("MoE route segments must share one exact contract")
+        return self
+
+    @property
+    def num_experts(self) -> int:
+        return self.segments[0].num_experts
+
     @property
     def shape(self) -> tuple[int, int, int]:
         first = self.segments[0]
@@ -58,7 +101,10 @@ def iter_slices(
                 slices.append(
                     (
                         overlap_start,
-                        segment[overlap_start - offset : overlap_end - offset],
+                        cast(
+                            MoeRouteArray,
+                            segment[overlap_start - offset : overlap_end - offset],
+                        ),
                     )
                 )
             offset = segment_end
@@ -71,9 +117,6 @@ class PackedMoeRoutingReplay(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     expert_indices: Any
-    token_mask: Any
-    num_layers: int
-    topk: int
     num_experts: int
     pack_stats: MoeRoutingPackStats
 
@@ -82,27 +125,18 @@ def _validate(self) -> "PackedMoeRoutingReplay":
         if self.expert_indices.ndim != 4:
             raise RuntimeError(
                 "expert_indices must have shape "
-                "[num_sequences, sequence_length, num_layers, topk], got "
+                "[num_layers, num_sequences, sequence_length, topk], got "
                 f"{tuple(self.expert_indices.shape)}"
             )
-        if self.token_mask.shape != self.expert_indices.shape[:2]:
-            raise RuntimeError(
-                "token_mask shape must match packed route tokens, got "
-                f"{tuple(self.token_mask.shape)} vs "
-                f"{tuple(self.expert_indices.shape[:2])}"
-            )
-        if self.num_layers != int(self.expert_indices.shape[2]):
-            raise RuntimeError(
-                f"num_layers={self.num_layers} does not match "
-                f"expert_indices.shape[2]={self.expert_indices.shape[2]}"
-            )
-        if self.topk != int(self.expert_indices.shape[3]):
+        if min(map(int, self.expert_indices.shape)) <= 0:
+            raise RuntimeError("expert_indices axes must be non-empty")
+        expected_dtype = str(moe_route_dtype(self.num_experts))
+        actual_dtype = str(self.expert_indices.dtype).removeprefix("torch.")
+        if actual_dtype != expected_dtype:
             raise RuntimeError(
-                f"topk={self.topk} does not match "
-                f"expert_indices.shape[3]={self.expert_indices.shape[3]}"
+                f"{self.num_experts} experts require {expected_dtype} replay ids, "
+                f"got {actual_dtype}"
             )
-        if self.num_experts <= 0:
-            raise RuntimeError(f"num_experts must be >0, got {self.num_experts}")
         if self.topk > self.num_experts:
             raise RuntimeError(
                 f"MoE routing topk cannot exceed num_experts: topk={self.topk}, "
@@ -110,19 +144,31 @@ def _validate(self) -> "PackedMoeRoutingReplay":
             )
         return self
 
+    @property
+    def num_layers(self) -> int:
+        return int(self.expert_indices.shape[0])
+
+    @property
+    def topk(self) -> int:
+        return int(self.expert_indices.shape[3])
+
 
 def attach_moe_routing_metadata_to_choice(
     *,
     choice: Choice,
     response_payload: dict[str, Any],
     choice_index: int = 0,
-    routed_experts: MoeRouteArray | None = None,
+    routed_experts: np.ndarray | None = None,
+    num_experts: int | None = None,
 ) -> None:
     if routed_experts is None:
         return
+    num_experts = int(num_experts or getattr(routed_experts, "num_experts", 0))
+    routes = MoeRouteArray(routed_experts, num_experts=num_experts)
     metadata: dict[str, Any] = {
         PROMPT_TOKEN_IDS_KEY: response_payload.get(PROMPT_TOKEN_IDS_KEY),
-        ROUTED_EXPERTS_KEY: routed_experts,
+        ROUTED_EXPERTS_KEY: routes,
+        NUM_EXPERTS_KEY: num_experts,
     }
     raw_choices = response_payload.get("choices")
     if isinstance(raw_choices, list) and choice_index < len(raw_choices):
@@ -142,7 +188,6 @@ def attach_moe_routing_metadata_to_choice(
             )
     _normalize_token_ids(metadata[PROMPT_TOKEN_IDS_KEY])
     _normalize_token_ids(metadata.get(COMPLETION_TOKEN_IDS_KEY))
-    _validate_route_array(routed_experts, field_name=ROUTED_EXPERTS_KEY)
     extra = choice.model_extra
     if extra is None:
         raise RuntimeError("OpenAI Choice.model_extra is unavailable for route capture")
@@ -170,10 +215,11 @@ def align_choice_routes_to_tokenized_result(
             f"choices={len(choices)}, offsets={len(choice_offsets)}, "
             f"lengths={len(choice_token_lengths)}"
         )
-    aligned: MoeRouteArray | None = None
+    aligned: np.ndarray | None = None
     route_mask: np.ndarray | None = None
     route_segments: list[MoeRouteArray] = []
     route_shape: tuple[int, int] | None = None
+    num_experts: int | None = None
     covered_until = 0
     stats = MoeRoutingAlignmentStats()
     saw_routing = False
@@ -195,6 +241,10 @@ def align_choice_routes_to_tokenized_result(
             completion_token_count=len(completion_token_ids),
             stats=stats,
         )
+        if num_experts is None:
+            num_experts = prompt_routes.num_experts
+        elif num_experts != prompt_routes.num_experts:
+            raise RuntimeError("MoE route captures disagree on exact expert count")
         timing_start = _route_alignment_time_ns()
         if prompt_token_ids != token_ids[:offset]:
             raise RuntimeError(
@@ -264,27 +314,32 @@ def align_choice_routes_to_tokenized_result(
         raise RuntimeError("Some trainable choices had MoE routes while others did not")
     if not saw_routing:
         return None, stats
+    if num_experts is None:
+        raise RuntimeError("MoE routing metadata omitted exact expert count")
     if aligned is not None:
-        return aligned, stats
+        assert route_mask is not None
+        _fill_missing_routes(aligned, route_mask, num_experts=num_experts)
+        return MoeRouteArray(aligned, num_experts=num_experts), stats
     if covered_until == len(token_ids):
         if len(route_segments) == 1:
             return route_segments[0], stats
         return MoeRouteSegments(segments=tuple(route_segments)), stats
     if route_shape is None:
         raise RuntimeError("MoE routing metadata did not contain any routed tokens")
-    aligned, route_mask = _materialize_route_segments(
-        token_count=len(token_ids),
+    missing = deterministic_moe_routes(
+        np.arange(covered_until, len(token_ids), dtype=np.int64),
         route_shape=route_shape,
-        route_segments=route_segments,
+        num_experts=num_experts,
     )
-    stats.routed_tokens = int(route_mask.sum())
-    return aligned, stats
+    route_segments.append(missing)
+    stats.routed_tokens = covered_until
+    return MoeRouteSegments(segments=tuple(route_segments)), stats
 
 
 def _timed_append_or_overlay_routes(
     *,
     stats: MoeRoutingAlignmentStats,
-    aligned: MoeRouteArray | None,
+    aligned: np.ndarray | None,
     route_mask: np.ndarray | None,
     route_segments: list[MoeRouteArray],
     covered_until: int,
@@ -292,7 +347,7 @@ def _timed_append_or_overlay_routes(
     route_shape: tuple[int, int],
     start: int,
     routes: MoeRouteArray,
-) -> tuple[MoeRouteArray | None, np.ndarray | None, int]:
+) -> tuple[np.ndarray | None, np.ndarray | None, int]:
     timing_start = _route_alignment_time_ns()
     try:
         return _append_or_overlay_routes(
@@ -311,7 +366,7 @@ def _timed_append_or_overlay_routes(
 
 def _append_or_overlay_routes(
     *,
-    aligned: MoeRouteArray | None,
+    aligned: np.ndarray | None,
     route_mask: np.ndarray | None,
     route_segments: list[MoeRouteArray],
     covered_until: int,
@@ -319,7 +374,7 @@ def _append_or_overlay_routes(
     route_shape: tuple[int, int],
     start: int,
     routes: MoeRouteArray,
-) -> tuple[MoeRouteArray | None, np.ndarray | None, int]:
+) -> tuple[np.ndarray | None, np.ndarray | None, int]:
     if routes.shape[0] == 0:
         return aligned, route_mask, covered_until
     if aligned is None and start == covered_until:
@@ -341,13 +396,10 @@ def _materialize_route_segments(
     token_count: int,
     route_shape: tuple[int, int],
     route_segments: list[MoeRouteArray],
-) -> tuple[MoeRouteArray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     num_layers, topk = route_shape
-    aligned = np.full(
-        (token_count, num_layers, topk),
-        MISSING_EXPERT_ID,
-        dtype=np.int32,
-    )
+    dtype = route_segments[0].dtype if route_segments else np.dtype(np.uint8)
+    aligned = np.zeros((token_count, num_layers, topk), dtype=dtype)
     route_mask = np.zeros(token_count, dtype=np.bool_)
     offset = 0
     for routes in route_segments:
@@ -357,7 +409,7 @@ def _materialize_route_segments(
 
 
 def _overlay_routes(
-    aligned: MoeRouteArray,
+    aligned: np.ndarray,
     route_mask: np.ndarray,
     start: int,
     routes: MoeRouteArray,
@@ -366,6 +418,10 @@ def _overlay_routes(
         return
     end = start + routes.shape[0]
     existing = route_mask[start:end]
+    if bool(existing.any()) and not np.array_equal(
+        aligned[start:end][existing], routes[existing]
+    ):
+        raise RuntimeError("Overlapping routed experts disagree for the same token")
     fill = ~existing
     if bool(fill.any()):
         aligned[start:end][fill] = routes[fill]
@@ -387,6 +443,23 @@ def _validate_route_array(array: MoeRouteArray, *, field_name: str) -> None:
         )
     if array.shape[0] > 0 and (array.shape[1] <= 0 or array.shape[2] <= 0):
         raise RuntimeError(f"{field_name} must have non-empty layer and topk axes")
+    expected_dtype = moe_route_dtype(array.num_experts)
+    if array.dtype != expected_dtype:
+        raise RuntimeError(
+            f"{array.num_experts} experts require {expected_dtype} routes, "
+            f"got {array.dtype}"
+        )
+    if array.shape[-1] > array.num_experts:
+        raise RuntimeError("MoE routing top-k exceeds exact expert count")
+    flat = array.reshape(-1, array.shape[-1])
+    for start in range(0, len(flat), 1 << 20):
+        rows = np.sort(flat[start : start + (1 << 20)], axis=1)
+        if rows.size and int(rows.max()) >= array.num_experts:
+            raise RuntimeError("MoE route expert id is outside the exact model range")
+        if rows.shape[1] > 1 and bool(np.any(rows[:, 1:] == rows[:, :-1])):
+            raise RuntimeError(
+                "MoE route expert ids must be distinct per token and layer"
+            )
 
 
 def _common_route_shape(*arrays: MoeRouteArray) -> tuple[int, int]:
@@ -421,8 +494,12 @@ def _choice_routes(
     routes = metadata.get(ROUTED_EXPERTS_KEY)
     if not isinstance(routes, np.ndarray):
         raise RuntimeError("Missing binary routed experts")
-    _validate_route_array(routes, field_name=ROUTED_EXPERTS_KEY)
-    routes.flags.writeable = False
+    num_experts = int(metadata.get(NUM_EXPERTS_KEY, 0))
+    if isinstance(routes, MoeRouteArray):
+        if routes.num_experts != num_experts:
+            raise RuntimeError("MoE route array disagrees with its exact expert count")
+    else:
+        routes = MoeRouteArray(routes, num_experts=num_experts)
     expected_lengths = {
         len(prompt_token_ids) + completion_token_count,
         len(prompt_token_ids) + max(completion_token_count - 1, 0),
@@ -440,9 +517,46 @@ def _choice_routes(
     return prompt_routes, completion_routes
 
 
-def _readonly_route_view(routes: MoeRouteArray) -> MoeRouteArray:
-    routes.flags.writeable = False
-    return routes
+def _readonly_route_view(routes: np.ndarray) -> MoeRouteArray:
+    route_view = cast(MoeRouteArray, routes)
+    route_view.flags.writeable = False
+    return route_view
+
+
+def _fill_missing_routes(
+    routes: np.ndarray, mask: np.ndarray, *, num_experts: int
+) -> None:
+    missing = np.flatnonzero(~mask)
+    if missing.size:
+        routes[missing] = deterministic_moe_routes(
+            missing,
+            route_shape=(int(routes.shape[1]), int(routes.shape[2])),
+            num_experts=num_experts,
+        )
+        mask[missing] = True
+
+
+def deterministic_moe_routes(
+    positions: np.ndarray,
+    *,
+    route_shape: tuple[int, int],
+    num_experts: int,
+) -> MoeRouteArray:
+    num_layers, topk = route_shape
+    if num_layers <= 0 or not 1 <= topk <= num_experts:
+        raise RuntimeError(
+            "MoE route shape requires positive layers and top-k in expert range"
+        )
+    routes = np.empty(
+        (len(positions), num_layers, topk), dtype=moe_route_dtype(num_experts)
+    )
+    base = (
+        (positions.astype(np.uint64, copy=False)[:, None] + 1) * 1_299_709
+        + np.arange(1, num_layers + 1, dtype=np.uint64)[None, :] * 97_003
+    ) % num_experts
+    for slot in range(topk):
+        routes[:, :, slot] = (base + slot) % num_experts
+    return MoeRouteArray(routes, num_experts=num_experts, validate=False)
 
 
 def _route_alignment_time_ns() -> int:
diff --git a/src/art/preprocessing/pack.py b/src/art/preprocessing/pack.py
index 5ef3396b5..a4149039a 100644
--- a/src/art/preprocessing/pack.py
+++ b/src/art/preprocessing/pack.py
@@ -16,11 +16,12 @@
 )
 from ..types import Verbosity
 from .moe_routing import (
-    MISSING_EXPERT_ID,
     MoeRouteArray,
     MoeRouteSegments,
     MoeRoutingPackStats,
     PackedMoeRoutingReplay,
+    deterministic_moe_routes,
+    moe_route_dtype,
 )
 from .tokenize import TokenizedResult
 
@@ -56,22 +57,6 @@ class DiskPackedTensors(TypedDict):
     image_grid_thw: NotRequired[tuple[int, list[int]]]
 
 
-class _PackedPrefixTreeRow(NamedTuple):
-    token_ids: np.ndarray
-    group_ids: np.ndarray
-    parent_ids: np.ndarray
-    input_pos: np.ndarray
-    assistant_mask: np.ndarray
-    logprobs: np.ndarray
-    advantages: np.ndarray
-    weights: np.ndarray
-    pixel_values: torch.Tensor | None
-    image_grid_thw: torch.Tensor | None
-    route_tensor: np.ndarray | None = None
-    route_mask: np.ndarray | None = None
-    max_expert_id: int = 0
-
-
 class _PrefixTreePackItem(NamedTuple):
     token_ids: tuple[int, ...]
     input_pos: np.ndarray
@@ -284,7 +269,7 @@ def prefix_tree_pack(
     )
     if not planned_rows:
         raise RuntimeError("No tokenized results were packable")
-    random.shuffle(planned_rows)
+    random.Random(len(planned_rows)).shuffle(planned_rows)
     rows = [row for row, _ in planned_rows]
     row_plans = [plan for _, plan in planned_rows]
 
@@ -299,31 +284,26 @@ def prefix_tree_pack(
     weights_np = np.zeros((num_sequences, seq_len), dtype=np.float32)
     pixel_values: list[torch.Tensor | None] = []
     image_grid_thw: list[torch.Tensor | None] = []
-    route_shape = next(
-        (
-            shape
-            for row in rows
-            if (shape := _first_item_moe_route_shape(row)) is not None
-        ),
-        None,
-    )
+    route_contract = _moe_route_contract(rows) if include_moe_routing else None
     route_tensor_np: np.ndarray | None = None
-    route_mask_np: np.ndarray | None = None
-    max_expert_id = 0
     if include_moe_routing:
-        if route_shape is None:
+        if route_contract is None:
             raise RuntimeError("No MoE routes were packed")
-        num_layers, topk = route_shape
-        route_tensor_np = np.zeros(
-            (num_sequences, seq_len, num_layers, topk), dtype=np.int32
+        num_experts, num_layers, topk = route_contract
+        padding = deterministic_moe_routes(
+            np.arange(seq_len, dtype=np.int64),
+            route_shape=(num_layers, topk),
+            num_experts=num_experts,
         )
-        route_mask_np = np.zeros((num_sequences, seq_len), dtype=np.bool_)
+        route_tensor_np = np.broadcast_to(
+            np.moveaxis(padding, 1, 0)[:, None],
+            (num_layers, num_sequences, seq_len, topk),
+        ).copy()
 
     for index, (row, plan) in enumerate(zip(rows, row_plans, strict=True)):
         row_route_tensor = (
-            route_tensor_np[index] if route_tensor_np is not None else None
+            route_tensor_np[:, index] if route_tensor_np is not None else None
         )
-        row_route_mask = route_mask_np[index] if route_mask_np is not None else None
         _materialize_prefix_tree_row(
             row,
             plan=plan,
@@ -336,17 +316,11 @@ def prefix_tree_pack(
             advantages=advantages_np[index],
             weights=weights_np[index],
             route_tensor=row_route_tensor,
-            route_mask=row_route_mask,
-            route_shape=route_shape,
+            route_shape=(None if route_contract is None else route_contract[1:]),
             include_moe_routing=include_moe_routing,
         )
         pixel_values.append(_packed_row_tensor_list(row, "pixel_values"))
         image_grid_thw.append(_packed_row_tensor_list(row, "image_grid_thw"))
-    if include_moe_routing:
-        assert route_tensor_np is not None and route_mask_np is not None
-        if bool(route_mask_np.any()):
-            max_expert_id = int(route_tensor_np.max())
-
     assistant_mask_tensor = torch.from_numpy(assistant_mask_np)
     weights_tensor = torch.from_numpy(weights_np)
     weights_tensor = torch.where(
@@ -396,18 +370,12 @@ def prefix_tree_pack(
         },
     }
     if include_moe_routing:
-        assert route_tensor_np is not None and route_mask_np is not None
-        assert route_shape is not None
-        num_layers, topk = route_shape
-        if not bool(route_mask_np.any()):
-            raise RuntimeError("No MoE routes were packed")
-        moe_routing_pack_stats.packed_tokens = int(route_mask_np.sum())
+        assert route_tensor_np is not None and route_contract is not None
+        num_experts, _num_layers, _topk = route_contract
+        moe_routing_pack_stats.packed_tokens = sum(plan.length for plan in row_plans)
         packed_tensors["moe_routing_replay"] = PackedMoeRoutingReplay(
             expert_indices=torch.from_numpy(route_tensor_np),
-            token_mask=torch.from_numpy(route_mask_np),
-            num_layers=num_layers,
-            topk=topk,
-            num_experts=max(topk, max_expert_id + 1),
+            num_experts=num_experts,
             pack_stats=moe_routing_pack_stats,
         )
     return packed_tensors
@@ -734,7 +702,6 @@ def _materialize_prefix_tree_row(
     advantages: np.ndarray,
     weights: np.ndarray,
     route_tensor: np.ndarray | None,
-    route_mask: np.ndarray | None,
     route_shape: tuple[int, int] | None,
     include_moe_routing: bool,
 ) -> None:
@@ -763,12 +730,11 @@ def _materialize_prefix_tree_row(
                 src_end=src_end,
             )
         if include_moe_routing:
-            assert route_tensor is not None and route_mask is not None
+            assert route_tensor is not None
             assert route_shape is not None
             assert item.moe_routes is not None
             _copy_moe_route_slice(
                 route_tensor=route_tensor,
-                route_mask=route_mask,
                 dst_start=dst_start,
                 src_start=src_start,
                 src_end=src_end,
@@ -777,93 +743,6 @@ def _materialize_prefix_tree_row(
             )
 
 
-def _pack_prefix_tree_row(
-    row: list[_PrefixTreePackItem],
-    *,
-    seq_len: int,
-    pack_results: bool,
-    include_moe_routing: bool,
-    min_shared_segment_length: int = DEFAULT_MIN_PREFIX_TREE_SHARED_SEGMENT_LENGTH,
-) -> _PackedPrefixTreeRow:
-    if not row:
-        empty_i64 = np.empty((0,), dtype=np.int64)
-        empty_f32 = np.empty((0,), dtype=np.float32)
-        return _PackedPrefixTreeRow(
-            token_ids=empty_i64,
-            group_ids=empty_i64,
-            parent_ids=empty_i64,
-            input_pos=empty_i64,
-            assistant_mask=np.empty((0,), dtype=np.bool_),
-            logprobs=empty_f32,
-            advantages=empty_f32,
-            weights=empty_f32,
-            pixel_values=None,
-            image_grid_thw=None,
-        )
-    plan = _prefix_tree_row_plan(
-        row,
-        seq_len=seq_len,
-        pack_results=pack_results,
-        min_shared_segment_length=min_shared_segment_length,
-    )
-    length = plan.length
-    token_ids = np.empty(length, dtype=np.int64)
-    group_ids = np.empty(length, dtype=np.int64)
-    parent_ids = np.empty(length, dtype=np.int64)
-    input_pos = np.zeros(length, dtype=np.int64)
-    assistant_mask = np.zeros(length, dtype=np.bool_)
-    logprobs = np.full(length, np.nan, dtype=np.float32)
-    advantages = np.zeros(length, dtype=np.float32)
-    weights = np.zeros(length, dtype=np.float32)
-    route_shape = _first_item_moe_route_shape(row) if include_moe_routing else None
-    route_tensor: np.ndarray | None = None
-    route_mask: np.ndarray | None = None
-    max_expert_id = 0
-    if route_shape is not None:
-        route_tensor = np.zeros(
-            (length, route_shape[0], route_shape[1]), dtype=np.int32
-        )
-        route_mask = np.zeros(length, dtype=np.bool_)
-    _materialize_prefix_tree_row(
-        row,
-        plan=plan,
-        token_ids=token_ids,
-        group_ids=group_ids,
-        parent_ids=parent_ids,
-        input_pos=input_pos,
-        assistant_mask=assistant_mask,
-        logprobs=logprobs,
-        advantages=advantages,
-        weights=weights,
-        route_tensor=route_tensor,
-        route_mask=route_mask,
-        route_shape=route_shape,
-        include_moe_routing=include_moe_routing,
-    )
-    max_expert_id = (
-        int(route_tensor.max())
-        if route_tensor is not None
-        and route_mask is not None
-        and bool(route_mask.any())
-        else 0
-    )
-    return _PackedPrefixTreeRow(
-        token_ids=token_ids[:length],
-        group_ids=group_ids[:length],
-        parent_ids=parent_ids[:length],
-        input_pos=input_pos,
-        assistant_mask=assistant_mask,
-        logprobs=logprobs,
-        advantages=advantages,
-        weights=weights,
-        pixel_values=_packed_row_tensor_list(row, "pixel_values"),
-        image_grid_thw=_packed_row_tensor_list(row, "image_grid_thw"),
-        route_tensor=route_tensor,
-        route_mask=route_mask,
-        max_expert_id=max_expert_id,
-    )
-
-
 def _validate_shared_prefix_tree_segment(
     row: list[_PrefixTreePackItem],
     *,
@@ -881,6 +760,8 @@ def _validate_shared_prefix_tree_segment(
             raise RuntimeError(
                 "Prefix-tree pack cannot share mismatched input positions"
             )
+        if (item.moe_routes is None) != (reference.moe_routes is None):
+            raise RuntimeError("Prefix-tree shared routes are incomplete")
 
 
 def _packed_row_tensor_list(
@@ -901,39 +782,35 @@ def _packed_row_tensor_list(
     return torch.concat(tensors) if tensors else None
 
 
-def _first_item_moe_route_shape(
-    row: list[_PrefixTreePackItem],
-) -> tuple[int, int] | None:
-    for item in row:
-        if item.moe_routes is not None:
-            shape = _moe_route_shape(item.moe_routes)
-            if shape is not None:
-                return shape
-    return None
-
-
-def _moe_route_shape(raw: MoeRouteArray | MoeRouteSegments) -> tuple[int, int] | None:
-    if isinstance(raw, MoeRouteSegments):
-        return int(raw.shape[1]), int(raw.shape[2])
-    routes = _coerce_moe_routes(raw)
-    if routes.shape[0] == 0:
-        return None
-    return int(routes.shape[1]), int(routes.shape[2])
+def _moe_route_contract(
+    rows: list[list[_PrefixTreePackItem]],
+) -> tuple[int, int, int] | None:
+    contracts = {
+        (
+            routes.num_experts,
+            int(routes.shape[1]),
+            int(routes.shape[2]),
+        )
+        for row in rows
+        for item in row
+        if (routes := item.moe_routes) is not None and routes.shape[0] > 0
+    }
+    if len(contracts) > 1:
+        raise RuntimeError("Packed MoE routes must share one exact contract")
+    return next(iter(contracts), None)
 
 
 def _coerce_moe_routes(raw: MoeRouteArray | MoeRouteSegments) -> MoeRouteArray:
-    if not isinstance(raw, np.ndarray):
+    if not isinstance(raw, MoeRouteArray):
         raise RuntimeError(f"Expected MoE routes array, got {type(raw)}")
-    routes = np.asarray(raw, dtype=np.int32)
-    if routes.ndim != 3 or routes.shape[1] <= 0 or routes.shape[2] <= 0:
-        raise RuntimeError(f"Packed MoE routes must be rank 3, got {routes.shape}")
-    return routes
+    if raw.dtype != moe_route_dtype(raw.num_experts):
+        raise RuntimeError("Packed MoE routes use the wrong exact ID dtype")
+    return raw
 
 
 def _copy_moe_route_slice(
     *,
     route_tensor: np.ndarray,
-    route_mask: np.ndarray,
     dst_start: int,
     src_start: int,
     src_end: int,
@@ -952,12 +829,9 @@ def _copy_moe_route_slice(
             if tuple(segment.shape[1:]) != route_shape:
                 raise RuntimeError("Packed MoE routes must have one rectangular shape")
             segment_dst_start = dst_start + segment_start - src_start
-            _copy_valid_moe_route_chunk(
-                route_tensor=route_tensor,
-                route_mask=route_mask,
-                dst_start=segment_dst_start,
-                routes=segment,
-                assume_valid=True,
+            segment_dst_end = segment_dst_start + int(segment.shape[0])
+            route_tensor[:, segment_dst_start:segment_dst_end] = np.moveaxis(
+                segment, 1, 0
             )
             covered_until = segment_start + int(segment.shape[0])
         if covered_until != src_end:
@@ -968,93 +842,14 @@ def _copy_moe_route_slice(
     route_slice = routes[src_start:src_end]
     if tuple(route_slice.shape[1:]) != route_shape:
         raise RuntimeError("Packed MoE routes must have one rectangular shape")
-    _copy_valid_moe_route_chunk(
-        route_tensor=route_tensor,
-        route_mask=route_mask,
-        dst_start=dst_start,
-        routes=route_slice,
-    )
-
-
-def _copy_valid_moe_route_chunk(
-    *,
-    route_tensor: np.ndarray,
-    route_mask: np.ndarray,
-    dst_start: int,
-    routes: np.ndarray,
-    assume_valid: bool = False,
-) -> None:
-    if int(routes.shape[0]) == 0:
-        return
-    if assume_valid:
-        dst_end = dst_start + int(routes.shape[0])
-        route_tensor[dst_start:dst_end] = routes
-        route_mask[dst_start:dst_end] = True
-        return
-    valid = np.all(routes != MISSING_EXPERT_ID, axis=(1, 2))
-    if not bool(valid.any()):
-        return
-    if bool(valid.all()):
-        dst_end = dst_start + int(routes.shape[0])
-        route_tensor[dst_start:dst_end] = routes
-        route_mask[dst_start:dst_end] = True
-        return
-    valid_offsets = np.nonzero(valid)[0]
-    route_tensor[dst_start + valid_offsets] = routes[valid_offsets]
-    route_mask[dst_start + valid_offsets] = True
-
-
-def _copy_source_moe_route(
-    *,
-    route_tensor: np.ndarray,
-    route_mask: np.ndarray,
-    dst_index: int,
-    source_index: int,
-    raw_routes: MoeRouteArray | MoeRouteSegments,
-    route_shape: tuple[int, int],
-) -> int:
-    if isinstance(raw_routes, MoeRouteSegments):
-        for segment_start, segment in raw_routes.iter_slices(
-            source_index, source_index + 1
-        ):
-            if tuple(segment.shape[1:]) != route_shape:
-                raise RuntimeError("Packed MoE routes must have one rectangular shape")
-            route = segment[source_index - segment_start]
-            return _copy_valid_moe_route(
-                route_tensor=route_tensor,
-                route_mask=route_mask,
-                dst_index=dst_index,
-                route=route,
-            )
-        raise RuntimeError(f"Segmented MoE routes did not cover row {source_index}")
-
-    routes = _coerce_moe_routes(raw_routes)
-    route = routes[source_index]
-    if tuple(route.shape) != route_shape:
-        raise RuntimeError("Packed MoE routes must have one rectangular shape")
-    return _copy_valid_moe_route(
-        route_tensor=route_tensor,
-        route_mask=route_mask,
-        dst_index=dst_index,
-        route=route,
+    dst_end = dst_start + int(route_slice.shape[0])
+    route_tensor[:, dst_start:dst_end] = np.moveaxis(
+        route_slice,
+        1,
+        0,
     )
 
 
-def _copy_valid_moe_route(
-    *,
-    route_tensor: np.ndarray,
-    route_mask: np.ndarray,
-    dst_index: int,
-    route: np.ndarray,
-) -> int:
-    valid = bool(np.all(route != MISSING_EXPERT_ID))
-    if not valid:
-        return 0
-    route_tensor[dst_index] = route
-    route_mask[dst_index] = True
-    return int(route.max()) if route.size else 0
-
-
 def packed_tensors_from_dir(**kwargs: Unpack[DiskPackedTensors]) -> PackedTensors:
     os.makedirs(kwargs["dir"], exist_ok=True)
     packed_tensors = {
diff --git a/src/art/preprocessing/policy_spans.py b/src/art/preprocessing/policy_spans.py
index 856400880..4372fe854 100644
--- a/src/art/preprocessing/policy_spans.py
+++ b/src/art/preprocessing/policy_spans.py
@@ -10,6 +10,14 @@
 
 
 class PolicyTokenSpan(BaseModel):
+    """Half-open completion-token interval scored by one executing policy state.
+
+    The version identifies the adapter used by the target model execution that
+    produced the returned token and logprob, not request admission or response
+    delivery. Adjacent intervals may merge only when all policy identity fields
+    match.
+    """
+
     model_config = ConfigDict(extra="forbid")
 
     start_token: int = Field(ge=0)
diff --git a/src/art/preprocessing/tokenize.py b/src/art/preprocessing/tokenize.py
index 12554de75..1ca8e0df0 100644
--- a/src/art/preprocessing/tokenize.py
+++ b/src/art/preprocessing/tokenize.py
@@ -30,6 +30,7 @@
 from ..trajectories._selection import ModelSelector, resolve_training_model
 from ..types import MessagesAndChoices
 from ..utils.chat_template import (
+    TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR,
     default_chat_template_kwargs_for_tokenizer,
     merge_chat_template_kwargs,
     normalize_tool_call_arguments_for_chat_template,
@@ -240,13 +241,20 @@ def _slice_moe_routes(
         if start <= 0:
             return routes
         if start >= routes.shape[0]:
-            return np.empty((0, routes.shape[1], routes.shape[2]), dtype=np.int32)
+            return MoeRouteArray(
+                np.empty(
+                    (0, routes.shape[1], routes.shape[2]),
+                    dtype=routes.segments[0].dtype,
+                ),
+                num_experts=routes.num_experts,
+                validate=False,
+            )
         return MoeRouteSegments(
             segments=tuple(
                 segment for _, segment in routes.iter_slices(start, routes.shape[0])
             )
         )
-    return routes[start:]
+    return cast(MoeRouteArray, routes[start:])
 
 
 class _TokenDecoder(Protocol):
@@ -324,7 +332,11 @@ def _normalize_tool_call_arguments_for_chat_template(
     messages: list[dict[str, Any]],
 ) -> list[dict[str, Any]]:
     return normalize_tool_call_arguments_for_chat_template(
-        messages, tokenizer.chat_template
+        messages,
+        tokenizer.chat_template,
+        require_mapping=bool(
+            getattr(tokenizer, TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR, False)
+        ),
     )
 
 
@@ -725,7 +737,7 @@ def tokenize_trajectory_groups(
     model: ModelSelector | str | None = None,
     _max_sequence_length: int | None = None,
 ) -> Generator["TokenizedResult", None, None]:
-    for group in trajectory_groups:
+    for prompt_id, group in enumerate(trajectory_groups):
         if not group:
             continue
         results: list[TokenizedResult] = []
@@ -903,8 +915,6 @@ def tokenize_trajectory_groups(
             for result in trajectory_results:
                 result.weight = weight
             results.extend(trajectory_results)
-        # Choose a random prompt id
-        prompt_id = random.randint(-(2**63), 2**63 - 1)
         # Find the longest shared prefix
         # TODO: Potentially support multiple prompts per group
         # Initial thought is to sort the results by token_ids and then
@@ -933,7 +943,7 @@ def tokenize_trajectory_groups(
             result.prompt_id = prompt_id
             result.prompt_length = prompt_length
         if shuffle_group_trajectories:
-            random.shuffle(results)
+            random.Random(prompt_id).shuffle(results)
         yield from results
 
 
diff --git a/src/art/serving_capabilities.py b/src/art/serving_capabilities.py
index 6c6649701..16cd253d1 100644
--- a/src/art/serving_capabilities.py
+++ b/src/art/serving_capabilities.py
@@ -1,7 +1,17 @@
+from ipaddress import ip_address
 from typing import Literal
 
 import httpx
-from pydantic import BaseModel, ConfigDict
+from pydantic import (
+    AnyHttpUrl,
+    BaseModel,
+    ConfigDict,
+    Field,
+    FiniteFloat,
+    model_validator,
+)
+
+ART_SERVING_PROTOCOL_VERSION = 4
 
 ServingFeature = Literal[
     "binary_routed_experts",
@@ -12,17 +22,58 @@
 ]
 
 
+class FastMetricsEndpoint(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    url: AnyHttpUrl
+
+    @model_validator(mode="after")
+    def _validate_url(self) -> "FastMetricsEndpoint":
+        host = self.url.host
+        if host is None:
+            raise ValueError("fast metrics URL must include a host")
+        try:
+            unspecified = ip_address(host.strip("[]")).is_unspecified
+        except ValueError:
+            unspecified = False
+        if unspecified:
+            raise ValueError("fast metrics URL must not use an unspecified host")
+        return self
+
+
+class FastMetricsSnapshot(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True, strict=True)
+
+    schema_version: Literal[1]
+    source: Literal["art_vllm_runtime"]
+    last_update_unix_s: FiniteFloat = Field(ge=0)
+    record_count: int = Field(ge=0)
+    engine_count: int = Field(ge=0)
+    metrics: dict[str, FiniteFloat]
+    process_uuid: str = Field(min_length=1)
+    generation: int = Field(ge=0)
+
+
 class ServingCapabilities(BaseModel):
     model_config = ConfigDict(extra="forbid", frozen=True)
 
     runtime: Literal["openai_compatible", "art_vllm"]
     protocol_version: int
     binary_routed_experts: bool = False
-    fast_metrics: bool = False
+    fast_metrics: FastMetricsEndpoint | None = None
     inplace_lora_load: bool = False
     in_flight_lora_updates: bool = False
     policy_token_spans: bool = False
 
+    @model_validator(mode="after")
+    def _validate_protocol(self) -> "ServingCapabilities":
+        expected = ART_SERVING_PROTOCOL_VERSION if self.runtime == "art_vllm" else 0
+        if self.protocol_version != expected:
+            raise ValueError(
+                f"{self.runtime} serving protocol must be version {expected}"
+            )
+        return self
+
     @classmethod
     def openai_compatible(cls) -> "ServingCapabilities":
         return cls(runtime="openai_compatible", protocol_version=0)
diff --git a/src/art/tinker/backend.py b/src/art/tinker/backend.py
index 4a30d54c5..3189f3f92 100644
--- a/src/art/tinker/backend.py
+++ b/src/art/tinker/backend.py
@@ -71,11 +71,14 @@ async def _get_service(self, model: TrainableModel) -> ModelService:
                 TinkerTrainingClientArgs,
                 config["tinker_args"].get("training_client_args") or {},
             )
-            self._services[storage_key] = TinkerService(
-                model_name=model.name,
-                base_model=model.base_model,
-                config=config,
-                output_dir=get_model_dir(model=model, art_path=self._path),
+            self._services[storage_key] = cast(
+                ModelService,
+                TinkerService(
+                    model_name=model.name,
+                    base_model=model.base_model,
+                    config=config,
+                    output_dir=get_model_dir(model=model, art_path=self._path),
+                ),
             )
             if not self._in_process:
                 self._services[storage_key] = move_to_child_process(
diff --git a/src/art/trainer_rank/_impl.py b/src/art/trainer_rank/_impl.py
index 84c1a7ea2..24946416d 100644
--- a/src/art/trainer_rank/_impl.py
+++ b/src/art/trainer_rank/_impl.py
@@ -782,6 +782,13 @@ def __init__(
         memory_safety_factor: float = 1.10,
         memory_reserve_fraction: float = 0.03,
     ) -> None:
+        pp_size = int(getattr(runtime.provider, "pipeline_model_parallel_size", 1) or 1)
+        if pp_size > 1 or len(runtime.model) > 1:
+            raise NotImplementedError(
+                "TrainerRank does not use the MCore forward/backward schedule and "
+                "therefore requires PP=1 with exactly one local model chunk; "
+                f"got pp={pp_size}, chunks={len(runtime.model)}"
+            )
         if head_chunk_tokens < 1:
             raise ValueError("head_chunk_tokens must be >= 1")
         if shared_prefix_max_depth < 0:
@@ -3481,7 +3488,9 @@ def _hybridep_rows(
                     parent_ids=batch.parent_ids,
                     topology=topology,
                     config=_context_parallel_config_for_provider(
-                        self.runtime.provider, self.device
+                        self.runtime.provider,
+                        self.device,
+                        handler,
                     ),
                     original_seq_len=sequence_length,
                     build_gdn_execution_spec=handler.build_gdn_execution_spec,
@@ -3532,7 +3541,11 @@ def _prepare_context_parallel_forward(
         prepared = prepare_cp_micro(
             micro=sparse_micro,
             topology=topology,
-            config=_context_parallel_config_for_provider(provider, self.device),
+            config=_context_parallel_config_for_provider(
+                provider,
+                self.device,
+                handler,
+            ),
             cp_group=ps.get_context_parallel_group(check_initialized=False),
             cp_rank=ps.get_context_parallel_rank(),
             build_gdn_execution_spec=handler.build_gdn_execution_spec,
diff --git a/src/art/trajectories/__init__.py b/src/art/trajectories/__init__.py
index 2da1bcb7c..6f172cd1c 100644
--- a/src/art/trajectories/__init__.py
+++ b/src/art/trajectories/__init__.py
@@ -538,6 +538,7 @@ class Trajectory(_CompactModel):
     metadata: dict[str, MetadataValue] = pydantic.Field(default_factory=dict)
     logs: list[str] = pydantic.Field(default_factory=list)
     start_time: datetime = pydantic.Field(default_factory=datetime.now, exclude=True)
+    _policy_token_counts: dict[int, int] | None = pydantic.PrivateAttr(default=None)
 
     @pydantic.field_serializer("messages_and_choices", when_used="json")
     def serialize_messages_and_choices(self, value: MessagesAndChoices) -> list[Any]:
@@ -555,9 +556,6 @@ def validate_representation(self) -> Trajectory:
             )
         return self
 
-    def _intern_strings(self, pool: _StringPool | None = None) -> None:
-        _intern_string_graph(self, pool)
-
     def compact_dump(self) -> CompactTrajectoryPayload:
         """Return the explicit string-table representation of this trajectory."""
 
@@ -853,6 +851,9 @@ class TrajectoryGroup(_CompactModel):
     logs: list[str] = pydantic.Field(default_factory=list)
     _collect_packing_shape: bool = pydantic.PrivateAttr(default=False)
     _packed_group_shape: Any = pydantic.PrivateAttr(default=None)
+    _distributed_lease: Any = pydantic.PrivateAttr(default=None)
+    _prepared_training_batch: Any = pydantic.PrivateAttr(default=None)
+    _prepared_log_path: str | None = pydantic.PrivateAttr(default=None)
 
     def _intern_strings(self, pool: _StringPool | None = None) -> None:
         _intern_string_graph(self, pool)
@@ -1171,7 +1172,7 @@ def metadata(self, value: dict[str, MetadataValue]) -> None:
         self.trajectory.metadata = value
 
     @pydantic.model_validator(mode="after")
-    def _intern_source_graph(self) -> TokenizedMultiHistoryTrajectory:
+    def _bind_source_graph(self) -> TokenizedMultiHistoryTrajectory:
         for history in self.histories:
             _rebind_history_sources(history.history, self.trajectory)
         return self
@@ -1217,7 +1218,7 @@ def metadata(self, value: dict[str, MetadataValue]) -> None:
         self.trajectory_group.metadata = value
 
     @pydantic.model_validator(mode="after")
-    def _intern_source_graph(self) -> TokenizedTrajectoryGroup[TokenizedTrajectoryT]:
+    def _bind_source_graph(self) -> TokenizedTrajectoryGroup[TokenizedTrajectoryT]:
         if len(self.trajectories) != len(self.trajectory_group.trajectories):
             raise ValueError("Tokenized group differs in length from its source group")
         for tokenized, trajectory in zip(
@@ -1266,6 +1267,13 @@ def tensorize(
 _CompactValidated: TypeAlias = Union[CompactDumpable, list[CompactDumpable]]
 
 
+def compact_memory[T](value: T) -> T:
+    """Deduplicate equal strings in a supported object graph in place."""
+
+    _intern_string_graph(value)
+    return value
+
+
 def compact_dump(
     value: CompactDumpable | Iterable[CompactDumpable],
 ) -> CompactTrajectoryPayload:
@@ -1428,6 +1436,7 @@ def __dir__() -> list[str]:
     "CompactDumpable",
     "CompactTrajectoryKind",
     "CompactTrajectoryPayload",
+    "compact_memory",
     "compact_dump",
     "compact_validate",
     "current_trajectory",
diff --git a/src/art/trajectories/_compact.py b/src/art/trajectories/_compact.py
index c058df12b..68faae99f 100644
--- a/src/art/trajectories/_compact.py
+++ b/src/art/trajectories/_compact.py
@@ -132,9 +132,7 @@ def validate(
             _validate_value(item, singular, target_model, device=device)
             for item in data
         ]
-        for value in values:
-            _finish(value)
-        return cast(_CompactValidated, values)
+        return cast(_CompactValidated, [_finish(value) for value in values])
     return _finish(_validate_value(data, kind, target_model, device=device))
 
 
diff --git a/src/art/trajectories/_serialization.py b/src/art/trajectories/_serialization.py
index feaca3549..7b31db4c9 100644
--- a/src/art/trajectories/_serialization.py
+++ b/src/art/trajectories/_serialization.py
@@ -43,6 +43,12 @@ def _intern_value(value: object, pool: _StringPool, memo: dict[int, object]) ->
         return pool.setdefault(value, value)
     if isinstance(value, (bytes, bytearray, memoryview)) or value is None:
         return value
+    if type(value) in (bool, float, int):
+        return value
+    if isinstance(value, list) and all(
+        item is None or type(item) in (bool, float, int) for item in value
+    ):
+        return value
 
     value_id = id(value)
     if value_id in memo:
diff --git a/src/art/types.py b/src/art/types.py
index d54f86420..91e3a8017 100644
--- a/src/art/types.py
+++ b/src/art/types.py
@@ -1,3 +1,4 @@
+from collections.abc import Awaitable
 from dataclasses import dataclass, field
 from typing import Annotated, Literal
 
@@ -29,6 +30,7 @@ class TrainConfig(pydantic.BaseModel):
     kl_penalty_source: Literal["current_learner", "sample"] = "current_learner"
     grad_accumulation_sequences: int | None = pydantic.Field(default=None, ge=1)
     optimizer_save_interval: int = pydantic.Field(default=5, ge=1)
+    final_training_step: int | None = pydantic.Field(default=None, ge=1)
 
 
 class MegatronTopologyConfig(pydantic.BaseModel):
@@ -37,14 +39,27 @@ class MegatronTopologyConfig(pydantic.BaseModel):
     ep: int = pydantic.Field(default_factory=_visible_device_count, ge=1)
     pp: int = pydantic.Field(default=1, ge=1)
     vpp: int | None = pydantic.Field(default=None, ge=1)
+    vpp_microbatch_group_size: int | None = pydantic.Field(default=None, ge=1)
     etp: int = pydantic.Field(default=1, ge=1)
 
+    @pydantic.model_validator(mode="after")
+    def _validate_vpp_group(self) -> "MegatronTopologyConfig":
+        if self.vpp_microbatch_group_size is None:
+            return self
+        if self.vpp is None:
+            raise ValueError("vpp_microbatch_group_size requires vpp")
+        if self.vpp_microbatch_group_size < self.pp:
+            raise ValueError("vpp_microbatch_group_size must be at least pp")
+        return self
+
 
 class MegatronRuntimeConfig(pydantic.BaseModel):
     model_config = pydantic.ConfigDict(frozen=True)
 
     topology: MegatronTopologyConfig
     packed_sequence_length: int = pydantic.Field(ge=1)
+    snapshot_pool_capacity: int = pydantic.Field(default=2, ge=1, le=4)
+    compile_cache: bool = False
     # The default 2 resident layers / 4 slots is the tested recommendation.
     # Set ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD_{NUM_LAYERS,NUM_SLOTS,RESIDENT_LAYERS}
     # before worker startup only when benchmarking a different streaming policy.
@@ -92,9 +107,14 @@ class LocalTrainResult(TrainResult):
         metrics: Aggregated training metrics (loss, gradient norms, etc.).
         checkpoint_path: Path to the saved checkpoint directory, or None if
             no checkpoint was saved.
+        checkpoint_ready: Completion signal for an asynchronously materialized
+            checkpoint. None when checkpoint_path is already usable.
     """
 
     checkpoint_path: str | None = None
+    checkpoint_ready: Awaitable[None] | None = field(
+        default=None, repr=False, compare=False
+    )
 
 
 @dataclass
diff --git a/src/art/unsloth/service.py b/src/art/unsloth/service.py
index e478e441f..8ab66f487 100644
--- a/src/art/unsloth/service.py
+++ b/src/art/unsloth/service.py
@@ -5,7 +5,6 @@
 from functools import cached_property
 import logging
 import os
-import socket
 from typing import Any, AsyncIterator, Literal, TypedDict, cast
 
 import torch
@@ -15,7 +14,6 @@
 from ..adapter_leases import in_flight_lora_name
 from ..dev.validate import is_dedicated_mode
 from ..local.checkpoints import get_last_checkpoint_dir
-from ..preprocessing.inputs import TrainInputs
 from ..preprocessing.pack import DiskPackedTensors
 from ..preprocessing.tokenize import SFTBatch
 from ..serving_capabilities import (
@@ -34,12 +32,6 @@
     ManagedVllmRuntime,
     VllmRuntimeLaunchConfig,
 )
-from ..weight_transfer import (
-    DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-    DEFAULT_PACKED_NUM_BUFFERS,
-    trainer_init,
-    trainer_send_weights,
-)
 from .train import (
     UnslothTrainContext,
     create_unsloth_train_context,
@@ -114,21 +106,6 @@ def save_checkpoint(
     return checkpoint_dir
 
 
-def _find_free_tcp_port() -> int:
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
-        sock.bind(("127.0.0.1", 0))
-        return int(sock.getsockname()[1])
-
-
-def _normalize_merged_checkpoint_name(name: str) -> str:
-    # PEFT wraps adapted modules under `.base_layer`, but vLLM expects the
-    # original checkpoint parameter names during update_weights().
-    normalized = name.removeprefix("base_model.model.")
-    while ".base_layer." in normalized:
-        normalized = normalized.replace(".base_layer.", ".")
-    return normalized
-
-
 # ============================================================================
 # Service
 # ============================================================================
@@ -147,7 +124,6 @@ class UnslothService:
         init=False,
         repr=False,
     )
-    _weight_transfer_group: Any = field(default=None, init=False, repr=False)
     _lifecycle: ServiceLifecycle = field(
         default_factory=ServiceLifecycle,
         init=False,
@@ -194,12 +170,6 @@ def _raise_if_child_failed(self) -> None:
     def is_dedicated(self) -> bool:
         return is_dedicated_mode(self.config)
 
-    @property
-    def rollout_weights_mode(self) -> Literal["lora", "merged"]:
-        mode = self.config["rollout_weights_mode"]
-        assert mode in {"lora", "merged"}
-        return mode
-
     @property
     def rollout_weight_update_mode(self) -> Literal["step_lora", "in_flight_lora"]:
         mode = self.config.get("rollout_weight_update_mode", "step_lora")
@@ -212,10 +182,7 @@ def _in_flight_lora_slot(self) -> str:
 
     @property
     def _initial_served_model_name(self) -> str:
-        if (
-            self.rollout_weights_mode == "lora"
-            and self.rollout_weight_update_mode == "in_flight_lora"
-        ):
+        if self.rollout_weight_update_mode == "in_flight_lora":
             return self._in_flight_lora_slot
         return f"{self.model_name}@{self._latest_step}"
 
@@ -244,10 +211,6 @@ def _vllm_port(self, port: int) -> None:
     def _vllm_api_key(self) -> str | None:
         return self._vllm_runtime.api_key
 
-    @property
-    def _vllm_nccl_so_path(self) -> str | None:
-        return self._vllm_runtime.nccl_so_path
-
     def _runtime_cuda_visible_devices(self) -> str:
         if self.is_dedicated:
             return ",".join(str(gpu_id) for gpu_id in self.config["inference_gpu_ids"])
@@ -262,13 +225,8 @@ def _runtime_engine_args(
         if config and "engine_args" in config:
             engine_args.update(dict(config["engine_args"]))
         engine_args.setdefault("generation_config", "vllm")
-        if self.rollout_weights_mode == "merged":
-            engine_args["weight_transfer_config"] = {"backend": "nccl"}
-            engine_args.pop("enable_lora", None)
-            engine_args.pop("max_loras", None)
-        else:
-            engine_args["enable_lora"] = True
-            engine_args.setdefault("max_loras", 2)
+        engine_args["enable_lora"] = True
+        engine_args.setdefault("max_loras", 2)
         for key in ("model", "served_model_name"):
             engine_args.pop(key, None)
         return engine_args
@@ -334,7 +292,6 @@ async def _start_vllm_subprocess(
                 cuda_visible_devices=self._runtime_cuda_visible_devices(),
                 lora_path=lora_path,
                 served_model_name=self._initial_served_model_name,
-                rollout_weights_mode=self.rollout_weights_mode,
                 engine_args=self._runtime_engine_args(config),
                 server_args=server_args,
             ),
@@ -352,218 +309,6 @@ async def _start_vllm_subprocess(
         )
         return location
 
-    async def _set_served_model_name(self, step: int) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        served_model_name = f"{self.model_name}@{step}"
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self._vllm_base_url}/art/set_served_model_name",
-                json={"name": served_model_name},
-                **self._runtime_request_kwargs(),
-                timeout=30.0,
-            )
-            response.raise_for_status()
-        logger.info(
-            "[DEDICATED] Updated merged rollout alias to %s",
-            served_model_name,
-        )
-
-    async def _init_merged_weight_transfer(self) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        if self._weight_transfer_group is not None:
-            return
-
-        async with httpx.AsyncClient() as client:
-            world_size_response = await client.get(
-                f"{self._vllm_base_url}/get_world_size",
-                **self._runtime_request_kwargs(),
-                timeout=30.0,
-            )
-            try:
-                world_size_response.raise_for_status()
-            except httpx.HTTPStatusError as exc:
-                raise RuntimeError(
-                    "Merged rollout weights require a vLLM build with the "
-                    "/get_world_size endpoint"
-                ) from exc
-            inference_world_size = int(world_size_response.json()["world_size"])
-            if self._vllm_nccl_so_path is None:
-                raise RuntimeError("vLLM runtime NCCL path is not initialized")
-
-            master_port = _find_free_tcp_port()
-            init_info = {
-                "master_address": "127.0.0.1",
-                "master_port": master_port,
-                "rank_offset": 1,
-                "world_size": inference_world_size + 1,
-            }
-
-            remote_init_task = asyncio.create_task(
-                client.post(
-                    f"{self._vllm_base_url}/init_weight_transfer_engine",
-                    json={"init_info": init_info},
-                    **self._runtime_request_kwargs(),
-                    timeout=300.0,
-                )
-            )
-            self._weight_transfer_group = await asyncio.to_thread(
-                trainer_init,
-                {
-                    "master_address": init_info["master_address"],
-                    "master_port": init_info["master_port"],
-                    "world_size": init_info["world_size"],
-                    "nccl_so_path": self._vllm_nccl_so_path,
-                },
-            )
-            remote_init_response = await remote_init_task
-            try:
-                remote_init_response.raise_for_status()
-            except httpx.HTTPStatusError as exc:
-                raise RuntimeError(
-                    "Merged rollout weights require a vLLM build with the "
-                    "/init_weight_transfer_engine endpoint"
-                ) from exc
-
-        logger.info(
-            "[DEDICATED] Initialized merged weight transfer: inference_world_size=%d",
-            inference_world_size,
-        )
-
-    def _merged_checkpoint_weights_for_vllm(self) -> list[tuple[str, torch.Tensor]]:
-        model = self._state.peft_model.base_model.model
-        device = next(model.parameters()).device
-        assert device.type == "cuda"
-
-        weights: list[tuple[str, torch.Tensor]] = []
-        normalized_names: set[str] = set()
-        for name, tensor in model.state_dict().items():
-            if "lora_" in name:
-                continue
-            normalized_name = _normalize_merged_checkpoint_name(name)
-            assert normalized_name not in normalized_names
-            normalized_names.add(normalized_name)
-            detached = tensor.detach()
-            if detached.device != device:
-                detached = detached.to(device=device, non_blocking=True)
-            weights.append((normalized_name, detached))
-
-        assert weights
-        return weights
-
-    async def _sync_merged_weights(
-        self,
-        step: int,
-        pause_generation: bool,
-    ) -> None:
-        import httpx
-
-        self._raise_if_child_failed()
-        assert self._weight_transfer_group is not None
-
-        peft_model = self._state.peft_model
-        merged = False
-        error: Exception | None = None
-        logger.info("[DEDICATED] Syncing merged rollout weights for step %d", step)
-
-        async with httpx.AsyncClient() as client:
-            try:
-                if pause_generation:
-                    response = await client.post(
-                        f"{self._vllm_base_url}/pause",
-                        params={"mode": "wait"},
-                        **self._runtime_request_kwargs(),
-                        timeout=300.0,
-                    )
-                    response.raise_for_status()
-
-                peft_model.merge_adapter()
-                merged = True
-                torch.cuda.synchronize()
-
-                weights = self._merged_checkpoint_weights_for_vllm()
-                response = await client.post(
-                    f"{self._vllm_base_url}/start_weight_update",
-                    json={"is_checkpoint_format": True},
-                    **self._runtime_request_kwargs(),
-                    timeout=300.0,
-                )
-                response.raise_for_status()
-                update_info = {
-                    "names": [name for name, _ in weights],
-                    "dtype_names": [
-                        str(tensor.dtype).removeprefix("torch.")
-                        for _, tensor in weights
-                    ],
-                    "shapes": [list(tensor.shape) for _, tensor in weights],
-                    "packed": True,
-                    "packed_buffer_size_bytes": DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-                    "packed_num_buffers": DEFAULT_PACKED_NUM_BUFFERS,
-                }
-
-                _, update_response = await asyncio.gather(
-                    asyncio.to_thread(
-                        trainer_send_weights,
-                        iter(weights),
-                        {
-                            "group": self._weight_transfer_group,
-                            "packed": True,
-                            "packed_buffer_size_bytes": DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-                            "packed_num_buffers": DEFAULT_PACKED_NUM_BUFFERS,
-                        },
-                    ),
-                    client.post(
-                        f"{self._vllm_base_url}/update_weights",
-                        json={"update_info": update_info},
-                        **self._runtime_request_kwargs(),
-                        timeout=600.0,
-                    ),
-                )
-                try:
-                    update_response.raise_for_status()
-                except httpx.HTTPStatusError as exc:
-                    raise RuntimeError(
-                        "Merged rollout weights require a vLLM build with the "
-                        "/update_weights endpoint"
-                    ) from exc
-                response = await client.post(
-                    f"{self._vllm_base_url}/finish_weight_update",
-                    **self._runtime_request_kwargs(),
-                    timeout=600.0,
-                )
-                response.raise_for_status()
-                self._latest_step = step
-                await self._set_served_model_name(step)
-            except Exception as exc:
-                error = exc
-                raise
-            finally:
-                if merged:
-                    peft_model.unmerge_adapter()
-                    torch.cuda.synchronize()
-                if pause_generation:
-                    try:
-                        response = await client.post(
-                            f"{self._vllm_base_url}/resume",
-                            **self._runtime_request_kwargs(),
-                            timeout=30.0,
-                        )
-                        response.raise_for_status()
-                    except Exception:
-                        if error is None:
-                            raise
-                        logger.exception(
-                            "Failed to resume generation after merged weight sync error"
-                        )
-
-        logger.info(
-            "[DEDICATED] Merged rollout sync complete for step %d",
-            step,
-        )
-
     async def _reload_adapter(self, checkpoint_path: str, step: int) -> None:
         """Reload LoRA adapter in vLLM subprocess via HTTP."""
         import httpx
@@ -630,8 +375,6 @@ async def _load_rollout_lora_for_step(
             await self._reload_adapter(checkpoint_path, step)
 
     async def acquire_exact_adapter(self, step: int, checkpoint_path: str) -> str:
-        if self.rollout_weights_mode != "lora":
-            raise RuntimeError("Exact checkpoint eval requires LoRA rollout serving")
         lora_name = self._exact_lora_name(step)
         async with self._exact_adapter_lock:
             loaded_steps = (
@@ -698,7 +441,7 @@ async def _unload_exact_adapter(self, step: int) -> None:
         self._loaded_exact_adapter_steps.discard(step)
 
     async def prune_loaded_adapters(self, *, retain_steps: set[int]) -> None:
-        if self.rollout_weights_mode != "lora" or self._vllm_port == 0:
+        if self._vllm_port == 0:
             return
         async with self._exact_adapter_lock:
             for step in sorted(self._loaded_exact_adapter_steps - retain_steps):
@@ -715,20 +458,12 @@ def close(self) -> None:
         """Terminate vLLM subprocess if running."""
         if not self._lifecycle.begin_close():
             return
-        weight_transfer_group = self._weight_transfer_group
-        self._weight_transfer_group = None
         try:
-            try:
-                if weight_transfer_group is not None:
-                    close = getattr(weight_transfer_group, "close", None)
-                    if close is not None:
-                        close()
-            finally:
-                self._child_processes.close()
-                self._vllm_runtime.close()
-                self._loaded_adapter_steps.clear()
-                self._loaded_exact_adapter_steps.clear()
-                self._exact_adapter_refcounts.clear()
+            self._child_processes.close()
+            self._vllm_runtime.close()
+            self._loaded_adapter_steps.clear()
+            self._loaded_exact_adapter_steps.clear()
+            self._exact_adapter_refcounts.clear()
         finally:
             self._lifecycle.restore_parent_cleanup()
 
@@ -770,15 +505,10 @@ async def start_openai_server(
                 headers=self._runtime_headers(),
                 allow_openai_compatible=False,
             )
-            if self.rollout_weights_mode == "lora":
-                if self.rollout_weight_update_mode == "in_flight_lora":
-                    await self._update_in_flight_adapter(lora_path, self._latest_step)
-                else:
-                    self._loaded_adapter_steps.add(self._latest_step)
-            if self.rollout_weights_mode == "merged":
-                _ = self._state
-                await self._init_merged_weight_transfer()
-                await self._sync_merged_weights(self._latest_step, False)
+            if self.rollout_weight_update_mode == "in_flight_lora":
+                await self._update_in_flight_adapter(lora_path, self._latest_step)
+            else:
+                self._loaded_adapter_steps.add(self._latest_step)
         except BaseException as exc:
             await cleanup_after_failure(
                 exc,
@@ -819,10 +549,7 @@ async def _wake_runtime(self) -> None:
         self._is_sleeping = False
 
     async def register_lora_for_step(self, step: int, checkpoint_dir: str) -> None:
-        if self.rollout_weights_mode == "merged":
-            await self._set_served_model_name(step)
-        else:
-            await self._load_rollout_lora_for_step(checkpoint_dir, step)
+        await self._load_rollout_lora_for_step(checkpoint_dir, step)
         self._latest_step = step
 
     async def train(
@@ -879,18 +606,11 @@ async def _train_dedicated(
         )
 
         new_step = int(os.path.basename(checkpoint_dir))
-        if self.rollout_weights_mode == "merged":
-            logger.info(
-                "[DEDICATED] _train_dedicated: saved checkpoint step=%s, syncing merged weights...",
-                new_step,
-            )
-            await self._sync_merged_weights(new_step, True)
-        else:
-            logger.info(
-                "[DEDICATED] _train_dedicated: saved checkpoint step=%s, reloading adapter...",
-                new_step,
-            )
-            await self._load_rollout_lora_for_step(checkpoint_dir, new_step)
+        logger.info(
+            "[DEDICATED] _train_dedicated: saved checkpoint step=%s, reloading adapter...",
+            new_step,
+        )
+        await self._load_rollout_lora_for_step(checkpoint_dir, new_step)
         self._latest_step = new_step
         logger.info(
             f"[DEDICATED] _train_dedicated: inference weights updated for step {new_step}"
diff --git a/src/art/utils/cache_dirs.py b/src/art/utils/cache_dirs.py
new file mode 100644
index 000000000..f76529612
--- /dev/null
+++ b/src/art/utils/cache_dirs.py
@@ -0,0 +1,120 @@
+from collections.abc import MutableMapping
+import os
+from pathlib import Path
+import re
+
+_DEFAULT_CACHE_ROOT = Path("/tmp/art-cache")
+
+
+def compiler_cache_root(
+    cache_root: str | Path,
+    environ: MutableMapping[str, str] | None = None,
+) -> Path:
+    environ = os.environ if environ is None else environ
+    arch = environ.get("TORCH_CUDA_ARCH_LIST") or environ.get("CUDA_ARCH_LIST")
+    arch_tag = re.sub(r"[^A-Za-z0-9._-]+", "_", arch or "unknown")
+    return Path(cache_root) / "compiled" / arch_tag
+
+
+def _set_path(
+    environ: MutableMapping[str, str],
+    name: str,
+    default: str | Path,
+    *,
+    previous_default: Path | None = None,
+) -> Path:
+    value = environ.get(name)
+    path = Path(value or default).expanduser()
+    if previous_default is not None and path == previous_default:
+        path = Path(default).expanduser()
+    environ[name] = str(path)
+    return path
+
+
+def configure_model_cache_env(
+    environ: MutableMapping[str, str] | None = None,
+    *,
+    cache_root: str | Path | None = None,
+) -> Path:
+    """Set node-local cache defaults while preserving explicit paths."""
+    environ = os.environ if environ is None else environ
+    previous_art = environ.get("ART_MEGATRON_CACHE_ROOT")
+    previous_root = (
+        Path(previous_art).expanduser() if previous_art else _DEFAULT_CACHE_ROOT
+    )
+    previous_xdg = Path(environ.get("XDG_CACHE_HOME") or previous_root).expanduser()
+    previous_hf = Path(
+        environ.get("HF_HOME") or previous_xdg / "huggingface"
+    ).expanduser()
+    previous_hub = Path(
+        environ.get("HF_HUB_CACHE")
+        or environ.get("HUGGINGFACE_HUB_CACHE")
+        or previous_hf / "hub"
+    ).expanduser()
+
+    selected_root = cache_root if cache_root is not None else previous_art
+    art_root = Path(selected_root).expanduser() if selected_root is not None else None
+    if art_root is not None:
+        environ["ART_MEGATRON_CACHE_ROOT"] = str(art_root)
+    rebase = cache_root is not None
+    xdg_root = _set_path(
+        environ,
+        "XDG_CACHE_HOME",
+        art_root or _DEFAULT_CACHE_ROOT,
+        previous_default=previous_root if rebase else None,
+    )
+    hf_home = _set_path(
+        environ,
+        "HF_HOME",
+        xdg_root / "huggingface",
+        previous_default=previous_xdg / "huggingface" if rebase else None,
+    )
+    legacy_hub_cache = environ.get("HUGGINGFACE_HUB_CACHE")
+    hub_default = (
+        Path(legacy_hub_cache).expanduser()
+        if legacy_hub_cache
+        and (not rebase or Path(legacy_hub_cache).expanduser() != previous_hf / "hub")
+        else hf_home / "hub"
+    )
+    hub_cache = _set_path(
+        environ,
+        "HF_HUB_CACHE",
+        hub_default,
+        previous_default=previous_hf / "hub" if rebase else None,
+    )
+    compiled_root = compiler_cache_root(xdg_root, environ)
+    previous_compiled_root = compiler_cache_root(previous_xdg, environ)
+    for name, default, previous_default in (
+        ("HUGGINGFACE_HUB_CACHE", hub_cache, previous_hf / "hub"),
+        ("TRANSFORMERS_CACHE", hub_cache, previous_hub),
+        ("TORCH_HOME", xdg_root / "torch", previous_xdg / "torch"),
+        (
+            "TORCH_EXTENSIONS_DIR",
+            compiled_root / "torch_extensions",
+            previous_compiled_root / "torch_extensions",
+        ),
+        (
+            "TORCHINDUCTOR_CACHE_DIR",
+            compiled_root / "torchinductor",
+            previous_compiled_root / "torchinductor",
+        ),
+        ("TRITON_HOME", xdg_root, previous_xdg),
+        (
+            "TRITON_CACHE_DIR",
+            compiled_root / "triton",
+            previous_compiled_root / "triton",
+        ),
+        (
+            "VLLM_CACHE_ROOT",
+            compiled_root / "vllm",
+            previous_compiled_root / "vllm",
+        ),
+        ("VLLM_CONFIG_ROOT", xdg_root / "vllm_config", previous_xdg / "vllm_config"),
+    ):
+        _set_path(
+            environ,
+            name,
+            default,
+            previous_default=previous_default if rebase else None,
+        )
+    return art_root or xdg_root
diff --git a/src/art/utils/chat_template.py b/src/art/utils/chat_template.py
index a4a009ba6..deaad7a52 100644
--- a/src/art/utils/chat_template.py
+++ b/src/art/utils/chat_template.py
@@ -6,6 +6,7 @@
     "enable_thinking": False,
     "preserve_thinking": True,
 }
+TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR = "_art_tool_call_arguments_as_mapping"
 _QWEN_DROP_PRIOR_THINKING = "{%- if loop.index0 > ns.last_query_index %}"
 _QWEN_PRESERVE_PRIOR_THINKING = (
     "{%- if (preserve_thinking is defined and preserve_thinking is true) or "
@@ -88,13 +89,17 @@ def _template_requires_structured_tool_arguments(chat_template: object) -> bool:
 def normalize_tool_call_arguments_for_chat_template(
     messages: list[dict[str, Any]],
     chat_template: object,
+    *,
+    require_mapping: bool = False,
 ) -> list[dict[str, Any]]:
     """Give chat templates the structured tool arguments they require.
 
     Templates that interpolate the raw JSON string must keep string arguments,
     so only templates that iterate structured arguments trigger normalization.
     """
-    if not _template_requires_structured_tool_arguments(chat_template):
+    if not require_mapping and not _template_requires_structured_tool_arguments(
+        chat_template
+    ):
         return messages
     normalized: list[dict[str, Any]] = []
     for message in messages:
diff --git a/src/art/utils/lifecycle.py b/src/art/utils/lifecycle.py
index 09fa373a8..531d5d723 100644
--- a/src/art/utils/lifecycle.py
+++ b/src/art/utils/lifecycle.py
@@ -10,11 +10,48 @@
 import sys
 import time
 from types import FrameType
-from typing import Any
+from typing import Any, TypeVar
 
 PROCESS_SHUTDOWN_TIMEOUT_SECONDS = 20.0
 _PROCESS_SHUTDOWN_LEVEL_STEP = 0.1
 _PROCESS_SHUTDOWN_SWEEP_GRACE_FRACTION = 0.05
+_T = TypeVar("_T")
+
+
+async def complete_task(
+    task: asyncio.Task[_T],
+) -> tuple[_T, asyncio.CancelledError | None]:
+    cancelled: asyncio.CancelledError | None = None
+    while not task.done():
+        try:
+            await asyncio.shield(task)
+        except asyncio.CancelledError as error:
+            if task.cancelled():
+                break
+            cancelled = cancelled or error
+        except BaseException:
+            break
+    try:
+        result = task.result()
+    except BaseException as error:
+        if cancelled is not None:
+            cancelled.add_note(f"operation also failed: {error}")
+            raise cancelled
+        raise
+    return result, cancelled
+
+
+async def complete_to_thread(
+    operation: Callable[[], _T],
+) -> tuple[_T, asyncio.CancelledError | None]:
+    return await complete_task(asyncio.create_task(asyncio.to_thread(operation)))
+
+
+def consume_future_exception(future: asyncio.Future[Any]) -> None:
+    try:
+        future.exception()
+    except asyncio.CancelledError:
+        pass
 
 
 def process_shutdown_timeout(level: int) -> float:
diff --git a/src/art/utils/managed_process.py b/src/art/utils/managed_process.py
index a6fbdce00..16d47eb87 100644
--- a/src/art/utils/managed_process.py
+++ b/src/art/utils/managed_process.py
@@ -62,8 +62,8 @@ def shutdown(sig: signal.Signals, exit_code: int) -> None:
         if shutting_down:
             return
         shutting_down = True
-        signal_child_group(sig)
         if process is not None:
+            process.send_signal(sig)
             try:
                 process.wait(timeout=args.child_timeout)
             except subprocess.TimeoutExpired:
diff --git a/src/art/utils/s3.py b/src/art/utils/s3.py
index a96acf19d..5aefade5f 100644
--- a/src/art/utils/s3.py
+++ b/src/art/utils/s3.py
@@ -209,13 +209,7 @@ async def pull_model_from_s3(
         prefix=prefix,
     )
     await ensure_bucket_exists(s3_bucket)
-    if verbose:
-        print(f"DEBUG: S3 sync from {s3_path} to {local_dir}")
     await s3_sync(s3_path, local_dir, verbose=verbose, delete=delete, exclude=exclude)
-    if verbose:
-        print(
-            f"DEBUG: After sync, local_dir contents: {os.listdir(local_dir) if os.path.exists(local_dir) else 'Does not exist'}"
-        )
 
     return local_model_dir
 
@@ -258,8 +252,6 @@ async def push_model_to_s3(
     )
 
     await ensure_bucket_exists(s3_bucket)
-    if verbose:
-        print(f"DEBUG: S3 sync from {local_model_dir} to {s3_path}")
     await s3_sync(local_model_dir, s3_path, verbose=verbose, delete=delete)
 
 
diff --git a/src/art/utils/safetensors.py b/src/art/utils/safetensors.py
new file mode 100644
index 000000000..1e883cb08
--- /dev/null
+++ b/src/art/utils/safetensors.py
@@ -0,0 +1,246 @@
+from collections import deque
+from itertools import islice
+import json
+import os
+from pathlib import Path
+import struct
+import sys
+import tempfile
+from typing import NamedTuple
+
+import torch
+
+_DTYPES = {
+    dtype: name
+    for name, dtype in {
+        "BOOL": torch.bool,
+        "U8": torch.uint8,
+        "I8": torch.int8,
+        "I16": torch.int16,
+        "I32": torch.int32,
+        "I64": torch.int64,
+        "F16": torch.float16,
+        "BF16": torch.bfloat16,
+        "F32": torch.float32,
+        "F64": torch.float64,
+        "C64": torch.complex64,
+        "U16": getattr(torch, "uint16", None),
+        "U32": getattr(torch, "uint32", None),
+        "U64": getattr(torch, "uint64", None),
+        "F8_E4M3": getattr(torch, "float8_e4m3fn", None),
+        "F8_E5M2": getattr(torch, "float8_e5m2", None),
+    }.items()
+    if dtype is not None
+}
+
+
+class PreparedSafetensors(NamedTuple):
+    chunks: tuple[torch.Tensor, ...]
+
+    @property
+    def nbytes(self) -> int:
+        return sum(chunk.numel() for chunk in self.chunks)
+
+
+class _TensorLayout(NamedTuple):
+    name: str
+    dtype: torch.dtype
+    shape: tuple[int, ...]
+    storage: int
+    offset: int
+    nbytes: int
+
+
+class _StorageLayout(NamedTuple):
+    nbytes: int
+    chunks: tuple[tuple[int, int], ...]
+
+
+class SafetensorsLayout:
+    """Reusable file layout for immutable CPU snapshots with stable shapes."""
+
+    def __init__(self, tensors: dict[str, torch.Tensor]) -> None:
+        storage_indices: dict[tuple[int, int], int] = {}
+        storages: list[list[tuple[int, int]]] = []
+        storage_bytes: list[int] = []
+        entries: list[_TensorLayout] = []
+        for name, tensor in sorted(tensors.items()):
+            _validate_tensor(name, tensor)
+            storage = tensor.untyped_storage()
+            key = storage.data_ptr(), storage.nbytes()
+            storage_index = storage_indices.get(key)
+            if storage_index is None:
+                storage_index = len(storages)
+                storage_indices[key] = storage_index
+                storages.append([])
+                storage_bytes.append(storage.nbytes())
+            offset = tensor.data_ptr() - storage.data_ptr()
+            entries.append(
+                _TensorLayout(
+                    name,
+                    tensor.dtype,
+                    tuple(tensor.shape),
+                    storage_index,
+                    offset,
+                    tensor.nbytes,
+                )
+            )
+            storages[storage_index].append((offset, tensor.nbytes))
+
+        layouts: list[_StorageLayout] = []
+        for size, intervals in zip(storage_bytes, storages, strict=True):
+            ordered = sorted(intervals)
+            cursor = 0
+            coalesced = True
+            for offset, length in ordered:
+                if offset != cursor:
+                    coalesced = False
+                    break
+                cursor += length
+            layouts.append(
+                _StorageLayout(
+                    size,
+                    ((0, size),) if coalesced and cursor == size else tuple(intervals),
+                )
+            )
+
+        data_offsets: dict[str, tuple[int, int]] = {}
+        output_offset = 0
+        for storage_index, layout in enumerate(layouts):
+            storage_entries = [
+                entry for entry in entries if entry.storage == storage_index
+            ]
+            if len(layout.chunks) == 1 and layout.chunks[0] == (0, layout.nbytes):
+                for entry in storage_entries:
+                    data_offsets[entry.name] = (
+                        output_offset + entry.offset,
+                        output_offset + entry.offset + entry.nbytes,
+                    )
+                output_offset += layout.nbytes
+                continue
+            for entry in storage_entries:
+                data_offsets[entry.name] = (
+                    output_offset,
+                    output_offset + entry.nbytes,
+                )
+                output_offset += entry.nbytes
+
+        header = {
+            entry.name: {
+                "dtype": _DTYPES[entry.dtype],
+                "shape": list(entry.shape),
+                "data_offsets": list(data_offsets[entry.name]),
+            }
+            for entry in entries
+        }
+        encoded = json.dumps(header, separators=(",", ":")).encode()
+        encoded += b" " * (-len(encoded) % 8)
+        self._entries = tuple(entries)
+        self._storages = tuple(layouts)
+        self._prefix = torch.frombuffer(
+            bytearray(struct.pack("<Q", len(encoded)) + encoded), dtype=torch.uint8
+        )
+
+    def bind(self, tensors: dict[str, torch.Tensor]) -> PreparedSafetensors:
+        bound: list[torch.Tensor | None] = [None] * len(self._storages)
+        for entry in self._entries:
+            tensor = tensors.get(entry.name)
+            if tensor is None:
+                raise RuntimeError(f"Safetensors tensor disappeared: {entry.name}")
+            _validate_tensor(entry.name, tensor)
+            storage = tensor.untyped_storage()
+            if (
+                tensor.dtype != entry.dtype
+                or tuple(tensor.shape) != entry.shape
+                or storage.nbytes() != self._storages[entry.storage].nbytes
+                or tensor.data_ptr() - storage.data_ptr() != entry.offset
+            ):
+                raise RuntimeError(f"Safetensors tensor layout changed: {entry.name}")
+            owner = bound[entry.storage]
+            if owner is None:
+                bound[entry.storage] = torch.empty(0, dtype=torch.uint8).set_(
+                    storage, 0, (storage.nbytes(),), (1,)
+                )
+            elif owner.untyped_storage().data_ptr() != storage.data_ptr():
+                raise RuntimeError("Safetensors storage aliasing changed")
+        if len(tensors) != len(self._entries):
+            raise RuntimeError("Safetensors tensor set changed")
+        owners = tuple(owner for owner in bound if owner is not None)
+        if len(owners) != len(bound):
+            raise RuntimeError("Safetensors storage disappeared")
+        return PreparedSafetensors(
+            (
+                self._prefix,
+                *(
+                    owner.narrow(0, offset, length)
+                    for owner, layout in zip(owners, self._storages, strict=True)
+                    for offset, length in layout.chunks
+                ),
+            )
+        )
+
+
+def _writev_all(fd: int, buffers: list[memoryview]) -> None:
+    pending = deque(buffer for buffer in buffers if buffer.nbytes)
+    iov_max = os.sysconf("SC_IOV_MAX")
+    while pending:
+        written = os.writev(fd, tuple(islice(pending, iov_max)))
+        if written <= 0:
+            raise OSError("Short vectored write")
+        while pending and written >= pending[0].nbytes:
+            written -= pending.popleft().nbytes
+        if written:
+            pending[0] = pending[0][written:]
+
+
+def _validate_tensor(name: str, tensor: torch.Tensor) -> None:
+    if sys.byteorder != "little":
+        raise RuntimeError("ART's zero-copy safetensors writer requires little endian")
+    if tensor.device.type != "cpu" or not tensor.is_contiguous():
+        raise RuntimeError(f"Tensor {name!r} must be contiguous CPU storage")
+    if tensor.dtype not in _DTYPES:
+        raise RuntimeError(f"Unsupported safetensors dtype: {tensor.dtype}")
+
+
+def prepare_safetensors(tensors: dict[str, torch.Tensor]) -> PreparedSafetensors:
+    entries: list[tuple[str, torch.Tensor]] = []
+    data_offsets: dict[str, tuple[int, int]] = {}
+    offset = 0
+    for name, tensor in sorted(tensors.items()):
+        _validate_tensor(name, tensor)
+        entries.append((name, tensor))
+        data_offsets[name] = offset, offset + tensor.nbytes
+        offset += tensor.nbytes
+    header = {
+        name: {
+            "dtype": _DTYPES[tensor.dtype],
+            "shape": list(tensor.shape),
+            "data_offsets": list(data_offsets[name]),
+        }
+        for name, tensor in entries
+    }
+    encoded = json.dumps(header, separators=(",", ":")).encode()
+    encoded += b" " * (-len(encoded) % 8)
+    prefix = torch.frombuffer(
+        bytearray(struct.pack("<Q", len(encoded)) + encoded), dtype=torch.uint8
+    )
+    return PreparedSafetensors(
+        (prefix, *(tensor.reshape(-1).view(torch.uint8) for _name, tensor in entries))
+    )
+
+
+def save_prepared_safetensors(prepared: PreparedSafetensors, path: Path) -> None:
+    """Stream a prepared safetensors payload without rebuilding tensor metadata."""
+    with tempfile.TemporaryDirectory(dir=path.parent) as temp_dir:
+        temporary_path = Path(temp_dir) / path.name
+        with temporary_path.open("wb", buffering=0) as output:
+            _writev_all(
+                output.fileno(),
+                [memoryview(chunk.numpy()) for chunk in prepared.chunks],
+            )
+        temporary_path.replace(path)
+
+
+def save_safetensors(tensors: dict[str, torch.Tensor], path: Path) -> None:
+    """Stream CPU tensor buffers without copying them into GIL-held bytes."""
+    save_prepared_safetensors(prepare_safetensors(tensors), path)
diff --git a/src/art/vllm_route_transport.py b/src/art/vllm_route_transport.py
index 8d3b5826f..66a0e857e 100644
--- a/src/art/vllm_route_transport.py
+++ b/src/art/vllm_route_transport.py
@@ -1,15 +1,13 @@
 from __future__ import annotations
 
 import struct
-from typing import TYPE_CHECKING
 
 from openai.types.chat import ChatCompletion
 
-if TYPE_CHECKING:
-    import numpy as np
+from art.preprocessing.moe_routing import MoeRouteArray
 
-MAGIC = b"ARTRTE1\0"
-HEADER = struct.Struct("<8sQI")
+MAGIC = b"ARTRTE2\0"
+HEADER = struct.Struct("<8sQII")
 ROUTE_HEADER = struct.Struct("<IB3xQQQ")
 DTYPES = {1: "u1", 2: "<u2"}
 
@@ -20,12 +18,12 @@ def is_routed_experts_response(body: bytes) -> bool:
 
 def decode_routed_experts_response(
     body: bytes,
-) -> tuple[ChatCompletion, dict[int, np.ndarray]]:
+) -> tuple[ChatCompletion, dict[int, MoeRouteArray]]:
     import numpy as np
 
     if len(body) < HEADER.size:
         raise RuntimeError("Truncated ART routed-experts response header")
-    magic, json_size, route_count = HEADER.unpack_from(body)
+    magic, json_size, route_count, num_experts = HEADER.unpack_from(body)
     if magic != MAGIC:
         raise RuntimeError("Invalid ART routed-experts response magic")
     offset = HEADER.size
@@ -34,7 +32,7 @@ def decode_routed_experts_response(
         raise RuntimeError("Truncated ART routed-experts JSON response")
     response = ChatCompletion.model_validate_json(body[offset:json_end])
     offset = json_end
-    routes: dict[int, np.ndarray] = {}
+    routes: dict[int, MoeRouteArray] = {}
     for _ in range(route_count):
         if offset + ROUTE_HEADER.size > len(body):
             raise RuntimeError("Truncated ART routed-experts array header")
@@ -55,7 +53,9 @@ def decode_routed_experts_response(
         array = np.frombuffer(
             body, dtype=dtype, count=tokens * layers * topk, offset=offset
         )
-        routes[choice_index] = array.reshape((tokens, layers, topk))
+        routes[choice_index] = MoeRouteArray(
+            array.reshape((tokens, layers, topk)), num_experts=num_experts
+        )
         offset = end
     if offset != len(body):
         raise RuntimeError("Unexpected trailing bytes in ART routed-experts response")
diff --git a/src/art/vllm_runtime.py b/src/art/vllm_runtime.py
index 2133db127..2076d31c4 100644
--- a/src/art/vllm_runtime.py
+++ b/src/art/vllm_runtime.py
@@ -14,8 +14,9 @@
 from urllib.parse import urlparse
 
 import httpx
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
+from .utils.cache_dirs import configure_model_cache_env
 from .utils.lifecycle import (
     ChildProcessSupervisor,
     managed_process_cmd,
@@ -41,18 +42,86 @@
 VLLM_RUNTIME_CLOSE_TIMEOUT = process_shutdown_timeout(1)
 
 
+def _managed_runtime_extra() -> Literal["cuda12", "cuda13"]:
+    override = os.environ.get("ART_VLLM_RUNTIME_CUDA_PROFILE")
+    if override is not None:
+        if override == "cuda12":
+            return "cuda12"
+        if override == "cuda13":
+            return "cuda13"
+        raise ValueError("ART_VLLM_RUNTIME_CUDA_PROFILE must be 'cuda12' or 'cuda13'")
+    cuda_home = Path(os.environ.get("CUDA_HOME", "/usr/local/cuda"))
+    commands = ([str(cuda_home / "bin" / "nvcc"), "--version"], ["nvidia-smi"])
+    for command in commands:
+        try:
+            output = subprocess.run(
+                command, capture_output=True, text=True, check=False
+            ).stdout
+        except FileNotFoundError:
+            continue
+        if "release 13." in output or "CUDA Version: 13." in output:
+            return "cuda13"
+    return "cuda12"
+
+
+MANAGED_RUNTIME_EXTRA = _managed_runtime_extra()
+
+
 class VllmRuntimeLaunchConfig(BaseModel):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     base_model: str
     port: int
     host: str = "127.0.0.1"
-    cuda_visible_devices: str
+    cuda_visible_devices: str | None = None
+    local_gpu_ids: tuple[int, ...] | None = None
     lora_path: str | None = None
     served_model_name: str
-    rollout_weights_mode: Literal["lora", "merged"]
     engine_args: dict[str, object] = Field(default_factory=dict)
     server_args: dict[str, object] = Field(default_factory=dict)
+    nnodes: int = Field(default=1, ge=1)
+    node_rank: int = Field(default=0, ge=0)
+    master_addr: str | None = None
+    master_port: int | None = Field(default=None, ge=1, le=65535)
+    headless: bool = False
+    replica_generation: int = Field(default=0, ge=0)
+    process_uuid: str | None = None
+    update_identity: str | None = None
+    initial_policy_version: int | None = Field(default=None, ge=0)
+
+    @model_validator(mode="after")
+    def _validate_native_member(self) -> "VllmRuntimeLaunchConfig":
+        explicit = self.local_gpu_ids
+        if explicit is not None:
+            if not explicit or any(gpu_id < 0 for gpu_id in explicit):
+                raise ValueError("local_gpu_ids must contain non-negative GPU IDs")
+            if len(set(explicit)) != len(explicit):
+                raise ValueError("local_gpu_ids must be unique")
+            visible = ",".join(map(str, explicit))
+            if self.cuda_visible_devices not in (None, visible):
+                raise ValueError("cuda_visible_devices must match local_gpu_ids")
+        elif not self.cuda_visible_devices:
+            raise ValueError("cuda_visible_devices or local_gpu_ids is required")
+        if self.node_rank >= self.nnodes:
+            raise ValueError("node_rank must be smaller than nnodes")
+        if self.nnodes == 1:
+            if self.node_rank or self.headless or self.master_addr or self.master_port:
+                raise ValueError("single-node launch cannot set native member options")
+        else:
+            if self.master_addr is None or self.master_port is None:
+                raise ValueError(
+                    "multi-node launch requires master_addr and master_port"
+                )
+            if self.headless != (self.node_rank != 0):
+                raise ValueError("exactly nonzero node ranks must be headless")
+        return self
+
+    @property
+    def visible_devices(self) -> str:
+        if self.local_gpu_ids is not None:
+            return ",".join(map(str, self.local_gpu_ids))
+        assert self.cuda_visible_devices is not None
+        return self.cuda_visible_devices
 
 
 class ExternalVllmRuntimeConfig(BaseModel):
@@ -92,6 +161,7 @@ class VllmRuntimeInstallMarker(BaseModel):
     protocol_version: int = RUNTIME_PROTOCOL_VERSION
     manifest_hash: str
     runtime_wheel_sha256: str
+    runtime_extra: Literal["cuda12", "cuda13"] = MANAGED_RUNTIME_EXTRA
     cache_root: str
 
 
@@ -194,7 +264,9 @@ def _drop_tilelang_env_paths(value: str | None) -> str | None:
     return os.pathsep.join(kept) if kept else None
 
 
-def _vllm_runtime_subprocess_env() -> dict[str, str]:
+def _vllm_runtime_subprocess_env(
+    runtime_command: list[str] | None = None,
+) -> dict[str, str]:
     """Build a child env isolated from runtime-specific JIT path leaks.
 
     TileLang mutates process env during import. If a vLLM runtime child inherits
@@ -206,12 +278,56 @@ def _vllm_runtime_subprocess_env() -> dict[str, str]:
     make one runtime compile kernels from another runtime's venv.
     """
     env = os.environ.copy()
+    configure_model_cache_env(env)
+    for key in ("PYTORCH_ALLOC_CONF", "PYTORCH_CUDA_ALLOC_CONF"):
+        options = [
+            option
+            for option in env.get(key, "").split(",")
+            if option and option != "expandable_segments:True"
+        ]
+        if options:
+            env[key] = ",".join(options)
+        else:
+            env.pop(key, None)
+    service_prefixes = {
+        key.removesuffix("_SERVICE_HOST")
+        for key in env
+        if key.startswith("VLLM_") and key.endswith("_SERVICE_HOST")
+    }
+    for key in tuple(env):
+        if any(
+            key.startswith(f"{prefix}_SERVICE_")
+            or key == f"{prefix}_PORT"
+            or key.startswith(f"{prefix}_PORT_")
+            for prefix in service_prefixes
+        ):
+            env.pop(key)
     for key in _TILELANG_ENV_KEYS:
         value = _drop_tilelang_env_paths(env.get(key))
         if value is None:
             env.pop(key, None)
         else:
             env[key] = value
+    runtime_dir = (
+        _runtime_dir_from_bin(Path(runtime_command[0])) if runtime_command else None
+    )
+    if runtime_dir is not None:
+        env.pop("PYTHONPATH", None)
+        env["PATH"] = os.pathsep.join(
+            (str(runtime_dir / ".venv" / "bin"), env.get("PATH", ""))
+        )
+        nvidia_libs = sorted(
+            str(path)
+            for site_packages in (runtime_dir / ".venv" / "lib").glob(
+                "python*/site-packages"
+            )
+            for path in (site_packages / "nvidia").glob("*/lib")
+            if path.is_dir()
+        )
+        inherited = env.get("LD_LIBRARY_PATH", "").split(os.pathsep)
+        env["LD_LIBRARY_PATH"] = os.pathsep.join(
+            (*nvidia_libs, *(path for path in inherited if "/nvidia/" not in path))
+        )
     env[_FLASHINFER_WORKSPACE_ENV] = str(_vllm_runtime_flashinfer_workspace_base())
     return env
 
@@ -221,7 +337,6 @@ def __init__(self, *, host: str = "127.0.0.1") -> None:
         self.host = host
         self.port = 0
         self.api_key: str | None = None
-        self.nccl_so_path: str | None = None
         self.process: subprocess.Popen[Any] | None = None
         self.log_file: Any = None
         self.log_path: str | None = None
@@ -248,12 +363,9 @@ async def start(
         self.host = launch_config.host
         self.port = launch_config.port
         api_key = launch_config.server_args.get("api_key")
-        self.api_key = api_key if isinstance(api_key, str) else None
-        self.nccl_so_path = (
-            str(get_vllm_runtime_nccl_so_path())
-            if launch_config.rollout_weights_mode == "merged"
-            else None
-        )
+        if api_key is not None and (not isinstance(api_key, str) or not api_key):
+            raise ValueError("vLLM api_key must be a non-empty string")
+        self.api_key = api_key
 
         cmd = build_vllm_runtime_server_cmd(launch_config)
         install_parent_cleanup()
@@ -261,10 +373,17 @@ async def start(
         os.makedirs(log_dir, exist_ok=True)
         self.log_path = os.path.join(log_dir, "vllm-runtime.log")
         self.log_file = open(self.log_path, "w", buffering=1)
+        env = _vllm_runtime_subprocess_env(cmd)
+        env.pop("VLLM_API_KEY", None)
+        if self.api_key is not None:
+            env["VLLM_API_KEY"] = self.api_key
         self.process = subprocess.Popen(
             managed_process_cmd(cmd),
-            cwd=str(get_vllm_runtime_working_dir()),
-            env=_vllm_runtime_subprocess_env(),
+            cwd=str(_vllm_runtime_subprocess_cwd(cmd)),
+            env={
+                **env,
+                "CUDA_VISIBLE_DEVICES": launch_config.visible_devices,
+            },
             stdout=self.log_file,
             stderr=subprocess.STDOUT,
             bufsize=1,
@@ -276,6 +395,24 @@ async def start(
             if timeout is not None
             else float(os.environ.get("ART_DEDICATED_VLLM_TIMEOUT", 1200))
         )
+        if launch_config.headless:
+            await asyncio.sleep(0.1)
+            if self.process.poll() is not None:
+                returncode = self.process.returncode
+                log_path = self.log_path
+                self._cleanup_after_start_error(cleanup_on_error)
+                raise RuntimeError(
+                    f"headless vLLM member exited with code {returncode}. "
+                    f"Check logs at {log_path}"
+                )
+            assert self.log_path is not None
+            child_processes.watch_popen(
+                f"vLLM headless member {launch_config.node_rank}",
+                self.process,
+                log_path=self.log_path,
+            )
+            return self.host, self.port
+
         async with httpx.AsyncClient() as client:
             try:
                 await wait_for_vllm_runtime(
@@ -283,6 +420,7 @@ async def start(
                     host=self.host,
                     port=self.port,
                     timeout=runtime_timeout,
+                    log_path=self.log_path,
                 )
             except TimeoutError as exc:
                 log_path = self.log_path
@@ -296,10 +434,36 @@ async def start(
                 log_path = self.log_path
                 self._cleanup_after_start_error(cleanup_on_error)
                 raise RuntimeError(
-                    f"vLLM subprocess exited with code {returncode}. "
+                    f"vLLM subprocess failed during startup "
+                    f"(returncode={returncode}): {exc}. "
                     f"Check logs at {log_path}"
                 ) from exc
 
+            if launch_config.process_uuid is not None:
+                try:
+                    response = await client.get(
+                        f"{self.base_url}/art/state",
+                        **self.request_kwargs(),
+                        timeout=5.0,
+                    )
+                    response.raise_for_status()
+                    state = response.json()
+                    expected = {
+                        "process_uuid": launch_config.process_uuid,
+                        "generation": launch_config.replica_generation,
+                    }
+                    if any(state.get(key) != value for key, value in expected.items()):
+                        raise RuntimeError(
+                            f"vLLM /art/state identity mismatch: {state!r}"
+                        )
+                except (httpx.HTTPError, RuntimeError, ValueError) as exc:
+                    log_path = self.log_path
+                    self._cleanup_after_start_error(cleanup_on_error)
+                    raise RuntimeError(
+                        "vLLM passed readiness but /art/state was invalid. "
+                        f"Check logs at {log_path}"
+                    ) from exc
+
             try:
                 response = await client.get(
                     f"{self.base_url}/v1/models",
@@ -341,7 +505,6 @@ def close(self) -> None:
             self.log_file = None
         self.log_path = None
         self.api_key = None
-        self.nccl_so_path = None
         self.port = 0
 
     def _cleanup_after_start_error(
@@ -371,16 +534,13 @@ def get_vllm_runtime_cache_root() -> Path:
     override = os.environ.get("ART_VLLM_RUNTIME_CACHE_DIR")
     if override:
         return Path(override).expanduser()
-    return Path.home() / ".cache" / "art" / "vllm_runtime"
+    return configure_model_cache_env(os.environ.copy()) / "vllm_runtime"
 
 
 def _vllm_runtime_flashinfer_workspace_base() -> Path:
     override = os.environ.get(_ART_FLASHINFER_WORKSPACE_ENV)
     if override:
         return Path(override).expanduser()
-    runtime_root = get_vllm_runtime_project_root()
-    if runtime_root.exists():
-        return runtime_root.resolve().parent / "scratch" / "vllm_runtime_flashinfer"
     return get_vllm_runtime_cache_root().expanduser() / "flashinfer_workspace"
 
 
@@ -401,6 +561,7 @@ def _runtime_python(runtime_dir: Path) -> Path:
 
 
 def _runtime_dir_from_bin(runtime_bin: Path) -> Path | None:
+    runtime_bin = runtime_bin.expanduser().resolve()
     if (
         runtime_bin.name == RUNTIME_SERVER
         and runtime_bin.parent.name == "bin"
@@ -410,6 +571,13 @@ def _runtime_dir_from_bin(runtime_bin: Path) -> Path | None:
     return None
 
 
+def _vllm_runtime_subprocess_cwd(runtime_command: list[str] | None = None) -> Path:
+    runtime_dir = (
+        _runtime_dir_from_bin(Path(runtime_command[0])) if runtime_command else None
+    )
+    return runtime_dir or get_vllm_runtime_working_dir()
+
+
 def _is_executable_file(path: Path) -> bool:
     return path.is_file() and os.access(path, os.X_OK)
 
@@ -423,7 +591,10 @@ def _sha256_file(path: Path) -> str:
 
 
 def _manifest_hash(manifest: VllmRuntimeManifest) -> str:
-    payload = json.dumps(manifest.model_dump(), sort_keys=True).encode()
+    payload = json.dumps(
+        {"manifest": manifest.model_dump(), "runtime_extra": MANAGED_RUNTIME_EXTRA},
+        sort_keys=True,
+    ).encode()
     return hashlib.sha256(payload).hexdigest()
 
 
@@ -533,6 +704,8 @@ def _validate_managed_runtime(
         return None
     if marker.runtime_wheel_sha256 != manifest.runtime_wheel_sha256:
         return None
+    if marker.runtime_extra != MANAGED_RUNTIME_EXTRA:
+        return None
     runtime_bin = _runtime_bin(runtime_dir)
     if not _is_executable_file(runtime_bin):
         return None
@@ -578,6 +751,8 @@ def _install_managed_runtime(
                 "sync",
                 "--project",
                 str(stage),
+                "--extra",
+                MANAGED_RUNTIME_EXTRA,
                 "--frozen",
                 "--no-install-project",
                 "--no-dev",
@@ -619,6 +794,7 @@ def _install_managed_runtime(
             protocol_version=manifest.protocol_version,
             manifest_hash=manifest_hash,
             runtime_wheel_sha256=manifest.runtime_wheel_sha256,
+            runtime_extra=MANAGED_RUNTIME_EXTRA,
             cache_root=str(cache_root.resolve()),
         )
         _install_marker_path(runtime_dir).write_text(
@@ -632,6 +808,7 @@ def _install_managed_runtime(
 
 
 def ensure_vllm_runtime() -> Path:
+    configure_model_cache_env()
     bundle_dir = _bundled_runtime_dir()
     manifest = _load_bundled_manifest(bundle_dir)
     manifest_hash = _manifest_hash(manifest)
@@ -658,67 +835,14 @@ def ensure_vllm_runtime() -> Path:
         )
 
 
-def _runtime_python_for_nccl_discovery() -> Path:
-    override = os.environ.get("ART_VLLM_RUNTIME_BIN")
-    if override:
-        runtime_bin = Path(shlex.split(override)[0]).expanduser().resolve()
-        runtime_dir = _runtime_dir_from_bin(runtime_bin)
-        if runtime_dir is None:
-            raise RuntimeError(
-                "Cannot infer vLLM runtime Python from ART_VLLM_RUNTIME_BIN. "
-                "Merged rollout weights require ART's source or managed vLLM runtime."
-            )
-        return _runtime_python(runtime_dir)
-
-    source_runtime_bin = _source_runtime_bin()
-    if source_runtime_bin.exists():
-        runtime_dir = _runtime_dir_from_bin(source_runtime_bin)
-        assert runtime_dir is not None
-        return _runtime_python(runtime_dir)
-
-    runtime_bin = ensure_vllm_runtime()
-    runtime_dir = _runtime_dir_from_bin(runtime_bin)
-    assert runtime_dir is not None
-    return _runtime_python(runtime_dir)
-
-
-def get_vllm_runtime_nccl_so_path() -> Path:
-    runtime_python = _runtime_python_for_nccl_discovery()
-    script = (
-        "from pathlib import Path\n"
-        "import importlib.util\n"
-        "spec = importlib.util.find_spec('nvidia.nccl')\n"
-        "if spec is None or spec.submodule_search_locations is None:\n"
-        "    raise SystemExit('vLLM runtime is missing nvidia-nccl-cu12')\n"
-        "package_dir = Path(next(iter(spec.submodule_search_locations)))\n"
-        "path = package_dir / 'lib' / 'libnccl.so.2'\n"
-        "if not path.exists():\n"
-        "    raise SystemExit(f'vLLM runtime is missing {path}')\n"
-        "print(path.resolve())\n"
-    )
-    result = subprocess.run(
-        [str(runtime_python), "-c", script],
-        capture_output=True,
-        text=True,
-    )
-    if result.returncode != 0:
-        output = (result.stdout + result.stderr)[-4000:]
-        raise RuntimeError(
-            "Failed to discover vLLM runtime NCCL library with "
-            f"{runtime_python}.\n{output}"
-        )
-    nccl_so_path = Path(result.stdout.strip()).resolve()
-    if not nccl_so_path.exists():
-        raise RuntimeError(
-            f"vLLM runtime reported a missing NCCL library: {nccl_so_path}"
-        )
-    return nccl_so_path
-
-
 def _runtime_command_prefix() -> list[str]:
     override = os.environ.get("ART_VLLM_RUNTIME_BIN")
     if override:
-        return shlex.split(override)
+        command = shlex.split(override)
+        runtime_dir = _runtime_dir_from_bin(Path(command[0]))
+        if runtime_dir is not None:
+            command[0] = str(_runtime_bin(runtime_dir))
+        return command
     runtime_bin = _source_runtime_bin()
     if runtime_bin.exists():
         return [str(runtime_bin)]
@@ -735,23 +859,47 @@ def _runtime_command_prefix() -> list[str]:
 
 
 def build_vllm_runtime_server_cmd(config: VllmRuntimeLaunchConfig) -> list[str]:
+    server_args = {
+        key: value for key, value in config.server_args.items() if key != "api_key"
+    }
     command = [
         *_runtime_command_prefix(),
         f"--model={config.base_model}",
         f"--port={config.port}",
         f"--host={config.host}",
-        f"--cuda-visible-devices={config.cuda_visible_devices}",
+        f"--cuda-visible-devices={config.visible_devices}",
     ]
     if config.lora_path is not None:
         command.append(f"--lora-path={config.lora_path}")
     command.extend(
         [
             f"--served-model-name={config.served_model_name}",
-            f"--rollout-weights-mode={config.rollout_weights_mode}",
             f"--engine-args-json={json.dumps(config.engine_args)}",
-            f"--server-args-json={json.dumps(config.server_args)}",
+            f"--server-args-json={json.dumps(server_args)}",
         ]
     )
+    if config.nnodes > 1:
+        command.extend(
+            [
+                f"--nnodes={config.nnodes}",
+                f"--node-rank={config.node_rank}",
+                f"--master-addr={config.master_addr}",
+                f"--master-port={config.master_port}",
+            ]
+        )
+        if config.headless:
+            command.append("--headless")
+    if config.process_uuid is not None:
+        command.extend(
+            [
+                f"--replica-generation={config.replica_generation}",
+                f"--process-uuid={config.process_uuid}",
+            ]
+        )
+    if config.update_identity is not None:
+        command.append(f"--update-identity={config.update_identity}")
+    if config.initial_policy_version is not None:
+        command.append(f"--initial-policy-version={config.initial_policy_version}")
     return command
 
 
@@ -761,15 +909,35 @@ async def wait_for_vllm_runtime(
     host: str,
     port: int,
     timeout: float,
+    log_path: str | None = None,
 ) -> None:
     deadline = asyncio.get_running_loop().time() + timeout
     url = f"http://{host}:{port}/health"
+    log_offset = 0
+    log_tail = ""
+    fatal_markers = (
+        "EngineCore failed to start",
+        "Engine core initialization failed",
+    )
     async with httpx.AsyncClient() as client:
         while True:
             if process.poll() is not None:
                 raise RuntimeError(
                     f"vLLM runtime exited with code {process.returncode}"
                 )
+            if log_path is not None:
+                try:
+                    with open(log_path, "rb") as log:
+                        log.seek(log_offset)
+                        payload = log.read()
+                        log_offset = log.tell()
+                except FileNotFoundError:
+                    payload = b""
+                log_tail = (log_tail + payload.decode(errors="replace"))[-8192:]
+                if marker := next(
+                    (marker for marker in fatal_markers if marker in log_tail), None
+                ):
+                    raise RuntimeError(f"vLLM reported fatal startup failure: {marker}")
             try:
                 response = await client.get(url, timeout=5.0)
                 if response.status_code == 200:
diff --git a/src/art/weight_transfer/__init__.py b/src/art/weight_transfer/__init__.py
deleted file mode 100644
index f8140bd78..000000000
--- a/src/art/weight_transfer/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-from .nccl import (
-    DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-    DEFAULT_PACKED_NUM_BUFFERS,
-    TrainerNcclCommunicator,
-    trainer_init,
-    trainer_send_weights,
-)
-
-__all__ = [
-    "DEFAULT_PACKED_BUFFER_SIZE_BYTES",
-    "DEFAULT_PACKED_NUM_BUFFERS",
-    "TrainerNcclCommunicator",
-    "trainer_init",
-    "trainer_send_weights",
-]
diff --git a/src/art/weight_transfer/nccl.py b/src/art/weight_transfer/nccl.py
deleted file mode 100644
index eb7adafb5..000000000
--- a/src/art/weight_transfer/nccl.py
+++ /dev/null
@@ -1,443 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Trainer-side NCCL transport subset extracted from vLLM."""
-
-import ctypes
-from datetime import timedelta
-import importlib.util
-import os
-from pathlib import Path
-import pickle
-import socket
-from typing import Any, cast
-
-from pydantic import BaseModel, ConfigDict
-import torch
-from torch.distributed import TCPStore
-
-from .packed_tensor import (
-    DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-    DEFAULT_PACKED_NUM_BUFFERS,
-    packed_broadcast_producer,
-)
-
-
-class TrainerNcclSendWeightsArgs(BaseModel):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    group: Any
-    src: int = 0
-    post_iter_func: Any = None
-    packed: bool = False
-    stream: Any = None
-    packed_buffer_size_bytes: int = DEFAULT_PACKED_BUFFER_SIZE_BYTES
-    packed_num_buffers: int = DEFAULT_PACKED_NUM_BUFFERS
-
-
-class _NcclUniqueId(ctypes.Structure):
-    _fields_ = [("internal", ctypes.c_byte * 128)]
-
-
-_nccl_result_t = ctypes.c_int
-_nccl_comm_t = ctypes.c_void_p
-_cuda_stream_t = ctypes.c_void_p
-_buffer_type = ctypes.c_void_p
-
-
-class _NcclDataType:
-    INT8 = 0
-    UINT8 = 1
-    INT32 = 2
-    INT64 = 4
-    FLOAT16 = 6
-    FLOAT32 = 7
-    FLOAT64 = 8
-    BFLOAT16 = 9
-
-    @classmethod
-    def from_torch(cls, dtype: torch.dtype) -> int:
-        if dtype == torch.int8:
-            return cls.INT8
-        if dtype == torch.uint8:
-            return cls.UINT8
-        if dtype == torch.int32:
-            return cls.INT32
-        if dtype == torch.int64:
-            return cls.INT64
-        if dtype == torch.float16:
-            return cls.FLOAT16
-        if dtype == torch.float32:
-            return cls.FLOAT32
-        if dtype == torch.float64:
-            return cls.FLOAT64
-        if dtype == torch.bfloat16:
-            return cls.BFLOAT16
-        raise ValueError(f"Unsupported NCCL dtype: {dtype}")
-
-
-class _NcclRedOp:
-    SUM = 0
-
-
-class _NcclLibrary:
-    def __init__(self, so_file: str | None = None):
-        self._lib = ctypes.CDLL(so_file or _find_nccl_library())
-        self._configure("ncclGetErrorString", ctypes.c_char_p, [_nccl_result_t])
-        self._configure(
-            "ncclGetUniqueId", _nccl_result_t, [ctypes.POINTER(_NcclUniqueId)]
-        )
-        self._configure(
-            "ncclCommInitRank",
-            _nccl_result_t,
-            [ctypes.POINTER(_nccl_comm_t), ctypes.c_int, _NcclUniqueId, ctypes.c_int],
-        )
-        self._configure("ncclCommDestroy", _nccl_result_t, [_nccl_comm_t])
-        self._configure("ncclCommAbort", _nccl_result_t, [_nccl_comm_t])
-        self._configure(
-            "ncclAllReduce",
-            _nccl_result_t,
-            [
-                _buffer_type,
-                _buffer_type,
-                ctypes.c_size_t,
-                ctypes.c_int,
-                ctypes.c_int,
-                _nccl_comm_t,
-                _cuda_stream_t,
-            ],
-        )
-        self._configure(
-            "ncclBroadcast",
-            _nccl_result_t,
-            [
-                _buffer_type,
-                _buffer_type,
-                ctypes.c_size_t,
-                ctypes.c_int,
-                ctypes.c_int,
-                _nccl_comm_t,
-                _cuda_stream_t,
-            ],
-        )
-
-    def _configure(self, name: str, restype: Any, argtypes: list[Any]) -> None:
-        function = getattr(self._lib, name)
-        function.restype = restype
-        function.argtypes = argtypes
-
-    def _check(self, result: int) -> None:
-        if result != 0:
-            error = self._lib.ncclGetErrorString(result).decode("utf-8")
-            raise RuntimeError(f"NCCL error: {error}")
-
-    def get_unique_id(self) -> _NcclUniqueId:
-        unique_id = _NcclUniqueId()
-        self._check(self._lib.ncclGetUniqueId(ctypes.byref(unique_id)))
-        return unique_id
-
-    def init_rank(self, world_size: int, unique_id: _NcclUniqueId, rank: int) -> Any:
-        comm = _nccl_comm_t()
-        self._check(
-            self._lib.ncclCommInitRank(ctypes.byref(comm), world_size, unique_id, rank)
-        )
-        return comm
-
-    def destroy_comm(self, comm: Any) -> None:
-        self._check(self._lib.ncclCommDestroy(comm))
-
-    def abort_comm(self, comm: Any) -> None:
-        self._check(self._lib.ncclCommAbort(comm))
-
-    def all_reduce(
-        self,
-        tensor: torch.Tensor,
-        comm: Any,
-        stream: torch.cuda.Stream,
-    ) -> None:
-        self._check(
-            self._lib.ncclAllReduce(
-                _buffer_type(tensor.data_ptr()),
-                _buffer_type(tensor.data_ptr()),
-                tensor.numel(),
-                _NcclDataType.from_torch(tensor.dtype),
-                _NcclRedOp.SUM,
-                comm,
-                _cuda_stream_t(stream.cuda_stream),
-            )
-        )
-
-    def broadcast(
-        self,
-        tensor: torch.Tensor,
-        comm: Any,
-        *,
-        rank: int,
-        src: int,
-        stream: torch.cuda.Stream,
-    ) -> None:
-        send_buffer = _buffer_type(tensor.data_ptr()) if rank == src else _buffer_type()
-        self._check(
-            self._lib.ncclBroadcast(
-                send_buffer,
-                _buffer_type(tensor.data_ptr()),
-                tensor.numel(),
-                _NcclDataType.from_torch(tensor.dtype),
-                src,
-                comm,
-                _cuda_stream_t(stream.cuda_stream),
-            )
-        )
-
-
-def _nccl_unique_id_to_bytes(unique_id: _NcclUniqueId) -> bytes:
-    return ctypes.string_at(ctypes.byref(unique_id), ctypes.sizeof(unique_id))
-
-
-def _nccl_unique_id_from_bytes(payload: bytes) -> _NcclUniqueId:
-    assert len(payload) == ctypes.sizeof(_NcclUniqueId)
-    unique_id = _NcclUniqueId()
-    ctypes.memmove(ctypes.byref(unique_id), payload, len(payload))
-    return unique_id
-
-
-class _BootstrapGroup:
-    def __init__(
-        self,
-        *,
-        host: str,
-        port: int,
-        rank: int,
-        world_size: int,
-        store_timeout: int = 300,
-    ) -> None:
-        launch_server = rank == 0
-        listen_socket = None
-        listen_fd = None
-        if launch_server:
-            listen_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-            listen_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-            listen_socket.bind((host, port))
-            listen_socket.listen()
-            listen_fd = listen_socket.fileno()
-        self.rank = rank
-        self.world_size = world_size
-        self.store: TCPStore | None = None
-        try:
-            self.store = TCPStore(
-                host_name=host,
-                port=port,
-                world_size=world_size,
-                is_master=launch_server,
-                timeout=timedelta(seconds=store_timeout),
-                use_libuv=False,
-                master_listen_fd=listen_fd,
-            )
-            if listen_socket is not None:
-                # TCPStore owns master_listen_fd after construction. Detach the
-                # Python socket so its close/finalizer cannot invalidate the
-                # store's listening fd while the bootstrap server is alive.
-                listen_socket.detach()
-                listen_socket = None
-        finally:
-            if listen_socket is not None:
-                listen_socket.close()
-        self._broadcast_send_counter = 0
-        self._broadcast_recv_counter = {value: 0 for value in range(world_size)}
-
-    def broadcast_obj(self, obj: Any | None, *, src: int) -> Any:
-        if self.store is None:
-            raise RuntimeError("NCCL bootstrap group is closed")
-        if self.rank == src:
-            key = f"broadcast_from/{src}/{self._broadcast_send_counter}"
-            self.store.set(key, cast(Any, pickle.dumps(obj)))
-            self._broadcast_send_counter += 1
-            return obj
-        key = f"broadcast_from/{src}/{self._broadcast_recv_counter[src]}"
-        received = pickle.loads(self.store.get(key))
-        self._broadcast_recv_counter[src] += 1
-        return received
-
-    def close(self) -> None:
-        self.store = None
-
-
-def _canonical_cuda_device(device: int | torch.device) -> torch.device:
-    cuda_device = torch.device(f"cuda:{device}") if isinstance(device, int) else device
-    if cuda_device.type != "cuda":
-        raise RuntimeError(f"NCCL weight transfer requires a CUDA device, got {device}")
-    if cuda_device.index is None:
-        return torch.device("cuda", torch.cuda.current_device())
-    return cuda_device
-
-
-class TrainerNcclCommunicator:
-    def __init__(
-        self,
-        *,
-        host: str,
-        port: int,
-        rank: int,
-        world_size: int,
-        device: int | torch.device,
-        nccl_so_path: str | None = None,
-    ) -> None:
-        self.device = _canonical_cuda_device(device)
-        bootstrap_group = _BootstrapGroup(
-            host=host,
-            port=port,
-            rank=rank,
-            world_size=world_size,
-        )
-        self._bootstrap_group = bootstrap_group
-        self.rank = rank
-        self.world_size = world_size
-        self._nccl = _NcclLibrary(nccl_so_path)
-        self._comm = None
-        unique_id_bytes = (
-            _nccl_unique_id_to_bytes(self._nccl.get_unique_id()) if rank == 0 else None
-        )
-        try:
-            unique_id = _nccl_unique_id_from_bytes(
-                bootstrap_group.broadcast_obj(unique_id_bytes, src=0)
-            )
-            with torch.cuda.device(self.device):
-                self._comm = self._nccl.init_rank(world_size, unique_id, rank)
-                stream = torch.cuda.current_stream(self.device)
-                warmup = torch.zeros(1, device=self.device)
-                self.all_reduce(warmup, stream=stream)
-                stream.synchronize()
-        finally:
-            self._close_bootstrap_group()
-
-    def _close_bootstrap_group(self) -> None:
-        bootstrap_group = self._bootstrap_group
-        self._bootstrap_group = None
-        if bootstrap_group is not None:
-            bootstrap_group.close()
-
-    def _require_comm(self) -> Any:
-        if self._comm is None:
-            raise RuntimeError("NCCL weight transfer communicator is closed")
-        return self._comm
-
-    def _validate_collective_tensor(self, tensor: torch.Tensor) -> None:
-        if not tensor.is_cuda:
-            raise RuntimeError(
-                f"NCCL weight transfer requires a CUDA tensor, got {tensor.device}"
-            )
-        if tensor.device != self.device:
-            raise RuntimeError(
-                "NCCL weight transfer tensor device mismatch: "
-                f"expected {self.device}, got {tensor.device}"
-            )
-        if not tensor.is_contiguous():
-            raise RuntimeError("NCCL weight transfer requires contiguous tensors")
-
-    def close(self) -> None:
-        comm = self._comm
-        if comm is None:
-            return
-        self._comm = None
-        try:
-            self._nccl.destroy_comm(comm)
-        finally:
-            self._close_bootstrap_group()
-
-    def abort(self) -> None:
-        comm = self._comm
-        if comm is None:
-            return
-        self._comm = None
-        try:
-            self._nccl.abort_comm(comm)
-        finally:
-            self._close_bootstrap_group()
-
-    def all_reduce(
-        self,
-        tensor: torch.Tensor,
-        *,
-        stream: torch.cuda.Stream | None = None,
-    ) -> None:
-        self._validate_collective_tensor(tensor)
-        self._nccl.all_reduce(
-            tensor,
-            self._require_comm(),
-            stream=stream or torch.cuda.current_stream(self.device),
-        )
-
-    def broadcast(
-        self,
-        tensor: torch.Tensor,
-        *,
-        src: int,
-        stream: torch.cuda.Stream | None = None,
-    ) -> None:
-        self._validate_collective_tensor(tensor)
-        self._nccl.broadcast(
-            tensor,
-            self._require_comm(),
-            rank=self.rank,
-            src=src,
-            stream=stream or torch.cuda.current_stream(self.device),
-        )
-
-
-def _find_nccl_library() -> str:
-    if override := os.environ.get("VLLM_NCCL_SO_PATH"):
-        return override
-    if torch.version.cuda is not None:
-        spec = importlib.util.find_spec("nvidia.nccl")
-        if spec is None or spec.submodule_search_locations is None:
-            raise RuntimeError(
-                "CUDA weight transfer requires the nvidia-nccl-cu12 package."
-            )
-        nccl_library = (
-            Path(next(iter(spec.submodule_search_locations))) / "lib" / "libnccl.so.2"
-        )
-        if not nccl_library.exists():
-            raise RuntimeError(f"nvidia-nccl-cu12 is missing {nccl_library}")
-        return str(nccl_library)
-    if torch.version.hip is not None:
-        return "librccl.so.1"
-    raise ValueError("NCCL only supports CUDA and ROCm backends.")
-
-
-def trainer_init(init_info: dict[str, object]) -> TrainerNcclCommunicator:
-    return TrainerNcclCommunicator(
-        host=str(init_info["master_address"]),
-        port=int(cast(Any, init_info["master_port"])),
-        rank=0,
-        world_size=int(cast(Any, init_info["world_size"])),
-        device=torch.cuda.current_device(),
-        nccl_so_path=cast(str | None, init_info.get("nccl_so_path")),
-    )
-
-
-def trainer_send_weights(
-    iterator: Any,
-    trainer_args: dict[str, Any] | TrainerNcclSendWeightsArgs,
-) -> None:
-    args = (
-        TrainerNcclSendWeightsArgs(**trainer_args)
-        if isinstance(trainer_args, dict)
-        else trainer_args
-    )
-    post_iter_func = args.post_iter_func or (lambda item: item[1])
-    if args.packed:
-        packed_broadcast_producer(
-            iterator=iterator,
-            group=args.group,
-            src=args.src,
-            post_iter_func=post_iter_func,
-            buffer_size_bytes=args.packed_buffer_size_bytes,
-            num_buffers=args.packed_num_buffers,
-        )
-        return
-    for item in iterator:
-        tensor = post_iter_func(item)
-        args.group.broadcast(
-            tensor,
-            src=args.src,
-            stream=args.stream or torch.cuda.current_stream(tensor.device),
-        )
diff --git a/src/art/weight_transfer/packed_tensor.py b/src/art/weight_transfer/packed_tensor.py
deleted file mode 100644
index c8bc41f8f..000000000
--- a/src/art/weight_transfer/packed_tensor.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Packed tensor utilities for efficient trainer-side weight transfer."""
-
-from collections.abc import Callable, Iterator
-import math
-from typing import Any
-
-import torch
-
-DEFAULT_PACKED_BUFFER_SIZE_BYTES = 1024 * 1024 * 1024
-DEFAULT_PACKED_NUM_BUFFERS = 2
-
-
-def packed_broadcast_producer(
-    iterator: Iterator[tuple[str, torch.Tensor]],
-    group: Any,
-    src: int,
-    post_iter_func: Callable[[tuple[str, torch.Tensor]], torch.Tensor],
-    buffer_size_bytes: int = DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-    num_buffers: int = DEFAULT_PACKED_NUM_BUFFERS,
-) -> None:
-    """Pack and broadcast tensors on side streams with stable ring buffers.
-
-    The caller owns producer-side ordering: source tensors must already be on the
-    active CUDA device, must not be mutated while this function may read them,
-    and any prior writer streams must be ordered before entry. Each ring-buffer
-    slot is synchronized before reuse, and the function returns only after every
-    side-stream broadcast has completed.
-    """
-    target_packed_tensor_size = buffer_size_bytes
-    streams = [torch.cuda.Stream() for _ in range(num_buffers)]
-    buffer_idx = 0
-    packing_tensor_list: list[list[torch.Tensor]] = [[] for _ in range(num_buffers)]
-    packing_tensor_sizes: list[int] = [0 for _ in range(num_buffers)]
-    packed_tensors: list[torch.Tensor] = [
-        torch.empty(0, dtype=torch.uint8, device="cuda") for _ in range(num_buffers)
-    ]
-
-    while True:
-        streams[buffer_idx].synchronize()
-        with torch.cuda.stream(streams[buffer_idx]):
-            try:
-                packing_tensor_list[buffer_idx] = []
-                packing_tensor_sizes[buffer_idx] = 0
-                while True:
-                    tensor = (
-                        post_iter_func(next(iterator))
-                        .contiguous()
-                        .view(torch.uint8)
-                        .view(-1)
-                    )
-                    packing_tensor_list[buffer_idx].append(tensor)
-                    packing_tensor_sizes[buffer_idx] += tensor.numel()
-                    if packing_tensor_sizes[buffer_idx] > target_packed_tensor_size:
-                        break
-                packed_tensors[buffer_idx] = torch.cat(
-                    packing_tensor_list[buffer_idx], dim=0
-                )
-                group.broadcast(packed_tensors[buffer_idx], src=src)
-                buffer_idx = (buffer_idx + 1) % num_buffers
-            except StopIteration:
-                if packing_tensor_list[buffer_idx]:
-                    packed_tensors[buffer_idx] = torch.cat(
-                        packing_tensor_list[buffer_idx], dim=0
-                    )
-                    group.broadcast(packed_tensors[buffer_idx], src=src)
-                break
-    for stream in streams:
-        stream.synchronize()
-
-
-def packed_broadcast_consumer(
-    iterator: Iterator[tuple[str, tuple[list[int], torch.dtype]]],
-    group: Any,
-    src: int,
-    post_unpack_func: Callable[[list[tuple[str, torch.Tensor]]], None],
-    buffer_size_bytes: int = DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-    num_buffers: int = DEFAULT_PACKED_NUM_BUFFERS,
-) -> None:
-    """Receive packed tensors on side streams and unpack views for a callback.
-
-    The tensors passed to ``post_unpack_func`` are backed by the current packed
-    receive buffer. The callback must copy into durable storage before returning
-    if it needs to keep them, and it must add its own stream waits or lifetime
-    recording if it launches consumers outside the active side stream.
-    """
-
-    def unpack_tensor(
-        packed_tensor: torch.Tensor,
-        names: list[str],
-        shapes: list[list[int]],
-        dtypes: list[torch.dtype],
-        tensor_sizes: list[int],
-    ) -> list[tuple[str, torch.Tensor]]:
-        unpacked_tensors = packed_tensor.split(tensor_sizes)
-        return [
-            (name, tensor.contiguous().view(dtype).view(*shape))
-            for name, shape, dtype, tensor in zip(
-                names, shapes, dtypes, unpacked_tensors
-            )
-        ]
-
-    target_packed_tensor_size = buffer_size_bytes
-    streams = [torch.cuda.Stream() for _ in range(num_buffers)]
-    buffer_idx = 0
-    packing_tensor_meta_data: list[list[tuple[str, list[int], torch.dtype, int]]] = [
-        [] for _ in range(num_buffers)
-    ]
-    packing_tensor_sizes: list[int] = [0 for _ in range(num_buffers)]
-    packed_tensors: list[torch.Tensor] = [
-        torch.empty(0, dtype=torch.uint8, device="cuda") for _ in range(num_buffers)
-    ]
-
-    while True:
-        streams[buffer_idx].synchronize()
-        with torch.cuda.stream(streams[buffer_idx]):
-            packing_tensor_meta_data[buffer_idx] = []
-            packing_tensor_sizes[buffer_idx] = 0
-            try:
-                while True:
-                    name, (shape, dtype) = next(iterator)
-                    tensor_size = math.prod(shape) * dtype.itemsize
-                    packing_tensor_meta_data[buffer_idx].append(
-                        (name, shape, dtype, tensor_size)
-                    )
-                    packing_tensor_sizes[buffer_idx] += tensor_size
-                    if packing_tensor_sizes[buffer_idx] > target_packed_tensor_size:
-                        break
-                packed_tensors[buffer_idx] = torch.empty(
-                    packing_tensor_sizes[buffer_idx], dtype=torch.uint8, device="cuda"
-                )
-                group.broadcast(packed_tensors[buffer_idx], src=src)
-                names, shapes, dtypes, tensor_sizes = zip(
-                    *packing_tensor_meta_data[buffer_idx]
-                )
-                post_unpack_func(
-                    unpack_tensor(
-                        packed_tensors[buffer_idx],
-                        list(names),
-                        list(shapes),
-                        list(dtypes),
-                        list(tensor_sizes),
-                    )
-                )
-                buffer_idx = (buffer_idx + 1) % num_buffers
-            except StopIteration:
-                if packing_tensor_meta_data[buffer_idx]:
-                    packed_tensors[buffer_idx] = torch.empty(
-                        packing_tensor_sizes[buffer_idx],
-                        dtype=torch.uint8,
-                        device="cuda",
-                    )
-                    group.broadcast(packed_tensors[buffer_idx], src=src)
-                    names, shapes, dtypes, tensor_sizes = zip(
-                        *packing_tensor_meta_data[buffer_idx]
-                    )
-                    post_unpack_func(
-                        unpack_tensor(
-                            packed_tensors[buffer_idx],
-                            list(names),
-                            list(shapes),
-                            list(dtypes),
-                            list(tensor_sizes),
-                        )
-                    )
-                break
diff --git a/tests/integration/distributed/test_model_service.py b/tests/integration/distributed/test_model_service.py
new file mode 100644
index 000000000..b49571086
--- /dev/null
+++ b/tests/integration/distributed/test_model_service.py
@@ -0,0 +1,299 @@
+from types import MethodType, SimpleNamespace
+from typing import Any, cast
+from unittest.mock import AsyncMock, Mock
+
+import pytest
+
+from art.distributed import art_runtime as runtime_module
+from art.distributed.art_runtime import ArtRuntime
+from art.distributed.specs import (
+    EndpointSpec,
+    ModelServiceMemberSpec,
+    ModelServiceSpec,
+    VllmParallelSpec,
+)
+from art.distributed.vllm_replica import ReplicaFailure, ReplicaState
+from art.local import checkpoints as checkpoints_module
+from art.megatron import distributed_service as service_module
+from art.megatron.backend import MegatronBackend
+from art.megatron.distributed_service import DistributedMegatronService
+from art.serving_capabilities import ART_SERVING_PROTOCOL_VERSION, ServingCapabilities
+
+
+def _spec() -> ModelServiceSpec:
+    return ModelServiceSpec(
+        name="model",
+        members=(
+            ModelServiceMemberSpec(
+                member_id="node0", host_id="host0", node_rank=0, gpu_ids=(0,)
+            ),
+        ),
+        leader_endpoint=EndpointSpec(host="10.0.0.1", port=8000),
+        rendezvous=EndpointSpec(host="10.0.0.1", port=29500),
+        base_model="base",
+        model_revision="revision",
+        runtime_fingerprint="runtime",
+        parallel=VllmParallelSpec(),
+    )
+
+
+def _service(tmp_path, runtime) -> DistributedMegatronService:
+    return DistributedMegatronService(
+        model_name="model",
+        base_model="base",
+        config={},
+        output_dir=str(tmp_path),
+        runtime=runtime,
+        enable_expert_replay=False,
+    )
+
+
+@pytest.mark.asyncio
+async def test_runtime_retains_model_service_until_stop_succeeds(monkeypatch) -> None:
+    spec = _spec()
+    manager = SimpleNamespace(
+        start=AsyncMock(side_effect=RuntimeError("start failed")),
+        stop=AsyncMock(side_effect=RuntimeError("stop failed")),
+    )
+    runtime = ArtRuntime.__new__(ArtRuntime)
+    runtime.topology = SimpleNamespace(
+        model_services=(spec,),
+        cluster=SimpleNamespace(startup_timeout_s=1, rpc_timeout_s=1),
+    )
+    runtime._host_services = {"host0": object()}
+    runtime._adapter_services = {"host0": object()}
+    runtime._model_services = {}
+    runtime._started, runtime._closed = True, False
+    runtime._preflight_launch = AsyncMock()
+    monkeypatch.setattr(
+        runtime_module, "MonarchVllmHostLauncher", lambda *_args: object()
+    )
+    monkeypatch.setattr(runtime_module, "ReplicaManager", lambda *_a, **_kw: manager)
+
+    with pytest.raises(RuntimeError, match="start failed"):
+        await runtime.start_model_service(spec, SimpleNamespace())
+    assert runtime.model_service("model") is manager
+
+    with pytest.raises(RuntimeError, match="stop failed"):
+        await runtime.stop_model_service("model")
+    assert runtime.model_service("model") is manager
+
+    manager.stop = AsyncMock(return_value="stopped")
+    assert await runtime.stop_model_service("model") == "stopped"
+    with pytest.raises(RuntimeError, match="not managed"):
+        runtime.model_service("model")
+
+
+@pytest.mark.asyncio
+async def test_failed_recovery_unpublishes_dead_endpoint(tmp_path) -> None:
+    failure = ReplicaFailure(
+        replica_id="model", generation=2, generation_digest="digest", reason="dead"
+    )
+    manager = SimpleNamespace(
+        state=ReplicaState(
+            replica_id="model",
+            generation=2,
+            generation_digest="digest",
+            phase="quarantined",
+        )
+    )
+    service = _service(
+        tmp_path,
+        SimpleNamespace(model_service=lambda _name: manager),
+    )
+    service._managed_service_name = "model"
+    service._base_url = "http://10.0.0.1:8000"
+    service._loaded_adapter_steps = {1, 2}
+    service._loaded_exact_adapter_steps = {1}
+    service._recover_replica_locked = AsyncMock(
+        side_effect=RuntimeError("restart failed")
+    )
+
+    await service._recover_failed_replica(failure)
+
+    assert service._managed_service_name == "model"
+    assert service._base_url is None
+    assert not service._loaded_adapter_steps
+    assert not service._loaded_exact_adapter_steps
+    with pytest.raises(RuntimeError, match="unavailable"):
+        await service.start_openai_server(None)
+
+
+@pytest.mark.asyncio
+async def test_recovery_rebuilds_loaded_adapter_index(monkeypatch, tmp_path) -> None:
+    spec = _spec()
+    ready = ReplicaState(
+        replica_id="model",
+        generation=3,
+        generation_digest="generation",
+        phase="ready",
+    )
+    manager = SimpleNamespace(
+        restart=AsyncMock(return_value=ready),
+        prepare_update=Mock(return_value=ready),
+        verify_update=Mock(return_value=ready),
+        quarantine=Mock(),
+        stop=AsyncMock(),
+    )
+    runtime = SimpleNamespace(
+        topology=SimpleNamespace(model_services=(spec,)),
+        model_service=lambda _name: manager,
+    )
+    service = _service(tmp_path, runtime)
+    capabilities = ServingCapabilities(
+        runtime="art_vllm",
+        protocol_version=ART_SERVING_PROTOCOL_VERSION,
+    )
+    service._latest_step = 5
+    service._serving_step = 5
+    service._managed_service_name = "model"
+    service._base_url = spec.leader_endpoint.url
+    service._serving_capabilities = capabilities
+    service._current_lora_name = "model@5"
+    service._loaded_adapter_steps = {1, 3, 5}
+    service._loaded_exact_adapter_steps = {2}
+    service._exact_adapter_refcounts = {2: 1}
+    service._published_adapters[5] = cast(
+        Any,
+        SimpleNamespace(
+            generation_id="policy",
+            identity=str(tmp_path / "checkpoints" / "0005"),
+        ),
+    )
+    service._load_adapter_at = AsyncMock(return_value=("model@2", "/step/2"))
+    monkeypatch.setattr(
+        service_module,
+        "discover_serving_capabilities",
+        AsyncMock(return_value=capabilities),
+    )
+
+    await service._recover_replica_locked(
+        ReplicaFailure(
+            replica_id="model",
+            generation=2,
+            generation_digest="old",
+            reason="dead",
+        )
+    )
+
+    assert service._loaded_adapter_steps == {5}
+    assert service._loaded_exact_adapter_steps == {2}
+    assert service._exact_adapter_refcounts == {2: 1}
+    service._load_adapter_at.assert_awaited_once()
+
+
+@pytest.mark.asyncio
+async def test_recovery_uses_serving_generation_while_learner_is_ahead(
+    monkeypatch, tmp_path
+) -> None:
+    spec = _spec()
+    ready = ReplicaState(
+        replica_id="model",
+        generation=4,
+        generation_digest="restarted",
+        phase="ready",
+    )
+    manager = SimpleNamespace(
+        restart=AsyncMock(return_value=ready),
+        prepare_update=Mock(return_value=ready),
+        verify_update=Mock(return_value=ready),
+        quarantine=Mock(),
+        stop=AsyncMock(),
+    )
+    service = _service(
+        tmp_path,
+        SimpleNamespace(
+            topology=SimpleNamespace(model_services=(spec,)),
+            model_service=lambda _name: manager,
+        ),
+    )
+    capabilities = ServingCapabilities(
+        runtime="art_vllm", protocol_version=ART_SERVING_PROTOCOL_VERSION
+    )
+    serving_path = str(tmp_path / "checkpoints" / "0005")
+    service._latest_step = 6
+    service._serving_step = 5
+    service._managed_service_name = "model"
+    service._base_url = spec.leader_endpoint.url
+    service._serving_capabilities = capabilities
+    service._current_lora_name = "model@5"
+    service._published_adapters[5] = cast(
+        Any,
+        SimpleNamespace(
+            generation_id="serving-generation",
+            identity=serving_path,
+        ),
+    )
+    monkeypatch.setattr(
+        service_module,
+        "discover_serving_capabilities",
+        AsyncMock(return_value=capabilities),
+    )
+
+    await service._recover_replica_locked(
+        ReplicaFailure(
+            replica_id="model",
+            generation=3,
+            generation_digest="failed",
+            reason="dead",
+        )
+    )
+
+    manager.restart.assert_awaited_once_with(
+        served_model_name="model@5",
+        lora_path=serving_path,
+        initial_policy_version=5,
+    )
+    report = manager.verify_update.call_args.args[0]
+    assert report.policy_version == "5"
+    assert report.policy_digest == "serving-generation"
+    assert service._latest_step == 6
+    assert service._serving_step == 5
+    assert service._loaded_adapter_steps == {5}
+
+
+@pytest.mark.asyncio
+async def test_retention_protects_absent_learner_and_serving_steps(
+    monkeypatch, tmp_path
+) -> None:
+    model = SimpleNamespace(
+        project="project", name="model", _storage_name=lambda: "model"
+    )
+    output_dir = tmp_path / "project" / "models" / "model"
+    service = _service(output_dir, SimpleNamespace())
+    service._latest_step = 3
+    service._serving_step = 2
+    service._loaded_adapter_steps = {1, 2, 3}
+    service._unload_adapter = AsyncMock()
+
+    await service.prune_loaded_adapters(retain_steps={3})
+    assert service._loaded_adapter_steps == {2, 3}
+    service._unload_adapter.assert_awaited_once_with("model@1")
+
+    checkpoints = output_dir / "checkpoints"
+    for step in (1, 2, 4):
+        (checkpoints / f"{step:04d}").mkdir(parents=True)
+    staging = output_dir / "staging-0003"
+    staging.mkdir()
+    original_delete = checkpoints_module.delete_checkpoints
+
+    def publish_during_retention(path: str, excluding: list[int]) -> None:
+        staging.rename(checkpoints / "0003")
+        original_delete(path, excluding)
+
+    monkeypatch.setattr(
+        checkpoints_module, "delete_checkpoints", publish_during_retention
+    )
+    backend = object.__new__(MegatronBackend)
+    backend._runtime = object()
+    backend._path = str(tmp_path)
+
+    async def get_service(_self, _model):
+        return service
+
+    backend._get_service = MethodType(get_service, backend)
+    await backend._delete_checkpoint_files(model, [1])
+    assert (checkpoints / "0001").is_dir()
+    assert (checkpoints / "0002").is_dir()
+    assert (checkpoints / "0003").is_dir()
+    assert not (checkpoints / "0004").exists()
diff --git a/tests/integration/distributed/test_public_api.py b/tests/integration/distributed/test_public_api.py
new file mode 100644
index 000000000..8ada2636c
--- /dev/null
+++ b/tests/integration/distributed/test_public_api.py
@@ -0,0 +1,110 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+import subprocess
+import sys
+
+import art
+from art.distributed import PackingRequest
+
+EXAMPLE_DIR = Path(__file__).parents[3] / "examples" / "multinode"
+
+
+def test_packing_request_from_public_groups() -> None:
+    model = art.TrainableModel(
+        name="packing-public-api",
+        project="test",
+        run_name="packing-public-api",
+        base_model="not-loaded",
+    )
+    group = art.TrajectoryGroup(
+        [
+            art.Trajectory(
+                messages_and_choices=[
+                    {"role": "user", "content": "Respond with maybe."},
+                    {"role": "assistant", "content": "maybe"},
+                ],
+                reward=1.0,
+                initial_policy_version=3,
+            )
+        ],
+        metadata={"split": "smoke"},
+    )
+
+    request = PackingRequest.from_groups(
+        model,
+        [group],
+        packed_sequence_length=128,
+        allow_training_without_logprobs=True,
+        group_ids=("maybe",),
+        min_source_version=3,
+        max_source_version=3,
+    )
+
+    assert request.model.build().base_model == "not-loaded"
+    assert request.trajectory_groups[0].build().model_dump(mode="json") == (
+        group.model_dump(mode="json")
+    )
+    assert request.group_ids == ("maybe",)
+    assert request.min_source_version == request.max_source_version == 3
+
+
+def test_distributed_package_import_is_lazy() -> None:
+    subprocess.run(
+        [
+            sys.executable,
+            "-c",
+            """
+import sys
+import art.distributed as distributed
+assert "art.distributed.art_runtime" not in sys.modules
+from art.distributed import (
+    ArtRuntime, ClusterSpec, NcclTransportSpec, PackingRequest, compile_topology,
+)
+assert all(value is not None for value in (
+    ArtRuntime, ClusterSpec, NcclTransportSpec, PackingRequest, compile_topology
+))
+assert "monarch" not in sys.modules
+assert "PackingRequest" in distributed.__all__
+assert "NcclTransportSpec" in distributed.__all__
+""",
+        ],
+        check=True,
+    )
+
+
+def test_documented_rollout_is_installed_and_bounded() -> None:
+    environment = os.environ.copy()
+    environment["PYTHONPATH"] = os.pathsep.join(
+        (str(EXAMPLE_DIR), environment.get("PYTHONPATH", ""))
+    )
+    subprocess.run(
+        [
+            sys.executable,
+            "-c",
+            """
+import asyncio
+import art
+from art.distributed import InstalledAsyncCallable
+import program
+
+async def check():
+    reference = InstalledAsyncCallable.from_callable(program.rollout)
+    assert (reference.module, reference.qualname) == ("program", "rollout")
+    model = art.TrainableModel(
+        name="documented-rollout",
+        project="test",
+        run_name="documented-rollout",
+        base_model="not-loaded",
+    )
+    trajectory = await program.rollout(model, "maybe", None)
+    assert trajectory.reward == 1.0
+    assert trajectory.metadata["answer"] == "maybe"
+
+asyncio.run(check())
+""",
+        ],
+        check=True,
+        env=environment,
+    )
diff --git a/tests/integration/distributed/test_replica_recovery.py b/tests/integration/distributed/test_replica_recovery.py
new file mode 100644
index 000000000..43b6a6c5f
--- /dev/null
+++ b/tests/integration/distributed/test_replica_recovery.py
@@ -0,0 +1,123 @@
+from typing import Any, cast
+
+import pytest
+
+from art.distributed.specs import (
+    EndpointSpec,
+    ModelServiceMemberSpec,
+    ModelServiceSpec,
+    VllmParallelSpec,
+)
+from art.distributed.vllm_replica import (
+    HostMemberState,
+    ReplicaFailure,
+    ReplicaLaunchTemplate,
+    ReplicaManager,
+)
+
+
+class Launcher:
+    def __init__(self, host_id: str, events: list[str]) -> None:
+        self.host_id = host_id
+        self.events = events
+        self.requests = []
+        self.states = {}
+        self.failed = False
+        self.stops = []
+
+    async def start_member(self, request):
+        self.requests.append(request)
+        state = HostMemberState(
+            replica_id=request.replica_id,
+            member_id=request.member.member_id,
+            generation=request.generation,
+            generation_digest=request.generation_digest,
+            process_uuid=request.process_uuid,
+            phase="ready",
+        )
+        self.states[
+            (request.replica_id, request.member.member_id, request.generation)
+        ] = state
+        return state
+
+    async def member_state(self, replica_id, member_id, generation):
+        state = self.states[(replica_id, member_id, generation)]
+        return state.model_copy(update={"phase": "failed"}) if self.failed else state
+
+    async def stop_member(self, replica_id, member_id, generation):
+        self.events.append(f"stop:{self.host_id}")
+        self.stops.append((replica_id, member_id, generation))
+
+
+def _spec() -> ModelServiceSpec:
+    return ModelServiceSpec(
+        name="model",
+        members=tuple(
+            ModelServiceMemberSpec(
+                member_id=f"node{rank}",
+                host_id=f"host{rank}",
+                node_rank=rank,
+                gpu_ids=(0, 1),
+            )
+            for rank in range(2)
+        ),
+        leader_endpoint=EndpointSpec(host="10.0.0.1", port=8000),
+        rendezvous=EndpointSpec(host="10.0.0.1", port=29500),
+        base_model="base",
+        model_revision="revision",
+        runtime_fingerprint="runtime",
+        parallel=VllmParallelSpec(tp=2, pp=2),
+    )
+
+
+@pytest.mark.asyncio
+async def test_failure_stops_whole_gang_before_callback_and_restarts_generation() -> (
+    None
+):
+    events: list[str] = []
+    launchers = {f"host{rank}": Launcher(f"host{rank}", events) for rank in range(2)}
+    failures: list[ReplicaFailure] = []
+
+    async def failed(event: ReplicaFailure) -> None:
+        events.append("callback")
+        failures.append(event)
+
+    manager = ReplicaManager(
+        _spec(),
+        cast(Any, launchers),
+        ReplicaLaunchTemplate(served_model_name="model@0", lora_path="/step/0000"),
+        on_failure=failed,
+        monitor_interval_s=60,
+    )
+    await manager.start()
+    launchers["host1"].failed = True
+
+    await manager.poll()
+
+    assert manager.state.phase == "quarantined"
+    assert set(events[:2]) == {"stop:host0", "stop:host1"}
+    assert events[2:] == ["callback"]
+    assert [(event.replica_id, event.generation) for event in failures] == [
+        ("model", 0)
+    ]
+
+    launchers["host1"].failed = False
+    restarted = await manager.restart(
+        served_model_name="model@1",
+        lora_path="/step/0001",
+        initial_policy_version=1,
+    )
+
+    assert restarted.phase == "ready"
+    assert restarted.generation == 1
+    assert restarted.generation_digest != failures[0].generation_digest
+    for launcher in launchers.values():
+        assert [request.launch_config.port for request in launcher.requests] == [
+            8000,
+            8000,
+        ]
+        assert [request.launch_config.master_port for request in launcher.requests] == [
+            29500,
+            29500,
+        ]
+    await manager.stop()
diff --git a/tests/integration/distributed/test_replica_update.py b/tests/integration/distributed/test_replica_update.py
new file mode 100644
index 000000000..ee0e12b78
--- /dev/null
+++ b/tests/integration/distributed/test_replica_update.py
@@ -0,0 +1,166 @@
+from __future__ import annotations
+
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any, cast
+
+import pytest
+
+from art.distributed.specs import (
+    EndpointSpec,
+    ModelServiceMemberSpec,
+    ModelServiceSpec,
+    VllmParallelSpec,
+)
+from art.distributed.vllm_replica import (
+    HostMemberState,
+    ReplicaLaunchTemplate,
+    ReplicaManager,
+    ReplicaState,
+)
+from art.megatron import distributed_service as service_module
+from art.megatron.distributed_service import DistributedMegatronService
+
+
+def manager(*, engine_args: dict[str, object] | None = None) -> ReplicaManager:
+    members = tuple(
+        ModelServiceMemberSpec(
+            member_id=f"node{rank}",
+            host_id=f"host{rank}",
+            node_rank=rank,
+            gpu_ids=(0, 1),
+        )
+        for rank in range(2)
+    )
+    spec = ModelServiceSpec(
+        name="model",
+        members=members,
+        leader_endpoint=EndpointSpec(host="10.0.0.1", port=8000),
+        rendezvous=EndpointSpec(host="10.0.0.1", port=29500),
+        base_model="base",
+        model_revision="revision",
+        runtime_fingerprint="runtime",
+        parallel=VllmParallelSpec(tp=1, pp=2, dp=2, enable_expert_parallel=True),
+    )
+    value = ReplicaManager(
+        spec,
+        cast(
+            Any,
+            {"host0": SimpleNamespace(), "host1": SimpleNamespace()},
+        ),
+        ReplicaLaunchTemplate(
+            served_model_name="model@1", engine_args=engine_args or {}
+        ),
+    )
+    value._state = ReplicaState(
+        replica_id="model",
+        generation=0,
+        generation_digest=value.state.generation_digest,
+        phase="ready",
+        members=tuple(
+            HostMemberState(
+                replica_id="model",
+                member_id=member.member_id,
+                generation=0,
+                generation_digest=value.state.generation_digest,
+                process_uuid=f"process-{member.node_rank}",
+                phase="ready",
+            )
+            for member in reversed(members)
+        ),
+    )
+    return value
+
+
+@pytest.mark.asyncio
+async def test_in_flight_update_is_the_only_acknowledgement_call(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    value = manager()
+    runtime = SimpleNamespace(
+        topology=SimpleNamespace(cluster=SimpleNamespace(rpc_timeout_s=5)),
+        model_service=lambda name: value if name == "model" else None,
+    )
+    service = DistributedMegatronService(
+        model_name="model",
+        base_model="base",
+        config={"rollout_weight_update_mode": "in_flight_lora"},
+        output_dir=str(tmp_path),
+        runtime=cast(Any, runtime),
+        enable_expert_replay=False,
+    )
+    calls: list[tuple[str, dict[str, Any], dict[str, str] | None]] = []
+
+    class Response:
+        def raise_for_status(self) -> None:
+            return None
+
+    class Client:
+        def __init__(self, **_kwargs: Any) -> None:
+            pass
+
+        async def __aenter__(self) -> Client:
+            return self
+
+        async def __aexit__(self, *_args: Any) -> None:
+            return None
+
+        async def post(self, url: str, *, json: dict[str, Any], headers):
+            calls.append((url, json, headers))
+            return Response()
+
+    monkeypatch.setattr(service_module.httpx, "AsyncClient", Client)
+    service._latest_step = 1
+    service._serving_step = 0
+    service._base_url = "http://leader.test:8000"
+    service._api_key_value = "secret"
+    name, path = await service._load_adapter("/step/0001", 1)
+
+    assert (name, path) == ("model:active", "/step/0001")
+    assert len(calls) == 1
+    assert calls[0][0] == "http://leader.test:8000/art/in_flight_lora_update"
+    assert calls[0][1]["policy_version"] == 1
+    assert calls[0][2] == {"Authorization": "Bearer secret"}
+
+
+def test_launch_preserves_user_args_and_owns_native_gang_topology() -> None:
+    value = manager(
+        engine_args={
+            "enable_prefix_caching": False,
+            "block_size": 32,
+            "prefill_context_parallel_size": 2,
+        }
+    )
+    leader = value._launch_request(value.spec.members[0]).launch_config
+    follower = value._launch_request(value.spec.members[1]).launch_config
+
+    assert leader.engine_args == {
+        "enable_prefix_caching": False,
+        "block_size": 32,
+        "prefill_context_parallel_size": 2,
+        "revision": "revision",
+        "tokenizer_revision": "revision",
+        "tensor_parallel_size": 1,
+        "pipeline_parallel_size": 2,
+        "data_parallel_size": 2,
+        "enable_expert_parallel": True,
+    }
+    assert leader.host == "10.0.0.1" and not leader.headless
+    assert follower.host == "127.0.0.1" and follower.headless
+    assert leader.nnodes == follower.nnodes == 2
+    assert "kv_events_config" not in leader.engine_args
+
+
+def test_conflicting_untyped_revision_is_rejected() -> None:
+    value = manager()
+    with pytest.raises(ValueError, match="revision conflicts"):
+        ReplicaManager(
+            value.spec,
+            cast(
+                Any,
+                {"host0": SimpleNamespace(), "host1": SimpleNamespace()},
+            ),
+            ReplicaLaunchTemplate(
+                served_model_name="model@1", engine_args={"revision": "other"}
+            ),
+        )
diff --git a/tests/integration/distributed/test_trajectory_queue.py b/tests/integration/distributed/test_trajectory_queue.py
new file mode 100644
index 000000000..a18dcd557
--- /dev/null
+++ b/tests/integration/distributed/test_trajectory_queue.py
@@ -0,0 +1,223 @@
+import asyncio
+from collections.abc import Callable
+from unittest.mock import AsyncMock
+
+import pytest
+
+from art.distributed.rollout import (
+    DistributedTrajectoryQueue,
+    DistributedTrajectorySelection,
+    _InProcessTrajectoryQueueEndpoint,
+)
+from art.distributed.trajectory_store import (
+    TrajectoryCapacityError,
+    TrajectoryEnqueueResult,
+    TrajectoryGroupAnnotations,
+    TrajectoryGroupDescriptor,
+    TrajectoryGroupRef,
+    TrajectoryQueueItem,
+    TrajectoryRecordRef,
+)
+
+
+def _item(
+    result_id: str, *, records: int = 1, byte_count: int = 1
+) -> TrajectoryQueueItem:
+    return TrajectoryQueueItem(
+        ref=TrajectoryGroupRef(
+            result_id=result_id,
+            owner_actor_id="owner",
+            lease_id=f"lease-{result_id}",
+            records=tuple(
+                TrajectoryRecordRef(
+                    record_id=f"{result_id}-{index}",
+                    owner_actor_id="owner",
+                    byte_count=1,
+                )
+                for index in range(records)
+            ),
+            descriptor=TrajectoryGroupDescriptor(
+                grouping_key=result_id,
+                trajectory_count=records,
+                exception_count=0,
+                rewards=(0.0,) * records,
+                initial_policy_versions=(0,) * records,
+                completion_tokens=(1.0,) * records,
+                policy_token_counts={},
+                trajectory_initial_policy_versions=(0,) * records,
+                trajectory_final_policy_versions=(0,) * records,
+                trajectory_policy_token_counts=({},) * records,
+                trajectory_metrics=({},) * records,
+                trajectory_metadata=({},) * records,
+                group_metadata={},
+                group_metrics={},
+                exceptions=(),
+                byte_count=byte_count,
+            ),
+        ),
+        annotations=TrajectoryGroupAnnotations(
+            initial_policy_version=0,
+            final_policy_version=0,
+        ),
+    )
+
+
+async def _put(queue: DistributedTrajectoryQueue, item: TrajectoryQueueItem) -> bool:
+    accepted, _ = await queue.put(
+        item.ref,
+        metadata={},
+        initial_policy_version=0,
+        final_policy_version=0,
+        rollout_wall_s=0.0,
+        actor_idle_s=0.0,
+    )
+    return accepted
+
+
+async def _wait_until(condition: Callable[[], bool]) -> None:
+    for _ in range(100):
+        if condition():
+            return
+        await asyncio.sleep(0)
+    raise AssertionError("condition was not reached")
+
+
+class _ObservedQueueEndpoint(_InProcessTrajectoryQueueEndpoint):
+    def __init__(self) -> None:
+        super().__init__()
+        self.enqueue_results: list[TrajectoryEnqueueResult] = []
+
+    async def enqueue(
+        self, queue_id: str, item: TrajectoryQueueItem
+    ) -> TrajectoryEnqueueResult:
+        result = await super().enqueue(queue_id, item)
+        self.enqueue_results.append(result)
+        return result
+
+
+@pytest.mark.asyncio
+async def test_packing_occupancy_backpressures_until_lease_release() -> None:
+    endpoint = _ObservedQueueEndpoint()
+    queue = DistributedTrajectoryQueue(
+        endpoint=endpoint,
+        owner_endpoints={"owner": AsyncMock()},
+        maxsize=6,
+        capacity_records=8,
+        capacity_bytes=8,
+    )
+    await queue.start()
+    for index in range(6):
+        assert await _put(queue, _item(f"initial-{index}"))
+
+    groups, closed = await queue.get_many(6, wait=True)
+    assert len(groups) == 6
+    assert not closed
+    snapshot = await queue.snapshot()
+    assert (
+        snapshot.ready_groups,
+        snapshot.packing_groups,
+        snapshot.packed_groups,
+        len(snapshot.items),
+        snapshot.max_ready_groups,
+    ) == (0, 6, 0, 6, 6)
+
+    pending_take = asyncio.create_task(queue.get_many(2, wait=True))
+    await _wait_until(lambda: queue._minimum_take_size == 2)
+    blocked_put = asyncio.create_task(_put(queue, _item("blocked")))
+    await _wait_until(lambda: len(endpoint.enqueue_results) == 7)
+    assert endpoint.enqueue_results[-1].status == "full"
+    await asyncio.sleep(0)
+    assert not blocked_put.done()
+
+    selections = []
+    for group in groups:
+        selection = group._distributed_lease
+        assert isinstance(selection, DistributedTrajectorySelection)
+        selections.append(selection)
+    await queue.mark_packed(selections, "generation")
+    await queue.release_selections(
+        selections,
+        disposition="consumed",
+        generation_id="generation",
+    )
+    assert await blocked_put
+    assert await _put(queue, _item("unblocks-minimum"))
+
+    acquired, closed = await pending_take
+    assert len(acquired) == 2
+    assert not closed
+    for group in acquired:
+        await queue.discard_group(group)
+    await queue.close()
+
+
+@pytest.mark.parametrize(
+    ("capacity_records", "capacity_bytes", "blocker"),
+    ((1, 8, "record capacity"), (8, 1, "byte capacity")),
+)
+@pytest.mark.asyncio
+async def test_ready_occupancy_makes_limit_failure_sticky(
+    capacity_records: int, capacity_bytes: int, blocker: str
+) -> None:
+    queue = DistributedTrajectoryQueue(
+        endpoint=_InProcessTrajectoryQueueEndpoint(),
+        owner_endpoints={"owner": AsyncMock()},
+        maxsize=6,
+        capacity_records=capacity_records,
+        capacity_bytes=capacity_bytes,
+    )
+    await queue.start()
+    assert await _put(queue, _item("ready"))
+    pending_take = asyncio.create_task(queue.get_many(2, wait=True))
+    await _wait_until(lambda: queue._minimum_take_size == 2)
+
+    with pytest.raises(TrajectoryCapacityError) as blocked_error:
+        await _put(queue, _item("blocked"))
+    with pytest.raises(TrajectoryCapacityError) as take_error:
+        await pending_take
+    with pytest.raises(TrajectoryCapacityError) as sticky_error:
+        await _put(queue, _item("later"))
+    assert blocker in str(blocked_error.value)
+    assert str(take_error.value) == str(blocked_error.value)
+    assert str(sticky_error.value) == str(blocked_error.value)
+    await queue.close()
+
+
+@pytest.mark.asyncio
+async def test_minimum_larger_than_group_capacity_fails_promptly() -> None:
+    queue = DistributedTrajectoryQueue(
+        endpoint=_InProcessTrajectoryQueueEndpoint(),
+        owner_endpoints={},
+        maxsize=6,
+        capacity_records=8,
+        capacity_bytes=8,
+    )
+    await queue.start()
+    with pytest.raises(
+        TrajectoryCapacityError,
+        match="minimum acquisition requires 7 trajectory groups",
+    ):
+        await queue.get_many(7, wait=True)
+    await queue.close()
+
+
+@pytest.mark.asyncio
+async def test_pending_minimum_defers_shrink_until_cancelled() -> None:
+    queue = DistributedTrajectoryQueue(
+        endpoint=_InProcessTrajectoryQueueEndpoint(),
+        owner_endpoints={},
+        maxsize=6,
+        capacity_records=8,
+        capacity_bytes=8,
+    )
+    await queue.start()
+    pending_take = asyncio.create_task(queue.get_many(2, wait=True))
+    await _wait_until(lambda: queue._minimum_take_size == 2)
+
+    queue.set_maxsize(1)
+    assert (await queue.snapshot()).max_ready_groups == 2
+    pending_take.cancel()
+    with pytest.raises(asyncio.CancelledError):
+        await pending_take
+    assert (await queue.snapshot()).max_ready_groups == 1
+    await queue.close()
diff --git a/tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_train_prepare.py b/tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_train_prepare.py
index 4a0622c6c..dc4e399a1 100644
--- a/tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_train_prepare.py
+++ b/tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_train_prepare.py
@@ -25,6 +25,7 @@
     ParallelTopology,
 )
 from art.megatron.gdn.gdn_prefix_tree import GdnPlannerConfig  # noqa: E402
+from art.megatron.selective_lm_head import LmHeadTokenSelection  # noqa: E402
 from art.preprocessing.pack import PackedTensors  # noqa: E402
 
 from .cases import default_phase0_cases  # noqa: E402
@@ -171,9 +172,14 @@ def test_main_loss_matches_shifted_dispatched_loss_inputs() -> None:
     )
     ref_logprobs = torch.tensor([[-0.9, -0.7, -0.6, -0.8, -0.55, -0.5]])
     entropies = torch.tensor([[0.0, 0.2, 0.4, 0.6, 0.8, 0.0]])
+    dispatched_labels = torch.where(
+        shift_tensor(packed["assistant_mask"], False),
+        shift_tensor(packed["tokens"], -100),
+        torch.full_like(packed["tokens"], -100),
+    )
     dispatched = DispatchedPackedTensors(
         tokens=packed["tokens"],
-        labels=shift_tensor(packed["tokens"], -100),
+        labels=dispatched_labels,
         input_pos=packed["input_pos"],
         assistant_mask=shift_tensor(packed["assistant_mask"], False),
         group_ids=shift_tensor(packed["group_ids"], 0),
@@ -181,6 +187,7 @@ def test_main_loss_matches_shifted_dispatched_loss_inputs() -> None:
         advantages=shift_tensor(packed["advantages"], 0.0),
         weights=shift_tensor(packed["weights"], 0.0),
         valid_lengths=(6,),
+        lm_head_selection=LmHeadTokenSelection.from_labels(dispatched_labels),
         original_logprobs=shift_tensor(packed["original_logprobs"], 0.0),
         ref_logprobs=ref_logprobs,
     )
diff --git a/tests/integration/megatron/lora/merged_vllm_serving.py b/tests/integration/megatron/lora/merged_vllm_serving.py
deleted file mode 100644
index 6909ca461..000000000
--- a/tests/integration/megatron/lora/merged_vllm_serving.py
+++ /dev/null
@@ -1,225 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-from contextlib import contextmanager
-import os
-from pathlib import Path
-import socket
-from typing import Any, Iterator, cast
-
-from pydantic import BaseModel, Field
-import torch
-
-import art
-from art import dev
-from art.megatron.service import MegatronService
-
-from ..model_support.oracle_harness import (
-    ORACLE_TOPOLOGY,
-    OracleCaseConfig,
-    Topology,
-    ensure_case_artifacts,
-)
-from ..model_support.oracle_worker import provider_topology_env
-from ..model_support.workflow_resources import (
-    handler_workflow_resources_for_base_model,
-    resolve_stage_resources_for_visible_gpus,
-    validate_dedicated_test_resources,
-)
-
-_TRAINER_GPU_IDS_ENV = "ART_MODEL_SUPPORT_TRAINER_GPU_IDS"
-_INFERENCE_GPU_IDS_ENV = "ART_MODEL_SUPPORT_INFERENCE_GPU_IDS"
-
-
-class MergedVllmServingReport(BaseModel):
-    base_model: str
-    output_dir: str
-    host: str
-    port: int
-    trainer_gpu_ids: list[int]
-    inference_gpu_ids: list[int]
-    served_model_name: str
-    model_ids: list[str] = Field(default_factory=list)
-    completion_text: str = ""
-
-
-def _find_free_port() -> int:
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
-        sock.bind(("127.0.0.1", 0))
-        return int(sock.getsockname()[1])
-
-
-def _parse_gpu_id_env(name: str) -> list[int] | None:
-    raw = os.environ.get(name)
-    if raw is None or raw.strip() == "":
-        return None
-    return [int(part.strip()) for part in raw.split(",") if part.strip()]
-
-
-def _resolve_dedicated_gpu_ids() -> tuple[list[int], list[int]]:
-    trainer_gpu_ids = _parse_gpu_id_env(_TRAINER_GPU_IDS_ENV)
-    inference_gpu_ids = _parse_gpu_id_env(_INFERENCE_GPU_IDS_ENV)
-    if trainer_gpu_ids is not None or inference_gpu_ids is not None:
-        if trainer_gpu_ids is None or inference_gpu_ids is None:
-            raise RuntimeError(
-                f"{_TRAINER_GPU_IDS_ENV} and {_INFERENCE_GPU_IDS_ENV} must both be set"
-            )
-        return trainer_gpu_ids, inference_gpu_ids
-
-    visible_gpu_count = int(torch.cuda.device_count())
-    if visible_gpu_count < 2:
-        raise RuntimeError(
-            f"Need at least 2 visible GPUs for merged serving, found {visible_gpu_count}"
-        )
-    return [0], [1]
-
-
-@contextmanager
-def _temporary_env(updates: dict[str, str]) -> Iterator[None]:
-    previous = {name: os.environ.get(name) for name in updates}
-    os.environ.update(updates)
-    try:
-        yield
-    finally:
-        for name, value in previous.items():
-            if value is None:
-                os.environ.pop(name, None)
-            else:
-                os.environ[name] = value
-
-
-def _init_runtime_config(case_config: OracleCaseConfig, topology: Any) -> None:
-    art.init_megatron_runtime_config(
-        topology=art.MegatronTopologyConfig(
-            tp=topology.tp,
-            cp=topology.cp,
-            ep=topology.ep,
-            pp=topology.pp,
-            etp=topology.etp,
-        ),
-        packed_sequence_length=case_config.packed_tensors.sequence_length,
-    )
-
-
-async def _run_merged_vllm_serving(
-    case_config: OracleCaseConfig,
-) -> MergedVllmServingReport:
-    workflow_resources = handler_workflow_resources_for_base_model(
-        case_config.base_model,
-        allow_unvalidated_arch=case_config.allow_unvalidated_arch,
-    )
-    stage_resources = (
-        workflow_resources.merged_vllm_serving
-        if workflow_resources is not None
-        else None
-    )
-    topology: Topology = ORACLE_TOPOLOGY
-    megatron_env: dict[str, str] = {}
-    engine_args: dev.EngineArgs = dev.EngineArgs()
-    if stage_resources is not None:
-        stage_resources = resolve_stage_resources_for_visible_gpus(
-            "merged_vllm_serving",
-            stage_resources,
-            visible_gpu_count=int(torch.cuda.device_count()),
-        )
-        if stage_resources.megatron is None or stage_resources.vllm is None:
-            raise RuntimeError(
-                "merged_vllm_serving resources require Megatron and vLLM"
-            )
-        trainer_gpu_ids = list(stage_resources.megatron.gpu_ids)
-        inference_gpu_ids = list(stage_resources.vllm.gpu_ids)
-        validate_dedicated_test_resources(
-            stage_name="merged_vllm_serving",
-            trainer_gpu_ids=trainer_gpu_ids,
-            inference_gpu_ids=inference_gpu_ids,
-            allow_overlap=stage_resources.allow_gpu_overlap,
-        )
-        resource_topology = stage_resources.megatron.topology
-        topology = Topology(
-            tp=resource_topology.tp,
-            ep=resource_topology.ep,
-            etp=resource_topology.etp,
-            dp=resource_topology.dp,
-            cp=resource_topology.cp,
-            pp=resource_topology.pp,
-            sp=resource_topology.sp,
-        )
-        megatron_env = dict(stage_resources.megatron_env)
-        engine_args = cast(dev.EngineArgs, stage_resources.vllm.engine_args())
-    else:
-        trainer_gpu_ids, inference_gpu_ids = _resolve_dedicated_gpu_ids()
-    service_name = "model_support_merged_validation"
-    case_artifacts = ensure_case_artifacts(case_config)
-    output_dir = str(Path(case_artifacts.case_dir) / "merged_vllm_serving")
-    os.makedirs(output_dir, exist_ok=True)
-    internal_config = dev.InternalModelConfig(
-        trainer_gpu_ids=trainer_gpu_ids,
-        inference_gpu_ids=inference_gpu_ids,
-        rollout_weights_mode="merged",
-        allow_unvalidated_arch=case_config.allow_unvalidated_arch,
-        engine_args=engine_args,
-    )
-    if stage_resources is None:
-        dev.validate_dedicated_config(internal_config)
-    with _temporary_env(megatron_env), provider_topology_env(topology):
-        _init_runtime_config(case_config, topology)
-        service = MegatronService(
-            model_name=service_name,
-            base_model=case_config.base_model,
-            config=internal_config,
-            output_dir=output_dir,
-        )
-        port = _find_free_port()
-        try:
-            host, resolved_port = await service.start_openai_server(
-                {"server_args": {"port": port}}
-            )
-            import httpx
-
-            async with httpx.AsyncClient() as client:
-                models_response = await client.get(
-                    f"http://{host}:{resolved_port}/v1/models",
-                    timeout=60.0,
-                )
-                models_response.raise_for_status()
-                model_ids = [
-                    str(model_info["id"])
-                    for model_info in models_response.json().get("data", [])
-                    if isinstance(model_info, dict) and "id" in model_info
-                ]
-
-                served_model_name = f"{service_name}@{service._latest_step}"
-                completion_response = await client.post(
-                    f"http://{host}:{resolved_port}/v1/completions",
-                    json={
-                        "model": served_model_name,
-                        "prompt": "Hello",
-                        "max_tokens": 1,
-                        "temperature": 0.0,
-                    },
-                    timeout=900.0,
-                )
-                completion_response.raise_for_status()
-                completion_json = completion_response.json()
-                completion_text = str(
-                    completion_json.get("choices", [{}])[0].get("text", "")
-                )
-            return MergedVllmServingReport(
-                base_model=case_config.base_model,
-                output_dir=output_dir,
-                host=host,
-                port=resolved_port,
-                trainer_gpu_ids=trainer_gpu_ids,
-                inference_gpu_ids=inference_gpu_ids,
-                served_model_name=served_model_name,
-                model_ids=model_ids,
-                completion_text=completion_text,
-            )
-        finally:
-            service.close()
-
-
-def run_merged_vllm_serving(
-    case_config: OracleCaseConfig,
-) -> MergedVllmServingReport:
-    return asyncio.run(_run_merged_vllm_serving(case_config))
diff --git a/tests/integration/megatron/lora/native_vllm_lora.py b/tests/integration/megatron/lora/native_vllm_lora.py
deleted file mode 100644
index d0040e860..000000000
--- a/tests/integration/megatron/lora/native_vllm_lora.py
+++ /dev/null
@@ -1,231 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import os
-from pathlib import Path
-import shutil
-import socket
-import tempfile
-from typing import cast
-
-from pydantic import BaseModel, Field
-import torch
-
-import art
-from art import dev
-from art.megatron.service import MegatronService
-from art.utils.output_dirs import get_step_checkpoint_dir
-
-from ..model_support.oracle_harness import (
-    ORACLE_TOPOLOGY,
-    OracleCaseConfig,
-    ensure_case_artifacts,
-)
-from ..model_support.oracle_worker import provider_topology_env
-from ..model_support.workflow_resources import (
-    handler_workflow_resources_for_base_model,
-    resolve_stage_resources_for_visible_gpus,
-    validate_dedicated_test_resources,
-)
-
-_TRAINER_GPU_IDS_ENV = "ART_MODEL_SUPPORT_TRAINER_GPU_IDS"
-_INFERENCE_GPU_IDS_ENV = "ART_MODEL_SUPPORT_INFERENCE_GPU_IDS"
-
-
-class NativeVllmLoraServingReport(BaseModel):
-    base_model: str
-    output_dir: str
-    host: str
-    port: int
-    trainer_gpu_ids: list[int]
-    inference_gpu_ids: list[int]
-    rollout_weights_mode: str = "lora"
-    step0_name: str
-    step1_name: str
-    model_ids_before: list[str] = Field(default_factory=list)
-    model_ids_after: list[str] = Field(default_factory=list)
-    step0_served: bool
-    step1_served: bool
-    step0_completion_text: str = ""
-    step1_completion_text: str = ""
-
-
-def _find_free_port() -> int:
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
-        sock.bind(("127.0.0.1", 0))
-        return int(sock.getsockname()[1])
-
-
-def _parse_gpu_id_env(name: str) -> list[int] | None:
-    raw = os.environ.get(name)
-    if raw is None or raw.strip() == "":
-        return None
-    return [int(part.strip()) for part in raw.split(",") if part.strip()]
-
-
-def _resolve_dedicated_gpu_ids() -> tuple[list[int], list[int]]:
-    trainer_gpu_ids = _parse_gpu_id_env(_TRAINER_GPU_IDS_ENV)
-    inference_gpu_ids = _parse_gpu_id_env(_INFERENCE_GPU_IDS_ENV)
-    if trainer_gpu_ids is not None or inference_gpu_ids is not None:
-        if trainer_gpu_ids is None or inference_gpu_ids is None:
-            raise RuntimeError(
-                f"{_TRAINER_GPU_IDS_ENV} and {_INFERENCE_GPU_IDS_ENV} must both be set"
-            )
-        return trainer_gpu_ids, inference_gpu_ids
-
-    visible_gpu_count = int(torch.cuda.device_count())
-    if visible_gpu_count < 2:
-        raise RuntimeError(
-            f"Need at least 2 visible GPUs for native LoRA serving, found {visible_gpu_count}"
-        )
-    return [0], [1]
-
-
-async def _model_ids(client, base_url: str) -> list[str]:
-    response = await client.get(f"{base_url}/v1/models", timeout=60.0)
-    response.raise_for_status()
-    return [
-        str(model_info["id"])
-        for model_info in response.json().get("data", [])
-        if isinstance(model_info, dict) and "id" in model_info
-    ]
-
-
-async def _completion_text(client, base_url: str, model_name: str) -> str:
-    response = await client.post(
-        f"{base_url}/v1/completions",
-        json={
-            "model": model_name,
-            "prompt": "Hello",
-            "max_tokens": 1,
-            "temperature": 0.0,
-        },
-        timeout=900.0,
-    )
-    response.raise_for_status()
-    return str(response.json().get("choices", [{}])[0].get("text", ""))
-
-
-def _copy_adapter_checkpoint(source_dir: str, dest_dir: str) -> None:
-    os.makedirs(dest_dir, exist_ok=True)
-    for filename in ("adapter_model.safetensors", "adapter_config.json"):
-        shutil.copy(Path(source_dir) / filename, Path(dest_dir) / filename)
-
-
-def _init_runtime_config(case_config: OracleCaseConfig) -> None:
-    art.init_megatron_runtime_config(
-        topology=art.MegatronTopologyConfig(
-            tp=ORACLE_TOPOLOGY.tp,
-            cp=ORACLE_TOPOLOGY.cp,
-            ep=ORACLE_TOPOLOGY.ep,
-            pp=ORACLE_TOPOLOGY.pp,
-            etp=ORACLE_TOPOLOGY.etp,
-        ),
-        packed_sequence_length=case_config.packed_tensors.sequence_length,
-    )
-
-
-async def _run_native_vllm_lora(
-    case_config: OracleCaseConfig,
-) -> NativeVllmLoraServingReport:
-    workflow_resources = handler_workflow_resources_for_base_model(
-        case_config.base_model,
-        allow_unvalidated_arch=case_config.allow_unvalidated_arch,
-    )
-    stage_resources = (
-        workflow_resources.native_vllm_lora if workflow_resources is not None else None
-    )
-    if stage_resources is not None:
-        stage_resources = resolve_stage_resources_for_visible_gpus(
-            "native_vllm_lora",
-            stage_resources,
-            visible_gpu_count=int(torch.cuda.device_count()),
-        )
-        if stage_resources.vllm is None:
-            raise RuntimeError("native_vllm_lora resources require vLLM")
-        trainer_gpu_ids = [0]
-        inference_gpu_ids = list(stage_resources.vllm.gpu_ids)
-        validate_dedicated_test_resources(
-            stage_name="native_vllm_lora",
-            trainer_gpu_ids=trainer_gpu_ids,
-            inference_gpu_ids=inference_gpu_ids,
-            allow_overlap=True,
-        )
-        engine_args = cast(dev.EngineArgs, stage_resources.vllm.engine_args())
-    else:
-        trainer_gpu_ids, inference_gpu_ids = _resolve_dedicated_gpu_ids()
-        engine_args = dev.EngineArgs()
-    service_name = "model_support_native_lora_validation"
-    case_artifacts = ensure_case_artifacts(case_config)
-    output_root = Path(case_artifacts.case_dir) / "native_vllm_lora"
-    output_root.mkdir(parents=True, exist_ok=True)
-    output_dir = tempfile.mkdtemp(prefix="run_", dir=output_root)
-    internal_config = dev.InternalModelConfig(
-        trainer_gpu_ids=trainer_gpu_ids,
-        inference_gpu_ids=inference_gpu_ids,
-        rollout_weights_mode="lora",
-        allow_unvalidated_arch=case_config.allow_unvalidated_arch,
-        engine_args=engine_args,
-    )
-    if stage_resources is None:
-        dev.validate_dedicated_config(internal_config)
-    with provider_topology_env(ORACLE_TOPOLOGY):
-        _init_runtime_config(case_config)
-        service = MegatronService(
-            model_name=service_name,
-            base_model=case_config.base_model,
-            config=internal_config,
-            output_dir=output_dir,
-        )
-        port = _find_free_port()
-        try:
-            host, resolved_port = await service.start_openai_server(
-                {"server_args": {"port": port}}
-            )
-            import httpx
-
-            base_url = f"http://{host}:{resolved_port}"
-            step0_name = f"{service_name}@0"
-            step1_name = f"{service_name}@1"
-            async with httpx.AsyncClient() as client:
-                model_ids_before = await _model_ids(client, base_url)
-                step0_completion_text = await _completion_text(
-                    client,
-                    base_url,
-                    step0_name,
-                )
-                step0_dir = get_step_checkpoint_dir(output_dir, 0)
-                step1_dir = get_step_checkpoint_dir(output_dir, 1)
-                _copy_adapter_checkpoint(step0_dir, step1_dir)
-                await service.register_lora_for_step(1, step1_dir)
-                model_ids_after = await _model_ids(client, base_url)
-                step1_completion_text = await _completion_text(
-                    client,
-                    base_url,
-                    step1_name,
-                )
-
-            return NativeVllmLoraServingReport(
-                base_model=case_config.base_model,
-                output_dir=output_dir,
-                host=host,
-                port=resolved_port,
-                trainer_gpu_ids=trainer_gpu_ids,
-                inference_gpu_ids=inference_gpu_ids,
-                step0_name=step0_name,
-                step1_name=step1_name,
-                model_ids_before=model_ids_before,
-                model_ids_after=model_ids_after,
-                step0_served=True,
-                step1_served=True,
-                step0_completion_text=step0_completion_text,
-                step1_completion_text=step1_completion_text,
-            )
-        finally:
-            service.close()
-
-
-def run_native_vllm_lora(
-    case_config: OracleCaseConfig,
-) -> NativeVllmLoraServingReport:
-    return asyncio.run(_run_native_vllm_lora(case_config))
diff --git a/tests/integration/megatron/lora/test_dynamic_lora_slots.py b/tests/integration/megatron/lora/test_dynamic_lora_slots.py
index 71b37e1ec..70059b43a 100644
--- a/tests/integration/megatron/lora/test_dynamic_lora_slots.py
+++ b/tests/integration/megatron/lora/test_dynamic_lora_slots.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from collections.abc import Sequence
 from contextlib import contextmanager
 import os
 from pathlib import Path
@@ -596,11 +597,7 @@ def _trainer_for(lora: LoRA, device: torch.device) -> TrainerRank:
     trainer.runtime = SimpleNamespace(
         model=[lora],
         optimizer=None,
-        model_support_handler=SimpleNamespace(
-            canonicalize_loaded_lora_state=lambda state, _model: state,
-            zero_internal_padding_grads=lambda _model: None,
-            zero_internal_padding_params=lambda _model: None,
-        ),
+        model_support_handler=_IdentityModelSupportHandler(),
     )
     trainer.device = device
     trainer._slot_stack = []
@@ -616,6 +613,21 @@ def _trainer_for(lora: LoRA, device: torch.device) -> TrainerRank:
     return trainer
 
 
+class _IdentityModelSupportHandler:
+    def zero_internal_padding_grads(
+        self, model_chunks: Sequence[torch.nn.Module]
+    ) -> None:
+        del model_chunks
+
+    def canonicalize_loaded_lora_state(
+        self,
+        state: dict[str, torch.Tensor],
+        model_chunks: Sequence[torch.nn.Module],
+    ) -> dict[str, torch.Tensor]:
+        del model_chunks
+        return state
+
+
 @contextmanager
 def _single_rank_model_parallel():
     os.environ.setdefault("MASTER_ADDR", "127.0.0.1")
diff --git a/tests/integration/megatron/lora/test_lora_disk_codecs.py b/tests/integration/megatron/lora/test_lora_disk_codecs.py
index 92606d6a8..d3b379af3 100644
--- a/tests/integration/megatron/lora/test_lora_disk_codecs.py
+++ b/tests/integration/megatron/lora/test_lora_disk_codecs.py
@@ -1,10 +1,8 @@
-import importlib.util
 import json
 import os
 from pathlib import Path
 import shutil
 import subprocess
-import sys
 from types import SimpleNamespace
 from typing import Any, cast
 
@@ -154,37 +152,6 @@ def _save_adapter(path: Path, tensors: dict[str, torch.Tensor], config: dict) ->
     (path / "adapter_config.json").write_text(json.dumps(config), encoding="utf-8")
 
 
-def _old_merge_shard_files_to_vllm(
-    lora_path: Path,
-    *,
-    handler,
-    adapter_config: dict,
-) -> None:
-    entries_by_key: dict[str, list[tuple[dict, torch.Tensor]]] = {}
-    shard_paths = sorted(lora_path.glob("adapter_model-*-of-*.safetensors"))
-    manifest_paths = sorted(lora_path.glob("adapter_manifest-*-of-*.json"))
-    for shard_path in shard_paths:
-        suffix = shard_path.name.removeprefix("adapter_model-").removesuffix(
-            ".safetensors"
-        )
-        manifest = json.loads(
-            (lora_path / f"adapter_manifest-{suffix}.json").read_text()
-        )
-        shard_tensors = load_file(shard_path)
-        assert set(shard_tensors) == set(manifest)
-        for key, tensor in shard_tensors.items():
-            entries_by_key.setdefault(key, []).append((manifest[key], tensor))
-
-    merged = merge_sharded_adapter_entries(entries_by_key)
-    vllm_tensors, adapter_config = handler.to_vllm_lora_tensors(
-        merged,
-        adapter_config=adapter_config,
-    )
-    save_vllm_lora_tensors(lora_path, vllm_tensors, adapter_config)
-    for path in [*shard_paths, *manifest_paths]:
-        path.unlink()
-
-
 def _assert_stock_vllm_loads(
     path: Path,
     *,
@@ -891,6 +858,25 @@ def test_dsv4_vllm_canonical_moe_roundtrip(tmp_path: Path) -> None:
     assert "model.layers.4.attn.compressor.wgate" in loaded_modules
     assert "model.layers.4.attn.compressor.wkv" in loaded_modules
 
+    packed_art = {
+        key.replace(".ffn.experts", ".mlp.experts"): tensor
+        for key, tensor in vllm_tensors.items()
+        if ".ffn.experts" in key
+    }
+    reexported, _ = DSV4_HANDLER.to_vllm_lora_tensors(
+        packed_art,
+        adapter_config=config,
+    )
+    _assert_tensors_equal(
+        reexported,
+        {key: tensor for key, tensor in vllm_tensors.items() if ".ffn.experts" in key},
+    )
+    assert all(
+        reexported[key].data_ptr()
+        == packed_art[key.replace(".ffn.experts", ".mlp.experts")].data_ptr()
+        for key in reexported
+    )
+
 
 def test_gemma4_shared_experts_plural_keys_map_to_vllm_dense_mlp(tmp_path: Path):
     art_prefix = "base_model.model.model.layers.0"
@@ -1305,7 +1291,6 @@ def sharded(rank_id: int, dim: int) -> dict:
         handler=QWEN3_5_MOE_HANDLER,
     )
     _assert_tensors_equal(roundtrip, full)
-    final_config = json.loads((adapter_dir / "adapter_config.json").read_text())
     loaded_modules = _assert_stock_vllm_loads(
         adapter_dir,
         expected_modules={"experts"},
@@ -1452,117 +1437,6 @@ def test_lora_publish_planner_maps_expert_owner_ranks(monkeypatch):
     assert LoRAPublishPlanner._expert_owner_rank(ep_rank=3, shard_rank=1) == 7
 
 
-def test_batched_lora_publish_matches_old_shard_merge_exactly(tmp_path: Path):
-    uniform_key = "base_model.model.model.layers.0.self_attn.q_proj.lora_B.weight"
-    componentwise_key = (
-        "base_model.model.model.layers.0.mlp.experts.gate_up_proj.lora_B.weight"
-    )
-    unsharded_key = "base_model.model.model.layers.0.self_attn.q_proj.lora_A.weight"
-    full_uniform = torch.arange(8, dtype=torch.float32).reshape(4, 2)
-    full_componentwise = torch.tensor(
-        [[0.0], [1.0], [10.0], [11.0], [2.0], [3.0], [12.0], [13.0]]
-    )
-    shard0 = {
-        unsharded_key: torch.arange(4, dtype=torch.float32).reshape(2, 2) + 100,
-        uniform_key: full_uniform[:2],
-        componentwise_key: torch.tensor([[0.0], [1.0], [2.0], [3.0]]),
-    }
-    shard1 = {
-        uniform_key: full_uniform[2:],
-        componentwise_key: torch.tensor([[10.0], [11.0], [12.0], [13.0]]),
-    }
-    unsharded_manifest = {"sharded": False, "shard_world_size": 1, "shard_rank": 0}
-    uniform_manifest = {
-        "sharded": True,
-        "shard_world_size": 2,
-        "export_shard_dim": 0,
-        "export_shard_strategy": "uniform",
-    }
-    componentwise_manifest = {
-        "sharded": True,
-        "shard_world_size": 2,
-        "export_shard_dim": 0,
-        "export_shard_strategy": "componentwise",
-        "component_sizes": [4, 4],
-    }
-    manifest0 = {
-        unsharded_key: unsharded_manifest,
-        uniform_key: {**uniform_manifest, "shard_rank": 0},
-        componentwise_key: {**componentwise_manifest, "shard_rank": 0},
-    }
-    manifest1 = {
-        uniform_key: {**uniform_manifest, "shard_rank": 1},
-        componentwise_key: {**componentwise_manifest, "shard_rank": 1},
-    }
-
-    class IdentityHandler:
-        def to_vllm_lora_tensors(self, tensors, *, adapter_config):
-            return dict(tensors), dict(adapter_config)
-
-    old_dir = tmp_path / "old"
-    current_dir = tmp_path / "current"
-    old_dir.mkdir()
-    save_file(shard0, old_dir / "adapter_model-01-of-02.safetensors")
-    save_file(shard1, old_dir / "adapter_model-02-of-02.safetensors")
-    (old_dir / "adapter_manifest-01-of-02.json").write_text(
-        json.dumps(manifest0, sort_keys=True)
-    )
-    (old_dir / "adapter_manifest-02-of-02.json").write_text(
-        json.dumps(manifest1, sort_keys=True)
-    )
-    adapter_config = _config("Qwen/Qwen3-30B-A3B")
-    handler = IdentityHandler()
-    _old_merge_shard_files_to_vllm(
-        old_dir,
-        handler=handler,
-        adapter_config=adapter_config,
-    )
-
-    metadata = [
-        LoraShardMeta(
-            key=key,
-            owner_rank=0,
-            shape=tuple(tensor.shape),
-            dtype_name=str(tensor.dtype).removeprefix("torch."),
-            manifest=manifest0[key],
-            block="base_model.model.model.layers.0",
-        )
-        for key, tensor in shard0.items()
-    ] + [
-        LoraShardMeta(
-            key=key,
-            owner_rank=1,
-            shape=tuple(tensor.shape),
-            dtype_name=str(tensor.dtype).removeprefix("torch."),
-            manifest=manifest1[key],
-            block="base_model.model.model.layers.0",
-        )
-        for key, tensor in shard1.items()
-    ]
-    lora_publish._save_rank0_vllm_lora(
-        metadata=metadata,
-        tensors_by_owner_key={
-            **{(0, key): tensor for key, tensor in shard0.items()},
-            **{(1, key): tensor for key, tensor in shard1.items()},
-        },
-        handler=handler,
-        adapter_config=adapter_config,
-        output_dir=str(current_dir),
-    )
-
-    old_tensors = load_file(old_dir / "adapter_model.safetensors")
-    current_tensors = load_file(current_dir / "adapter_model.safetensors")
-    _assert_tensors_equal(current_tensors, old_tensors)
-    assert torch.equal(current_tensors[uniform_key], full_uniform)
-    assert torch.equal(current_tensors[componentwise_key], full_componentwise)
-    assert (current_dir / "adapter_model.safetensors").read_bytes() == (
-        old_dir / "adapter_model.safetensors"
-    ).read_bytes()
-    assert json.loads((current_dir / "adapter_config.json").read_text()) == json.loads(
-        (old_dir / "adapter_config.json").read_text()
-    )
-
-
 def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path):
     prefix = "base_model.model.model.layers.0.mlp.experts.0"
     full = {
@@ -1587,7 +1461,7 @@ def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path)
         out_features=8,
         rank=1,
         alpha=1,
-        dtype=torch.float32,
+        dtype=torch.bfloat16,
         device=torch.device("cpu"),
     )
     gate_up_lora.A_T.data.copy_(full[f"{prefix}.gate_up_proj.lora_A.weight"].T)
@@ -1598,7 +1472,7 @@ def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path)
         out_features=2,
         rank=1,
         alpha=1,
-        dtype=torch.float32,
+        dtype=torch.bfloat16,
         device=torch.device("cpu"),
     )
     down_lora.A_T.data.copy_(full[f"{prefix}.down_proj.lora_A.weight"].T)
@@ -1607,7 +1481,7 @@ def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path)
     publish_dir = tmp_path / "published_from_model"
     save_vllm_lora_from_model(
         model=cast(Any, [torch.nn.Sequential(gate_up_lora, down_lora)]),
-        adapter_dtypes={key: tensor.dtype for key, tensor in full.items()},
+        adapter_dtypes={},
         handler=QWEN3_5_MOE_HANDLER,
         adapter_config=_config("Qwen/Qwen3.5-35B-A3B", rank=1, alpha=1),
         output_dir=str(publish_dir),
@@ -1620,7 +1494,10 @@ def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path)
         str(publish_dir),
         handler=QWEN3_5_MOE_HANDLER,
     )
-    _assert_tensors_equal(roundtrip, full)
+    _assert_tensors_equal(
+        roundtrip,
+        {key: tensor.bfloat16() for key, tensor in full.items()},
+    )
 
 
 def test_trainer_rank_publishes_named_checkpoint_slot_without_mutating_base(
@@ -1665,6 +1542,7 @@ def test_trainer_rank_publishes_named_checkpoint_slot_without_mutating_base(
 @pytest.mark.parametrize(
     ("handler", "base_model"),
     (
+        (QWEN3_MOE_HANDLER, "Qwen/Qwen3-30B-A3B-Instruct-2507"),
         (QWEN3_5_MOE_HANDLER, "Qwen/Qwen3.5-35B-A3B"),
         (DSV4_HANDLER, "deepseek-ai/DeepSeek-V4-Flash"),
     ),
@@ -1685,41 +1563,33 @@ def test_direct_3d_packed_expert_publish_matches_handler_vllm_exactly(
     intermediate = 4
     group_prefix = "base_model.model.model.layers.0.mlp.experts"
     full: dict[str, torch.Tensor] = {}
-    gate_up_lora = LoRA(
-        adapter_model_prefix=f"{group_prefix}.{{expert}}.gate_up_proj",
-        in_features=hidden,
-        out_features=2 * intermediate,
-        rank=rank,
-        alpha=rank,
-        dtype=torch.float32,
-        device=torch.device("cpu"),
-        num_local_experts=2,
-    )
-    down_lora = LoRA(
-        adapter_model_prefix=f"{group_prefix}.{{expert}}.down_proj",
-        in_features=intermediate,
-        out_features=hidden,
-        rank=rank,
-        alpha=rank,
-        dtype=torch.float32,
-        device=torch.device("cpu"),
-        num_local_experts=2,
-    )
+    projection_loras = {
+        projection: LoRA(
+            adapter_model_prefix=f"{group_prefix}.{{expert}}.{projection}",
+            in_features=hidden if projection != "down_proj" else intermediate,
+            out_features=(
+                hidden
+                if projection == "down_proj"
+                else 2 * intermediate
+                if projection == "gate_up_proj"
+                else intermediate
+            ),
+            rank=rank,
+            alpha=rank,
+            dtype=torch.float32,
+            device=torch.device("cpu"),
+            num_local_experts=2,
+        )
+        for projection in (
+            ("gate_proj", "up_proj", "down_proj")
+            if handler is QWEN3_MOE_HANDLER
+            else ("gate_up_proj", "down_proj")
+        )
+    }
     offset = 0
     for expert in range(2):
         expert_prefix = f"{group_prefix}.{expert}"
         tensors = {
-            "gate_up_proj.lora_A.weight": torch.arange(
-                rank * hidden,
-                dtype=torch.float32,
-            ).reshape(rank, hidden)
-            + offset,
-            "gate_up_proj.lora_B.weight": torch.arange(
-                2 * intermediate * rank,
-                dtype=torch.float32,
-            ).reshape(2 * intermediate, rank)
-            + offset
-            + 100,
             "down_proj.lora_A.weight": torch.arange(
                 rank * intermediate,
                 dtype=torch.float32,
@@ -1733,20 +1603,36 @@ def test_direct_3d_packed_expert_publish_matches_handler_vllm_exactly(
             + offset
             + 300,
         }
+        for projection_index, projection in enumerate(
+            ("gate_proj", "up_proj")
+            if handler is QWEN3_MOE_HANDLER
+            else ("gate_up_proj",)
+        ):
+            output = intermediate if handler is QWEN3_MOE_HANDLER else 2 * intermediate
+            tensors[f"{projection}.lora_A.weight"] = (
+                torch.arange(rank * hidden, dtype=torch.float32).reshape(rank, hidden)
+                + offset
+                + projection_index * 10
+            )
+            tensors[f"{projection}.lora_B.weight"] = (
+                torch.arange(output * rank, dtype=torch.float32).reshape(output, rank)
+                + offset
+                + 100
+                + projection_index * 10
+            )
         for suffix, tensor in tensors.items():
             full[f"{expert_prefix}.{suffix}"] = tensor
-        gate_up_lora.A_T.data[expert].copy_(tensors["gate_up_proj.lora_A.weight"].T)
-        gate_up_lora.B_T.data[expert].copy_(tensors["gate_up_proj.lora_B.weight"].T)
-        down_lora.A_T.data[expert].copy_(tensors["down_proj.lora_A.weight"].T)
-        down_lora.B_T.data[expert].copy_(tensors["down_proj.lora_B.weight"].T)
+        for projection, lora in projection_loras.items():
+            lora.A_T.data[expert].copy_(tensors[f"{projection}.lora_A.weight"].T)
+            lora.B_T.data[expert].copy_(tensors[f"{projection}.lora_B.weight"].T)
         offset += 1000
 
     slot_ref = LoRASlotRef("checkpoint", "student") if dynamic_slot else None
     if slot_ref is not None:
-        assert gate_up_lora.load_lora_slot(
-            slot_ref, full, alpha=rank, requires_grad=True
+        assert all(
+            lora.load_lora_slot(slot_ref, full, alpha=rank, requires_grad=True)
+            for lora in projection_loras.values()
         )
-        assert down_lora.load_lora_slot(slot_ref, full, alpha=rank, requires_grad=True)
 
     adapter_config = _config(base_model, rank=rank, alpha=rank)
     old_dir = tmp_path / "old"
@@ -1757,7 +1643,7 @@ def test_direct_3d_packed_expert_publish_matches_handler_vllm_exactly(
     )
     save_vllm_lora_tensors(old_dir, old_tensors, old_config)
     save_vllm_lora_from_model(
-        model=cast(Any, [torch.nn.Sequential(gate_up_lora, down_lora)]),
+        model=cast(Any, [torch.nn.Sequential(*projection_loras.values())]),
         adapter_dtypes={key: tensor.dtype for key, tensor in full.items()},
         handler=handler,
         adapter_config=dict(adapter_config),
diff --git a/tests/integration/megatron/lora/test_merged_weight_export.py b/tests/integration/megatron/lora/test_merged_weight_export.py
deleted file mode 100644
index fc95cfa42..000000000
--- a/tests/integration/megatron/lora/test_merged_weight_export.py
+++ /dev/null
@@ -1,282 +0,0 @@
-from types import SimpleNamespace
-from typing import Any, cast
-
-import httpx
-import torch
-
-from art.megatron.runtime.jobs import (
-    MergedWeightTransferInitInfo,
-    MergedWeightTransferSpec,
-)
-import art.megatron.weights.merged_weight_export as export
-
-
-def _spec() -> MergedWeightTransferSpec:
-    return MergedWeightTransferSpec(
-        init_info=MergedWeightTransferInitInfo(
-            master_address="127.0.0.1",
-            master_port=23456,
-            rank_offset=1,
-            world_size=3,
-        ),
-        vllm_base_url="http://runtime.test",
-        served_model_name="model@7",
-        nccl_so_path="/runtime/libnccl.so.2",
-    )
-
-
-class _OkResponse:
-    def raise_for_status(self) -> None:
-        return None
-
-
-def test_ensure_merged_weight_transfer_group_rank_zero_initializes_runtime_and_trainer(
-    monkeypatch,
-) -> None:
-    spec = _spec()
-    calls: list[tuple[str, object]] = []
-
-    def fake_trainer_init(init_info: dict[str, object]) -> str:
-        calls.append(("trainer_init", init_info))
-        return "trainer-group"
-
-    def fake_post(url: str, *, json: dict[str, object], timeout: float) -> _OkResponse:
-        calls.append(("post", (url, json, timeout)))
-        return _OkResponse()
-
-    monkeypatch.setattr(export, "trainer_init", fake_trainer_init)
-    monkeypatch.setattr(httpx, "post", fake_post)
-    monkeypatch.setattr(export, "_maybe_distributed_barrier", lambda world_size: None)
-
-    group, init_info = export.ensure_merged_weight_transfer_group(
-        rank=0,
-        world_size=2,
-        merged_weight_transfer_group=None,
-        merged_weight_transfer_init_info=None,
-        spec=spec,
-    )
-
-    assert group == "trainer-group"
-    assert init_info == spec.init_info
-    assert sorted(calls, key=lambda item: item[0]) == [
-        (
-            "post",
-            (
-                "http://runtime.test/init_weight_transfer_engine",
-                {"init_info": spec.init_info.model_dump()},
-                300.0,
-            ),
-        ),
-        (
-            "trainer_init",
-            {
-                "master_address": "127.0.0.1",
-                "master_port": 23456,
-                "world_size": 3,
-                "nccl_so_path": "/runtime/libnccl.so.2",
-            },
-        ),
-    ]
-
-
-def test_ensure_merged_weight_transfer_group_non_sender_skips_runtime_init(
-    monkeypatch,
-) -> None:
-    spec = _spec()
-    barriers: list[int] = []
-
-    monkeypatch.setattr(
-        export,
-        "trainer_init",
-        lambda init_info: (_ for _ in ()).throw(
-            AssertionError("unexpected trainer_init")
-        ),
-    )
-    monkeypatch.setattr(
-        httpx,
-        "post",
-        lambda *args, **kwargs: (_ for _ in ()).throw(
-            AssertionError("unexpected post")
-        ),
-    )
-    monkeypatch.setattr(export, "_maybe_distributed_barrier", barriers.append)
-
-    group, init_info = export.ensure_merged_weight_transfer_group(
-        rank=1,
-        world_size=2,
-        merged_weight_transfer_group=None,
-        merged_weight_transfer_init_info=None,
-        spec=spec,
-    )
-
-    assert group is None
-    assert init_info == spec.init_info
-    assert barriers == []
-
-
-def test_sync_merged_weights_to_vllm_non_sender_only_builds_lora_payload(
-    monkeypatch,
-) -> None:
-    spec = _spec()
-    barrier_calls: list[int] = []
-    build_ranks: list[int] = []
-
-    monkeypatch.setattr(
-        export,
-        "ensure_merged_weight_transfer_group",
-        lambda **kwargs: (None, spec.init_info),
-    )
-    monkeypatch.setattr(
-        export,
-        "build_vllm_lora_tensors_from_model",
-        lambda **kwargs: build_ranks.append(kwargs["rank"]) or None,
-    )
-
-    monkeypatch.setattr(export, "_maybe_distributed_barrier", barrier_calls.append)
-    monkeypatch.setattr(torch.cuda, "synchronize", lambda: None)
-    monkeypatch.setattr(
-        export,
-        "trainer_send_weights",
-        lambda *args, **kwargs: (_ for _ in ()).throw(
-            AssertionError("unexpected send")
-        ),
-    )
-    monkeypatch.setattr(
-        httpx,
-        "Client",
-        lambda: (_ for _ in ()).throw(AssertionError("unexpected http client")),
-    )
-
-    group, init_info = export.sync_merged_weights_to_vllm(
-        bridge=object(),
-        model=cast(Any, object()),
-        model_support_handler=object(),
-        adapter_model={},
-        adapter_config={},
-        rank=1,
-        world_size=2,
-        merged_weight_transfer_group=None,
-        merged_weight_transfer_init_info=None,
-        spec=spec,
-        pause_generation=True,
-    )
-
-    assert group is None
-    assert init_info == spec.init_info
-    assert build_ranks == [1]
-    assert barrier_calls == [2]
-
-
-def test_sync_merged_weights_to_vllm_sender_controls_runtime_and_sends(
-    monkeypatch,
-) -> None:
-    spec = _spec()
-    barrier_calls: list[int] = []
-    sent_items: list[list[tuple[str, torch.Tensor]]] = []
-    posts: list[
-        tuple[str, dict[str, object] | None, dict[str, object] | None, float]
-    ] = []
-
-    monkeypatch.setattr(
-        export,
-        "ensure_merged_weight_transfer_group",
-        lambda **kwargs: ("trainer-group", spec.init_info),
-    )
-    published_config = {"r": 2, "lora_alpha": 4}
-
-    def fake_build(**kwargs):
-        return (
-            {
-                "layer.b.lora_B.weight": torch.zeros((3,), dtype=torch.float32),
-                "layer.a.lora_A.weight": torch.zeros((2, 3), dtype=torch.float16),
-            },
-            published_config,
-        )
-
-    def fake_send(iterator, trainer_args):
-        sent_items.append(list(iterator))
-        assert trainer_args["group"] == "trainer-group"
-        assert trainer_args["packed"] is True
-
-    class FakeClient:
-        def __enter__(self):
-            return self
-
-        def __exit__(self, exc_type, exc, tb):
-            return None
-
-        def post(
-            self,
-            url: str,
-            *,
-            json: dict[str, object] | None = None,
-            params: dict[str, object] | None = None,
-            timeout: float,
-        ) -> _OkResponse:
-            posts.append((url, json, params, timeout))
-            return _OkResponse()
-
-    monkeypatch.setattr(export, "build_vllm_lora_tensors_from_model", fake_build)
-    monkeypatch.setattr(export, "trainer_send_weights", fake_send)
-    monkeypatch.setattr(export, "_maybe_distributed_barrier", barrier_calls.append)
-    monkeypatch.setattr(torch.cuda, "synchronize", lambda: None)
-    monkeypatch.setattr(httpx, "Client", FakeClient)
-
-    group, init_info = export.sync_merged_weights_to_vllm(
-        bridge=object(),
-        model=cast(Any, object()),
-        model_support_handler=object(),
-        adapter_model={},
-        adapter_config=published_config,
-        rank=0,
-        world_size=2,
-        merged_weight_transfer_group=None,
-        merged_weight_transfer_init_info=None,
-        spec=spec,
-        pause_generation=True,
-    )
-
-    assert group == "trainer-group"
-    assert init_info == spec.init_info
-    assert [name for name, _ in sent_items[0]] == [
-        "layer.a.lora_A.weight",
-        "layer.b.lora_B.weight",
-    ]
-    assert posts == [
-        ("http://runtime.test/pause", None, {"mode": "wait"}, 300.0),
-        (
-            "http://runtime.test/start_weight_update",
-            {"is_checkpoint_format": False},
-            None,
-            300.0,
-        ),
-        (
-            "http://runtime.test/update_weights",
-            {
-                "update_info": {
-                    "art_weight_update_kind": "lora_delta",
-                    "art_lora_config": published_config,
-                    "names": [
-                        "layer.a.lora_A.weight",
-                        "layer.b.lora_B.weight",
-                    ],
-                    "dtype_names": ["float16", "float32"],
-                    "shapes": [[2, 3], [3]],
-                    "packed": True,
-                    "packed_buffer_size_bytes": export.DEFAULT_PACKED_BUFFER_SIZE_BYTES,
-                    "packed_num_buffers": export.DEFAULT_PACKED_NUM_BUFFERS,
-                }
-            },
-            None,
-            600.0,
-        ),
-        ("http://runtime.test/finish_weight_update", None, None, 600.0),
-        (
-            "http://runtime.test/art/set_served_model_name",
-            {"name": "model@7"},
-            None,
-            30.0,
-        ),
-        ("http://runtime.test/resume", None, None, 30.0),
-    ]
-    assert barrier_calls == [2]
diff --git a/tests/integration/megatron/lora/test_weight_transfer_bootstrap_contract.py b/tests/integration/megatron/lora/test_weight_transfer_bootstrap_contract.py
deleted file mode 100644
index ee85f325b..000000000
--- a/tests/integration/megatron/lora/test_weight_transfer_bootstrap_contract.py
+++ /dev/null
@@ -1,168 +0,0 @@
-from contextlib import nullcontext
-from types import SimpleNamespace
-from typing import Any, cast
-
-import pytest
-import torch
-
-import art.weight_transfer.nccl as nccl
-
-
-def test_trainer_nccl_unique_id_round_trips_as_raw_bytes() -> None:
-    payload = bytes(range(128))
-    unique_id = nccl._nccl_unique_id_from_bytes(payload)
-    assert nccl._nccl_unique_id_to_bytes(unique_id) == payload
-
-
-def test_trainer_nccl_communicator_releases_bootstrap_group_after_init(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    payload = bytes(range(128))
-    bootstrap_closed = False
-
-    def close_bootstrap() -> None:
-        nonlocal bootstrap_closed
-        bootstrap_closed = True
-
-    bootstrap_group = SimpleNamespace(
-        broadcast_obj=lambda obj, src: obj if obj is not None else payload,
-        close=close_bootstrap,
-    )
-    loaded_so_paths: list[str | None] = []
-
-    class FakeNcclLibrary:
-        def __init__(self, so_file: str | None = None):
-            loaded_so_paths.append(so_file)
-
-        def get_unique_id(self):
-            return nccl._nccl_unique_id_from_bytes(payload)
-
-        def init_rank(self, world_size, unique_id, rank):
-            assert world_size == 2
-            assert rank == 0
-            assert nccl._nccl_unique_id_to_bytes(unique_id) == payload
-            return "comm"
-
-    monkeypatch.setattr(nccl, "_BootstrapGroup", lambda **kwargs: bootstrap_group)
-    monkeypatch.setattr(nccl, "_NcclLibrary", FakeNcclLibrary)
-    monkeypatch.setattr(torch.cuda, "device", lambda device: nullcontext())
-    monkeypatch.setattr(
-        torch.cuda,
-        "current_stream",
-        lambda device=None: SimpleNamespace(synchronize=lambda: None),
-    )
-    monkeypatch.setattr(
-        nccl.TrainerNcclCommunicator,
-        "all_reduce",
-        lambda self, tensor, *, stream=None: None,
-    )
-    monkeypatch.setattr(
-        torch,
-        "zeros",
-        lambda *args, **kwargs: SimpleNamespace(device=torch.device("cuda:0")),
-    )
-
-    communicator = nccl.TrainerNcclCommunicator(
-        host="127.0.0.1",
-        port=12345,
-        rank=0,
-        world_size=2,
-        device=0,
-        nccl_so_path="/runtime/libnccl.so.2",
-    )
-    assert communicator._bootstrap_group is None
-    assert bootstrap_closed is True
-    assert loaded_so_paths == ["/runtime/libnccl.so.2"]
-
-
-def test_trainer_init_passes_explicit_nccl_so_path(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    seen: dict[str, object] = {}
-
-    def fake_communicator(**kwargs):
-        seen.update(kwargs)
-        return "communicator"
-
-    monkeypatch.setattr(nccl, "TrainerNcclCommunicator", fake_communicator)
-    monkeypatch.setattr(torch.cuda, "current_device", lambda: 3)
-
-    assert (
-        nccl.trainer_init(
-            {
-                "master_address": "127.0.0.1",
-                "master_port": 23456,
-                "world_size": 4,
-                "nccl_so_path": "/runtime/libnccl.so.2",
-            }
-        )
-        == "communicator"
-    )
-    assert seen == {
-        "host": "127.0.0.1",
-        "port": 23456,
-        "rank": 0,
-        "world_size": 4,
-        "device": 3,
-        "nccl_so_path": "/runtime/libnccl.so.2",
-    }
-
-
-def test_trainer_nccl_communicator_closes_nccl_and_bootstrap_group() -> None:
-    communicator = object.__new__(nccl.TrainerNcclCommunicator)
-    calls: list[str] = []
-    communicator._comm = "comm"
-    communicator._nccl = SimpleNamespace(
-        destroy_comm=lambda comm: calls.append(f"destroy:{comm}")
-    )
-    communicator._bootstrap_group = SimpleNamespace(
-        close=lambda: calls.append("bootstrap_close")
-    )
-
-    communicator.close()
-    communicator.close()
-
-    assert calls == ["destroy:comm", "bootstrap_close"]
-    assert communicator._comm is None
-
-
-def test_trainer_nccl_communicator_aborts_nccl_and_bootstrap_group() -> None:
-    communicator = object.__new__(nccl.TrainerNcclCommunicator)
-    calls: list[str] = []
-    communicator._comm = "comm"
-    communicator._nccl = SimpleNamespace(
-        abort_comm=lambda comm: calls.append(f"abort:{comm}")
-    )
-    communicator._bootstrap_group = SimpleNamespace(
-        close=lambda: calls.append("bootstrap_close")
-    )
-
-    communicator.abort()
-    communicator.abort()
-
-    assert calls == ["abort:comm", "bootstrap_close"]
-    assert communicator._comm is None
-
-
-def test_trainer_nccl_communicator_rejects_invalid_collective_tensors() -> None:
-    communicator = object.__new__(nccl.TrainerNcclCommunicator)
-    communicator.device = torch.device("cuda:0")
-
-    with pytest.raises(RuntimeError, match="requires a CUDA tensor"):
-        communicator._validate_collective_tensor(torch.empty(1))
-
-    wrong_device = SimpleNamespace(
-        is_cuda=True,
-        device=torch.device("cuda:1"),
-        is_contiguous=lambda: True,
-    )
-    with pytest.raises(RuntimeError, match="tensor device mismatch"):
-        communicator._validate_collective_tensor(cast(Any, wrong_device))
-
-    non_contiguous = SimpleNamespace(
-        is_cuda=True,
-        device=torch.device("cuda:0"),
-        is_contiguous=lambda: False,
-    )
-    with pytest.raises(RuntimeError, match="requires contiguous tensors"):
-        communicator._validate_collective_tensor(cast(Any, non_contiguous))
diff --git a/tests/integration/megatron/model_support/base_megatron_session.py b/tests/integration/megatron/model_support/base_megatron_session.py
new file mode 100644
index 000000000..26fffc2f5
--- /dev/null
+++ b/tests/integration/megatron/model_support/base_megatron_session.py
@@ -0,0 +1,233 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+from contextvars import ContextVar
+import random
+from typing import Any, Iterator
+
+from megatron.core import parallel_state as ps
+import numpy as np
+from pydantic import BaseModel, ConfigDict
+import torch
+
+
+def initialize_single_rank_process_group() -> None:
+    if torch.distributed.is_initialized():  # type: ignore[possibly-missing-attribute]
+        if torch.distributed.get_world_size() != 1:  # type: ignore[possibly-missing-attribute]
+            raise RuntimeError(
+                "single-rank validation found a multi-rank process group"
+            )
+        return
+    torch.distributed.init_process_group(  # type: ignore[possibly-missing-attribute]
+        backend="nccl",
+        store=torch.distributed.HashStore(),  # type: ignore[possibly-missing-attribute]
+        rank=0,
+        world_size=1,
+    )
+
+
+class BaseMegatronSessionKey(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    base_model: str
+    model_key: str
+    num_layers: int
+    precision: str
+    allow_unvalidated_arch: bool
+
+
+class BaseMegatronResetReport(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    process_group_reused: bool
+    model_reused: bool
+    parameter_count: int
+    parameter_versions_unchanged: bool
+    buffer_count: int
+    buffers_restored: int
+    gradient_tensors_cleared: int
+    gradients_cleared: bool
+    optimizer_released: bool
+    moe_replay_was_active: bool
+    moe_replay_cleared: bool
+    rng_restored: bool
+
+
+class BaseMegatronSession:
+    def __init__(self) -> None:
+        self.runtime: Any | None = None
+        self.key: BaseMegatronSessionKey | None = None
+        self.reset_report: BaseMegatronResetReport | None = None
+        self._parameters: list[tuple[str, torch.nn.Parameter, int]] = []
+        self._buffers: list[tuple[str, torch.Tensor, torch.Tensor]] = []
+        self._rng_state: tuple[Any, Any, torch.Tensor, list[torch.Tensor]] | None = None
+        self._process_group: Any | None = None
+        self._model_chunks: tuple[Any, ...] = ()
+
+    def capture_runtime(self, runtime: Any, *, key: BaseMegatronSessionKey) -> None:
+        if self.runtime is not None:
+            raise RuntimeError("base Megatron session already owns a runtime")
+        if not torch.distributed.is_initialized():  # type: ignore[possibly-missing-attribute]
+            raise RuntimeError(
+                "base Megatron session requires an initialized process group"
+            )
+        if runtime.model_support_spec.key != key.model_key:
+            raise RuntimeError(
+                f"base Megatron handler mismatch: {runtime.model_support_spec.key} != {key.model_key}"
+            )
+        self.runtime = runtime
+        self.key = key
+        self._parameters = [
+            (f"{chunk_index}:{name}", parameter, parameter._version)
+            for chunk_index, chunk in enumerate(runtime.model)
+            for name, parameter in chunk.named_parameters()
+        ]
+        self._buffers = [
+            (f"{chunk_index}:{name}", buffer, buffer.detach().clone())
+            for chunk_index, chunk in enumerate(runtime.model)
+            for name, buffer in chunk.named_buffers()
+        ]
+        self._rng_state = (
+            random.getstate(),
+            np.random.get_state(),
+            torch.get_rng_state(),
+            torch.cuda.get_rng_state_all(),
+        )
+        self._process_group = torch.distributed.group.WORLD  # type: ignore[possibly-missing-attribute]
+        self._model_chunks = tuple(runtime.model)
+
+    def owns_runtime(self, runtime: Any) -> bool:
+        return self.runtime is runtime
+
+    def reset_for_packing(self, *, key: BaseMegatronSessionKey) -> Any:
+        from art.megatron import train as megatron_train
+
+        runtime = self.runtime
+        if runtime is None or self.key != key or self._rng_state is None:
+            raise RuntimeError(
+                f"base Megatron session is incompatible: retained={self.key}, requested={key}"
+            )
+        process_group_reused = (
+            torch.distributed.is_initialized()  # type: ignore[possibly-missing-attribute]
+            and torch.distributed.group.WORLD is self._process_group  # type: ignore[possibly-missing-attribute]
+        )
+        model_reused = len(runtime.model) == len(self._model_chunks) and all(
+            current is retained
+            for current, retained in zip(runtime.model, self._model_chunks, strict=True)
+        )
+        if not process_group_reused or not model_reused:
+            raise RuntimeError("base Megatron process group or model was replaced")
+        changed_parameters = [
+            name
+            for name, parameter, version in self._parameters
+            if parameter._version != version
+        ]
+        if changed_parameters:
+            raise RuntimeError(
+                "HF parity mutated base parameters: "
+                + ", ".join(changed_parameters[:8])
+            )
+
+        had_replay = runtime.moe_routing_replay_controller is not None
+        megatron_train.configure_moe_routing_replay(runtime)
+        if runtime.optimizer is not None:
+            runtime.optimizer.zero_grad()
+        megatron_train._zero_grad_buffers(runtime.model)
+        gradient_tensors_cleared = 0
+        for _name, parameter, _version in self._parameters:
+            if parameter.grad is not None:
+                parameter.grad = None
+                gradient_tensors_cleared += 1
+            main_grad = getattr(parameter, "main_grad", None)
+            if isinstance(main_grad, torch.Tensor):
+                main_grad.zero_()
+                gradient_tensors_cleared += 1
+        runtime.optimizer = None
+        gradients_cleared = all(
+            parameter.grad is None
+            and (
+                not isinstance(
+                    main_grad := getattr(parameter, "main_grad", None), torch.Tensor
+                )
+                or not bool(torch.count_nonzero(main_grad).item())
+            )
+            for _name, parameter, _version in self._parameters
+        )
+        if not gradients_cleared:
+            raise RuntimeError("base Megatron gradient reset was incomplete")
+
+        buffers_restored = 0
+        with torch.no_grad():
+            for name, buffer, initial in self._buffers:
+                if buffer.shape != initial.shape or buffer.dtype != initial.dtype:
+                    raise RuntimeError(f"HF parity changed buffer metadata for {name}")
+                if not torch.equal(buffer, initial):
+                    buffer.copy_(initial)
+                    buffers_restored += 1
+        python_state, numpy_state, torch_state, cuda_states = self._rng_state
+        random.setstate(python_state)
+        np.random.set_state(numpy_state)
+        torch.set_rng_state(torch_state)
+        torch.cuda.set_rng_state_all(cuda_states)
+        for chunk in runtime.model:
+            chunk.eval()
+        self.reset_report = BaseMegatronResetReport(
+            process_group_reused=True,
+            model_reused=True,
+            parameter_count=len(self._parameters),
+            parameter_versions_unchanged=True,
+            buffer_count=len(self._buffers),
+            buffers_restored=buffers_restored,
+            gradient_tensors_cleared=gradient_tensors_cleared,
+            gradients_cleared=True,
+            optimizer_released=runtime.optimizer is None,
+            moe_replay_was_active=had_replay,
+            moe_replay_cleared=runtime.moe_routing_replay_controller is None,
+            rng_restored=True,
+        )
+        return runtime
+
+    def close(self) -> None:
+        runtime, self.runtime = self.runtime, None
+        try:
+            if runtime is not None:
+                from art.megatron import train as megatron_train
+
+                megatron_train.configure_moe_routing_replay(runtime)
+            if getattr(ps, "model_parallel_is_initialized", lambda: False)():
+                ps.destroy_model_parallel()
+            if torch.distributed.is_initialized():  # type: ignore[possibly-missing-attribute]
+                torch.distributed.destroy_process_group()  # type: ignore[possibly-missing-attribute]
+        finally:
+            self.key = None
+            self._parameters.clear()
+            self._buffers.clear()
+            self._rng_state = None
+            self._process_group = None
+            self._model_chunks = ()
+            del runtime
+            torch.cuda.empty_cache()
+
+
+_ACTIVE_SESSION: ContextVar[BaseMegatronSession | None] = ContextVar(
+    "base_megatron_session", default=None
+)
+
+
+def active_base_megatron_session() -> BaseMegatronSession | None:
+    return _ACTIVE_SESSION.get()
+
+
+@contextmanager
+def base_megatron_session() -> Iterator[BaseMegatronSession]:
+    if _ACTIVE_SESSION.get() is not None:
+        raise RuntimeError("nested base Megatron sessions are not supported")
+    session = BaseMegatronSession()
+    token = _ACTIVE_SESSION.set(session)
+    try:
+        yield session
+    finally:
+        try:
+            session.close()
+        finally:
+            _ACTIVE_SESSION.reset(token)
diff --git a/tests/integration/megatron/model_support/forward_trace.py b/tests/integration/megatron/model_support/forward_trace.py
index 8dcba458c..0b812c953 100644
--- a/tests/integration/megatron/model_support/forward_trace.py
+++ b/tests/integration/megatron/model_support/forward_trace.py
@@ -72,7 +72,47 @@ def _trace_hook(fn: Callable[..., Any]) -> Callable[..., Any]:
 
 def _normalize_trace_module_name(module_name: str) -> str:
     """Strips compile-wrapper path segments from trace module names."""
-    return module_name.replace("._orig_mod", "")
+    normalized = module_name.replace("._orig_mod", "")
+    chunk, separator, remainder = normalized.partition(".")
+    if (
+        separator
+        and chunk.startswith("chunk")
+        and chunk.removeprefix("chunk").isdigit()
+    ):
+        return remainder
+    return normalized
+
+
+def _global_trace_module_name(
+    module_name: str,
+    module_by_name: dict[str, Any],
+    *,
+    chunk_index: int,
+) -> str:
+    local_layer_index = _module_layer_index(module_name)
+    normalized = _normalize_trace_module_name(module_name)
+    if local_layer_index is None:
+        return f"chunk{chunk_index}.{normalized}"
+    marker = "decoder.layers."
+    marker_index = module_name.find(marker)
+    layer_name_end = marker_index + len(marker) + len(str(local_layer_index))
+    layer = module_by_name[module_name[:layer_name_end]]
+    layer_number = getattr(layer, "layer_number", None)
+    if layer_number is None:
+        layer_number = getattr(getattr(layer, "_orig_mod", None), "layer_number", None)
+    if layer_number is None:
+        raise RuntimeError(
+            f"Transformer layer has no global layer_number: {module_name}"
+        )
+    normalized_marker_index = normalized.find(marker)
+    normalized_layer_start = normalized_marker_index + len(marker)
+    normalized_layer_end = normalized_layer_start + len(str(local_layer_index))
+    return "chunk{}.{}{}{}".format(
+        chunk_index,
+        normalized[:normalized_layer_start],
+        int(layer_number) - 1,
+        normalized[normalized_layer_end:],
+    )
 
 
 def _safe_int(value: Any, default: int = 0) -> int:
@@ -249,7 +289,16 @@ def _extract_router_topk(
         topk_scores = probs.new_zeros((probs.shape[0], 0))
         topk_ids = torch.zeros((probs.shape[0], 0), dtype=torch.int64)
     else:
-        topk_scores, topk_ids = torch.topk(probs, k=topk, dim=-1)
+        expert_ids = torch.arange(probs.shape[-1]).expand_as(routing_map)
+        topk_ids = (
+            expert_ids.masked_fill(~routing_map, probs.shape[-1])
+            .sort(dim=-1)
+            .values[:, :topk]
+        )
+        valid = topk_ids < probs.shape[-1]
+        topk_scores = probs.gather(-1, topk_ids.clamp_max(probs.shape[-1] - 1))
+        topk_ids = topk_ids.masked_fill(~valid, -1)
+        topk_scores = topk_scores.masked_fill(~valid, 0)
     return topk_ids.contiguous(), topk_scores.contiguous()
 
 
@@ -311,7 +360,9 @@ def __init__(
             tuple[int | None, int, int | None, torch.Tensor, torch.Tensor | None]
         ] = []
         self._trace_metadata_by_name: dict[str, dict[str, Any]] = {}
-        self._next_micro_order = 0
+        self._root_module_ids: set[int] = set()
+        self._root_output_module_ids: set[int] = set()
+        self._next_micro_order_by_root: dict[int, int] = {}
         self._inside_root_forward = False
         self._hook_handles: list[Any] = []
         if not enabled:
@@ -321,13 +372,19 @@ def __init__(
     def _register_hooks(self, model_chunks: list[Any]) -> None:
         if not model_chunks:
             raise RuntimeError("Expected at least one model chunk for forward tracing")
-        root_module = model_chunks[0]
-        self._hook_handles.append(
-            root_module.register_forward_pre_hook(_trace_hook(self._root_pre_hook))
-        )
-        self._hook_handles.append(
-            root_module.register_forward_hook(_trace_hook(self._root_post_hook))
-        )
+        from art.megatron.training.pipeline_schedule import chunk_post_process
+
+        self._root_module_ids = {id(chunk) for chunk in model_chunks}
+        self._root_output_module_ids = {
+            id(chunk) for chunk in model_chunks if chunk_post_process(chunk)
+        }
+        for root_module in model_chunks:
+            self._hook_handles.append(
+                root_module.register_forward_pre_hook(_trace_hook(self._root_pre_hook))
+            )
+            self._hook_handles.append(
+                root_module.register_forward_hook(_trace_hook(self._root_post_hook))
+            )
         for chunk_index, chunk in enumerate(model_chunks):
             named_modules = list(chunk.named_modules())
             module_by_name = dict(named_modules)
@@ -339,8 +396,10 @@ def _register_hooks(self, model_chunks: list[Any]) -> None:
                     and layer_index > self.max_layer_index
                 ):
                     continue
-                trace_module_name = _normalize_trace_module_name(
-                    f"chunk{chunk_index}.{module_name}"
+                trace_module_name = _global_trace_module_name(
+                    module_name,
+                    module_by_name,
+                    chunk_index=chunk_index,
                 )
                 metadata = self._build_module_trace_metadata(
                     module_name=module_name,
@@ -421,7 +480,9 @@ def _sequence_parallel_enabled(module: Any) -> bool:
     @staticmethod
     def _lora_primary_output_merge_hint(module: Any) -> dict[str, Any] | None:
         """Infers the correct output merge op for LoRA modules."""
-        if module.__class__.__name__ != "LoRA":
+        from art.megatron.lora import LoRA
+
+        if not isinstance(module, LoRA):
             return None
         lora_module = module
         b_param = getattr(lora_module, "B_T", None)
@@ -454,6 +515,8 @@ def _lora_primary_output_merge_hint(module: Any) -> dict[str, Any] | None:
         a_world_size = _shard_world_size_for_domain(a_domain)
         if bool(getattr(a_param, "lora_tp_sharded", False)) and a_world_size > 1:
             return {"op": "sum"}
+        if a_world_size > 1 and a_domain == b_domain:
+            return {"op": "replicated"}
         return None
 
     def _infer_primary_output_merge_hint(
@@ -671,32 +734,34 @@ def _root_pre_hook(self, _module: Any, _args: Any) -> None:
         if self.current_step_index is None:
             return
         self._inside_root_forward = True
-        micro_order = self._next_micro_order
+        micro_order = self._next_micro_order_by_root[id(_module)]
         sample_index = self._sample_index_for_micro(micro_order)
         self.begin_micro(sample_index=sample_index, micro_order=micro_order)
 
     def _root_post_hook(self, _module: Any, _inputs: Any, output: Any) -> None:
         if self.current_step_index is None:
             return
-        output_tensor = self.guess_primary_tensor(output)
-        if output_tensor is None:
-            raise RuntimeError(
-                f"Expected root forward output to contain a tensor, got {type(output)}"
-            )
-        sample_index = self.current_micro_sample_index
-        micro_order = self.current_micro_order
-        self.current_step_outputs.append(
-            (
-                sample_index,
-                micro_order,
-                None
-                if sample_index is not None
-                else _local_dummy_micro_slot(micro_order),
-                output_tensor.float(),
-                getattr(_module, "_art_root_output_token_uids", None),
+        module_id = id(_module)
+        if module_id in self._root_output_module_ids:
+            output_tensor = self.guess_primary_tensor(output)
+            if output_tensor is None:
+                raise RuntimeError(
+                    f"Expected root forward output to contain a tensor, got {type(output)}"
+                )
+            sample_index = self.current_micro_sample_index
+            micro_order = self.current_micro_order
+            self.current_step_outputs.append(
+                (
+                    sample_index,
+                    micro_order,
+                    None
+                    if sample_index is not None
+                    else _local_dummy_micro_slot(micro_order),
+                    output_tensor.float(),
+                    getattr(_module, "_art_root_output_token_uids", None),
+                )
             )
-        )
-        self._next_micro_order = micro_order + 1
+        self._next_micro_order_by_root[module_id] += 1
         self._inside_root_forward = False
 
     def set_step(
@@ -711,7 +776,9 @@ def set_step(
         self.current_micro_sample_index = None
         self.current_micro_order = 0
         self.current_micro_module_call_counts = {}
-        self._next_micro_order = 0
+        self._next_micro_order_by_root = {
+            module_id: 0 for module_id in self._root_module_ids
+        }
         self._inside_root_forward = False
 
     def begin_micro(self, sample_index: int | None, micro_order: int) -> None:
@@ -1253,8 +1320,7 @@ def _canonicalize_row_aligned_value(
     @classmethod
     def _canonicalize_call_row_token_order(cls, call: dict[str, Any]) -> None:
         """Canonicalizes all row-aligned call tensors to global token order."""
-        cls._align_exact_zero_padding_row_token_uids(call)
-        cls._drop_exact_zero_padding_rows(call)
+        cls._drop_padding_rows(call)
         row_token_uids = call.get("row_token_uids")
         if not isinstance(row_token_uids, torch.Tensor) or row_token_uids.ndim != 1:
             return
@@ -1276,8 +1342,8 @@ def _canonicalize_call_row_token_order(cls, call: dict[str, Any]) -> None:
         call["row_token_uids"] = row_token_uids.index_select(0, order).contiguous()
 
     @classmethod
-    def _drop_exact_zero_padding_rows(cls, call: dict[str, Any]) -> None:
-        """Removes traced sequence-padding rows before comparing compact CP traces."""
+    def _drop_padding_rows(cls, call: dict[str, Any]) -> None:
+        """Removes rows explicitly marked as sequence padding by their token UID."""
         row_token_uids = call.get("row_token_uids")
         tensor = call.get("primary_output")
         if (
@@ -1292,9 +1358,6 @@ def _drop_exact_zero_padding_rows(cls, call: dict[str, Any]) -> None:
         padding_rows = row_token_uids < 0
         if row_count == 0 or not bool(padding_rows.any().item()):
             return
-        flat = tensor.detach().reshape(row_count, -1)
-        if not bool((flat[padding_rows] == 0).all().item()):
-            return
         valid_rows = torch.nonzero(~padding_rows, as_tuple=False).reshape(-1)
         original_call = dict(call)
         for key, value in original_call.items():
@@ -1307,48 +1370,6 @@ def _drop_exact_zero_padding_rows(cls, call: dict[str, Any]) -> None:
             )
         call["row_token_uids"] = row_token_uids.index_select(0, valid_rows).contiguous()
 
-    @staticmethod
-    def _align_exact_zero_padding_row_token_uids(call: dict[str, Any]) -> None:
-        """Moves padding UID markers onto exact-zero sequence-parallel pad rows."""
-        row_token_uids = call.get("row_token_uids")
-        tensor = call.get("primary_output")
-        if (
-            not isinstance(row_token_uids, torch.Tensor)
-            or row_token_uids.ndim != 1
-            or not isinstance(tensor, torch.Tensor)
-            or tensor.ndim == 0
-            or int(tensor.shape[0]) != int(row_token_uids.numel())
-        ):
-            return
-        row_count = int(row_token_uids.numel())
-        if row_count <= 1 or not bool((row_token_uids < 0).any().item()):
-            return
-        flat = tensor.detach().reshape(row_count, -1)
-        zero_rows = torch.nonzero(
-            (flat == 0).all(dim=1) & (row_token_uids >= 0),
-            as_tuple=False,
-        ).reshape(-1)
-        negative_rows = torch.nonzero(
-            (row_token_uids < 0) & ~(flat == 0).all(dim=1),
-            as_tuple=False,
-        ).reshape(-1)
-        if int(zero_rows.numel()) == 0 or int(zero_rows.numel()) != int(
-            negative_rows.numel()
-        ):
-            return
-        aligned = row_token_uids.clone()
-        for zero_pos, negative_pos in zip(
-            zero_rows.tolist(), negative_rows.tolist(), strict=True
-        ):
-            zero_pos = int(zero_pos)
-            negative_pos = int(negative_pos)
-            if zero_pos >= negative_pos:
-                return
-            shifted = aligned[zero_pos:negative_pos].clone()
-            aligned[zero_pos] = -1
-            aligned[zero_pos + 1 : negative_pos + 1] = shifted
-        call["row_token_uids"] = aligned
-
     @classmethod
     def _canonicalize_primary_output_tensor(
         cls,
@@ -1641,6 +1662,13 @@ def _merge_rank_values(
             raise RuntimeError("Cannot merge empty rank value list")
         if all(isinstance(value, torch.Tensor) for value in values_by_rank):
             tensors = cast(list[torch.Tensor], values_by_rank)
+            if preferred_reduce == "replicated":
+                if not all(
+                    tensors[0].shape == tensor.shape and torch.equal(tensors[0], tensor)
+                    for tensor in tensors[1:]
+                ):
+                    raise RuntimeError("Replicated trace outputs diverged across ranks")
+                return tensors[0]
             if preferred_reduce == "sum" and all(
                 tensors[0].shape == tensor.shape for tensor in tensors[1:]
             ):
@@ -1801,8 +1829,8 @@ def _merge_rank_call_entries(
                 preferred_cat_dim = None
                 preferred_reduce = None
                 if isinstance(primary_hint, dict):
-                    if primary_hint.get("op") == "sum":
-                        preferred_reduce = "sum"
+                    if primary_hint.get("op") in {"sum", "replicated"}:
+                        preferred_reduce = str(primary_hint["op"])
                     elif primary_hint.get("op") == "concat" and isinstance(
                         primary_hint.get("dim"), int
                     ):
@@ -1851,8 +1879,8 @@ def _merge_rank_call_entries(
                         dim = selected_hint.get("dim")
                         if isinstance(dim, int):
                             preferred_cat_dim = dim
-                    elif op == "sum":
-                        preferred_reduce = "sum"
+                    elif op in {"sum", "replicated"}:
+                        preferred_reduce = op
                 if (
                     preferred_reduce is None
                     and preferred_cat_dim == 0
@@ -1942,7 +1970,7 @@ def _merge_rank_values_with_cp_groups(
                 preferred_cat_dim=preferred_cat_dim,
                 preferred_reduce=preferred_reduce,
             )
-        if preferred_cat_dim != -1 and preferred_reduce != "sum":
+        if preferred_cat_dim != -1 and preferred_reduce not in {"sum", "replicated"}:
             return cls._merge_rank_values(
                 values_by_rank,
                 preferred_cat_dim=preferred_cat_dim,
diff --git a/tests/integration/megatron/model_support/fp32_grouped_gemm.py b/tests/integration/megatron/model_support/fp32_grouped_gemm.py
index 2da228fef..272150757 100644
--- a/tests/integration/megatron/model_support/fp32_grouped_gemm.py
+++ b/tests/integration/megatron/model_support/fp32_grouped_gemm.py
@@ -1,15 +1,17 @@
 from __future__ import annotations
 
+import functools
 import os
 import sys
 from typing import Any
 
 _GUARD_ATTR = "__art_te_cutlass_grouped_gemm_guard__"
 _ORIGINAL_ATTR = "__art_original_general_grouped_gemm__"
+_REFERENCE_ATTR = "__art_fp32_grouped_linear_reference__"
 
 
 def allow_fp32_grouped_gemm_fallback_for_model_support_tests() -> None:
-    """Use TE's fp32 grouped-GEMM fallback in semantic model-support tests."""
+    """Use topology-stable fp32 expert GEMMs in semantic model-support tests."""
     os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = "0"
     os.environ["NVTE_CUTLASS_GROUPED_GEMM_WARN_FALLBACK"] = "0"
     try:
@@ -27,6 +29,41 @@ def allow_fp32_grouped_gemm_fallback_for_model_support_tests() -> None:
         "transformer_engine.pytorch.module.grouped_linear", current, original
     )
     _patch_if_guarded("transformer_engine.pytorch.module.linear", current, original)
+    _install_fp32_grouped_linear_reference()
+
+
+def _install_fp32_grouped_linear_reference() -> None:
+    from megatron.core.extensions.transformer_engine import TEGroupedLinear
+    import torch
+
+    assert TEGroupedLinear is not None
+    current = TEGroupedLinear.forward
+    if getattr(current, _REFERENCE_ATTR, False):
+        return
+
+    @functools.wraps(current)
+    def forward(self, x, m_splits):
+        if x.dtype is not torch.float32:
+            return current(self, x, m_splits)
+        counts = [int(count) for count in m_splits]
+        weights = self._get_weight_tensors()
+        biases = self._get_bias_tensors()
+        outputs = [
+            torch.nn.functional.linear(
+                rows,
+                weight,
+                bias if self.apply_bias else None,
+            )
+            for rows, weight, bias in zip(x.split(counts), weights, biases, strict=True)
+        ]
+        output = torch.cat(outputs)
+        self.is_first_microbatch = False
+        if self.te_return_bias:
+            return output, biases
+        return output, None
+
+    setattr(forward, _REFERENCE_ATTR, True)
+    setattr(TEGroupedLinear, "forward", forward)
 
 
 def _patch_if_guarded(module_name: str, guarded: Any, original: Any) -> None:
diff --git a/tests/integration/megatron/model_support/hf_parity.py b/tests/integration/megatron/model_support/hf_parity.py
index 55b355838..622bce83b 100644
--- a/tests/integration/megatron/model_support/hf_parity.py
+++ b/tests/integration/megatron/model_support/hf_parity.py
@@ -4,7 +4,7 @@
 from pathlib import Path
 import subprocess
 import sys
-from typing import Any
+from typing import Any, Callable
 
 from pydantic import BaseModel, Field
 
@@ -40,6 +40,16 @@
 HF_PARITY_ARTIFACT_SUITE_NAME = "Megatron HF parity artifacts"
 
 
+def _hf_parity_worker_env() -> dict[str, str]:
+    return {
+        "RANK": "0",
+        "WORLD_SIZE": "1",
+        "LOCAL_RANK": "0",
+        "LOCAL_WORLD_SIZE": "1",
+        "PYTHONUNBUFFERED": "1",
+    }
+
+
 class HfParityMetricRow(BaseModel):
     phase: str
     param: str
@@ -98,6 +108,22 @@ def _hf_parity_phase_pass_fns() -> dict[str, PhasePassFn]:
     }
 
 
+def _hf_parity_phase_pass_fns_for_case(
+    case_config: OracleCaseConfig,
+) -> dict[str, PhasePassFn]:
+    if case_config.precision == "fp32":
+        return _hf_parity_phase_pass_fns()
+    from art.megatron.model_support.registry import get_model_support_handler
+
+    handler = get_model_support_handler(
+        case_config.base_model,
+        allow_unvalidated_arch=case_config.allow_unvalidated_arch,
+    )
+    return handler.correctness_phase_pass_fns(sys.modules[__name__]) or (
+        _hf_parity_phase_pass_fns()
+    )
+
+
 def hf_parity_case_config(case_config: OracleCaseConfig) -> OracleCaseConfig:
     return case_config.model_copy(
         update={"packed_tensors": HF_PARITY_PACKED_TENSORS.model_copy(deep=True)}
@@ -170,6 +196,7 @@ def build_tensor_map_metric_rows(
     reference: dict[str, Any],
     candidate: dict[str, Any],
     phase_pass_fns: dict[str, PhasePassFn] | None = None,
+    group_by: Callable[[str], str] | None = None,
 ) -> list[HfParityMetricRow]:
     reference_keys = set(reference.keys())
     candidate_keys = set(candidate.keys())
@@ -186,6 +213,12 @@ def build_tensor_map_metric_rows(
             )
         ]
     rows: list[HfParityMetricRow] = []
+    accumulators: dict[str, DiffAccumulator] = {}
+    diagnostic_pass_fns = dict(phase_pass_fns or _hf_parity_phase_pass_fns())
+    diagnostic_phase = f"{phase}_diagnostic"
+    diagnostic_pass_fns[diagnostic_phase] = MetricThresholdRule(
+        minimums={"typical_abs_scale": 0.0, "candidate_abs_scale": 0.0}
+    )
     for key in sorted(reference_keys):
         if tuple(reference[key].shape) != tuple(candidate[key].shape):
             rows.append(
@@ -198,6 +231,19 @@ def build_tensor_map_metric_rows(
                 )
             )
             continue
+        if group_by is not None:
+            summary = summarize_tensor_pair(reference[key], candidate[key])
+            rows.append(
+                _build_metric_row(
+                    phase=diagnostic_phase,
+                    param=key,
+                    summary=summary,
+                    phase_pass_fns=diagnostic_pass_fns,
+                )
+            )
+            accumulator = accumulators.setdefault(group_by(key), DiffAccumulator())
+            accumulator.update(reference[key], candidate[key])
+            continue
         rows.append(
             _build_metric_row(
                 phase=phase,
@@ -206,6 +252,15 @@ def build_tensor_map_metric_rows(
                 phase_pass_fns=phase_pass_fns,
             )
         )
+    rows.extend(
+        _build_metric_row(
+            phase=phase,
+            param=group,
+            summary=accumulator.as_summary(),
+            phase_pass_fns=phase_pass_fns,
+        )
+        for group, accumulator in sorted(accumulators.items())
+    )
     return rows
 
 
@@ -301,11 +356,10 @@ def run_hf_parity_subprocess(request: HfParityRunRequest, output_dir: Path) -> N
         "--run-request",
         str(request_path),
     ]
-    env = {**os.environ, "PYTHONUNBUFFERED": "1"}
     run = subprocess.run(
         command,
         cwd=str(worker_cwd),
-        env=env,
+        env={**os.environ, **_hf_parity_worker_env()},
         capture_output=True,
         text=True,
         check=False,
@@ -319,13 +373,28 @@ def run_hf_parity_subprocess(request: HfParityRunRequest, output_dir: Path) -> N
         )
 
 
+def _run_hf_parity_in_process(
+    request: HfParityRunRequest,
+    output_dir: Path,
+) -> None:
+    from .hf_parity_worker import run_worker_cli
+    from .workflow import _redirect_output, _temporary_env
+
+    request_path = output_dir / "run_request.json"
+    _write_json(request_path, request.model_dump(mode="json"))
+    with _temporary_env(**_hf_parity_worker_env()):
+        with _redirect_output(output_dir / "worker.log"):
+            run_worker_cli(request_path)
+
+
 def run_hf_parity(
     *,
     case_config: OracleCaseConfig,
+    in_process: bool = False,
 ) -> HfParityReport:
     case_config = hf_parity_case_config(case_config)
-    if case_config.precision != "fp32":
-        raise ValueError("HF parity currently requires fp32 precision")
+    if case_config.precision not in {"bf16", "fp32"}:
+        raise ValueError(f"Unsupported HF parity precision {case_config.precision!r}")
     if case_config.num_steps != 1:
         raise ValueError("HF parity currently requires num_steps=1")
 
@@ -357,7 +426,8 @@ def run_hf_parity(
         coverage=coverage,
     )
     with provider_topology_env(ORACLE_TOPOLOGY):
-        run_hf_parity_subprocess(request, output_dir)
+        runner = _run_hf_parity_in_process if in_process else run_hf_parity_subprocess
+        runner(request, output_dir)
     report = HfParityReport.model_validate(_read_json(report_path))
     assert_hf_parity_pass(report, report_path=report_path)
     _prune_case_artifacts(Path(case_artifacts.case_dir))
@@ -371,7 +441,7 @@ def build_hf_parity_report(
     loss_summary: dict[str, float],
     grads_rows: list[HfParityMetricRow],
 ) -> HfParityReport:
-    phase_pass_fns = _hf_parity_phase_pass_fns()
+    phase_pass_fns = _hf_parity_phase_pass_fns_for_case(request.case_config)
     rows = [
         _build_metric_row(
             phase="outputs",
diff --git a/tests/integration/megatron/model_support/hf_parity_worker.py b/tests/integration/megatron/model_support/hf_parity_worker.py
index 5ca8ff6d3..f9bf5419e 100644
--- a/tests/integration/megatron/model_support/hf_parity_worker.py
+++ b/tests/integration/megatron/model_support/hf_parity_worker.py
@@ -9,11 +9,15 @@
 import sys
 import time
 from typing import Any, cast
+from unittest.mock import patch
 
 import torch
 import torch.nn.functional as F
 
 from art.megatron import train as megatron_train
+from art.megatron.context_parallel.block_mask import prepare_block_mask_context
+from art.megatron.prefix_tree import parse_prefix_tree_row
+from art.megatron.prefix_tree_state import create_prefix_tree_state
 from art.megatron.routing_replay import (
     MoeRoutingReplayBundle,
     RouterCallRoute,
@@ -23,10 +27,16 @@
 from art.megatron.routing_replay import (
     ParallelTopology as ReplayParallelTopology,
 )
+from art.megatron.training import microbatches as megatron_microbatches
 from art.megatron.training.trace import prepare_replay_local_input_token_uids
-from art.megatron.weights.merged_weight_export import build_art_conversion_tasks
+from art.megatron.weights.conversion_tasks import build_art_conversion_tasks
 from art.preprocessing.pack import packed_tensors_from_dir
 
+from .base_megatron_session import (
+    BaseMegatronSessionKey,
+    active_base_megatron_session,
+    initialize_single_rank_process_group,
+)
 from .fp32_grouped_gemm import (
     allow_fp32_grouped_gemm_fallback_for_model_support_tests,
 )
@@ -34,7 +44,7 @@
 from .hf_parity import (
     HF_PARITY_REPORT_FILENAME,
     HfParityRunRequest,
-    _hf_parity_phase_pass_fns,
+    _hf_parity_phase_pass_fns_for_case,
     build_hf_parity_report,
     build_parity_sample_indices,
     build_tensor_map_metric_rows,
@@ -76,6 +86,12 @@
 _REPLAY_ROUTER_LAYER_PATTERN = re.compile(
     r"^chunk_\d+\.layer_(?P<layer>\d+)\.mlp\.router$"
 )
+_DISTRIBUTED_PROCESS_ENV = (
+    "RANK",
+    "WORLD_SIZE",
+    "LOCAL_RANK",
+    "LOCAL_WORLD_SIZE",
+)
 _GATE_WEIGHT_PATTERN = re.compile(
     r"^model(?:\.language_model)?\.layers\.(?P<layer>\d+)\.mlp\.gate\.weight$"
 )
@@ -123,17 +139,50 @@ def _hf_router_num_experts(module: Any, router_scores: torch.Tensor) -> int:
     )
 
 
+def _glm_router_output(
+    module: Any, router_logits: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    scores = router_logits.sigmoid()
+    choice = scores + module.e_score_correction_bias
+    groups = int(module.n_group)
+    group_scores = (
+        choice.view(choice.shape[0], groups, -1).topk(2, dim=-1).values.sum(-1)
+    )
+    selected_groups = group_scores.topk(
+        int(module.topk_group), dim=-1, sorted=False
+    ).indices
+    group_mask = torch.zeros_like(group_scores, dtype=torch.bool)
+    group_mask.scatter_(1, selected_groups, True)
+    choice = choice.masked_fill(
+        ~group_mask.unsqueeze(-1)
+        .expand_as(choice.view(choice.shape[0], groups, -1))
+        .reshape_as(choice),
+        float("-inf"),
+    )
+    indices = choice.topk(int(module.top_k), dim=-1, sorted=False).indices
+    weights = scores.gather(1, indices)
+    if bool(module.norm_topk_prob):
+        weights = weights / (weights.sum(-1, keepdim=True) + 1e-20)
+    return weights * float(module.routed_scaling_factor), indices
+
+
 class _HfMoeRoutingCapture:
     def __init__(self, model: Any) -> None:
         self._handles: list[Any] = []
         self._routes: dict[str, dict[int, RouterCallRoute]] = {}
         self._active_sample_index: int | None = None
         self._active_micro_slot = 0
+        self._active_token_uids: torch.Tensor | None = None
+        self._active_token_span: int | None = None
+        self._assembled_routes: dict[str, dict[int, RouterCallRoute]] = {}
+        self._assembled_filled: dict[str, dict[int, torch.Tensor]] = {}
         for module_name, module in model.named_modules():
             router_key = _hf_moe_router_key(module_name)
             if router_key is None:
                 continue
             self._routes[router_key] = {}
+            self._assembled_routes[router_key] = {}
+            self._assembled_filled[router_key] = {}
             self._handles.append(
                 module.register_forward_hook(self._make_hook(router_key, module))
             )
@@ -142,9 +191,18 @@ def __init__(self, model: Any) -> None:
     def enabled(self) -> bool:
         return bool(self._handles)
 
-    def set_active_micro(self, sample_index: int | None, micro_slot: int) -> None:
+    def set_active_micro(
+        self,
+        sample_index: int | None,
+        micro_slot: int,
+        *,
+        token_uids: torch.Tensor | None = None,
+        token_span: int | None = None,
+    ) -> None:
         self._active_sample_index = sample_index
         self._active_micro_slot = micro_slot
+        self._active_token_uids = token_uids
+        self._active_token_span = token_span
 
     def close(self) -> None:
         for handle in self._handles:
@@ -162,9 +220,16 @@ def build_replay_bundle(
         max_topk = 0
         num_global_tokens: int | None = None
         for router_key in sorted(self._routes):
-            calls = self._routes[router_key]
+            assembled = self._assembled_routes[router_key]
+            calls = assembled if assembled else self._routes[router_key]
             if not calls:
                 raise RuntimeError(f"HF parity captured no routes for '{router_key}'")
+            for micro_slot, filled in self._assembled_filled[router_key].items():
+                if not bool(filled.all()):
+                    raise RuntimeError(
+                        f"HF parity did not assemble all route rows for {router_key} "
+                        f"micro {micro_slot}: {int(filled.sum())}/{int(filled.numel())}"
+                    )
             routers[router_key] = StepRouterRoutes(calls=calls)
             for route in calls.values():
                 max_topk = max(max_topk, route.max_topk)
@@ -195,12 +260,17 @@ def build_replay_bundle(
 
     def _make_hook(self, router_key: str, module: Any) -> Any:
         def _hook(_module: Any, _inputs: Any, output: Any) -> None:
-            if not isinstance(output, tuple) or len(output) < 3:
+            if isinstance(output, torch.Tensor) and hasattr(
+                module, "e_score_correction_bias"
+            ):
+                router_scores, router_indices = _glm_router_output(module, output)
+            elif isinstance(output, tuple) and len(output) >= 3:
+                router_scores = output[1]
+                router_indices = output[2]
+            else:
                 raise RuntimeError(
-                    f"Expected HF router tuple output for '{router_key}', got {type(output)}"
+                    f"Unsupported HF router output for '{router_key}': {type(output)}"
                 )
-            router_scores = output[1]
-            router_indices = output[2]
             if not isinstance(router_scores, torch.Tensor) or not isinstance(
                 router_indices, torch.Tensor
             ):
@@ -208,12 +278,12 @@ def _hook(_module: Any, _inputs: Any, output: Any) -> None:
                     f"Expected tensor router outputs for '{router_key}', "
                     f"got scores={type(router_scores)} indices={type(router_indices)}"
                 )
+            indices = router_indices.detach().cpu().to(torch.int32)
+            scores = router_scores.detach().cpu().to(torch.float32)
             route = RouterCallRoute(
-                expert_indices=router_indices.detach().cpu().to(torch.int32),
-                expert_probs=router_scores.detach().cpu().to(torch.float32),
-                expert_mask=torch.ones_like(
-                    router_indices.detach().cpu(), dtype=torch.bool
-                ),
+                expert_indices=indices,
+                expert_probs=scores,
+                expert_mask=torch.ones_like(indices, dtype=torch.bool),
                 num_experts=_hf_router_num_experts(module, router_scores),
                 sample_index=self._active_sample_index,
                 micro_slot=(
@@ -222,10 +292,66 @@ def _hook(_module: Any, _inputs: Any, output: Any) -> None:
                     else self._active_micro_slot
                 ),
             )
+            if self._active_token_uids is not None:
+                self._assemble_route(router_key, route)
+                return
             self._routes[router_key][len(self._routes[router_key])] = route
 
         return _hook
 
+    def _assemble_route(self, router_key: str, route: RouterCallRoute) -> None:
+        token_uids = cast(torch.Tensor, self._active_token_uids).cpu().long()
+        token_span = self._active_token_span
+        if token_span is None or int(token_uids.numel()) != route.num_global_tokens:
+            raise RuntimeError("HF parity route path metadata does not match routes")
+        micro_slot = self._active_micro_slot
+        assembled = self._assembled_routes[router_key].get(micro_slot)
+        filled = self._assembled_filled[router_key].get(micro_slot)
+        if assembled is None:
+            assembled = route.model_copy(
+                update={
+                    "expert_indices": torch.full(
+                        (token_span, route.max_topk), -1, dtype=torch.int32
+                    ),
+                    "expert_probs": torch.zeros(
+                        (token_span, route.max_topk), dtype=torch.float32
+                    ),
+                    "expert_mask": torch.zeros(
+                        (token_span, route.max_topk), dtype=torch.bool
+                    ),
+                }
+            )
+            filled = torch.zeros(token_span, dtype=torch.bool)
+            self._assembled_routes[router_key][micro_slot] = assembled
+            self._assembled_filled[router_key][micro_slot] = filled
+        assert filled is not None
+        repeated = filled.index_select(0, token_uids)
+        if bool(repeated.any()):
+            path_rows = torch.where(repeated)[0]
+            existing_rows = token_uids.index_select(0, path_rows)
+            if not torch.equal(
+                assembled.expert_indices.index_select(0, existing_rows),
+                route.expert_indices.index_select(0, path_rows),
+            ):
+                raise RuntimeError("HF parity repeated path changed expert ids")
+            assert assembled.expert_probs is not None
+            assert route.expert_probs is not None
+            if not torch.allclose(
+                assembled.expert_probs.index_select(0, existing_rows),
+                route.expert_probs.index_select(0, path_rows),
+                rtol=3e-5,
+                atol=3e-6,
+            ):
+                raise RuntimeError("HF parity repeated path changed expert scores")
+        assembled.expert_indices.index_copy_(0, token_uids, route.expert_indices)
+        assert assembled.expert_probs is not None
+        assert route.expert_probs is not None
+        assembled.expert_probs.index_copy_(0, token_uids, route.expert_probs)
+        assert assembled.expert_mask is not None
+        assert route.expert_mask is not None
+        assembled.expert_mask.index_copy_(0, token_uids, route.expert_mask)
+        filled.index_fill_(0, token_uids, True)
+
 
 def _debug(message: str) -> None:
     if os.environ.get(HF_PARITY_DEBUG_ENV, "").strip().lower() not in {
@@ -350,12 +476,15 @@ def _load_hf_model(
     num_layers: int,
     device: torch.device,
     dtype: torch.dtype,
+    allow_unvalidated_arch: bool,
 ) -> Any:
     from transformers import AutoConfig, AutoModelForCausalLM
 
     from art.megatron.model_support.registry import get_model_support_handler
 
-    handler = get_model_support_handler(base_model)
+    handler = get_model_support_handler(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
     ensure_hf_reference_registered = getattr(
         handler, "ensure_hf_reference_registered", None
     )
@@ -384,12 +513,18 @@ def _load_hf_model(
         **extra_kwargs,
     )
     model.train()
-    return cast(Any, model).to(device)
+    model = cast(Any, model).to(device)
+    prepare_hf_reference_model = getattr(handler, "prepare_hf_reference_model", None)
+    if prepare_hf_reference_model is not None:
+        model = prepare_hf_reference_model(model)
+    return model
 
 
 def _collect_hf_grads(model: Any) -> dict[str, torch.Tensor]:
     grads: dict[str, torch.Tensor] = {}
     for name, param in model.named_parameters():
+        if not param.requires_grad:
+            continue
         grad = param.grad
         if grad is None:
             grad = torch.zeros_like(param)
@@ -410,20 +545,27 @@ def _normalize_hf_reference_state_for_hf_parity(
     base_model: str,
     model: Any,
     state: dict[str, torch.Tensor],
+    allow_unvalidated_arch: bool,
 ) -> dict[str, torch.Tensor]:
     from art.megatron.model_support.registry import get_model_support_handler
 
-    handler = get_model_support_handler(base_model)
+    handler = get_model_support_handler(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
     normalize = getattr(handler, "normalize_hf_reference_state_for_hf_parity", None)
     if normalize is not None:
         normalize(state, config=model.config)
     return state
 
 
-def _use_hf_reference_state_for_hf_parity(base_model: str) -> bool:
+def _use_hf_reference_state_for_hf_parity(
+    base_model: str, *, allow_unvalidated_arch: bool
+) -> bool:
     from art.megatron.model_support.registry import get_model_support_handler
 
-    handler = get_model_support_handler(base_model)
+    handler = get_model_support_handler(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
     enabled = getattr(handler, "use_hf_reference_state_for_hf_parity", None)
     return bool(enabled()) if enabled is not None else False
 
@@ -582,6 +724,154 @@ def _focus_derivative_tensor_map(
     return focused
 
 
+def _dense_prefix_tree_attention_mask(
+    *,
+    group_ids: torch.Tensor,
+    parent_ids: torch.Tensor,
+    position_ids: torch.Tensor,
+    device: torch.device,
+    dtype: torch.dtype,
+    sliding_window: int | None = None,
+) -> torch.Tensor:
+    context = prepare_block_mask_context(
+        group_ids=group_ids,
+        parent_ids=parent_ids,
+        input_pos=position_ids,
+    )
+    seq_len = int(group_ids.numel())
+    absolute = torch.arange(seq_len)
+    group_enter = torch.from_numpy(context.group_enter_np)
+    group_exit = torch.from_numpy(context.group_exit_np)
+    allowed = (absolute[:, None] >= absolute[None, :]) & (
+        (group_enter[None, :] <= group_enter[:, None])
+        & (group_enter[:, None] < group_exit[None, :])
+    )
+    if sliding_window is not None:
+        positions = position_ids.detach().cpu().reshape(-1)
+        delta = positions[:, None] - positions[None, :]
+        allowed &= (delta >= 0) & (delta < sliding_window)
+    mask = torch.full(
+        (seq_len, seq_len),
+        torch.finfo(dtype).min,
+        device=device,
+        dtype=dtype,
+    )
+    return mask.masked_fill(allowed.to(device), 0).unsqueeze(0).unsqueeze(0)
+
+
+def _hf_prefix_tree_forward_inputs(
+    model: Any,
+    micro: dict[str, torch.Tensor],
+    *,
+    actual_len: int,
+    device: torch.device,
+    dtype: torch.dtype,
+) -> tuple[torch.Tensor | dict[str, torch.Tensor], torch.Tensor]:
+    group_ids = micro["group_ids"].reshape(-1)[:actual_len]
+    parent_ids = micro["parent_ids"].reshape(-1)[:actual_len]
+    position_ids = micro["position_ids"].reshape(-1)[:actual_len]
+    full_mask = _dense_prefix_tree_attention_mask(
+        group_ids=group_ids,
+        parent_ids=parent_ids,
+        position_ids=position_ids,
+        device=device,
+        dtype=dtype,
+    )
+    config = model.config
+    get_text_config = getattr(config, "get_text_config", None)
+    text_config = get_text_config() if callable(get_text_config) else config
+    layer_types = tuple(getattr(text_config, "layer_types", ()))
+    attention_mask: torch.Tensor | dict[str, torch.Tensor] = full_mask
+    if "sliding_attention" in layer_types:
+        attention_mask = {
+            "full_attention": full_mask,
+            "sliding_attention": _dense_prefix_tree_attention_mask(
+                group_ids=group_ids,
+                parent_ids=parent_ids,
+                position_ids=position_ids,
+                device=device,
+                dtype=dtype,
+                sliding_window=int(text_config.sliding_window),
+            ),
+        }
+    return attention_mask, position_ids.unsqueeze(0).to(device=device)
+
+
+def _prepare_hf_parity_megatron_micro(
+    micro: dict[str, torch.Tensor],
+    *,
+    device: torch.device,
+    provider: Any,
+    model_support_handler: Any,
+) -> megatron_train.PreparedSFTMicroInputs:
+    prepared = megatron_train._prepare_dense_sft_micro(
+        micro,
+        device=device,
+        provider=provider,
+        model_support_handler=model_support_handler,
+    )
+    seq_len = int(prepared.input_ids.shape[1])
+    position_ids = micro["position_ids"].reshape(-1)[:seq_len].unsqueeze(0)
+    attention_state = create_prefix_tree_state(
+        group_ids=micro["group_ids"].reshape(-1)[:seq_len].unsqueeze(0),
+        parent_ids=micro["parent_ids"].reshape(-1)[:seq_len].unsqueeze(0),
+        target_device=device,
+        input_pos=position_ids,
+        sliding_windows=megatron_microbatches._art_flex_sliding_windows(provider),
+        build_gdn_execution_spec=bool(
+            getattr(model_support_handler, "build_gdn_execution_spec", False)
+        ),
+        model_support_handler=model_support_handler,
+        attention_head_dim=getattr(provider, "kv_channels", None),
+        attention_value_head_dim=getattr(provider, "kv_channels", None),
+        gdn_planner_config=megatron_microbatches._gdn_planner_config_for_provider(
+            provider,
+            model_support_handler,
+        ),
+    )
+    return prepared.model_copy(
+        update={
+            "position_ids": position_ids.to(device=device),
+            "attention_state": attention_state,
+        }
+    )
+
+
+def _hf_requires_recurrent_prefix_paths(
+    base_model: str, *, allow_unvalidated_arch: bool
+) -> bool:
+    from art.megatron.model_support.registry import get_model_support_handler
+
+    handler = get_model_support_handler(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
+    return bool(getattr(handler, "build_gdn_execution_spec", False))
+
+
+def _prepare_hf_reference_forward(
+    model: Any,
+    micro: dict[str, torch.Tensor],
+    *,
+    base_model: str,
+    actual_len: int,
+    allow_unvalidated_arch: bool,
+) -> None:
+    from art.megatron.model_support.registry import get_model_support_handler
+
+    handler = get_model_support_handler(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
+    prepare_forward = getattr(handler, "prepare_hf_reference_forward", None)
+    if prepare_forward is None:
+        return
+    prepare_forward(
+        model,
+        position_ids=micro["position_ids"].reshape(-1)[:actual_len],
+        group_ids=micro["group_ids"].reshape(-1)[:actual_len],
+        parent_ids=micro["parent_ids"].reshape(-1)[:actual_len],
+    )
+
+
 def _run_hf_sft_step(
     *,
     base_model: str,
@@ -591,6 +881,7 @@ def _run_hf_sft_step(
     topology: ReplayParallelTopology,
     device: torch.device,
     dtype: torch.dtype,
+    allow_unvalidated_arch: bool,
 ) -> tuple[
     torch.Tensor,
     torch.Tensor,
@@ -604,9 +895,13 @@ def _run_hf_sft_step(
         num_layers=num_layers,
         device=device,
         dtype=dtype,
+        allow_unvalidated_arch=allow_unvalidated_arch,
     )
     if dtype == torch.float32:
         _install_hf_qwen35_gdn_fp32_reference(model, base_model=base_model)
+    recurrent_prefix_paths = _hf_requires_recurrent_prefix_paths(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
     route_capture = _HfMoeRoutingCapture(model)
     _debug("running HF forward/backward")
     model.zero_grad(set_to_none=True)
@@ -623,15 +918,45 @@ def _run_hf_sft_step(
     for micro_slot, (micro, sample_index) in enumerate(
         zip(micro_inputs, sample_indices, strict=True)
     ):
-        route_capture.set_active_micro(sample_index, micro_slot)
         attention_mask = micro["attention_mask"].reshape(-1)
         actual_len = max(int(attention_mask.sum().item()), 1)
+        if recurrent_prefix_paths:
+            micro_losses = _run_hf_recurrent_prefix_tree_micro(
+                model=model,
+                route_capture=route_capture,
+                micro=micro,
+                sample_index=sample_index,
+                micro_slot=micro_slot,
+                actual_len=actual_len,
+                total_token_count=total_token_count,
+                device=device,
+                dtype=dtype,
+            )
+            trainable_losses.append(micro_losses.detach().cpu())
+            loss_sum = loss_sum + micro_losses.detach().sum()
+            token_count += int(micro_losses.numel())
+            continue
+        route_capture.set_active_micro(sample_index, micro_slot)
+        _prepare_hf_reference_forward(
+            model,
+            micro,
+            base_model=base_model,
+            actual_len=actual_len,
+            allow_unvalidated_arch=allow_unvalidated_arch,
+        )
         input_ids = micro["input_ids"].reshape(-1)[:actual_len].unsqueeze(0).to(device)
         labels = micro["labels"].reshape(-1)[:actual_len].unsqueeze(0).to(device)
-        hf_attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=device)
+        hf_attention_mask, position_ids = _hf_prefix_tree_forward_inputs(
+            model,
+            micro,
+            actual_len=actual_len,
+            device=device,
+            dtype=dtype,
+        )
         logits = model(
             input_ids=input_ids,
             attention_mask=hf_attention_mask,
+            position_ids=position_ids,
             use_cache=False,
         ).logits
         shifted_labels = megatron_train.shift_tensor(labels, -100)
@@ -653,8 +978,11 @@ def _run_hf_sft_step(
             base_model=base_model,
             model=model,
             state=_collect_hf_state_dict(model),
+            allow_unvalidated_arch=allow_unvalidated_arch,
+        )
+        if _use_hf_reference_state_for_hf_parity(
+            base_model, allow_unvalidated_arch=allow_unvalidated_arch
         )
-        if _use_hf_reference_state_for_hf_parity(base_model)
         else None
     )
     routing_replay_bundle = route_capture.build_replay_bundle(topology=topology)
@@ -674,6 +1002,112 @@ def _run_hf_sft_step(
     )
 
 
+def _run_hf_recurrent_prefix_tree_micro(
+    *,
+    model: Any,
+    route_capture: _HfMoeRoutingCapture,
+    micro: dict[str, torch.Tensor],
+    sample_index: int | None,
+    micro_slot: int,
+    actual_len: int,
+    total_token_count: int,
+    device: torch.device,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    input_ids = micro["input_ids"].reshape(-1)[:actual_len]
+    labels = micro["labels"].reshape(-1)[:actual_len]
+    position_ids = micro["position_ids"].reshape(-1)[:actual_len]
+    shifted_labels = megatron_train.shift_tensor(labels.unsqueeze(0), -100)[0]
+    expected_mask = shifted_labels != -100
+    claimed_mask = torch.zeros(actual_len, dtype=torch.bool)
+    claimed_targets = torch.full((actual_len,), -100, dtype=labels.dtype)
+    packed_losses = torch.empty(actual_len, dtype=torch.float32)
+    for path_indices in _hf_prefix_tree_paths(micro, actual_len=actual_len):
+        route_capture.set_active_micro(
+            sample_index,
+            micro_slot,
+            token_uids=path_indices,
+            token_span=actual_len,
+        )
+        path_input_ids = input_ids.index_select(0, path_indices).unsqueeze(0).to(device)
+        path_labels = labels.index_select(0, path_indices).unsqueeze(0).to(device)
+        path_positions = (
+            position_ids.index_select(0, path_indices).unsqueeze(0).to(device)
+        )
+        logits = model(
+            input_ids=path_input_ids,
+            attention_mask=torch.ones_like(path_input_ids, dtype=dtype),
+            position_ids=path_positions,
+            use_cache=False,
+        ).logits
+        path_shifted_labels = megatron_train.shift_tensor(path_labels, -100)[0]
+        per_token_loss = F.cross_entropy(
+            logits.float().reshape(-1, logits.shape[-1]),
+            path_shifted_labels,
+            reduction="none",
+            ignore_index=-100,
+        )
+        path_mask = path_shifted_labels != -100
+        path_uids = path_indices[path_mask.cpu()]
+        path_targets = path_shifted_labels[path_mask].detach().cpu()
+        repeated = claimed_mask.index_select(0, path_uids)
+        if bool(repeated.any()) and not torch.equal(
+            claimed_targets.index_select(0, path_uids[repeated]),
+            path_targets[repeated],
+        ):
+            raise RuntimeError("HF prefix paths assign different targets to one token")
+        unclaimed = ~repeated
+        selected_uids = path_uids[unclaimed]
+        selected_losses = per_token_loss[path_mask][unclaimed.to(device)]
+        packed_losses.index_copy_(0, selected_uids, selected_losses.detach().cpu())
+        claimed_targets.index_copy_(0, selected_uids, path_targets[unclaimed])
+        claimed_mask.index_fill_(0, selected_uids, True)
+        if selected_losses.numel():
+            (selected_losses.sum() / total_token_count).backward()
+    if not torch.equal(claimed_mask, expected_mask.cpu()):
+        missing = torch.where(expected_mask.cpu() & ~claimed_mask)[0].tolist()
+        extra = torch.where(claimed_mask & ~expected_mask.cpu())[0].tolist()
+        raise RuntimeError(
+            "HF prefix paths do not preserve packed loss positions: "
+            f"missing={missing} extra={extra}"
+        )
+    return packed_losses[expected_mask.cpu()]
+
+
+def _hf_prefix_tree_paths(
+    micro: dict[str, torch.Tensor], *, actual_len: int
+) -> tuple[torch.Tensor, ...]:
+    row = parse_prefix_tree_row(
+        group_ids=micro["group_ids"].reshape(-1)[:actual_len],
+        parent_ids=micro["parent_ids"].reshape(-1)[:actual_len],
+    )
+    if row.valid_tokens != actual_len:
+        raise RuntimeError(
+            f"HF prefix tree covers {row.valid_tokens}/{actual_len} valid tokens"
+        )
+    by_group = {segment.group_id: segment for segment in row.segments}
+    parent_groups = {
+        segment.parent_id
+        for segment in row.segments
+        if segment.parent_id != segment.group_id
+    }
+    paths: list[torch.Tensor] = []
+    for leaf in row.segments:
+        if leaf.group_id in parent_groups:
+            continue
+        path_segments = [by_group[group_id] for group_id in leaf.ancestors]
+        path_segments.append(leaf)
+        paths.append(
+            torch.cat(
+                [
+                    torch.arange(segment.start, segment.end, dtype=torch.long)
+                    for segment in path_segments
+                ]
+            )
+        )
+    return tuple(paths)
+
+
 def _install_hf_qwen35_gdn_fp32_reference(model: Any, *, base_model: str) -> None:
     model_key = base_model.lower()
     if "qwen3.5" not in model_key and "qwen3_5" not in model_key:
@@ -696,7 +1130,8 @@ def _build_megatron_runtime(
     moe_routing_replay_bundle: MoeRoutingReplayBundle | None = None,
 ) -> megatron_train.TrainingRuntime:
     use_hf_reference_state = _use_hf_reference_state_for_hf_parity(
-        request.case_config.base_model
+        request.case_config.base_model,
+        allow_unvalidated_arch=request.case_config.allow_unvalidated_arch,
     )
     return megatron_train.build_training_runtime(
         model_identifier=request.case_config.base_model,
@@ -789,7 +1224,18 @@ def _build_hf_parity_conversion_tasks(
     hf_keys: set[str],
 ) -> list[Any]:
     tasks = []
-    for task in build_art_conversion_tasks(bridge=bridge, model=model):
+    registry_type = type(bridge._model_bridge.mapping_registry())
+    lookup = registry_type.megatron_to_hf_lookup
+
+    def permissive_lookup(registry: Any, name: str) -> Any:
+        mapping = lookup(registry, name)
+        if mapping is not None:
+            mapping.allow_hf_name_mismatch = True
+        return mapping
+
+    with patch.object(registry_type, "megatron_to_hf_lookup", permissive_lookup):
+        conversion_tasks = build_art_conversion_tasks(bridge=bridge, model=model)
+    for task in conversion_tasks:
         mapping_names = _hf_param_names_for_mapping(task.mapping)
         if not mapping_names:
             tasks.append(task)
@@ -1076,6 +1522,18 @@ def _run_megatron_sft_step(
         )
     _debug("initializing Megatron optimizer state")
     megatron_train._eager_initialize_optimizer_state(runtime.optimizer)
+    session = active_base_megatron_session()
+    if session is not None:
+        session.capture_runtime(
+            runtime,
+            key=BaseMegatronSessionKey(
+                base_model=request.case_config.base_model,
+                model_key=runtime.model_support_spec.key,
+                num_layers=request.case_config.num_layers,
+                precision=request.case_config.precision,
+                allow_unvalidated_arch=request.case_config.allow_unvalidated_arch,
+            ),
+        )
     _debug(f"built {len(tasks)} Megatron conversion tasks")
     for chunk in runtime.model:
         if hasattr(chunk, "zero_grad_buffer"):
@@ -1091,7 +1549,7 @@ def _run_megatron_sft_step(
                 sample_indices[micro_order],
                 micro_order,
             )
-        prepared_micro = megatron_train._prepare_dense_sft_micro(
+        prepared_micro = _prepare_hf_parity_megatron_micro(
             micro,
             device=device,
             provider=runtime.provider,
@@ -1133,6 +1591,7 @@ def _run_megatron_sft_step(
     derivative_tasks = [
         task
         for task in tasks
+        if cast(torch.nn.Parameter, task.param_weight).requires_grad
         if _mapping_supports_derivative_parity(task.mapping)
         and _mapping_targets_language_only(task.mapping)
     ]
@@ -1183,10 +1642,29 @@ def _drop_gemma4_reparameterized_norm_grads(
     }
 
 
+def _validate_distributed_process_env() -> None:
+    missing = [name for name in _DISTRIBUTED_PROCESS_ENV if not os.environ.get(name)]
+    if missing:
+        raise RuntimeError(
+            f"HF parity worker requires explicit distributed environment: {missing}"
+        )
+    rank = int(os.environ["RANK"])
+    world_size = int(os.environ["WORLD_SIZE"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    local_world_size = int(os.environ["LOCAL_WORLD_SIZE"])
+    if not 0 <= rank < world_size or not 0 <= local_rank < local_world_size:
+        raise RuntimeError(
+            "Invalid HF parity rank environment: "
+            f"rank={rank}/{world_size} local_rank={local_rank}/{local_world_size}"
+        )
+
+
 def _worker_run(request: HfParityRunRequest) -> None:
+    _validate_distributed_process_env()
     if not torch.cuda.is_available():
         raise RuntimeError("HF parity requires at least one CUDA device")
     torch.cuda.set_device(0)
+    initialize_single_rank_process_group()
     _set_deterministic_seed(request.case_config.seed)
     _configure_cuda_precision(request.case_config)
     _enable_debug_traceback_dump()
@@ -1197,6 +1675,14 @@ def _worker_run(request: HfParityRunRequest) -> None:
     trajectory_tensors = build_sft_trajectory_tensors_from_packed_tensors(
         packed_tensors
     )
+    for index, trajectory in enumerate(trajectory_tensors):
+        trajectory.update(
+            {
+                "group_ids": packed_tensors["group_ids"][index].detach().clone(),
+                "parent_ids": packed_tensors["parent_ids"][index].detach().clone(),
+                "position_ids": packed_tensors["input_pos"][index].detach().clone(),
+            }
+        )
     zero_template = megatron_train._zero_contribution_sft_inputs(trajectory_tensors[0])
     sample_indices = build_parity_sample_indices(
         num_sequences=len(trajectory_tensors),
@@ -1246,6 +1732,7 @@ def _worker_run(request: HfParityRunRequest) -> None:
             topology=replay_topology,
             device=device,
             dtype=dtype,
+            allow_unvalidated_arch=request.case_config.allow_unvalidated_arch,
         )
         megatron_outputs, megatron_loss, megatron_grads = _run_megatron_sft_step(
             request=request,
@@ -1295,11 +1782,18 @@ def _worker_run(request: HfParityRunRequest) -> None:
         )
         outputs_summary = summarize_tensor_pair(hf_outputs, megatron_outputs)
         loss_summary = summarize_tensor_pair(hf_loss, megatron_loss)
+        from art.megatron.model_support.registry import get_model_support_handler
+
+        handler = get_model_support_handler(
+            request.case_config.base_model,
+            allow_unvalidated_arch=request.case_config.allow_unvalidated_arch,
+        )
         grads_rows = build_tensor_map_metric_rows(
             phase="grads",
             reference=normalized_hf_grads,
             candidate=megatron_grads,
-            phase_pass_fns=_hf_parity_phase_pass_fns(),
+            phase_pass_fns=_hf_parity_phase_pass_fns_for_case(request.case_config),
+            group_by=getattr(handler, "hf_parity_gradient_group", None),
         )
         report = build_hf_parity_report(
             request=request,
@@ -1314,7 +1808,11 @@ def _worker_run(request: HfParityRunRequest) -> None:
         _debug("wrote HF parity report")
     finally:
         flex_patch_stack.close()
-        if torch.distributed.is_initialized():  # ty: ignore[possibly-missing-attribute]
+        session = active_base_megatron_session()
+        if (
+            (session is None or session.runtime is None)
+            and torch.distributed.is_initialized()  # ty: ignore[possibly-missing-attribute]
+        ):
             torch.distributed.destroy_process_group()  # ty: ignore[possibly-missing-attribute]
 
 
diff --git a/tests/integration/megatron/model_support/lora_coverage.py b/tests/integration/megatron/model_support/lora_coverage.py
index eb06182c2..d6d50e92a 100644
--- a/tests/integration/megatron/model_support/lora_coverage.py
+++ b/tests/integration/megatron/model_support/lora_coverage.py
@@ -2,7 +2,6 @@
 
 from collections.abc import Iterator
 from contextlib import contextmanager
-import socket
 from typing import Any
 
 from megatron.core import parallel_state as ps
@@ -11,13 +10,13 @@
 import torch
 from torch.distributed import (
     destroy_process_group,
-    init_process_group,
     is_initialized,
 )
 
 from art.megatron import train as megatron_train
 from art.megatron.lora import LoRA
 
+from .base_megatron_session import initialize_single_rank_process_group
 from .fp32_grouped_gemm import (
     allow_fp32_grouped_gemm_fallback_for_model_support_tests,
 )
@@ -29,6 +28,8 @@
 _WRAPPED_TARGET_SUFFIXES: dict[str, tuple[str, ...]] = {
     "q_a_proj": (".self_attn.q_a_proj",),
     "q_b_proj": (".self_attn.q_b_proj",),
+    "kv_a_proj_with_mqa": (".self_attn.kv_a_proj_with_mqa",),
+    "kv_b_proj": (".self_attn.kv_b_proj",),
     "kv_proj": (".self_attn.kv_proj",),
     "o_a_proj": (".self_attn.o_a_proj",),
     "o_b_proj": (".self_attn.o_b_proj",),
@@ -61,12 +62,8 @@ class LoraCoverageReport(BaseModel):
     wrapped_adapter_prefix_count: int = 0
     export_base_count: int = 0
     export_adapter_count: int = 0
-
-
-def _find_free_port() -> int:
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
-        sock.bind(("127.0.0.1", 0))
-        return int(sock.getsockname()[1])
+    trainable_lora_parameter_count: int = 0
+    unexpected_trainable_parameter_names: list[str] = Field(default_factory=list)
 
 
 @contextmanager
@@ -76,12 +73,7 @@ def _single_rank_model_parallel() -> Iterator[None]:
     if is_initialized():
         raise RuntimeError("torch.distributed is already initialized in this process.")
     torch.cuda.set_device(0)
-    init_process_group(
-        backend="nccl",
-        init_method=f"tcp://127.0.0.1:{_find_free_port()}",
-        rank=0,
-        world_size=1,
-    )
+    initialize_single_rank_process_group()
     try:
         ps.initialize_model_parallel(
             tensor_model_parallel_size=1,
@@ -115,10 +107,22 @@ def _covered_wrapped_target_modules(adapter_prefixes: set[str]) -> set[str]:
 
 
 def _covered_exported_target_modules(
-    adapter_weights_by_base: dict[str, list[Any]],
+    adapter_weights_by_base: dict[str, list[Any | str | None]],
 ) -> set[str]:
     covered: set[str] = set()
     for base_name, adapter_weights in adapter_weights_by_base.items():
+        if base_name.endswith(".self_attention.linear_q_down_proj.weight"):
+            covered.add("q_a_proj")
+            continue
+        if base_name.endswith(".self_attention.linear_q_up_proj.weight"):
+            covered.add("q_b_proj")
+            continue
+        if base_name.endswith(".self_attention.linear_kv_down_proj.weight"):
+            covered.add("kv_a_proj_with_mqa")
+            continue
+        if base_name.endswith(".self_attention.linear_kv_up_proj.weight"):
+            covered.add("kv_b_proj")
+            continue
         if base_name.endswith(".self_attention.wq_a.weight"):
             covered.add("q_a_proj")
             continue
@@ -142,7 +146,11 @@ def _covered_exported_target_modules(
             continue
         if base_name.endswith(".self_attention.linear_qkv.weight"):
             for adapter_weight in adapter_weights:
-                adapter_key = getattr(adapter_weight, "adapter_key", None)
+                adapter_key = (
+                    adapter_weight
+                    if isinstance(adapter_weight, str) or adapter_weight is None
+                    else getattr(adapter_weight, "adapter_key", None)
+                )
                 if adapter_key == "adapter_q":
                     covered.add("q_proj")
                 elif adapter_key == "adapter_k":
@@ -176,6 +184,34 @@ def _covered_exported_target_modules(
     return covered
 
 
+def build_lora_coverage_report(
+    *,
+    base_model: str,
+    target_modules: list[str],
+    adapter_prefixes: set[str],
+    adapter_weights_by_base: dict[str, list[Any | str | None]],
+    trainable_lora_parameter_names: set[str] | None = None,
+    unexpected_trainable_parameter_names: set[str] | None = None,
+) -> LoraCoverageReport:
+    wrapped = sorted(_covered_wrapped_target_modules(adapter_prefixes))
+    exported = sorted(_covered_exported_target_modules(adapter_weights_by_base))
+    return LoraCoverageReport(
+        base_model=base_model,
+        target_modules=target_modules,
+        wrapped_target_modules=wrapped,
+        exported_target_modules=exported,
+        missing_wrapped_target_modules=sorted(set(target_modules) - set(wrapped)),
+        missing_exported_target_modules=sorted(set(target_modules) - set(exported)),
+        wrapped_adapter_prefix_count=len(adapter_prefixes),
+        export_base_count=len(adapter_weights_by_base),
+        export_adapter_count=sum(map(len, adapter_weights_by_base.values())),
+        trainable_lora_parameter_count=len(trainable_lora_parameter_names or ()),
+        unexpected_trainable_parameter_names=sorted(
+            unexpected_trainable_parameter_names or ()
+        ),
+    )
+
+
 def run_lora_coverage(case_config: OracleCaseConfig) -> LoraCoverageReport:
     topology = oracle_topology(is_moe=case_config.is_moe)
     with _single_rank_model_parallel():
@@ -200,25 +236,9 @@ def run_lora_coverage(case_config: OracleCaseConfig) -> LoraCoverageReport:
             runtime.provider_bundle.handler.build_adapter_weights_by_base(runtime.model)
         )
 
-    target_modules = list(runtime.provider_bundle.spec.default_target_modules)
-    wrapped_target_modules = sorted(_covered_wrapped_target_modules(adapter_prefixes))
-    exported_target_modules = sorted(
-        _covered_exported_target_modules(adapter_weights_by_base)
-    )
-    return LoraCoverageReport(
+    return build_lora_coverage_report(
         base_model=case_config.base_model,
-        target_modules=target_modules,
-        wrapped_target_modules=wrapped_target_modules,
-        exported_target_modules=exported_target_modules,
-        missing_wrapped_target_modules=sorted(
-            set(target_modules) - set(wrapped_target_modules)
-        ),
-        missing_exported_target_modules=sorted(
-            set(target_modules) - set(exported_target_modules)
-        ),
-        wrapped_adapter_prefix_count=len(adapter_prefixes),
-        export_base_count=len(adapter_weights_by_base),
-        export_adapter_count=sum(
-            len(adapter_weights) for adapter_weights in adapter_weights_by_base.values()
-        ),
+        target_modules=list(runtime.provider_bundle.spec.default_target_modules),
+        adapter_prefixes=adapter_prefixes,
+        adapter_weights_by_base=adapter_weights_by_base,
     )
diff --git a/tests/integration/megatron/model_support/oracle_harness.py b/tests/integration/megatron/model_support/oracle_harness.py
index 35da59e98..5f7976fff 100644
--- a/tests/integration/megatron/model_support/oracle_harness.py
+++ b/tests/integration/megatron/model_support/oracle_harness.py
@@ -7,6 +7,7 @@
 import os
 from pathlib import Path
 import re
+import secrets
 import shutil
 from typing import Any, Callable, Literal, TypeVar, cast
 
@@ -16,7 +17,13 @@
 from rich.table import Table
 import torch
 
-from art.megatron.routing_replay import ROUTER_KEY_FORMAT_VERSION
+from art.megatron.routing_replay import (
+    ROUTER_KEY_FORMAT_VERSION,
+    MoeRoutingReplayBundle,
+)
+from art.megatron.routing_replay import (
+    ParallelTopology as ReplayParallelTopology,
+)
 from art.megatron.training.streaming_weight_offload import StreamingWeightOffloadConfig
 
 from ..artifacts import GitRepoState, pinned_git_state
@@ -33,7 +40,12 @@
 ORACLE_OBJECTIVE_ENV = "ART_ORACLE_OBJECTIVE"
 ORACLE_BASE_MODEL_ENV = "ART_ORACLE_BASE_MODEL"
 KEEP_TOPOLOGY_ARTIFACTS_ENV = "ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS"
+SHARED_MEMORY_ROOT_ENV = "ART_ORACLE_SHARED_MEMORY_ROOT"
 ORACLE_ARTIFACT_SUITE_NAME = "Megatron oracle artifacts"
+MAX_COMPARISON_BYTES = 8 * 1024**3
+MAX_FAILURE_ROWS = 8
+MAX_FAILURE_VALUES = 32
+COMPARISON_PHASES = "outputs grads deltas forward router_scores router_topk_ids".split()
 
 OracleObjective = Literal["rl", "sft"]
 SUPPORTED_ORACLE_OBJECTIVES: tuple[OracleObjective, ...] = ("rl", "sft")
@@ -214,10 +226,11 @@ def world_size(self) -> int:
         return attention_world
 
 
+# Retained for focused/nightly sentinel runs; normal workflows use compositions below.
 TOPOLOGIES = [
     Topology(tp=1, ep=1, etp=1, dp=1, sp=False),
     Topology(tp=1, ep=2, etp=1, dp=1, cp=2, sp=False),
-    Topology(tp=2, ep=2, etp=1, dp=1, cp=2, sp=True),
+    Topology(tp=1, ep=2, etp=1, dp=1, cp=2, pp=2, vpp=2, sp=False),
     Topology(tp=2, ep=4, etp=2, dp=2, cp=2, sp=True),
 ]
 
@@ -233,15 +246,20 @@ def _without_context_parallel(topology: Topology) -> Topology:
 ]
 DENSE_TOPOLOGIES = [
     Topology(tp=1, ep=1, etp=1, dp=1, sp=False),
-    Topology(tp=2, ep=1, etp=1, dp=1, sp=True),
-    Topology(tp=1, ep=1, etp=1, dp=2, sp=False),
-    Topology(tp=2, ep=1, etp=1, dp=2, sp=True),
-    Topology(tp=1, ep=1, etp=1, dp=1, cp=2, sp=False),
-    Topology(tp=2, ep=1, etp=1, dp=1, cp=2, sp=True),
+    Topology(tp=2, ep=1, etp=1, dp=1, cp=2, sp=False),
     Topology(tp=2, ep=1, etp=1, dp=2, cp=2, sp=True),
 ]
 ORACLE_TOPOLOGY = TOPOLOGIES[0]
 DENSE_ORACLE_TOPOLOGY = DENSE_TOPOLOGIES[0]
+CP_MOE_COMPOSITION_TOPOLOGY = Topology(
+    tp=2, ep=2, etp=2, dp=1, cp=2, pp=2, vpp=2, sp=True
+)
+DENSE_COMPOSITION_TOPOLOGY = Topology(
+    tp=2, ep=1, etp=1, dp=1, cp=2, pp=2, vpp=2, sp=True
+)
+NO_CP_MOE_COMPOSITION_TOPOLOGY = Topology(
+    tp=2, ep=2, etp=2, dp=2, cp=1, pp=2, vpp=2, sp=True
+)
 SENSITIVITY_TOPOLOGY = Topology(tp=2, ep=2, etp=1, dp=1, sp=True)
 CP_ATTENTION_SENSITIVITY_TOPOLOGY = Topology(tp=1, ep=2, etp=1, dp=1, cp=2, sp=False)
 DENSE_SENSITIVITY_TOPOLOGY = Topology(tp=2, ep=1, etp=1, dp=1, sp=True)
@@ -265,11 +283,14 @@ def _without_context_parallel(topology: Topology) -> Topology:
 SENSITIVITY_TOPOLOGY_BY_MUTATION |= {
     k: Topology(tp=1, ep=2, etp=1, dp=2, sp=False)
     for k in [
-        "dp_grad_accumulation_seqs",
         "dp_local_token_normalization",
         "sft_local_token_normalization",
     ]
 }
+# Isolate DP sample assignment from HybridEP's independently planned micro extents.
+SENSITIVITY_TOPOLOGY_BY_MUTATION["dp_grad_accumulation_seqs"] = Topology(
+    tp=1, ep=1, etp=1, dp=2, sp=False
+)
 
 
 class PackedTensorConfig(BaseModel):
@@ -341,6 +362,8 @@ class OracleCaseConfig(BaseModel):
     """Contains all deterministic run parameters for one oracle case."""
 
     base_model: str
+    provider_model: str | None = None
+    model_support_key: str | None = None
     precision: Literal["bf16", "fp32"] = "fp32"
     num_layers: int = 4
     seed: int = 20260304
@@ -356,6 +379,12 @@ class OracleCaseConfig(BaseModel):
 
     @property
     def is_moe(self) -> bool:
+        if self.model_support_key is not None:
+            from art.megatron.model_support.registry import (
+                get_model_support_spec_by_key,
+            )
+
+            return get_model_support_spec_by_key(self.model_support_key).is_moe
         from art.megatron.model_support import model_uses_expert_parallel
 
         return model_uses_expert_parallel(
@@ -394,6 +423,8 @@ class WorkerRunRequest(BaseModel):
     topology_dir: str
     packed_tensors: DiskPackedTensorsSpec
     shared_init_adapter_path: str
+    comparison_dir: str = Field(default_factory=lambda: str(_new_comparison_dir()))
+    prepare_moe_routing_replay: bool = False
     mutation: SensitivityMutation | None = None
     moe_routing_replay_path: str | None = None
     moe_routing_replay_strict: bool = True
@@ -407,7 +438,7 @@ class WorkerRunRequest(BaseModel):
 
 
 class StepTrace(BaseModel):
-    """Tracks per-step trace artifact filenames and loss metadata."""
+    """Tracks one step's compact loss and sample metadata."""
 
     step_index: int
     loss: float
@@ -415,10 +446,6 @@ class StepTrace(BaseModel):
     micro_sample_indices: list[int | None] = Field(default_factory=list)
     micro_losses: list[float] = Field(default_factory=list)
     debug_files: dict[str, str] = Field(default_factory=dict)
-    output_file: str
-    grads_file: str
-    deltas_file: str
-    lora_file: str
 
 
 class RunManifest(BaseModel):
@@ -433,6 +460,7 @@ class RunManifest(BaseModel):
     world_size: int
     seed: int
     num_steps: int
+    comparison_dir: str | None = None
     packed_tensors: DiskPackedTensorsSpec
     offload_between_jobs: bool = True
     streaming_weight_offload: StreamingWeightOffloadConfig = Field(
@@ -499,7 +527,7 @@ def resolved_reference_slug(self) -> str:
 
 
 class VariantReport(BaseModel):
-    """Captures full comparison output for one variant run."""
+    """Captures compact comparison output for one variant run."""
 
     git: GitRepoState
     case_id: str
@@ -510,7 +538,6 @@ class VariantReport(BaseModel):
     signal: Literal["pass", "fail"]
     pass_count: int
     fail_count: int
-    step_summaries: dict[int, dict[str, Any]] = Field(repr=False)
     metrics: list[MetricRow] = Field(repr=False)
 
 
@@ -768,35 +795,41 @@ def oracle_topology(*, is_moe: bool = True) -> Topology:
     return ORACLE_TOPOLOGY if is_moe else DENSE_ORACLE_TOPOLOGY
 
 
-def _filter_context_parallel_support(
-    topologies: list[Topology],
-    *,
-    is_moe: bool,
-    cp_supported: bool,
-) -> list[Topology]:
-    if cp_supported:
-        return topologies
-    if is_moe:
-        return list(CP_UNSUPPORTED_MOE_TOPOLOGIES)
-    return [_without_context_parallel(topology) for topology in topologies]
-
-
 def selected_suite_topologies(
     *,
     is_moe: bool = True,
     cp_supported: bool = True,
 ) -> list[Topology]:
-    """Returns the correctness topology list for a model family."""
-    return _filter_context_parallel_support(
-        list(TOPOLOGIES if is_moe else DENSE_TOPOLOGIES),
-        is_moe=is_moe,
-        cp_supported=cp_supported,
-    )
+    """Returns TP1 plus one composed correctness topology for a model family."""
+    if is_moe:
+        composition = (
+            CP_MOE_COMPOSITION_TOPOLOGY
+            if cp_supported
+            else NO_CP_MOE_COMPOSITION_TOPOLOGY
+        )
+    else:
+        composition = DENSE_COMPOSITION_TOPOLOGY
+    return [oracle_topology(is_moe=is_moe), composition]
 
 
 def stable_case_id(case_config: OracleCaseConfig) -> str:
     """Builds a deterministic case id from case config contents."""
     payload = case_config.model_dump(mode="json")
+    if case_config.model_support_key is not None:
+        from art.megatron.model_support.registry import get_model_support_spec_by_key
+
+        payload["runtime_target_modules"] = list(
+            get_model_support_spec_by_key(
+                case_config.model_support_key
+            ).default_target_modules
+        )
+    else:
+        from art.megatron.model_support import default_target_modules_for_model
+
+        payload["runtime_target_modules"] = default_target_modules_for_model(
+            case_config.base_model,
+            allow_unvalidated_arch=case_config.allow_unvalidated_arch,
+        )
     encoded = json.dumps(payload, sort_keys=True, separators=(",", ":"))
     digest = hashlib.sha256(encoded.encode("utf-8")).hexdigest()[:16]
     model_tag = (
@@ -821,6 +854,75 @@ def _read_json(path: Path) -> dict[str, Any]:
         return json.load(handle)
 
 
+def _comparison_dir(path: str | Path) -> Path:
+    path = Path(path)
+    root = Path(os.environ.get(SHARED_MEMORY_ROOT_ENV, "/dev/shm"))
+    if path.parent.resolve() != root.resolve() or not path.name.startswith(
+        "art_oracle_"
+    ):
+        raise RuntimeError(f"Invalid oracle comparison directory: {path}")
+    return path
+
+
+def _new_comparison_dir() -> Path:
+    root = Path(os.environ.get(SHARED_MEMORY_ROOT_ENV, "/dev/shm"))
+    if not root.is_dir():
+        raise RuntimeError(f"Oracle shared-memory root is unavailable: {root}")
+    return root / f"art_oracle_{os.getpid()}_{secrets.token_hex(8)}"
+
+
+def _remove_comparison_dir(path: str | Path) -> None:
+    path = _comparison_dir(path)
+    if path.exists():
+        shutil.rmtree(path)
+
+
+def _write_comparison_sink(
+    path: Path,
+    maps: dict[str, dict[str, torch.Tensor]],
+) -> None:
+    """Writes exactly compared tensors into one bounded shared-memory file."""
+    from safetensors.torch import save_file  # ty: ignore[unresolved-import]
+
+    _comparison_dir(path.parent).mkdir(exist_ok=True)
+    tensors = {
+        f"{phase}/{name}": tensor.detach().cpu().contiguous()
+        for phase, tensor_map in maps.items()
+        for name, tensor in tensor_map.items()
+    }
+    raw_bytes = sum(
+        tensor.numel() * tensor.element_size() for tensor in tensors.values()
+    )
+    used_bytes = sum(item.stat().st_size for item in path.parent.iterdir())
+    if not tensors or used_bytes + raw_bytes > MAX_COMPARISON_BYTES:
+        raise RuntimeError(
+            "Oracle comparison sink exceeds its bound: "
+            f"used={used_bytes} raw={raw_bytes} limit={MAX_COMPARISON_BYTES}"
+        )
+    save_file(tensors, str(path))
+    encoded_bytes = path.stat().st_size
+    if used_bytes + encoded_bytes > MAX_COMPARISON_BYTES:
+        path.unlink()
+        raise RuntimeError(
+            "Encoded oracle comparison sink exceeds its bound: "
+            f"used={used_bytes} encoded={encoded_bytes} limit={MAX_COMPARISON_BYTES}"
+        )
+
+
+def _load_comparison_sink(path: Path) -> dict[str, dict[str, torch.Tensor]]:
+    from safetensors.torch import load_file  # ty: ignore[unresolved-import]
+
+    _comparison_dir(path.parent)
+    tensors = load_file(str(path))
+    maps: dict[str, dict[str, torch.Tensor]] = {
+        phase: {} for phase in COMPARISON_PHASES
+    }
+    for key, tensor in tensors.items():
+        phase, name = key.split("/", 1)
+        maps[phase][name] = tensor
+    return maps
+
+
 def _current_git_state() -> GitRepoState:
     return pinned_git_state(ORACLE_ARTIFACT_SUITE_NAME)
 
@@ -892,20 +994,67 @@ def ensure_case_artifacts(case_config: OracleCaseConfig) -> CaseArtifacts:
     )
 
 
+def _manifest_has_live_comparisons(path: Path) -> bool:
+    try:
+        manifest = _load_manifest(path)
+        directory = _comparison_dir(
+            _require_not_none(manifest.comparison_dir, "comparison_dir")
+        )
+        return bool(manifest.steps) and all(
+            (directory / f"step_{step.step_index:03d}.safetensors").is_file()
+            for step in manifest.steps
+        )
+    except Exception:
+        return False
+
+
+def _release_comparisons(path: Path) -> None:
+    for name in ("manifest.json", "run_request.json"):
+        try:
+            directory = _read_json(path / name).get("comparison_dir")
+        except Exception:
+            continue
+        if directory is not None:
+            _remove_comparison_dir(directory)
+
+
 def _replace_topology_dir(path: Path) -> None:
     """Resets one topology output directory before regeneration."""
     if path.exists():
+        _release_comparisons(path)
         shutil.rmtree(path)
     path.mkdir(parents=True, exist_ok=True)
-    (path / "traces").mkdir(parents=True, exist_ok=True)
+
+
+def _replay_bundle_for_topology(
+    source: Path,
+    *,
+    topology: Topology,
+    output_dir: Path,
+) -> Path:
+    bundle = MoeRoutingReplayBundle.from_dir(source)
+    runtime_topology = ReplayParallelTopology.model_validate(
+        topology.model_dump(
+            include={"tp", "ep", "etp", "dp", "sp", "cp", "pp", "vpp"},
+            mode="python",
+        )
+    )
+    if bundle.topology == runtime_topology:
+        return source
+    bundle.model_copy(update={"topology": runtime_topology}).to_dir(output_dir)
+    return output_dir
 
 
 def _prune_topology_artifacts(path: Path) -> None:
     """Keeps small diagnostics and removes tensors that are only needed for comparison."""
-    if keep_topology_artifacts() or not path.exists():
+    if not path.exists():
+        return
+    _release_comparisons(path)
+    if keep_topology_artifacts():
         return
     for child in path.iterdir():
         if child.name in {
+            "failure_tensors.safetensors",
             "manifest.json",
             "variant_report.json",
             "run_request.json",
@@ -938,19 +1087,81 @@ def _load_manifest(topology_dir: Path) -> RunManifest:
     return RunManifest.model_validate(_read_json(manifest_path))
 
 
-def _load_output_tensor(topology_dir: Path, step: StepTrace):
-    """Loads one output trace tensor referenced by a step trace entry."""
-    import torch
-
-    path = topology_dir / step.output_file
-    return torch.load(path, map_location="cpu")
+def _sample_valid_lengths(
+    packed_tensors: dict[str, torch.Tensor],
+) -> tuple[int, ...]:
+    from art.megatron.context_parallel.builder import build_prefix_tree_attention_spec
 
+    return tuple(
+        int(
+            build_prefix_tree_attention_spec(
+                group_ids=packed_tensors["group_ids"][row : row + 1],
+                parent_ids=packed_tensors["parent_ids"][row : row + 1],
+            )
+            .rows[0]
+            .valid_tokens
+        )
+        for row in range(int(packed_tensors["group_ids"].shape[0]))
+    )
 
-def _load_safetensor_map(path: Path) -> dict[str, Any]:
-    """Loads one safetensor map from disk."""
-    from safetensors.torch import load_file  # ty: ignore[unresolved-import]
 
-    return load_file(str(path))
+def _trim_trace_padding(
+    trace: dict[str, list[dict[str, Any]]],
+    *,
+    valid_lengths: tuple[int, ...],
+    sequence_length: int,
+) -> dict[str, list[dict[str, Any]]]:
+    """Applies the existing valid-token trim before tensors enter the sink."""
+    if sequence_length <= 0:
+        return trace
+    for calls in trace.values():
+        for call in calls:
+            sample_index = call.get("micro_sample_index")
+            if not isinstance(sample_index, int):
+                continue
+            valid_length = valid_lengths[sample_index]
+            row_token_uids = call.get("row_token_uids")
+            if isinstance(row_token_uids, torch.Tensor) and row_token_uids.ndim == 1:
+                local_token_uids = torch.remainder(row_token_uids, sequence_length)
+                keep_rows = torch.nonzero(
+                    (row_token_uids >= 0) & (local_token_uids < valid_length),
+                    as_tuple=False,
+                ).reshape(-1)
+                if int(keep_rows.numel()) < int(row_token_uids.numel()):
+                    call["row_token_uids"] = row_token_uids.index_select(
+                        0, keep_rows
+                    ).contiguous()
+                    for key in (
+                        "primary_output",
+                        "router_topk_scores",
+                        "router_topk_ids",
+                    ):
+                        tensor = call.get(key)
+                        if (
+                            isinstance(tensor, torch.Tensor)
+                            and tensor.ndim > 0
+                            and int(tensor.shape[0]) == int(row_token_uids.numel())
+                        ):
+                            call[key] = tensor.index_select(0, keep_rows).contiguous()
+                    continue
+            if valid_length >= sequence_length:
+                continue
+            for key in ("primary_output", "router_topk_scores", "router_topk_ids"):
+                tensor = call.get(key)
+                if not isinstance(tensor, torch.Tensor) or tensor.ndim == 0:
+                    continue
+                leading_dim = int(tensor.shape[0])
+                if leading_dim <= valid_length:
+                    continue
+                if leading_dim % sequence_length == 0:
+                    target_rows = valid_length * (leading_dim // sequence_length)
+                elif leading_dim <= sequence_length:
+                    target_rows = valid_length
+                else:
+                    continue
+                if 0 < target_rows < leading_dim:
+                    call[key] = tensor[:target_rows].contiguous()
+    return trace
 
 
 def _align_sequence_parallel(reference, candidate):  # type: ignore[no-untyped-def]
@@ -966,14 +1177,6 @@ def _align_sequence_parallel(reference, candidate):  # type: ignore[no-untyped-d
     return None
 
 
-def _load_forward_trace(
-    topology_dir: Path, step_index: int
-) -> dict[str, list[dict[str, Any]]]:
-    """Loads one merged forward-trace file for a given step."""
-    trace_path = topology_dir / "traces" / f"forward_trace_step_{step_index:03d}.pt"
-    return ForwardTraceCapture.load_trace(trace_path)
-
-
 def _finite_metric(value: float, *, default: float = NON_FINITE_METRIC_VALUE) -> float:
     """Maps NaN/Inf metric values to a large finite sentinel for JSON-safe reports."""
     value_f = float(value)
@@ -1079,6 +1282,7 @@ def __init__(
         oracle_offload_between_jobs: bool = True,
         oracle_streaming_weight_offload: StreamingWeightOffloadConfig | None = None,
         use_fp32_lora_reference: bool = True,
+        paired_objective: OracleObjective | None = None,
         console: Console | None = None,
     ) -> None:
         self.objective = objective
@@ -1102,6 +1306,7 @@ def __init__(
             oracle_streaming_weight_offload or StreamingWeightOffloadConfig()
         )
         self.use_fp32_lora_reference = use_fp32_lora_reference
+        self.paired_objective = paired_objective
         self.shared_init_path = Path(self.case_artifacts.shared_init_adapter_path)
         self.oracle_flex_backend = _resolve_test_flex_backend(
             case_config, oracle_flex_backend
@@ -1112,153 +1317,15 @@ def __init__(
         self.console = console or Console(width=140)
         self._oracle_initialized = False
         self._oracle_regenerated = False
-        self._sample_valid_lengths_cache: tuple[int, ...] | None = None
-
-    def _sample_valid_lengths(self) -> tuple[int, ...]:
-        if self._sample_valid_lengths_cache is not None:
-            return self._sample_valid_lengths_cache
-        from art.megatron.context_parallel.builder import (
-            build_prefix_tree_attention_spec,
-        )
-        from art.preprocessing.pack import packed_tensors_from_dir
-
-        packed_tensors = packed_tensors_from_dir(
-            **self.case_artifacts.packed_tensors.model_dump(exclude_none=True)
-        )
-        group_ids = packed_tensors["group_ids"]
-        parent_ids = packed_tensors["parent_ids"]
-        self._sample_valid_lengths_cache = tuple(
-            int(
-                build_prefix_tree_attention_spec(
-                    group_ids=group_ids[row_index : row_index + 1],
-                    parent_ids=parent_ids[row_index : row_index + 1],
-                )
-                .rows[0]
-                .valid_tokens
-            )
-            for row_index in range(int(group_ids.shape[0]))
-        )
-        return self._sample_valid_lengths_cache
-
-    def _step_micro_sample_indices(self, step: StepTrace) -> list[int | None]:
-        base_sample_index = (
-            step.step_index * self.case_config.grad_accumulation_sequences
-        )
-        expected = [
-            sample_index
-            if sample_index < self.case_artifacts.packed_tensors.num_sequences
-            else None
-            for sample_index in range(
-                base_sample_index,
-                base_sample_index + self.case_config.grad_accumulation_sequences,
-            )
-        ]
-        if step.micro_sample_indices and len(step.micro_sample_indices) == len(
-            expected
-        ):
-            return list(step.micro_sample_indices)
-        return expected
-
-    def _load_output_tensor_map(
-        self,
-        topology_dir: Path,
-        step: StepTrace,
-    ) -> dict[str, torch.Tensor]:
-        tensor = _load_output_tensor(topology_dir, step)
-        if isinstance(tensor, list):
-            outputs = tensor
-        elif isinstance(tensor, torch.Tensor) and tensor.ndim >= 1:
-            outputs = [tensor[index] for index in range(int(tensor.shape[0]))]
-        else:
-            return {"logprobs": tensor}
-
-        sample_indices = self._step_micro_sample_indices(step)
-        valid_lengths = self._sample_valid_lengths()
-        output_map: dict[str, torch.Tensor] = {}
-        for output_index, output in enumerate(outputs):
-            key = f"logprobs.micro_{output_index:03d}"
-            if not isinstance(output, torch.Tensor):
-                output_map[key] = output
-                continue
-            if output_index < len(sample_indices):
-                sample_index = sample_indices[output_index]
-                if isinstance(sample_index, int):
-                    valid_length = int(valid_lengths[sample_index])
-                    target_length = max(valid_length - 1, 0)
-                    if output.ndim > 0 and int(output.shape[-1]) > target_length:
-                        output = output[..., :target_length].contiguous()
-            output_map[key] = output
-        return output_map
+        self._failure_samples: dict[
+            tuple[int, str, str], tuple[torch.Tensor, torch.Tensor]
+        ] = {}
 
     @staticmethod
     def _load_loss_tensor_map(step: StepTrace) -> dict[str, torch.Tensor]:
         return {"loss": torch.tensor([step.loss], dtype=torch.float32)}
 
-    def _trim_trace_padding(
-        self,
-        trace: dict[str, list[dict[str, Any]]],
-    ) -> dict[str, list[dict[str, Any]]]:
-        valid_lengths = self._sample_valid_lengths()
-        sequence_length = int(self.case_config.packed_tensors.sequence_length)
-        if sequence_length <= 0:
-            return trace
-
-        for calls in trace.values():
-            for call in calls:
-                sample_index = call.get("micro_sample_index")
-                if not isinstance(sample_index, int):
-                    continue
-                valid_length = int(valid_lengths[sample_index])
-                row_token_uids = call.get("row_token_uids")
-                if (
-                    isinstance(row_token_uids, torch.Tensor)
-                    and row_token_uids.ndim == 1
-                ):
-                    local_token_uids = torch.remainder(row_token_uids, sequence_length)
-                    keep_rows = torch.nonzero(
-                        (row_token_uids >= 0) & (local_token_uids < valid_length),
-                        as_tuple=False,
-                    ).reshape(-1)
-                    if int(keep_rows.numel()) < int(row_token_uids.numel()):
-                        call["row_token_uids"] = row_token_uids.index_select(
-                            0, keep_rows
-                        ).contiguous()
-                        for key in (
-                            "primary_output",
-                            "router_topk_scores",
-                            "router_topk_ids",
-                        ):
-                            tensor = call.get(key)
-                            if (
-                                isinstance(tensor, torch.Tensor)
-                                and tensor.ndim > 0
-                                and int(tensor.shape[0]) == int(row_token_uids.numel())
-                            ):
-                                call[key] = tensor.index_select(
-                                    0, keep_rows
-                                ).contiguous()
-                        continue
-                if valid_length >= sequence_length:
-                    continue
-                for key in ("primary_output", "router_topk_scores", "router_topk_ids"):
-                    tensor = call.get(key)
-                    if not isinstance(tensor, torch.Tensor) or tensor.ndim == 0:
-                        continue
-                    leading_dim = int(tensor.shape[0])
-                    if leading_dim <= valid_length:
-                        continue
-                    if leading_dim % sequence_length == 0:
-                        row_multiplier = leading_dim // sequence_length
-                        target_rows = valid_length * row_multiplier
-                    elif leading_dim <= sequence_length:
-                        target_rows = valid_length
-                    else:
-                        continue
-                    if 0 < target_rows < leading_dim:
-                        call[key] = tensor[:target_rows].contiguous()
-        return trace
-
-    def _run_topology(
+    def _prepare_topology(
         self,
         *,
         topology: Topology,
@@ -1266,21 +1333,29 @@ def _run_topology(
         mutation: SensitivityMutation | None,
         replay_bundle_dir: Path | None,
         capture_bundle_dir: Path | None,
+        prepare_moe_routing_replay: bool = False,
         regenerate: bool,
         flex_backend: FlexBackend | None = None,
         offload_between_jobs: bool = True,
         streaming_weight_offload: StreamingWeightOffloadConfig | None = None,
-    ) -> Path:
-        """Executes one topology worker run and returns its output directory."""
+    ) -> tuple[Path, WorkerRunRequest | None]:
+        """Prepares one topology output and returns any worker request it needs."""
         topology_dir = self.case_dir / output_slug
         manifest_path = topology_dir / "manifest.json"
         if (
             manifest_path.exists()
             and not regenerate
             and _manifest_matches_current_commit(manifest_path)
+            and _manifest_has_live_comparisons(topology_dir)
         ):
-            return topology_dir
+            return topology_dir, None
         _replace_topology_dir(topology_dir)
+        if replay_bundle_dir is not None and replay_bundle_dir.exists():
+            replay_bundle_dir = _replay_bundle_for_topology(
+                replay_bundle_dir,
+                topology=topology,
+                output_dir=topology_dir / "moe_routing_replay",
+            )
         run_case_config = self.case_config
         request = WorkerRunRequest(
             git=self.git,
@@ -1291,6 +1366,8 @@ def _run_topology(
             topology_dir=str(topology_dir),
             packed_tensors=self.case_artifacts.packed_tensors,
             shared_init_adapter_path=str(self.shared_init_path),
+            comparison_dir=str(_new_comparison_dir()),
+            prepare_moe_routing_replay=prepare_moe_routing_replay,
             mutation=mutation,
             moe_routing_replay_path=(
                 None if replay_bundle_dir is None else str(replay_bundle_dir)
@@ -1306,38 +1383,196 @@ def _run_topology(
             ),
             use_fp32_lora_reference=self.use_fp32_lora_reference,
         )
-        from .oracle_worker import run_worker_subprocess
+        return topology_dir, request
+
+    def _paired_topology_dir(self, topology_dir: Path) -> Path:
+        prefix = f"{self.objective}__"
+        if self.paired_objective is None or not topology_dir.name.startswith(prefix):
+            raise ValueError(f"Cannot pair oracle output '{topology_dir.name}'")
+        return self.case_dir / (
+            f"{self.paired_objective}__{topology_dir.name.removeprefix(prefix)}"
+        )
+
+    def _routing_bundle_dir(self, objective: OracleObjective) -> Path:
+        return self.case_dir / f"{objective}__{ORACLE_MOE_ROUTING_BUNDLE_DIRNAME}"
+
+    def _objective_artifact_paths(
+        self,
+    ) -> list[tuple[OracleObjective, Path, Path, Path]]:
+        oracle_dirs = [(self.objective, self.oracle_dir)]
+        if self.paired_objective is not None:
+            oracle_dirs.append(
+                (self.paired_objective, self._paired_topology_dir(self.oracle_dir))
+            )
+        return [
+            (
+                objective,
+                oracle_dir,
+                self._routing_bundle_dir(objective),
+                self.case_dir / f"{oracle_dir.name}__oracle_capture",
+            )
+            for objective, oracle_dir in oracle_dirs
+        ]
 
-        run_worker_subprocess(request, topology_dir, repo_root=REPO_ROOT)
+    def _paired_worker_request(
+        self,
+        request: WorkerRunRequest,
+        paired_dir: Path,
+    ) -> WorkerRunRequest:
+        objective = self.paired_objective
+        if objective is None:
+            raise ValueError("Cannot build a paired request without a paired objective")
+        updates: dict[str, Any] = {
+            "objective": objective,
+            "topology_dir": str(paired_dir),
+        }
+        if request.moe_routing_replay_path is not None:
+            source = self._routing_bundle_dir(objective)
+            updates["moe_routing_replay_path"] = str(
+                _replay_bundle_for_topology(
+                    source,
+                    topology=request.topology,
+                    output_dir=paired_dir / "moe_routing_replay",
+                )
+                if source.exists()
+                else source
+            )
+        if request.capture_moe_routing_bundle_path is not None:
+            updates["capture_moe_routing_bundle_path"] = str(
+                self._routing_bundle_dir(objective)
+            )
+        updates["comparison_dir"] = str(_new_comparison_dir())
+        return request.model_copy(update=updates)
+
+    def _run_prepared(self, prepared: list[tuple[Path, WorkerRunRequest]]) -> None:
+        requests: list[WorkerRunRequest] = []
+        topology_dirs: list[Path] = []
+        for topology_dir, request in prepared:
+            requests.append(request)
+            topology_dirs.append(topology_dir)
+            if self.paired_objective is not None:
+                paired_dir = self._paired_topology_dir(topology_dir)
+                _replace_topology_dir(paired_dir)
+                requests.append(self._paired_worker_request(request, paired_dir))
+                topology_dirs.append(paired_dir)
+        try:
+            from .oracle_worker import run_worker_subprocesses
+
+            run_worker_subprocesses(requests, topology_dirs, repo_root=REPO_ROOT)
+        except BaseException:
+            for request in requests:
+                _remove_comparison_dir(request.comparison_dir)
+            raise
+
+    def _run_topology(
+        self,
+        *,
+        topology: Topology,
+        output_slug: str,
+        mutation: SensitivityMutation | None,
+        replay_bundle_dir: Path | None,
+        capture_bundle_dir: Path | None,
+        regenerate: bool,
+        flex_backend: FlexBackend | None = None,
+        offload_between_jobs: bool = True,
+        streaming_weight_offload: StreamingWeightOffloadConfig | None = None,
+    ) -> Path:
+        """Executes one topology worker run and returns its output directory."""
+        replay_output_slug = (
+            self.oracle_slug if capture_bundle_dir is not None else None
+        )
+        topology_dir, request = self._prepare_topology(
+            topology=topology,
+            output_slug=output_slug,
+            mutation=mutation,
+            replay_bundle_dir=replay_bundle_dir,
+            capture_bundle_dir=capture_bundle_dir,
+            prepare_moe_routing_replay=replay_output_slug is not None,
+            regenerate=regenerate,
+            flex_backend=flex_backend,
+            offload_between_jobs=offload_between_jobs,
+            streaming_weight_offload=streaming_weight_offload,
+        )
+        prepared = [] if request is None else [(topology_dir, request)]
+        if replay_output_slug is not None:
+            replay_dir, replay_request = self._prepare_topology(
+                topology=topology,
+                output_slug=replay_output_slug,
+                mutation=mutation,
+                replay_bundle_dir=_require_not_none(
+                    capture_bundle_dir, "capture_bundle_dir"
+                ),
+                capture_bundle_dir=None,
+                prepare_moe_routing_replay=True,
+                regenerate=regenerate,
+                flex_backend=flex_backend,
+                offload_between_jobs=offload_between_jobs,
+                streaming_weight_offload=streaming_weight_offload,
+            )
+            if replay_request is not None:
+                prepared.append((replay_dir, replay_request))
+        if prepared:
+            self._run_prepared(prepared)
         return topology_dir
 
-    def ensure_oracle(self) -> Path:
+    def _prune_valid_moe_capture(
+        self,
+        capture_dir: Path,
+        *,
+        objective: OracleObjective | None = None,
+        bundle_dir: Path | None = None,
+    ) -> None:
+        """Prunes capture tensors only after persisted metadata reloads cleanly."""
+        objective = objective or self.objective
+        bundle_dir = bundle_dir or self.oracle_routing_bundle_dir
+        manifest = _load_manifest(capture_dir)
+        bundle = MoeRoutingReplayBundle.from_dir(bundle_dir)
+        expected_topology = ReplayParallelTopology.model_validate(
+            self.oracle_topology.model_dump(
+                include={"tp", "ep", "etp", "dp", "sp", "cp", "pp", "vpp"},
+                mode="python",
+            )
+        )
+        if (
+            manifest.git.commit != self.git.commit
+            or manifest.case_id != self.case_id
+            or manifest.objective != objective
+            or manifest.topology != self.oracle_topology.slug()
+            or manifest.num_steps != self.case_config.num_steps
+            or len(manifest.steps) != manifest.num_steps
+            or bundle.topology != expected_topology
+            or bundle.num_steps != manifest.num_steps
+        ):
+            raise RuntimeError("Persisted MoE routing capture metadata does not match")
+        _prune_topology_artifacts(capture_dir)
+
+    def ensure_oracle(self, *, require_existing: bool = False) -> Path:
         """Ensures routing capture and the canonical replay-backed oracle exist once."""
         regenerate = regenerate_requested()
         if self._oracle_initialized and (not regenerate or self._oracle_regenerated):
             return self.oracle_dir
         if regenerate and self.shared_init_path.exists():
             self.shared_init_path.unlink()
-        bundle_manifest = self.oracle_routing_bundle_dir / "manifest.json"
-        oracle_manifest = self.oracle_dir / "manifest.json"
-        capture_manifest = (
-            self.case_dir / f"{self.oracle_slug}__oracle_capture" / "manifest.json"
-        )
-        bundle_format_current = False
-        if bundle_manifest.exists():
+        objective_artifacts = self._objective_artifact_paths()
+        bundle_format_current = True
+        for _, _, bundle_dir, _ in objective_artifacts:
+            bundle_manifest = bundle_dir / "manifest.json"
             try:
-                bundle_format_current = (
-                    _read_json(bundle_manifest).get("format_version")
+                bundle_format_current &= (
+                    bundle_manifest.exists()
+                    and _read_json(bundle_manifest).get("format_version")
                     == ROUTER_KEY_FORMAT_VERSION
                 )
             except Exception:
                 bundle_format_current = False
         need_capture = (
             regenerate
-            or not bundle_manifest.exists()
             or not bundle_format_current
             or not self.shared_init_path.exists()
-            or not _manifest_matches_current_commit(capture_manifest)
+            or any(
+                not _manifest_matches_current_commit(capture_dir / "manifest.json")
+                for _, _, _, capture_dir in objective_artifacts
+            )
         )
         run_oracle_topology = partial(
             self._run_topology,
@@ -1349,17 +1584,31 @@ def ensure_oracle(self) -> Path:
             regenerate=True,
         )
         if self.case_config.is_moe and need_capture:
+            if require_existing:
+                raise RuntimeError(f"missing prepared oracle capture: {self.case_dir}")
             run_oracle_topology(
                 output_slug=f"{self.oracle_slug}__oracle_capture",
                 replay_bundle_dir=None,
                 capture_bundle_dir=self.oracle_routing_bundle_dir,
             )
-        if (
+            for objective, _, bundle_dir, capture_dir in objective_artifacts:
+                self._prune_valid_moe_capture(
+                    capture_dir,
+                    objective=objective,
+                    bundle_dir=bundle_dir,
+                )
+        need_oracle = not (self.case_config.is_moe and need_capture) and (
             regenerate
-            or not oracle_manifest.exists()
             or not self.shared_init_path.exists()
-            or not _manifest_matches_current_commit(oracle_manifest)
-        ):
+            or any(
+                not _manifest_matches_current_commit(oracle_dir / "manifest.json")
+                or not _manifest_has_live_comparisons(oracle_dir)
+                for _, oracle_dir, _, _ in objective_artifacts
+            )
+        )
+        if require_existing and need_oracle:
+            raise RuntimeError(f"missing prepared oracle reference: {self.case_dir}")
+        if need_oracle:
             run_oracle_topology(
                 output_slug=self.oracle_slug,
                 replay_bundle_dir=(
@@ -1495,16 +1744,16 @@ def _build_metric_rows_from_tensor_pairs(
                 reference_aligned, candidate_aligned
             )
             if aligned_candidate is None:
-                rows.append(
-                    self._build_metric_row(
-                        variant=variant,
-                        step_index=step_index,
-                        phase=phase,
-                        param=name,
-                        summary=self._inf_summary(),
-                        structural_failure="shape mismatch",
-                    )
+                row = self._build_metric_row(
+                    variant=variant,
+                    step_index=step_index,
+                    phase=phase,
+                    param=name,
+                    summary=self._inf_summary(),
+                    structural_failure="shape mismatch",
                 )
+                self._remember_failure(row, reference_aligned, candidate_aligned)
+                rows.append(row)
                 continue
             summary: dict[str, float]
             if router_ids:
@@ -1520,17 +1769,33 @@ def _build_metric_rows_from_tensor_pairs(
                 accumulator = DiffAccumulator()
                 accumulator.update(reference_aligned, aligned_candidate)
                 summary = accumulator.as_summary()
-            rows.append(
-                self._build_metric_row(
-                    variant=variant,
-                    step_index=step_index,
-                    phase=phase,
-                    param=name,
-                    summary=summary,
-                )
+            row = self._build_metric_row(
+                variant=variant,
+                step_index=step_index,
+                phase=phase,
+                param=name,
+                summary=summary,
             )
+            if not row.pass_signal:
+                self._remember_failure(row, reference_aligned, aligned_candidate)
+            rows.append(row)
         return rows
 
+    def _remember_failure(
+        self,
+        row: MetricRow,
+        reference: torch.Tensor,
+        candidate: torch.Tensor,
+    ) -> None:
+        key = (row.step_index, row.phase, row.param)
+        self._failure_samples.setdefault(
+            key,
+            (
+                reference.detach().cpu().reshape(-1)[:MAX_FAILURE_VALUES].clone(),
+                candidate.detach().cpu().reshape(-1)[:MAX_FAILURE_VALUES].clone(),
+            ),
+        )
+
     def _check_matching_keys(
         self,
         reference: dict[str, Any],
@@ -1617,16 +1882,6 @@ def _build_metric_rows_from_tensor_maps(
             )
         return rows
 
-    @staticmethod
-    def _build_step_summaries(rows: list[MetricRow]) -> dict[int, dict[str, Any]]:
-        """Builds step-indexed payloads directly from row model dumps."""
-        step_summaries: dict[int, dict[str, Any]] = {}
-        for row in rows:
-            step_entry = step_summaries.setdefault(row.step_index, {})
-            phase_entry = cast(dict[str, Any], step_entry.setdefault(row.phase, {}))
-            phase_entry[row.param] = row.model_dump(mode="json")
-        return step_summaries
-
     @staticmethod
     def _step_phase_rows(
         rows: list[MetricRow], step_index: int, phase: str
@@ -1677,12 +1932,19 @@ def _apply_forward_expert_lora_trace_noise_passes(
 
     def compare_variant(self, variant: VariantSpec) -> VariantReport:
         """Compares one candidate variant against its reference topology."""
+        self._failure_samples = {}
         reference_slug = variant.resolved_reference_slug()
         topology_slug = variant.resolved_output_slug()
         reference_dir = self.case_dir / reference_slug
         topology_dir = self.case_dir / topology_slug
         reference_manifest = _load_manifest(reference_dir)
         topology_manifest = _load_manifest(topology_dir)
+        reference_comparisons = _comparison_dir(
+            _require_not_none(reference_manifest.comparison_dir, "comparison_dir")
+        )
+        topology_comparisons = _comparison_dir(
+            _require_not_none(topology_manifest.comparison_dir, "comparison_dir")
+        )
         rows: list[MetricRow] = []
         if reference_manifest.objective != variant.objective:
             rows.append(
@@ -1727,23 +1989,21 @@ def compare_variant(self, variant: VariantSpec) -> VariantReport:
                 )
             )
 
-        import torch
-
         for reference_step, topology_step in zip(
             reference_manifest.steps, topology_manifest.steps
         ):
             step_index = reference_step.step_index
-            reference_trace = self._trim_trace_padding(
-                _load_forward_trace(reference_dir, step_index)
+            reference_maps = _load_comparison_sink(
+                reference_comparisons / f"step_{step_index:03d}.safetensors"
             )
-            topology_trace = self._trim_trace_padding(
-                _load_forward_trace(topology_dir, step_index)
+            topology_maps = _load_comparison_sink(
+                topology_comparisons / f"step_{step_index:03d}.safetensors"
             )
             map_phase_inputs = [
                 (
                     "outputs",
-                    self._load_output_tensor_map(reference_dir, reference_step),
-                    self._load_output_tensor_map(topology_dir, topology_step),
+                    reference_maps["outputs"],
+                    topology_maps["outputs"],
                     False,
                 ),
                 (
@@ -1754,35 +2014,29 @@ def compare_variant(self, variant: VariantSpec) -> VariantReport:
                 ),
                 (
                     "grads",
-                    _load_safetensor_map(reference_dir / reference_step.grads_file),
-                    _load_safetensor_map(topology_dir / topology_step.grads_file),
+                    reference_maps["grads"],
+                    topology_maps["grads"],
                     False,
                 ),
                 (
                     "deltas",
-                    _load_safetensor_map(reference_dir / reference_step.deltas_file),
-                    _load_safetensor_map(topology_dir / topology_step.deltas_file),
+                    reference_maps["deltas"],
+                    topology_maps["deltas"],
                     False,
                 ),
-                *[
-                    (
-                        phase,
-                        ForwardTraceCapture.flatten_trace_tensors(
-                            reference_trace,
-                            value_key=value_key,
-                        ),
-                        ForwardTraceCapture.flatten_trace_tensors(
-                            topology_trace,
-                            value_key=value_key,
-                        ),
-                        phase == "router_topk_ids",
-                    )
-                    for phase, value_key in (
-                        ("forward", "primary_output"),
-                        ("router_scores", "router_topk_scores"),
-                        ("router_topk_ids", "router_topk_ids"),
-                    )
-                ],
+                ("forward", reference_maps["forward"], topology_maps["forward"], False),
+                (
+                    "router_scores",
+                    reference_maps["router_scores"],
+                    topology_maps["router_scores"],
+                    False,
+                ),
+                (
+                    "router_topk_ids",
+                    reference_maps["router_topk_ids"],
+                    topology_maps["router_topk_ids"],
+                    True,
+                ),
             ]
             for phase, reference_map, candidate_map, router_ids in map_phase_inputs:
                 rows.extend(
@@ -1809,7 +2063,6 @@ def compare_variant(self, variant: VariantSpec) -> VariantReport:
             signal=signal,
             pass_count=pass_count,
             fail_count=fail_count,
-            step_summaries=self._build_step_summaries(rows),
             metrics=rows,
         )
 
@@ -1838,19 +2091,40 @@ def assert_expected_signal(
         )
 
     def _write_variant_report(self, topology_dir: Path, report: VariantReport) -> None:
-        """Persists full variant report JSON for debugging and regression inspection."""
+        """Persists compact metrics and bounded tensors only for failed rows."""
+        from safetensors.torch import save_file  # ty: ignore[unresolved-import]
+
+        failure_path = topology_dir / "failure_tensors.safetensors"
+        failure_path.unlink(missing_ok=True)
+        failures = [
+            (index, sample)
+            for index, row in enumerate(report.metrics)
+            if not row.pass_signal
+            and (
+                sample := self._failure_samples.get(
+                    (row.step_index, row.phase, row.param)
+                )
+            )
+            is not None
+        ][:MAX_FAILURE_ROWS]
+        failure_tensors = {
+            f"metric_{index:04d}.{side}": sample[offset]
+            for index, sample in failures
+            for offset, side in enumerate(("reference", "candidate"))
+        }
+        if failure_tensors:
+            save_file(failure_tensors, str(failure_path))
         _write_json(
             topology_dir / "variant_report.json", report.model_dump(mode="json")
         )
 
     def _prune_reference_artifacts(self) -> None:
         """Drops oracle-only tensors after all comparisons that need them are complete."""
-        _prune_topology_artifacts(self.oracle_dir)
-        if self.case_config.is_moe:
-            _prune_topology_artifacts(self.oracle_routing_bundle_dir)
-            _prune_topology_artifacts(
-                self.case_dir / f"{self.oracle_slug}__oracle_capture"
-            )
+        for _, oracle_dir, bundle_dir, capture_dir in self._objective_artifact_paths():
+            _prune_topology_artifacts(oracle_dir)
+            if self.case_config.is_moe:
+                _prune_topology_artifacts(bundle_dir)
+                _prune_topology_artifacts(capture_dir)
 
     def print_report(self, report: VariantReport) -> None:
         """Prints a row-level table excluding expert-specific rows."""
@@ -1906,7 +2180,6 @@ def run_variant(
         topology_dir = self.ensure_variant_artifacts(variant)
         report = self.compare_variant(variant)
         self._write_variant_report(topology_dir, report)
-        _prune_topology_artifacts(topology_dir)
         self.print_report(report)
         return report
 
@@ -1916,32 +2189,38 @@ def run_suite(
         *,
         prune_reference_artifacts: bool = True,
         prune_case_artifacts: bool = True,
+        prune_paired_artifacts: bool = True,
     ) -> list[VariantReport]:
         """Runs variants in order and stops at the first unexpected signal.
 
-        Reference and case artifacts are normally pruned when the suite exits.
         Callers that immediately run another comparison suite against the same
-        reference can defer that pruning so the second suite does not have to
-        regenerate or fail on missing forward traces.
+        reference can defer shared cleanup until all consumers finish.
         """
         reports: list[VariantReport] = []
         try:
             for variant in variants:
-                report = self.run_variant(variant)
-                reports.append(report)
-                self.assert_expected_signal(
-                    report,
-                    "Megatron correctness suite mismatch",
-                    report_path=self.case_dir
-                    / variant.resolved_output_slug()
-                    / "variant_report.json",
-                )
+                topology_dir = self.case_dir / variant.resolved_output_slug()
+                try:
+                    report = self.run_variant(variant)
+                    self.assert_expected_signal(
+                        report,
+                        "Megatron correctness suite mismatch",
+                        report_path=topology_dir / "variant_report.json",
+                    )
+                    reports.append(report)
+                finally:
+                    if topology_dir != self.oracle_dir:
+                        _prune_topology_artifacts(topology_dir)
+                    if self.paired_objective is not None and prune_paired_artifacts:
+                        _prune_topology_artifacts(
+                            self._paired_topology_dir(topology_dir)
+                        )
+            return reports
         finally:
             if prune_reference_artifacts:
                 self._prune_reference_artifacts()
             if prune_case_artifacts:
                 _prune_case_artifacts(self.case_dir)
-        return reports
 
 
 def _default_phase_pass_fns() -> dict[str, PhasePassFn]:
@@ -2011,6 +2290,115 @@ def _suite_variants(
     return variants
 
 
+def _prune_completed_runners(
+    runners: list[VariantRunner],
+    *,
+    prune_reference_artifacts: bool = True,
+    prune_case_artifacts: bool = True,
+) -> None:
+    """Prunes shared artifacts after every owning suite completes successfully."""
+    if prune_reference_artifacts:
+        for runner in runners:
+            runner._prune_reference_artifacts()
+    if prune_case_artifacts:
+        for case_dir in dict.fromkeys(runner.case_dir for runner in runners):
+            _prune_case_artifacts(case_dir)
+
+
+def prepare_suite_references(
+    *,
+    case_config: OracleCaseConfig,
+    oracle_flex_backend: FlexBackend | None = None,
+    use_fp32_lora_reference: bool = True,
+) -> None:
+    """Materializes the canonical references without running a candidate topology."""
+    objectives = selected_oracle_objectives()
+    paired = objectives == list(SUPPORTED_ORACLE_OBJECTIVES)
+    paired_objective = objectives[1] if paired else None
+    for objective in objectives[:1] if paired else objectives:
+        VariantRunner(
+            objective=objective,
+            case_config=case_config,
+            oracle_flex_backend=oracle_flex_backend,
+            use_fp32_lora_reference=use_fp32_lora_reference,
+            paired_objective=paired_objective,
+        ).ensure_oracle()
+
+
+def _run_paired_objective_suite(
+    *,
+    objectives: list[OracleObjective],
+    case_config: OracleCaseConfig,
+    max_world_size: int | None,
+    oracle_flex_backend: FlexBackend | None,
+    variant_flex_backend: FlexBackend | None,
+    cp_supported: bool,
+    phase_pass_fns: dict[str, PhasePassFn] | None,
+    use_fp32_lora_reference: bool,
+    require_existing_references: bool = False,
+    prune_reference_artifacts: bool,
+    prune_case_artifacts: bool,
+) -> list[VariantReport]:
+    """Runs RL/SFT pairs without rebuilding one topology twice."""
+    rl_objective, sft_objective = objectives
+
+    def runner(
+        objective: OracleObjective,
+        paired_objective: OracleObjective | None = None,
+    ) -> VariantRunner:
+        return VariantRunner(
+            objective=objective,
+            case_config=case_config,
+            oracle_flex_backend=oracle_flex_backend,
+            variant_flex_backend=variant_flex_backend,
+            use_fp32_lora_reference=use_fp32_lora_reference,
+            paired_objective=paired_objective,
+        )
+
+    def variants(objective: OracleObjective) -> list[VariantSpec]:
+        return _suite_variants(
+            objective,
+            is_moe=case_config.is_moe,
+            cp_supported=cp_supported,
+            max_world_size=max_world_size,
+            variant_flex_backend=variant_flex_backend,
+            phase_pass_fns=phase_pass_fns,
+        )
+
+    rl_runner = runner(rl_objective, sft_objective)
+    try:
+        if require_existing_references:
+            rl_runner.ensure_oracle(require_existing=True)
+        reports = rl_runner.run_suite(
+            variants(rl_objective),
+            prune_reference_artifacts=False,
+            prune_case_artifacts=False,
+            prune_paired_artifacts=False,
+        )
+        sft_runner = runner(sft_objective)
+        sft_runner._oracle_initialized = sft_runner._oracle_regenerated = True
+        reports.extend(
+            sft_runner.run_suite(
+                [
+                    variant.model_copy(update={"force_regenerate": False})
+                    for variant in variants(sft_objective)
+                ],
+                prune_reference_artifacts=False,
+                prune_case_artifacts=False,
+            )
+        )
+        return reports
+    finally:
+        _prune_completed_runners(
+            [rl_runner],
+            prune_reference_artifacts=prune_reference_artifacts,
+            prune_case_artifacts=prune_case_artifacts,
+        )
+
+
+_run_paired_dense_suite = _run_paired_objective_suite
+
+
 def run_suite(
     *,
     case_config: OracleCaseConfig,
@@ -2020,34 +2408,61 @@ def run_suite(
     cp_supported: bool = True,
     phase_pass_fns: dict[str, PhasePassFn] | None = None,
     use_fp32_lora_reference: bool = True,
+    require_existing_references: bool = False,
     prune_reference_artifacts: bool = True,
     prune_case_artifacts: bool = True,
 ) -> list[VariantReport]:
     """Runs non-oracle topologies against the canonical replay-backed oracle."""
-    reports: list[VariantReport] = []
-    for objective in selected_oracle_objectives():
-        runner = VariantRunner(
-            objective=objective,
+    objectives = selected_oracle_objectives()
+    if objectives == list(SUPPORTED_ORACLE_OBJECTIVES):
+        return _run_paired_objective_suite(
+            objectives=objectives,
             case_config=case_config,
+            max_world_size=max_world_size,
             oracle_flex_backend=oracle_flex_backend,
             variant_flex_backend=variant_flex_backend,
+            cp_supported=cp_supported,
+            phase_pass_fns=phase_pass_fns,
             use_fp32_lora_reference=use_fp32_lora_reference,
+            require_existing_references=require_existing_references,
+            prune_reference_artifacts=prune_reference_artifacts,
+            prune_case_artifacts=prune_case_artifacts,
         )
-        reports.extend(
-            runner.run_suite(
-                _suite_variants(
-                    objective,
-                    is_moe=case_config.is_moe,
-                    cp_supported=cp_supported,
-                    max_world_size=max_world_size,
-                    variant_flex_backend=variant_flex_backend,
-                    phase_pass_fns=phase_pass_fns,
-                ),
-                prune_reference_artifacts=prune_reference_artifacts,
-                prune_case_artifacts=prune_case_artifacts,
+    reports: list[VariantReport] = []
+    runners: list[VariantRunner] = []
+    try:
+        for objective in objectives:
+            runner = VariantRunner(
+                objective=objective,
+                case_config=case_config,
+                oracle_flex_backend=oracle_flex_backend,
+                variant_flex_backend=variant_flex_backend,
+                use_fp32_lora_reference=use_fp32_lora_reference,
+            )
+            runners.append(runner)
+            if require_existing_references:
+                runner.ensure_oracle(require_existing=True)
+            reports.extend(
+                runner.run_suite(
+                    _suite_variants(
+                        objective,
+                        is_moe=case_config.is_moe,
+                        cp_supported=cp_supported,
+                        max_world_size=max_world_size,
+                        variant_flex_backend=variant_flex_backend,
+                        phase_pass_fns=phase_pass_fns,
+                    ),
+                    prune_reference_artifacts=False,
+                    prune_case_artifacts=False,
+                )
             )
+        return reports
+    finally:
+        _prune_completed_runners(
+            runners,
+            prune_reference_artifacts=prune_reference_artifacts,
+            prune_case_artifacts=prune_case_artifacts,
         )
-    return reports
 
 
 def run_sensitivity_suite(
@@ -2061,6 +2476,7 @@ def run_sensitivity_suite(
     """Runs a list of sensitivity mutations and expects each to fail."""
     phase_pass = _default_phase_pass_fns()
     reports: list[VariantReport] = []
+    runners: list[VariantRunner] = []
     ran_any_variants = False
     for objective in selected_oracle_objectives():
         objective_mutations = selected_sensitivity_mutations_for_objective(
@@ -2154,8 +2570,16 @@ def run_sensitivity_suite(
             if not variants:
                 continue
             ran_any_variants = True
-            reports.extend(runner.run_suite(variants))
+            runners.append(runner)
+            reports.extend(
+                runner.run_suite(
+                    variants,
+                    prune_reference_artifacts=False,
+                    prune_case_artifacts=False,
+                )
+            )
     if ran_any_variants:
+        _prune_completed_runners(runners)
         return reports
     requested = ", ".join(mutations)
     supported = ", ".join(
diff --git a/tests/integration/megatron/model_support/oracle_worker.py b/tests/integration/megatron/model_support/oracle_worker.py
index 014871667..ad4be9658 100644
--- a/tests/integration/megatron/model_support/oracle_worker.py
+++ b/tests/integration/megatron/model_support/oracle_worker.py
@@ -1,6 +1,8 @@
 from __future__ import annotations
 
 import argparse
+import atexit
+from collections import deque
 from contextlib import ExitStack, contextmanager
 import faulthandler
 import hashlib
@@ -16,6 +18,9 @@
 import numpy as np
 import torch
 
+from art.megatron.routing_replay import (
+    ROUTER_NAME_TOKEN,
+)
 from art.megatron.routing_replay import (
     ParallelTopology as ReplayParallelTopology,
 )
@@ -24,7 +29,7 @@
 
 from ..routing_replay.bundle import build_bundle_from_forward_trace_dir
 from ..routing_replay.trace import install_moe_routing_trace_hooks
-from .forward_trace import ForwardTraceCapture
+from .forward_trace import CAPTURE_NAME_TOKENS, ForwardTraceCapture
 from .fp32_grouped_gemm import (
     allow_fp32_grouped_gemm_fallback_for_model_support_tests,
 )
@@ -38,8 +43,13 @@
     StepTrace,
     Topology,
     WorkerRunRequest,
+    _comparison_dir,
     _read_json,
+    _remove_comparison_dir,
     _require_not_none,
+    _sample_valid_lengths,
+    _trim_trace_padding,
+    _write_comparison_sink,
     _write_json,
 )
 from .test_inputs import build_sft_trajectory_tensors_from_packed_tensors
@@ -49,6 +59,8 @@
     "cp": "ART_MEGATRON_CONTEXT_PARALLEL_SIZE",
     "ep": "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE",
     "etp": "ART_MEGATRON_EXPERT_TENSOR_PARALLEL_SIZE",
+    "pp": "ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE",
+    "vpp": "ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE",
 }
 _ORACLE_DEBUG_ENV = "ART_ORACLE_DEBUG"
 _ATTACH_TOKEN_UIDS_ENV = "ART_MEGATRON_ATTACH_TOKEN_UIDS"
@@ -85,8 +97,29 @@ def run_worker_subprocess(
     repo_root: Path,
 ) -> None:
     """Runs one distributed worker subprocess and stores combined logs."""
-    request_path = topology_dir / "run_request.json"
-    _write_json(request_path, request.model_dump(mode="json"))
+    run_worker_subprocesses([request], [topology_dir], repo_root=repo_root)
+
+
+def run_worker_subprocesses(
+    requests: list[WorkerRunRequest],
+    topology_dirs: list[Path],
+    *,
+    repo_root: Path,
+) -> None:
+    """Runs compatible requests in one distributed rank-process lifetime."""
+    if not requests or len(requests) != len(topology_dirs):
+        raise ValueError(
+            "Worker requests and topology directories must be non-empty and aligned"
+        )
+    topology = requests[0].topology
+    if any(request.topology != topology for request in requests[1:]):
+        raise ValueError("One worker process lifetime requires one parallel topology")
+
+    request_paths: list[Path] = []
+    for request, topology_dir in zip(requests, topology_dirs, strict=True):
+        request_path = topology_dir / "run_request.json"
+        _write_json(request_path, request.model_dump(mode="json"))
+        request_paths.append(request_path)
     worker_module = "integration.megatron.model_support.oracle_worker"
     worker_cwd = repo_root / "tests"
 
@@ -96,35 +129,39 @@ def run_worker_subprocess(
         "torch.distributed.run",
         "--standalone",
         "--nproc_per_node",
-        str(request.topology.world_size()),
+        str(topology.world_size()),
         "-m",
         worker_module,
         "--worker-run",
-        "--run-request",
-        str(request_path),
     ]
-    combined_lines: list[str] = []
-    worker_log_path = topology_dir / "worker.log"
+    for request_path in request_paths:
+        command.extend(("--run-request", str(request_path)))
+    output_tail: deque[str] = deque(maxlen=80)
     live_log_raw = os.environ.get("ART_ORACLE_LIVE_TRAINING_LOG")
     live_log_path = None if not live_log_raw else Path(live_log_raw)
     run: subprocess.Popen[str] | None = None
-    worker_log_path.parent.mkdir(parents=True, exist_ok=True)
-    with worker_log_path.open("w", encoding="utf-8") as worker_log:
+    for topology_dir in topology_dirs:
+        topology_dir.mkdir(parents=True, exist_ok=True)
+    with ExitStack() as logs:
+        worker_logs = [
+            logs.enter_context(
+                (topology_dir / "worker.log").open("w", encoding="utf-8")
+            )
+            for topology_dir in topology_dirs
+        ]
         live_log = None
         try:
             if live_log_path is not None:
                 live_log_path.parent.mkdir(parents=True, exist_ok=True)
                 live_log = live_log_path.open("a", encoding="utf-8")
-                live_log.write(
-                    f"\n=== {request.objective} {request.topology.slug()} ===\n"
-                )
+                live_log.write(f"\n=== {requests[0].objective} {topology.slug()} ===\n")
                 live_log.flush()
             env = {
                 **os.environ,
                 "ART_MEGATRON_ATTACH_TOKEN_UIDS": "1",
                 "PYTHONUNBUFFERED": "1",
             }
-            if request.case_config.precision == "fp32":
+            if requests[0].case_config.precision == "fp32":
                 env["NVIDIA_TF32_OVERRIDE"] = "0"
             run = subprocess.Popen(
                 command,
@@ -137,10 +174,18 @@ def run_worker_subprocess(
                 start_new_session=True,
             )
             assert run.stdout is not None
+            active_log_index = 0
+            request_markers = {
+                f"=== oracle request {index} ===": index
+                for index in range(len(requests))
+            }
             for line in run.stdout:
-                combined_lines.append(line)
-                worker_log.write(line)
-                worker_log.flush()
+                output_tail.append(line.rstrip())
+                marker_index = request_markers.get(line.strip())
+                if marker_index is not None:
+                    active_log_index = marker_index
+                worker_logs[active_log_index].write(line)
+                worker_logs[active_log_index].flush()
                 if live_log is not None:
                     live_log.write(line)
                     live_log.flush()
@@ -150,12 +195,10 @@ def run_worker_subprocess(
                 terminate_popen_process_group(run)
             if live_log is not None:
                 live_log.close()
-    combined_output = "".join(combined_lines).strip()
     if run.returncode != 0:
-        tail = "\n".join(combined_output.splitlines()[-80:])
         raise RuntimeError(
-            f"Topology run failed for {request.topology.slug()} with exit code "
-            f"{run.returncode}.\n{tail}"
+            f"Topology run failed for {topology.slug()} with exit code "
+            f"{run.returncode}.\n" + "\n".join(output_tail)
         )
 
 
@@ -172,10 +215,9 @@ def _set_deterministic_seed(seed: int) -> None:
 
 def provider_topology_env_vars(topology: Topology) -> dict[str, str]:
     return {
-        _TOPOLOGY_ENV_VARS["tp"]: str(topology.tp),
-        _TOPOLOGY_ENV_VARS["cp"]: str(topology.cp),
-        _TOPOLOGY_ENV_VARS["ep"]: str(topology.ep),
-        _TOPOLOGY_ENV_VARS["etp"]: str(topology.etp),
+        env_var: str(getattr(topology, field))
+        for field, env_var in _TOPOLOGY_ENV_VARS.items()
+        if field != "vpp" or topology.vpp > 1
     }
 
 
@@ -234,6 +276,8 @@ def _gather_full_state(
 
 def _collect_lora_state(
     model_chunks: list[Any],
+    *,
+    optimizer_master: bool = False,
 ) -> dict[str, Any] | None:
     """Collects full LoRA adapter state for validation and delta computation."""
     local_state: dict[str, Any] = {}
@@ -248,9 +292,27 @@ def _collect_lora_state(
                             f"Duplicate manifest key while collecting state: {key}"
                         )
                     local_manifest[key] = value
-            if not hasattr(module, "sharded_lora_state_dict"):
+            if optimizer_master:
+                export_items = getattr(module, "_export_items", None)
+                if not callable(export_items):
+                    continue
+                module_state = {}
+                for key, param, expert in export_items():
+                    main_param = getattr(param, "main_param", None)
+                    if main_param is None and param.dtype == torch.float32:
+                        main_param = param
+                    if main_param is None or bool(
+                        getattr(param, "main_param_sharded", False)
+                    ):
+                        raise RuntimeError(
+                            f"Oracle requires a full FP32 optimizer master parameter for '{key}'"
+                        )
+                    value = main_param[expert] if expert is not None else main_param
+                    module_state[key] = value.T
+            elif hasattr(module, "sharded_lora_state_dict"):
+                module_state = module.sharded_lora_state_dict()
+            else:
                 continue
-            module_state = module.sharded_lora_state_dict()
             for key, value in module_state.items():
                 if key in local_state:
                     raise RuntimeError(
@@ -374,35 +436,45 @@ def _build_deterministic_shared_init(
     return initialized
 
 
-def _stack_output_tensors(outputs: list[torch.Tensor]) -> torch.Tensor:
-    """Stacks micro outputs, padding the trailing sequence axis when lengths differ."""
+def _output_tensor_map(
+    outputs: list[torch.Tensor],
+    sample_indices: list[int | None],
+    valid_lengths: tuple[int, ...],
+) -> dict[str, torch.Tensor]:
+    """Materializes the exact per-micro tensors consumed by comparison."""
     if not outputs:
-        raise RuntimeError("Expected at least one output tensor to stack")
+        raise RuntimeError("Expected at least one captured micro output")
     first = outputs[0]
-    if all(tensor.shape == first.shape for tensor in outputs[1:]):
-        return torch.stack(outputs, dim=0)
     if any(tensor.ndim != first.ndim for tensor in outputs[1:]) or any(
         tensor.shape[:-1] != first.shape[:-1] for tensor in outputs[1:]
     ):
         raise RuntimeError("Unable to stack output tensors with incompatible shapes")
-
-    max_last_dim = max(int(tensor.shape[-1]) for tensor in outputs)
-    padded_outputs: list[torch.Tensor] = []
-    for tensor in outputs:
-        if int(tensor.shape[-1]) == max_last_dim:
-            padded_outputs.append(tensor)
-            continue
-        pad_value = float("nan") if tensor.dtype.is_floating_point else 0
-        padded = tensor.new_full((*tensor.shape[:-1], max_last_dim), pad_value)
-        padded[..., : tensor.shape[-1]] = tensor
-        padded_outputs.append(padded)
-    return torch.stack(padded_outputs, dim=0)
+    max_last_dim = max(int(tensor.shape[-1]) for tensor in outputs) if first.ndim else 0
+    result: dict[str, torch.Tensor] = {}
+    for index, output in enumerate(outputs):
+        output = (-output).contiguous()
+        sample_index = sample_indices[index] if index < len(sample_indices) else None
+        target_length = max_last_dim
+        if isinstance(sample_index, int):
+            target_length = min(target_length, max(valid_lengths[sample_index] - 1, 0))
+        if output.ndim and int(output.shape[-1]) < target_length:
+            padded = output.new_full(
+                (*output.shape[:-1], target_length),
+                float("nan") if output.dtype.is_floating_point else 0,
+            )
+            padded[..., : output.shape[-1]] = output
+            output = padded
+        elif output.ndim and int(output.shape[-1]) > target_length:
+            output = output[..., :target_length].contiguous()
+        result[f"logprobs.micro_{index:03d}"] = output
+    return result
 
 
 def _configure_provider(
     provider: Any,
     topology: Topology,
     case_config: OracleCaseConfig,
+    prepare_moe_routing_replay: bool = False,
 ) -> None:
     """Applies deterministic topology/model overrides to provider config.
 
@@ -412,6 +484,10 @@ def _configure_provider(
     """
     del topology
     provider.num_layers = case_config.num_layers
+    for name in ("moe_layer_freq", "glm52_indexer_types"):
+        pattern = getattr(provider, name, None)
+        if isinstance(pattern, (list, tuple)):
+            setattr(provider, name, type(pattern)(pattern[: case_config.num_layers]))
     if case_config.precision == "fp32":
         provider.bf16 = False
         provider.fp16 = False
@@ -425,15 +501,14 @@ def _configure_provider(
         provider.attention_dropout = 0.0
     if hasattr(provider, "hidden_dropout"):
         provider.hidden_dropout = 0.0
-    from art.megatron.model_support.registry import get_model_support_handler
-
-    handler = get_model_support_handler(
-        case_config.base_model,
-        allow_unvalidated_arch=case_config.allow_unvalidated_arch,
-    )
+    handler = provider._art_model_support_handler
     configure_oracle_provider = getattr(handler, "configure_oracle_provider", None)
     if configure_oracle_provider is not None:
         configure_oracle_provider(provider, case_config=case_config)
+    if prepare_moe_routing_replay:
+        from art.megatron.train import _enable_native_moe_routing_replay
+
+        _enable_native_moe_routing_replay(provider)
 
 
 @contextmanager
@@ -451,7 +526,6 @@ def _oracle_finalize_provider_bundle(provider_bundle: Any) -> Any:
             provider.moe_token_dispatcher_type = "alltoall"
             provider.moe_flex_dispatcher_backend = None
             provider.moe_enable_deepep = False
-            provider.moe_shared_expert_overlap = True
             provider.overlap_moe_expert_parallel_comm = False
             provider.delay_wgrad_compute = False
             provider.ep_overlap_early_attn_memory_release = False
@@ -948,85 +1022,57 @@ def _apply_attention_async_comm_mutation(mutation: SensitivityMutation | None):
 
     from art.megatron.context_parallel import comm
 
-    original = comm.A2AVCommunicator.launch_kv_fetch
+    original = comm.A2AVCommunicator._launch_exchange
     comm_delay_cycles = 80_000_000
 
-    def _mutated_launch_kv_fetch(
+    def _mutated_launch_exchange(
         self: Any,
         *,
-        k_local: torch.Tensor,
-        v_local: torch.Tensor,
-        plan: Any,
+        tensor: torch.Tensor,
+        recv_buffer: torch.Tensor,
+        total_send_rows: int,
+        make_send_buffer: Callable[[], torch.Tensor],
+        output_split_sizes: list[int],
+        input_split_sizes: list[int],
         group: Any,
         async_op: bool,
-        range_meta_cache: dict[Any, Any] | None = None,
-        label: str = "kv_fetch",
-        input_layout: str = "token_major",
-        output_layout: str = "head_major",
+        input_layout: str,
+        row_factor: int = 2,
     ):
-        if group is None or comm._DIST.get_world_size(group) == 1:
-            return original(
-                self,
-                k_local=k_local,
-                v_local=v_local,
-                plan=plan,
-                group=group,
-                async_op=async_op,
-                range_meta_cache=range_meta_cache,
-                label=label,
-                input_layout=input_layout,
-                output_layout=output_layout,
-            )
-
-        total_send_rows = int(sum(plan.send_splits))
-        total_recv_rows = int(sum(plan.recv_splits))
-        recv_packed = k_local.new_empty(
-            comm._packed_peer_tensor_shape(
-                tensor=k_local,
-                total_rows=total_recv_rows,
-                input_layout=input_layout,
-            )
-        )
-        input_split_sizes = [split * 2 for split in plan.send_splits]
-        output_split_sizes = [split * 2 for split in plan.recv_splits]
-        stream = self._get_stream(k_local) if async_op else None
+        stream = self._get_stream(tensor) if async_op else None
         if stream is None:
             return original(
                 self,
-                k_local=k_local,
-                v_local=v_local,
-                plan=plan,
+                tensor=tensor,
+                recv_buffer=recv_buffer,
+                total_send_rows=total_send_rows,
+                make_send_buffer=make_send_buffer,
+                output_split_sizes=output_split_sizes,
+                input_split_sizes=input_split_sizes,
                 group=group,
                 async_op=async_op,
-                range_meta_cache=range_meta_cache,
-                label=label,
                 input_layout=input_layout,
-                output_layout=output_layout,
+                row_factor=row_factor,
             )
-        current_stream = torch.cuda.current_stream(k_local.device)
+        current_stream = torch.cuda.current_stream(tensor.device)
         if total_send_rows > 0:
             stream.wait_stream(current_stream)
         with torch.cuda.stream(stream):
             if total_send_rows <= 0:
-                send_buffer = k_local.new_empty(
+                send_buffer = tensor.new_empty(
                     comm._packed_peer_tensor_shape(
-                        tensor=k_local,
+                        tensor=tensor,
                         total_rows=0,
                         input_layout=input_layout,
+                        row_factor=row_factor,
                     )
                 )
             else:
-                send_buffer = comm._pack_gathered_tensors_per_peer(
-                    left_tensor=k_local,
-                    right_tensor=v_local,
-                    ranges_by_peer=plan.send_ranges_by_peer,
-                    range_meta_cache=range_meta_cache,
-                    input_layout=input_layout,
-                )
+                send_buffer = make_send_buffer()
             if total_send_rows > 0:
                 torch.cuda._sleep(comm_delay_cycles)
             handle = comm._launch_peer_exchange(
-                recv_buffer=recv_packed,
+                recv_buffer=recv_buffer,
                 send_buffer=send_buffer,
                 output_split_sizes=output_split_sizes,
                 input_split_sizes=input_split_sizes,
@@ -1035,21 +1081,13 @@ def _mutated_launch_kv_fetch(
             )
         if total_send_rows > 0 and send_buffer.numel() > 0:
             send_buffer.zero_()
-        return comm.KvFetchWork(
-            packed_buffer=recv_packed,
-            recv_splits=plan.recv_splits,
-            handle=handle,
-            send_buffer=send_buffer,
-            stream=stream,
-            label=label,
-            output_layout=output_layout,
-        )
+        return handle, send_buffer, stream
 
-    comm.A2AVCommunicator.launch_kv_fetch = _mutated_launch_kv_fetch  # type: ignore[invalid-assignment]
+    comm.A2AVCommunicator._launch_exchange = _mutated_launch_exchange  # type: ignore[invalid-assignment]
     try:
         yield
     finally:
-        comm.A2AVCommunicator.launch_kv_fetch = original
+        comm.A2AVCommunicator._launch_exchange = original
 
 
 @contextmanager
@@ -1137,7 +1175,7 @@ def _reference_forward(
         work_a = self.A_T.to(dtype=work_dtype)
         work_b = self.B_T.to(dtype=work_dtype)
 
-        if tokens_per_expert is None or self.num_local_experts == 1:
+        if tokens_per_expert is None or not self.is_expert:
             return (((work_x @ work_a) @ work_b) * self.scale).to(dtype=x.dtype)
 
         counts = (
@@ -1309,6 +1347,7 @@ def _patched_optimizer_step(
             *,
             model_support_handler: Any | None = None,
             model_chunks: Any | None = None,
+            before_step: Callable[[], None] | None = None,
         ):
             if pre_optimizer_step_hook is not None:
                 pre_optimizer_step_hook()
@@ -1317,6 +1356,7 @@ def _patched_optimizer_step(
                 learning_rate,
                 model_support_handler=model_support_handler,
                 model_chunks=model_chunks,
+                before_step=before_step,
             )
 
         megatron_train_module._optimizer_step = _patched_optimizer_step
@@ -1366,16 +1406,96 @@ def _scaled_loss_fn(*args: Any, **kwargs: Any):
             )
 
 
-def _worker_run(request: WorkerRunRequest) -> None:
-    """Executes one full distributed training trace generation worker run."""
-    os.environ.setdefault(_ATTACH_TOKEN_UIDS_ENV, "1")
-    from safetensors.torch import load_file, save_file  # ty: ignore[unresolved-import]
-    import torch
+class _WorkerSession:
+    """Owns reusable distributed model state for one parallel topology."""
 
-    from art import dev, types
+    def __init__(
+        self,
+        *,
+        request: WorkerRunRequest,
+        runtime: Any,
+        weight_offload: Any,
+        flex_patch_stack: ExitStack,
+    ) -> None:
+        self.request = request
+        self.runtime = runtime
+        self.weight_offload = weight_offload
+        self.flex_patch_stack = flex_patch_stack
+        self.rng_state: tuple[Any, Any, torch.Tensor, list[torch.Tensor]] | None = None
+
+    def begin_request(self) -> None:
+        self.weight_offload.before_job()
+        if self.rng_state is None:
+            self.rng_state = (
+                random.getstate(),
+                np.random.get_state(),
+                torch.get_rng_state(),
+                torch.cuda.get_rng_state_all(),
+            )
+            return
+        python_state, numpy_state, torch_state, cuda_states = self.rng_state
+        random.setstate(python_state)
+        np.random.set_state(numpy_state)
+        torch.set_rng_state(torch_state)
+        torch.cuda.set_rng_state_all(cuda_states)
+
+    def end_request(self) -> None:
+        self.weight_offload.after_job()
+
+    def close(self) -> None:
+        _debug("starting worker session close")
+        torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
+        self.flex_patch_stack.close()
+        torch.distributed.destroy_process_group()  # ty: ignore[possibly-missing-attribute]
+        _debug("finished worker session close")
+
+
+def _validate_session_request(
+    session_request: WorkerRunRequest,
+    request: WorkerRunRequest,
+) -> None:
+    """Rejects request differences that require rebuilding distributed state."""
+    per_run_fields = {
+        "objective",
+        "topology_dir",
+        "comparison_dir",
+        "mutation",
+        "moe_routing_replay_path",
+        "moe_routing_replay_strict",
+        "capture_moe_routing_bundle_path",
+    }
+    if session_request.model_dump(exclude=per_run_fields) != request.model_dump(
+        exclude=per_run_fields
+    ):
+        raise ValueError("Worker requests require different distributed runtimes")
+
+
+def _clear_optimizer_state(optimizer: Any) -> None:
+    chained = getattr(optimizer, "chained_optimizers", None)
+    if chained is not None:
+        for child in chained:
+            _clear_optimizer_state(child)
+        return
+    inner = getattr(optimizer, "optimizer", None)
+    state = getattr(inner, "state", None)
+    if state is None:
+        raise TypeError(f"{type(optimizer).__name__} has no mutable optimizer state")
+    state.clear()
+
+
+def _reset_optimizer_state(optimizer: Any) -> None:
+    from art.megatron import train as megatron_train
+
+    _clear_optimizer_state(optimizer)
+    megatron_train._eager_initialize_optimizer_state(optimizer)
+
+
+def _start_worker_session(request: WorkerRunRequest) -> _WorkerSession:
+    """Builds distributed model state once for compatible oracle requests."""
+    _debug("starting worker session setup")
+    os.environ.setdefault(_ATTACH_TOKEN_UIDS_ENV, "1")
     from art.megatron import train as megatron_train
     from art.megatron.training.weight_offload import WeightOffloadManager
-    from art.preprocessing.pack import packed_tensors_from_dir
 
     if request.case_config.precision == "fp32":
         allow_fp32_grouped_gemm_fallback_for_model_support_tests()
@@ -1416,16 +1536,22 @@ def _worker_run(request: WorkerRunRequest) -> None:
                 else torch.bfloat16
             )
             runtime = megatron_train.build_training_runtime(
-                model_identifier=request.case_config.base_model,
+                model_identifier=(
+                    request.case_config.provider_model or request.case_config.base_model
+                ),
                 provider_torch_dtype=provider_torch_dtype,
                 provider_configure=lambda provider: _configure_provider(
-                    provider, request.topology, request.case_config
+                    provider,
+                    request.topology,
+                    request.case_config,
+                    request.prepare_moe_routing_replay,
                 ),
                 optimizer_config=_build_optimizer_config(request.case_config),
                 moe_routing_replay_path=request.moe_routing_replay_path,
                 moe_routing_replay_strict=request.moe_routing_replay_strict,
                 print_env=False,
                 allow_unvalidated_arch=request.case_config.allow_unvalidated_arch,
+                model_support_key=request.case_config.model_support_key,
             )
         _debug("finished build_training_runtime")
     model_chunks = runtime.model
@@ -1440,18 +1566,65 @@ def _worker_run(request: WorkerRunRequest) -> None:
     )
     weight_offload.install()
     weight_offload.after_job()
-    weight_offload.before_job()
+    _debug("finished worker session setup")
+    return _WorkerSession(
+        request=request,
+        runtime=runtime,
+        weight_offload=weight_offload,
+        flex_patch_stack=flex_patch_stack,
+    )
+
+
+def _worker_run(
+    request: WorkerRunRequest,
+    session: _WorkerSession | None = None,
+) -> _WorkerSession:
+    """Executes one trace while retaining compatible distributed model state."""
+    from safetensors.torch import load_file, save_file  # ty: ignore[unresolved-import]
+
+    from art import dev, types
+    from art.megatron import train as megatron_train
+    from art.preprocessing.pack import packed_tensors_from_dir
+
+    reused_runtime = session is not None
+    if session is None:
+        session = _start_worker_session(request)
+    else:
+        _validate_session_request(session.request, request)
+    runtime = session.runtime
+    model_chunks = runtime.model
+    optimizer = runtime.optimizer
+    capture_routes = request.capture_moe_routing_bundle_path is not None
+    _debug(f"starting request objective={request.objective} capture={capture_routes}")
+    session.begin_request()
+    # Reloading LoRA masters does not clear moments from a prior paired objective.
+    _reset_optimizer_state(optimizer)
+    if reused_runtime:
+        had_replay = runtime.moe_routing_replay_controller is not None
+        megatron_train.configure_moe_routing_replay(
+            runtime,
+            replay_bundle_path=request.moe_routing_replay_path,
+            strict=request.moe_routing_replay_strict,
+        )
+        if not had_replay and request.moe_routing_replay_path is not None:
+            # Recompile with the full replay comparison hooks.
+            torch.compiler.reset()
 
     topology_dir = Path(request.topology_dir)
-    traces_dir = topology_dir / "traces"
-    traces_dir.mkdir(parents=True, exist_ok=True)
+    comparison_dir = _comparison_dir(request.comparison_dir)
+    rank0 = torch.distributed.get_rank() == 0  # ty: ignore[possibly-missing-attribute]
+    if rank0:
+        atexit.register(_remove_comparison_dir, comparison_dir)
+    routing_traces_dir = comparison_dir / "routing_traces"
+    if rank0 and capture_routes:
+        routing_traces_dir.mkdir(parents=True)
 
     # setup the shared initial lora
     shared_init_path = Path(request.shared_init_adapter_path)
     if not shared_init_path.exists():
         _debug("collecting initial lora state")
         initial_state = _collect_lora_state(model_chunks)
-        if torch.distributed.get_rank() == 0:  # ty: ignore[possibly-missing-attribute]
+        if rank0:
             _debug("building deterministic initial lora state")
             shared_init_path.parent.mkdir(parents=True, exist_ok=True)
             deterministic_init = _build_deterministic_shared_init(
@@ -1475,9 +1648,11 @@ def _worker_run(request: WorkerRunRequest) -> None:
         optimizer,
         model_support_handler=runtime.model_support_handler,
     )
+    optimizer.zero_grad()
+    megatron_train._zero_grad_buffers(model_chunks)
     _debug("collecting loaded lora state")
     loaded_state = _collect_lora_state(model_chunks)
-    if torch.distributed.get_rank() == 0:  # ty: ignore[possibly-missing-attribute]
+    if rank0:
         _debug("validating loaded lora state")
         _validate_loaded_state_matches_adapter(
             _require_not_none(loaded_state, "loaded_state"),
@@ -1492,6 +1667,11 @@ def _worker_run(request: WorkerRunRequest) -> None:
     packed_tensors = packed_tensors_from_dir(
         **request.packed_tensors.model_dump(exclude_none=True)
     )
+    valid_lengths = (
+        _sample_valid_lengths(cast(dict[str, torch.Tensor], packed_tensors))
+        if not capture_routes and rank0
+        else None
+    )
     sft_trajectory_tensors: list[dict[str, torch.Tensor]] | None = None
     rl_zero_template: PackedTensors | None = None
     sft_zero_template: dict[str, torch.Tensor] | None = None
@@ -1505,7 +1685,11 @@ def _worker_run(request: WorkerRunRequest) -> None:
         sft_zero_template = megatron_train._zero_contribution_sft_inputs(
             sft_trajectory_tensors[0]
         )
-    initial_lora_state = loaded_state
+    initial_optimizer_state = (
+        None
+        if capture_routes
+        else _collect_lora_state(model_chunks, optimizer_master=True)
+    )
     global_grad_accumulation_sequences = request.case_config.grad_accumulation_sequences
 
     train_config = types.TrainConfig(
@@ -1519,6 +1703,10 @@ def _worker_run(request: WorkerRunRequest) -> None:
     forward_trace_capture = ForwardTraceCapture(
         model_chunks,
         enabled=True,
+        capture_name_tokens=(
+            (ROUTER_NAME_TOKEN,) if capture_routes else CAPTURE_NAME_TOKENS
+        ),
+        capture_layer_outputs=not capture_routes,
     )
     install_moe_routing_trace_hooks(lambda: runtime.moe_routing_replay_controller)
     from megatron.core import parallel_state as ps
@@ -1553,7 +1741,9 @@ def _capture_lora_grads() -> None:
                 model_chunks,
                 request.mutation,
                 request.topology,
-                pre_optimizer_step_hook=_capture_lora_grads,
+                pre_optimizer_step_hook=(
+                    None if capture_routes else _capture_lora_grads
+                ),
                 loss_scale=request.case_config.loss_scale,
             )
         )
@@ -1634,60 +1824,85 @@ def _capture_lora_grads() -> None:
                 )
             _debug(f"finished step_index={step_index}")
             print(f"finished step_index={step_index}", flush=True)
-            ordered_step_outputs = (
-                forward_trace_capture.ordered_step_outputs_with_sample_indices()
-            )
-            if ordered_step_outputs is None:
-                ordered_micro_sample_indices = None
-                ordered_micro_outputs = None
+            ordered_micro_sample_indices = micro_sample_indices
+            if capture_routes:
+                forward_trace_capture.save_current_step(routing_traces_dir)
             else:
-                ordered_micro_sample_indices, ordered_micro_outputs = (
-                    ordered_step_outputs
+                ordered_step_outputs = (
+                    forward_trace_capture.ordered_step_outputs_with_sample_indices()
                 )
-            forward_trace_capture.save_current_step(traces_dir)
-            torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
-            current_lora_state = _collect_lora_state(model_chunks)
-
-            if torch.distributed.get_rank() == 0:  # ty: ignore[possibly-missing-attribute]
-                grads = _require_not_none(captured_grads, "captured_grads")
-                initial_state = _require_not_none(
-                    initial_lora_state, "initial_lora_state"
-                )
-                current_state = _require_not_none(
-                    current_lora_state, "current_lora_state"
-                )
-                deltas = _delta_state(initial_state, current_state)
-                saved_deltas = _apply_save_mutation_to_tensor_map(
-                    deltas,
-                    mutation=request.mutation,
+                ordered_micro_sample_indices, ordered_micro_outputs = (
+                    (micro_sample_indices, None)
+                    if ordered_step_outputs is None
+                    else ordered_step_outputs
                 )
-                saved_current_state = _apply_save_mutation_to_tensor_map(
-                    current_state,
-                    mutation=request.mutation,
+                gathered_traces: list[Any] | None = (
+                    [None] * torch.distributed.get_world_size()  # ty: ignore[possibly-missing-attribute]
+                    if rank0
+                    else None
                 )
-
-                output_rel = Path("traces") / f"output_step_{step_index:03d}.pt"
-                grads_rel = Path("traces") / f"grads_step_{step_index:03d}.safetensors"
-                deltas_rel = (
-                    Path("traces") / f"deltas_step_{step_index:03d}.safetensors"
+                torch.distributed.gather_object(  # ty: ignore[possibly-missing-attribute]
+                    forward_trace_capture.current_step_trace, gathered_traces, dst=0
                 )
-                lora_rel = Path(f"lora_step_{step_index:03d}.safetensors")
-                ordered_outputs = _require_not_none(
-                    ordered_micro_outputs, "ordered_micro_outputs"
+                merged_trace = (
+                    None
+                    if gathered_traces is None
+                    else forward_trace_capture.canonicalize_trace(
+                        forward_trace_capture._merge_rank_traces(
+                            cast(Any, gathered_traces)
+                        )
+                    )
                 )
-                if not ordered_outputs:
-                    raise RuntimeError("Expected at least one captured micro output")
-
-                torch.save(
-                    _stack_output_tensors(
-                        [(-output).contiguous() for output in ordered_outputs]
-                    ),
-                    topology_dir / output_rel,
+                torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
+                current_optimizer_state = _collect_lora_state(
+                    model_chunks, optimizer_master=True
                 )
-                save_file(grads, str(topology_dir / grads_rel))
-                save_file(saved_deltas, str(topology_dir / deltas_rel))
-                save_file(saved_current_state, str(topology_dir / lora_rel))
+                if rank0:
+                    trace = _trim_trace_padding(
+                        _require_not_none(merged_trace, "merged_trace"),
+                        valid_lengths=_require_not_none(valid_lengths, "valid_lengths"),
+                        sequence_length=request.case_config.packed_tensors.sequence_length,
+                    )
+                    ordered_outputs = _require_not_none(
+                        ordered_micro_outputs, "ordered_micro_outputs"
+                    )
+                    current_state = _require_not_none(
+                        current_optimizer_state, "current_optimizer_state"
+                    )
+                    _write_comparison_sink(
+                        comparison_dir / f"step_{step_index:03d}.safetensors",
+                        {
+                            "outputs": _output_tensor_map(
+                                ordered_outputs,
+                                ordered_micro_sample_indices,
+                                _require_not_none(valid_lengths, "valid_lengths"),
+                            ),
+                            "grads": _require_not_none(
+                                captured_grads, "captured_grads"
+                            ),
+                            "deltas": _apply_save_mutation_to_tensor_map(
+                                _delta_state(
+                                    _require_not_none(
+                                        initial_optimizer_state,
+                                        "initial_optimizer_state",
+                                    ),
+                                    current_state,
+                                ),
+                                mutation=request.mutation,
+                            ),
+                            "forward": ForwardTraceCapture.flatten_trace_tensors(
+                                trace, value_key="primary_output"
+                            ),
+                            "router_scores": ForwardTraceCapture.flatten_trace_tensors(
+                                trace, value_key="router_topk_scores"
+                            ),
+                            "router_topk_ids": ForwardTraceCapture.flatten_trace_tensors(
+                                trace, value_key="router_topk_ids"
+                            ),
+                        },
+                    )
 
+            if rank0:
                 step_traces.append(
                     StepTrace(
                         step_index=step_index,
@@ -1696,26 +1911,18 @@ def _capture_lora_grads() -> None:
                             / request.case_config.loss_scale
                         ),
                         probs_corr=step_result.probs_corr,
-                        micro_sample_indices=list(
-                            ordered_micro_sample_indices
-                            if ordered_micro_sample_indices is not None
-                            else micro_sample_indices
-                        ),
-                        output_file=str(output_rel),
-                        grads_file=str(grads_rel),
-                        deltas_file=str(deltas_rel),
-                        lora_file=str(lora_rel),
+                        micro_sample_indices=list(ordered_micro_sample_indices),
                     )
                 )
             torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
 
     forward_trace_capture.close()
 
-    if torch.distributed.get_rank() == 0:  # ty: ignore[possibly-missing-attribute]
+    if rank0:
         # build and save the moe routing replay bundle
-        if request.capture_moe_routing_bundle_path is not None:
+        if capture_routes:
             replay_bundle = build_bundle_from_forward_trace_dir(
-                traces_dir=traces_dir,
+                traces_dir=routing_traces_dir,
                 num_steps=request.case_config.num_steps,
                 topology=ReplayParallelTopology.model_validate(
                     request.topology.model_dump(
@@ -1724,7 +1931,13 @@ def _capture_lora_grads() -> None:
                     )
                 ),
             )
-            replay_bundle.to_dir(request.capture_moe_routing_bundle_path)
+            replay_bundle.to_dir(
+                _require_not_none(
+                    request.capture_moe_routing_bundle_path,
+                    "capture_moe_routing_bundle_path",
+                )
+            )
+            _remove_comparison_dir(comparison_dir)
 
         # build and save the run manifest
         manifest = RunManifest(
@@ -1737,6 +1950,7 @@ def _capture_lora_grads() -> None:
             world_size=request.topology.world_size(),
             seed=request.case_config.seed,
             num_steps=request.case_config.num_steps,
+            comparison_dir=None if capture_routes else str(comparison_dir),
             packed_tensors=request.packed_tensors,
             offload_between_jobs=request.offload_between_jobs,
             streaming_weight_offload=request.streaming_weight_offload,
@@ -1744,18 +1958,34 @@ def _capture_lora_grads() -> None:
             steps=step_traces,
         )
         _write_json(topology_dir / "manifest.json", manifest.model_dump(mode="json"))
-    weight_offload.after_job()
-    torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
-    flex_patch_stack.close()
-    torch.distributed.destroy_process_group()  # ty: ignore[possibly-missing-attribute]
-
-
-def run_worker_cli(run_request_path: Path) -> None:
-    """Loads a worker request and dispatches worker execution."""
-    request = WorkerRunRequest.model_validate(_read_json(run_request_path))
+    session.end_request()
+    _debug(f"finished request objective={request.objective}")
+    if rank0:
+        atexit.unregister(_remove_comparison_dir)
+    return session
+
+
+def run_worker_cli(run_request_paths: list[Path]) -> None:
+    """Loads compatible worker requests and dispatches them in one process lifetime."""
+    requests = [
+        WorkerRunRequest.model_validate(_read_json(run_request_path))
+        for run_request_path in run_request_paths
+    ]
+    session: _WorkerSession | None = None
     try:
-        _worker_run(request)
+        for index, request in enumerate(requests):
+            if index > 0:
+                torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
+                if torch.distributed.get_rank() == 0:  # ty: ignore[possibly-missing-attribute]
+                    print(
+                        f"=== oracle request {index} ===",
+                        flush=True,
+                    )
+                torch.distributed.barrier()  # ty: ignore[possibly-missing-attribute]
+            session = _worker_run(request, session)
     finally:
+        if session is not None:
+            session.close()
         if _oracle_debug_enabled():
             faulthandler.cancel_dump_traceback_later()
 
@@ -1764,7 +1994,7 @@ def _parse_args(argv: list[str]) -> argparse.Namespace:
     """Parses worker CLI arguments."""
     parser = argparse.ArgumentParser(description="Megatron oracle harness worker")
     parser.add_argument("--worker-run", action="store_true")
-    parser.add_argument("--run-request", type=Path)
+    parser.add_argument("--run-request", type=Path, action="append")
     return parser.parse_args(argv)
 
 
@@ -1773,7 +2003,7 @@ def _main(argv: list[str]) -> int:
     args = _parse_args(argv)
     if not args.worker_run:
         raise SystemExit("This module is intended for test imports or --worker-run")
-    if args.run_request is None:
+    if not args.run_request:
         raise SystemExit("--run-request is required with --worker-run")
     run_worker_cli(args.run_request)
     return 0
diff --git a/tests/integration/megatron/model_support/packing_invariance.py b/tests/integration/megatron/model_support/packing_invariance.py
index 68be89ada..70f4f3b70 100644
--- a/tests/integration/megatron/model_support/packing_invariance.py
+++ b/tests/integration/megatron/model_support/packing_invariance.py
@@ -1,13 +1,14 @@
 from __future__ import annotations
 
 import argparse
-from contextlib import ExitStack
+from contextlib import ExitStack, redirect_stderr, redirect_stdout
 import os
 from pathlib import Path
 import subprocess
 import sys
 import time
 from typing import Any, cast
+from unittest.mock import patch
 
 from megatron.core import parallel_state as ps
 from megatron.core.models.gpt.gpt_model import GPTModel
@@ -16,10 +17,21 @@
 
 from art.megatron import train as megatron_train
 from art.megatron.model_support.discovery import inspect_architecture
+from art.megatron.model_support.registry import (
+    get_model_support_handler_for_spec,
+    get_model_support_spec,
+)
+from art.megatron.model_support.spec import PrefixTreeModelStateContext
 from art.megatron.prefix_tree import parse_prefix_tree_row
 from art.megatron.prefix_tree_state import create_prefix_tree_state
 
 from ..artifacts import GitRepoState, pinned_git_state
+from .base_megatron_session import (
+    BaseMegatronResetReport,
+    BaseMegatronSessionKey,
+    active_base_megatron_session,
+    initialize_single_rank_process_group,
+)
 from .fp32_grouped_gemm import (
     allow_fp32_grouped_gemm_fallback_for_model_support_tests,
 )
@@ -42,13 +54,25 @@
 
 allow_fp32_grouped_gemm_fallback_for_model_support_tests()
 
-# Qwen3.5's single packed forward versus many shorter references has measured
-# up to 0.24% shape-dependent numerical drift. Use the standard 0.5% fp32 gate.
-_LOGITS_MEAN_ABS_PCT_LIMIT = 0.5
+_LOGITS_MEAN_ABS_PCT_LIMITS = {"fp32": 0.5, "bf16": 3.0}
 _DEBUG_ENV = "ART_PACKING_INVARIANCE_DEBUG"
 PACKING_INVARIANCE_REPORT_FILENAME = "report.json"
 PACKING_INVARIANCE_ARTIFACT_SUITE_NAME = "Megatron packing-invariance artifacts"
 REPO_ROOT = Path(__file__).resolve().parents[4]
+_SINGLE_ROTARY_OUTPUT_HANDLER_KEYS = frozenset(
+    {
+        "default_dense",
+        "default_moe",
+        "llama3_dense",
+        "qwen3_dense",
+        "qwen3_moe",
+        "qwen3_5_dense",
+        "qwen3_5_moe",
+        "dsv4",
+        "gpt_oss_moe",
+    }
+)
+_TUPLE_ROTARY_OUTPUT_HANDLER_KEYS = frozenset({"gemma4_dense", "gemma4_moe"})
 
 
 def _slugify(value: str) -> str:
@@ -147,6 +171,7 @@ class PackingInvarianceScenario(BaseModel):
     completion_pair_count: int
     logits_equivalent: bool
     logits_mean_abs_pct: float
+    logits_mean_abs_pct_limit: float
     logits_max_abs_diff: float
     matched: bool
 
@@ -156,6 +181,9 @@ class PackingInvarianceReport(BaseModel):
     base_model: str
     output_dir: str
     num_layers: int
+    precision: str
+    base_megatron_session_reused: bool = False
+    base_megatron_reset: BaseMegatronResetReport | None = None
     scenarios: list[PackingInvarianceScenario] = Field(default_factory=list)
 
 
@@ -270,18 +298,42 @@ def _rotary_grouping_check(
 
 def _rotary_outputs_for_validation(
     *,
+    handler: Any,
     preprocess_output: Any,
+    position_ids: torch.Tensor,
+    group_ids: torch.Tensor,
+    parent_ids: torch.Tensor,
 ) -> tuple[torch.Tensor | None, ...]:
+    handler_key = handler.key
+    if handler_key == "glm52":
+        model_state = handler.build_prefix_tree_model_state(
+            PrefixTreeModelStateContext(
+                input_pos=position_ids.detach().cpu(),
+                group_ids=group_ids.detach().cpu(),
+                parent_ids=parent_ids.detach().cpu(),
+                device=position_ids.device,
+            )
+        )
+        state = model_state.get("glm52")
+        if (
+            state is None
+            or not torch.is_tensor(state.rope_cos)
+            or not torch.is_tensor(state.rope_sin)
+        ):
+            raise RuntimeError("GLM-5.2 packed-position validation requires RoPE state")
+        return (torch.cat((state.rope_cos, state.rope_sin), dim=-1).permute(1, 0, 2),)
     rotary_output = preprocess_output[1]
-    if rotary_output is None or torch.is_tensor(rotary_output):
-        return (cast(torch.Tensor | None, rotary_output),)
-    if isinstance(rotary_output, tuple) and all(
-        item is None or torch.is_tensor(item) for item in rotary_output
-    ):
-        return cast(tuple[torch.Tensor | None, ...], rotary_output)
+    if handler_key in _SINGLE_ROTARY_OUTPUT_HANDLER_KEYS:
+        return (
+            cast(torch.Tensor | None, rotary_output)
+            if torch.is_tensor(rotary_output)
+            else None,
+        )
+    if handler_key in _TUPLE_ROTARY_OUTPUT_HANDLER_KEYS:
+        local_rotary, global_rotary = rotary_output
+        return local_rotary, global_rotary
     raise RuntimeError(
-        "Packed position validation received unsupported rotary outputs: "
-        f"{type(rotary_output).__name__}"
+        f"Packed position validation has no rotary output mapping for {handler_key!r}"
     )
 
 
@@ -294,6 +346,14 @@ def _build_art_realistic_packed_tensors(
     return build_complex_prefix_tree_packed_tensors(config, seed, deep=deep)
 
 
+def _dtype_for_precision(precision: str) -> torch.dtype:
+    if precision == "bf16":
+        return torch.bfloat16
+    if precision == "fp32":
+        return torch.float32
+    raise ValueError(f"Unsupported packed-position precision: {precision}")
+
+
 def _prefix_tree_leaf_paths(
     group_ids: torch.Tensor,
     parent_ids: torch.Tensor,
@@ -363,6 +423,7 @@ def _logits_equivalence_check(
     position_ids: torch.Tensor,
     group_ids: torch.Tensor,
     parent_ids: torch.Tensor,
+    mean_abs_pct_limit: float,
 ) -> tuple[int, bool, float, float]:
     _debug_log(
         "logits_check start "
@@ -472,12 +533,13 @@ def _logits_equivalence_check(
         mean_abs = logits_abs_sum / max(logits_numel, 1)
         typical_abs = logits_ref_abs_sum / max(logits_numel, 1)
         logits_mean_abs_pct = (mean_abs / (typical_abs + 1e-12)) * 100.0
-        logits_equivalent = logits_mean_abs_pct <= _LOGITS_MEAN_ABS_PCT_LIMIT
+        logits_equivalent = logits_mean_abs_pct <= mean_abs_pct_limit
         _debug_log(
             "logits_check done "
             f"pairs={completion_pair_count} "
             f"equivalent={logits_equivalent} "
             f"mean_abs_pct={logits_mean_abs_pct:.6f} "
+            f"limit={mean_abs_pct_limit:.6f} "
             f"max_abs_diff={logits_max_abs_diff:.6f}"
         )
         return (
@@ -522,6 +584,17 @@ def _run_packing_invariance_subprocess(
         )
 
 
+def _run_packing_invariance_in_process(
+    request: PackingInvarianceRunRequest,
+    output_dir: Path,
+) -> None:
+    request_path = output_dir / "run_request.json"
+    _write_json(request_path, request.model_dump(mode="json"))
+    with (output_dir / "worker.log").open("w", encoding="utf-8") as worker_log:
+        with redirect_stdout(worker_log), redirect_stderr(worker_log):
+            run_worker_cli(request_path)
+
+
 def _run_packing_invariance_worker(
     *,
     git: GitRepoState,
@@ -602,51 +675,89 @@ def _run_packing_invariance_worker(
             False,
         ),
     ]
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required for packing invariance validation")
+
+    spec = get_model_support_spec(
+        base_model,
+        allow_unvalidated_arch=allow_unvalidated_arch,
+    )
+    handler = get_model_support_handler_for_spec(spec)
+    precision = handler.correctness_precision()
+    mean_abs_pct_limit = _LOGITS_MEAN_ABS_PCT_LIMITS[precision]
     report = PackingInvarianceReport(
         git=git,
         base_model=base_model,
         output_dir=str(output_dir),
         num_layers=num_layers,
+        precision=precision,
     )
-
-    if not torch.cuda.is_available():
-        raise RuntimeError("CUDA is required for packing invariance validation")
-
     case_config = OracleCaseConfig(
         base_model=base_model,
-        precision="fp32",
+        precision=precision,
         num_layers=num_layers,
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
     runtime: megatron_train.TrainingRuntime | None = None
-    flex_patch_stack = ExitStack()
-    flex_patch_stack.enter_context(
+    session = active_base_megatron_session()
+    reused_runtime = session is not None
+    runtime_stack = ExitStack()
+    if not torch.distributed.is_initialized():
+        initialize_single_rank_process_group()
+        runtime_stack.enter_context(
+            patch.dict(
+                os.environ,
+                {
+                    "RANK": "0",
+                    "WORLD_SIZE": "1",
+                    "LOCAL_RANK": "0",
+                    "LOCAL_WORLD_SIZE": "1",
+                },
+            )
+        )
+    runtime_stack.enter_context(
         _apply_requested_flex_backend_patch(TEST_DEFAULT_FLEX_BACKEND)
     )
-    flex_patch_stack.enter_context(
-        _apply_test_flex_inner_fp32_patch(TEST_DEFAULT_FLEX_BACKEND)
-    )
-    flex_patch_stack.enter_context(
-        _apply_test_attention_full_fp32_patch(TEST_DEFAULT_FLEX_BACKEND)
-    )
+    if precision == "fp32":
+        runtime_stack.enter_context(
+            _apply_test_flex_inner_fp32_patch(TEST_DEFAULT_FLEX_BACKEND)
+        )
+        runtime_stack.enter_context(
+            _apply_test_attention_full_fp32_patch(TEST_DEFAULT_FLEX_BACKEND)
+        )
     try:
-        with provider_topology_env(ORACLE_TOPOLOGY):
-            runtime = _time_block(
-                "build_training_runtime",
-                lambda: megatron_train.build_training_runtime(
-                    model_identifier=base_model,
-                    provider_torch_dtype=torch.float32,
-                    provider_configure=lambda provider: _configure_provider(
-                        provider,
-                        ORACLE_TOPOLOGY,
-                        case_config,
-                    ),
-                    print_env=False,
-                    build_optimizer=False,
-                    trainable_parameter_mode="base_model",
+        if session is not None:
+            runtime = session.reset_for_packing(
+                key=BaseMegatronSessionKey(
+                    base_model=base_model,
+                    model_key=spec.key,
+                    num_layers=num_layers,
+                    precision=precision,
                     allow_unvalidated_arch=allow_unvalidated_arch,
-                ),
+                )
             )
+            report.base_megatron_session_reused = True
+            report.base_megatron_reset = session.reset_report
+        else:
+            with provider_topology_env(ORACLE_TOPOLOGY):
+                runtime = _time_block(
+                    "build_training_runtime",
+                    lambda: megatron_train.build_training_runtime(
+                        model_identifier=base_model,
+                        provider_torch_dtype=_dtype_for_precision(precision),
+                        provider_configure=lambda provider: _configure_provider(
+                            provider,
+                            ORACLE_TOPOLOGY,
+                            case_config,
+                        ),
+                        print_env=False,
+                        build_optimizer=False,
+                        trainable_parameter_mode="base_model",
+                        allow_unvalidated_arch=allow_unvalidated_arch,
+                    ),
+                )
+        if runtime is None:
+            raise RuntimeError("packing invariance did not acquire a Megatron runtime")
         model_chunks = cast(list[Any], runtime.model)
         gpt_module = _locate_gpt_module(model_chunks)
         for chunk in model_chunks:
@@ -687,7 +798,11 @@ def _run_packing_invariance_worker(
                 row_respected = True
                 row_repeated_count = 0
                 rotary_outputs = _rotary_outputs_for_validation(
+                    handler=runtime.model_support_handler,
                     preprocess_output=hooked_output,
+                    position_ids=row_position_ids,
+                    group_ids=group_ids[row_index : row_index + 1],
+                    parent_ids=parent_ids[row_index : row_index + 1],
                 )
                 for rotary_output in rotary_outputs:
                     checked, respected, repeated_count = _rotary_grouping_check(
@@ -720,6 +835,7 @@ def _run_packing_invariance_worker(
                     position_ids=position_ids,
                     group_ids=group_ids,
                     parent_ids=parent_ids,
+                    mean_abs_pct_limit=mean_abs_pct_limit,
                 ),
                 device=input_ids.device,
             )
@@ -756,6 +872,7 @@ def _run_packing_invariance_worker(
                     completion_pair_count=completion_pair_count,
                     logits_equivalent=logits_equivalent,
                     logits_mean_abs_pct=logits_mean_abs_pct,
+                    logits_mean_abs_pct_limit=mean_abs_pct_limit,
                     logits_max_abs_diff=logits_max_abs_diff,
                     matched=matched,
                 )
@@ -764,10 +881,11 @@ def _run_packing_invariance_worker(
         torch.cuda.empty_cache()
         _debug_log("run complete; model deleted and cuda cache emptied")
     finally:
-        flex_patch_stack.close()
-        del runtime
-        torch.cuda.empty_cache()
-        _cleanup_distributed_state()
+        runtime_stack.close()
+        if not reused_runtime:
+            del runtime
+            torch.cuda.empty_cache()
+            _cleanup_distributed_state()
 
     (output_dir / PACKING_INVARIANCE_REPORT_FILENAME).write_text(
         report.model_dump_json(indent=2),
@@ -781,14 +899,21 @@ def run_packing_invariance(
     base_model: str,
     num_layers: int | None = None,
     allow_unvalidated_arch: bool = False,
+    in_process: bool = False,
 ) -> PackingInvarianceReport:
     _debug_log(f"run start base_model={base_model} requested_num_layers={num_layers}")
+    spec = get_model_support_spec(
+        base_model,
+        allow_unvalidated_arch=allow_unvalidated_arch,
+    )
+    handler = get_model_support_handler_for_spec(spec)
+    dtype = _dtype_for_precision(handler.correctness_precision())
     resolved_num_layers = (
         max(
             1,
             inspect_architecture(
                 base_model,
-                torch_dtype=torch.float32,
+                torch_dtype=dtype,
                 allow_unvalidated_arch=allow_unvalidated_arch,
             ).recommended_min_layers,
         )
@@ -808,7 +933,12 @@ def run_packing_invariance(
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
     with provider_topology_env(ORACLE_TOPOLOGY):
-        _run_packing_invariance_subprocess(request, output_dir)
+        runner = (
+            _run_packing_invariance_in_process
+            if in_process
+            else _run_packing_invariance_subprocess
+        )
+        runner(request, output_dir)
     return PackingInvarianceReport.model_validate(_read_json(report_path))
 
 
diff --git a/tests/integration/megatron/model_support/resident_functional_session.py b/tests/integration/megatron/model_support/resident_functional_session.py
new file mode 100644
index 000000000..37ab7775f
--- /dev/null
+++ b/tests/integration/megatron/model_support/resident_functional_session.py
@@ -0,0 +1,164 @@
+from __future__ import annotations
+
+import asyncio
+from pathlib import Path
+import time
+from typing import Any, Literal
+
+from .lora_coverage import build_lora_coverage_report
+from .validation_spec import ValidationStageResult
+
+_PARITY_LEARNING_RATE = 1e-6
+
+
+async def run_resident_functional_session(
+    *,
+    base_model: str,
+    allow_unvalidated_arch: bool,
+    stage_dirs: dict[str, Path],
+    serving_ready: asyncio.Future[tuple[str, ...]] | None = None,
+) -> tuple[ValidationStageResult, ...]:
+    from art.megatron.runtime.specs import ResidentLoraInspectionResult
+
+    from ..train_inf_mismatch.real_path import (
+        config_from_env,
+        run_resident_train_inf_mismatch,
+    )
+    from ..train_inf_mismatch.workflow_stage import _attempt_limit
+    from ..trainability import test_live_length_trainability as length_trainability
+
+    coverage_report = None
+    coverage_rank_summaries: list[dict[str, Any]] = []
+    coverage_run_id: str | None = None
+    mismatch_report = None
+    coverage_s = 0.0
+    mismatch_s = 0.0
+    session_started = time.monotonic()
+    length_dir = stage_dirs["length_trainability"] / "artifacts"
+    length_trainability.LATEST_SUMMARY_LOG_PATH = (
+        stage_dirs["length_trainability"] / "length_trainability.log"
+    )
+
+    async def hook(
+        phase: Literal["registered", "first_update"],
+        backend: Any,
+        model: Any,
+        step: int,
+    ) -> None:
+        nonlocal coverage_report, coverage_rank_summaries, coverage_run_id
+        nonlocal coverage_s, mismatch_report, mismatch_s
+        if phase == "registered":
+            if step != 0:
+                raise RuntimeError(
+                    f"resident functional session must start at step 0, got {step}"
+                )
+            started = time.monotonic()
+            inspection = ResidentLoraInspectionResult.model_validate(
+                await backend.inspect_resident_lora(
+                    model, expected_learner_version=step
+                )
+            )
+            coverage_run_id = inspection.run_id
+            coverage_rank_summaries = [
+                summary.model_dump(mode="json") for summary in inspection.rank_summaries
+            ]
+            coverage_report = build_lora_coverage_report(
+                base_model=base_model,
+                target_modules=list(inspection.target_modules),
+                adapter_prefixes=set(inspection.wrapped_adapter_prefixes),
+                adapter_weights_by_base={
+                    export.base_name: list(export.adapter_keys)
+                    for export in inspection.exports
+                },
+                trainable_lora_parameter_names=set(
+                    inspection.trainable_lora_parameter_names
+                ),
+                unexpected_trainable_parameter_names=set(
+                    inspection.unexpected_trainable_parameter_names
+                ),
+            )
+            coverage_s = time.monotonic() - started
+            artifact_dir = stage_dirs["lora_coverage"] / "artifacts"
+            artifact_dir.mkdir(parents=True, exist_ok=True)
+            (artifact_dir / "resident_lora_coverage.json").write_text(
+                coverage_report.model_dump_json(indent=2) + "\n", encoding="utf-8"
+            )
+            return
+        if phase != "first_update" or step != 1:
+            raise RuntimeError(f"unexpected resident functional hook {phase}@{step}")
+        started = time.monotonic()
+        config = config_from_env()
+        config.output_parity.base_model = base_model
+        config.output_parity.allow_unvalidated_arch = allow_unvalidated_arch
+        mismatch_report = await run_resident_train_inf_mismatch(
+            backend=backend,
+            model=model,
+            policy_step=step,
+            config=config,
+            artifact_dir=stage_dirs["train_inf_mismatch"] / "artifacts",
+            max_attempts=_attempt_limit(),
+        )
+        if mismatch_report.run_id != coverage_run_id:
+            raise RuntimeError("resident functional phases used different trainer runs")
+        mismatch_s = time.monotonic() - started
+
+    try:
+        report = await length_trainability.run_length_trainability_async(
+            base_model=base_model,
+            artifact_dir=length_dir,
+            allow_unvalidated_arch=allow_unvalidated_arch,
+            resident_hook=hook,
+            registration_ready=serving_ready,
+            first_update_learning_rate=_PARITY_LEARNING_RATE,
+        )
+    finally:
+        from .workflow import _cleanup_stage_workspace
+
+        _cleanup_stage_workspace(length_dir / "megatron_dedicated_workspace")
+    if coverage_report is None or mismatch_report is None:
+        raise RuntimeError("resident functional session did not execute every phase")
+
+    first_update_s = sum(
+        phase.duration_s for phase in report.phases if phase.name == "first_update"
+    )
+    continuation_s = sum(
+        phase.duration_s for phase in report.phases if phase.name == "continuation"
+    )
+    session_s = time.monotonic() - session_started
+    common = {"resident_functional_session_s": session_s}
+    return (
+        ValidationStageResult(
+            name="lora_coverage",
+            passed=not coverage_report.missing_wrapped_target_modules
+            and not coverage_report.missing_exported_target_modules
+            and coverage_report.trainable_lora_parameter_count > 0
+            and not coverage_report.unexpected_trainable_parameter_names
+            and all(
+                summary["module_count"] > 0 and summary["trainable_parameter_count"] > 0
+                for summary in coverage_rank_summaries
+            ),
+            metrics=coverage_report.model_dump(mode="json")
+            | common
+            | {
+                "resident_rank_summaries": coverage_rank_summaries,
+                "workflow_stage_duration_s": coverage_s,
+            },
+            artifact_dir=str(stage_dirs["lora_coverage"] / "artifacts"),
+        ),
+        ValidationStageResult(
+            name="train_inf_mismatch",
+            passed=mismatch_report.passed,
+            metrics=mismatch_report.model_dump(mode="json")
+            | common
+            | {"workflow_stage_duration_s": first_update_s + mismatch_s},
+            artifact_dir=mismatch_report.artifact_dir,
+        ),
+        ValidationStageResult(
+            name="length_trainability",
+            passed=length_trainability.length_trainability_passed(report),
+            metrics=report.model_dump(mode="json")
+            | common
+            | {"workflow_stage_duration_s": continuation_s},
+            artifact_dir=str(length_dir),
+        ),
+    )
diff --git a/tests/integration/megatron/model_support/routing_replay_bundle.py b/tests/integration/megatron/model_support/routing_replay_bundle.py
index 008d8107f..b3eb9528f 100644
--- a/tests/integration/megatron/model_support/routing_replay_bundle.py
+++ b/tests/integration/megatron/model_support/routing_replay_bundle.py
@@ -142,6 +142,68 @@ def _rank_token_counts(
     return counts
 
 
+def _route_token_uids(
+    call_entry: dict[str, Any], token_count: int
+) -> torch.Tensor | None:
+    token_uids = call_entry.get("row_token_uids")
+    if not isinstance(token_uids, torch.Tensor):
+        return None
+    token_uids = token_uids.to(dtype=torch.int64).reshape(-1).contiguous()
+    if int(token_uids.numel()) != token_count:
+        raise RuntimeError(
+            "Router row token UID count must match route rows: "
+            f"uids={int(token_uids.numel())}, routes={token_count}"
+        )
+    if bool((token_uids < 0).any().item()) or int(token_uids.unique().numel()) != int(
+        token_uids.numel()
+    ):
+        raise RuntimeError("Router row token UIDs must be unique and non-negative")
+    return token_uids
+
+
+def _expand_route_to_token_span(
+    route: RouterCallRoute,
+    token_uids: torch.Tensor | None,
+    token_count: int,
+) -> RouterCallRoute:
+    if token_uids is None:
+        if route.num_global_tokens != token_count:
+            raise RuntimeError(
+                "A compact router route requires row token UIDs: "
+                f"routes={route.num_global_tokens}, token_span={token_count}"
+            )
+        return route
+    identity = torch.arange(token_count, dtype=torch.int64)
+    if route.num_global_tokens == token_count and torch.equal(token_uids, identity):
+        return route
+    if int(token_uids.numel()) > 0 and int(token_uids.max().item()) >= token_count:
+        raise RuntimeError(
+            "Router row token UID exceeds the replay token span: "
+            f"max_uid={int(token_uids.max().item())}, token_span={token_count}"
+        )
+
+    rows = torch.arange(token_count, dtype=torch.int64).unsqueeze(1)
+    slots = torch.arange(route.max_topk, dtype=torch.int64).unsqueeze(0)
+    expert_indices = ((rows + slots) % route.num_experts).to(torch.int32)
+    expert_indices.index_copy_(0, token_uids, route.expert_indices)
+    expert_probs = None
+    if route.expert_probs is not None:
+        expert_probs = torch.zeros((token_count, route.max_topk), dtype=torch.float32)
+        expert_probs.index_copy_(0, token_uids, route.expert_probs)
+    expert_mask = None
+    if route.expert_mask is not None:
+        expert_mask = torch.ones((token_count, route.max_topk), dtype=torch.bool)
+        expert_mask.index_copy_(0, token_uids, route.expert_mask)
+    return RouterCallRoute(
+        expert_indices=expert_indices,
+        expert_probs=expert_probs,
+        expert_mask=expert_mask,
+        num_experts=route.num_experts,
+        sample_index=route.sample_index,
+        micro_slot=route.micro_slot,
+    )
+
+
 def _dedupe_checkpoint_router_calls(
     call_entries: list[dict[str, Any]],
 ) -> list[dict[str, Any]]:
@@ -221,7 +283,7 @@ def build_bundle_from_forward_trace_dir(
 
         step_routers: dict[str, StepRouterRoutes] = {}
         step_global_tokens: int | None = None
-        token_count_by_call_key: dict[tuple[str, int], int] = {}
+        route_token_uids: dict[tuple[str, int], torch.Tensor | None] = {}
         for module_name in sorted(step_trace.keys()):
             if ROUTER_NAME_TOKEN not in module_name:
                 continue
@@ -240,29 +302,14 @@ def build_bundle_from_forward_trace_dir(
                     call_entry, compact_route.num_global_tokens
                 )
                 router_calls[call_index] = compact_route
+                token_uids = _route_token_uids(
+                    call_entry, compact_route.num_global_tokens
+                )
+                route_token_uids[(router_key, call_index)] = token_uids
                 max_topk = max(max_topk, compact_route.max_topk)
                 token_count = compact_route.num_global_tokens
-                call_key = (
-                    ("sample", int(sample_index))
-                    if sample_index is not None
-                    else (
-                        ("dummy_micro_slot", int(micro_slot))
-                        if micro_slot is not None
-                        else ("call_index", int(call_index))
-                    )
-                )
-                previous_token_count = token_count_by_call_key.get(call_key)
-                if (
-                    previous_token_count is not None
-                    and previous_token_count != token_count
-                ):
-                    raise RuntimeError(
-                        "Inconsistent token count across routers for the same micro: "
-                        f"step={step_index}, call_key={call_key}, "
-                        f"expected={previous_token_count}, got={token_count}, "
-                        f"router='{router_key}', call={call_index}"
-                    )
-                token_count_by_call_key[call_key] = token_count
+                if token_uids is not None and int(token_uids.numel()) > 0:
+                    token_count = max(token_count, int(token_uids.max().item()) + 1)
                 step_global_tokens = (
                     token_count
                     if step_global_tokens is None
@@ -284,6 +331,13 @@ def build_bundle_from_forward_trace_dir(
             raise RuntimeError(
                 f"Could not infer token count for step={step_index} from router traces"
             )
+        for router_key, router_routes in step_routers.items():
+            for call_index, route in router_routes.calls.items():
+                router_routes.calls[call_index] = _expand_route_to_token_span(
+                    route,
+                    route_token_uids[(router_key, call_index)],
+                    step_global_tokens,
+                )
         global_token_uids = torch.arange(step_global_tokens, dtype=torch.int64)
         steps[step_index] = StepRoutes(
             routers=step_routers,
diff --git a/tests/integration/megatron/model_support/test_bridge_runtime.py b/tests/integration/megatron/model_support/test_bridge_runtime.py
new file mode 100644
index 000000000..0d17ca185
--- /dev/null
+++ b/tests/integration/megatron/model_support/test_bridge_runtime.py
@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+from types import SimpleNamespace
+from typing import Any, cast
+
+import pytest
+import torch
+
+pytest.importorskip("megatron.bridge")
+
+from art.megatron.runtime.bridge_runtime import (
+    _optimized_load_weights_hf_to_megatron,
+)
+
+
+class _Mapping:
+    def __init__(self, megatron_param: str, hf_param: str) -> None:
+        self.megatron_param = megatron_param
+        self.hf_param = hf_param
+        self.tp_size = 1
+
+    def hf_to_megatron(
+        self, hf_weights: torch.Tensor, megatron_module: torch.nn.Module
+    ) -> torch.Tensor:
+        del megatron_module
+        return hf_weights
+
+
+class _Bridge:
+    def __init__(self, tasks: list[Any]) -> None:
+        self.tasks = tasks
+
+    def build_conversion_tasks(
+        self, hf_pretrained: Any, megatron_model: Any
+    ) -> list[Any]:
+        del hf_pretrained, megatron_model
+        return self.tasks
+
+    def _share_embeddings_and_output_weights(self, config: Any) -> bool:
+        return bool(config.share_embeddings_and_output_weights)
+
+    def _is_adapter_param_name(self, name: str) -> bool:
+        return ".adapter." in name
+
+    def _with_progress_tracking(self, tasks: list[Any], description: str) -> list[Any]:
+        del description
+        return tasks
+
+    def maybe_modify_loaded_hf_weight(
+        self, hf_param: str, state: dict[str, torch.Tensor]
+    ) -> torch.Tensor:
+        return state[hf_param]
+
+    def _broadcast_shared_embeddings(self, megatron_model: Any) -> None:
+        del megatron_model
+
+
+class _Model(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.config = SimpleNamespace(share_embeddings_and_output_weights=False)
+        self.local = torch.nn.Linear(1, 1, bias=False)
+
+
+def _task(
+    mapping: _Mapping,
+    *,
+    module: torch.nn.Module | None = None,
+    weight: torch.Tensor | None = None,
+) -> SimpleNamespace:
+    return SimpleNamespace(
+        mapping=mapping,
+        megatron_module=module,
+        param_weight=weight,
+        param_name=mapping.megatron_param,
+    )
+
+
+def test_pretrained_load_rejects_placeholder_for_required_local_parameter() -> None:
+    model = _Model()
+    bridge = _Bridge([_task(_Mapping("local.weight", "hf.weight"))])
+    pretrained = SimpleNamespace(state={}, model_name_or_path="empty-checkpoint")
+
+    with pytest.raises(
+        RuntimeError,
+        match=r"1 required local parameter\(s\): local.weight",
+    ):
+        _optimized_load_weights_hf_to_megatron(cast(Any, bridge), pretrained, model)
+
+
+def test_pretrained_load_allows_nonlocal_placeholder_tasks() -> None:
+    model = _Model()
+    local_mapping = _Mapping("local.weight", "hf.weight")
+    remote_mapping = _Mapping("remote.weight", "hf.remote_weight")
+    bridge = _Bridge(
+        [
+            _task(local_mapping, module=model.local, weight=model.local.weight),
+            _task(remote_mapping),
+        ]
+    )
+    expected = torch.tensor([[7.0]])
+    pretrained = SimpleNamespace(
+        state={"hf.weight": expected}, model_name_or_path="checkpoint"
+    )
+
+    result = _optimized_load_weights_hf_to_megatron(
+        cast(Any, bridge), pretrained, model
+    )
+
+    assert result == [model]
+    assert torch.equal(model.local.weight, expected)
diff --git a/tests/integration/megatron/model_support/test_compile_flags.py b/tests/integration/megatron/model_support/test_compile_flags.py
index ead5a48fc..29353e225 100644
--- a/tests/integration/megatron/model_support/test_compile_flags.py
+++ b/tests/integration/megatron/model_support/test_compile_flags.py
@@ -5,6 +5,7 @@
 import torch
 from torch._dynamo.testing import CompileCounter
 
+from art.megatron.flex_attn.compiled import _needs_blackwell_wide_head_tile
 from art.megatron.model_support.handlers.gemma4 import (
     GEMMA4_DENSE_HANDLER,
     GEMMA4_MOE_HANDLER,
@@ -82,8 +83,35 @@ def test_disabled_training_compile_does_not_change_dynamo_policy(
     )
 
 
+def test_wide_head_tile_workaround_is_blackwell_only(monkeypatch) -> None:
+    def selected(major: int) -> bool:
+        monkeypatch.setattr(
+            torch.cuda, "get_device_capability", lambda _device: (major, 0)
+        )
+        return _needs_blackwell_wide_head_tile(
+            backend="TRITON",
+            head_dim=512,
+            head_dim_v=512,
+            triton_num_stages_2_head_dims=(512,),
+            device=torch.device("cuda"),
+        )
+
+    assert selected(10)
+    assert not selected(11)
+
+
 def test_gemma4_wide_global_attention_uses_lower_triton_stage_count() -> None:
-    provider = type("Provider", (), {"global_head_dim": 512})()
+    provider = type(
+        "Provider",
+        (),
+        {
+            "global_head_dim": 512,
+            "hidden_size": 5376,
+            "kv_channels": 256,
+            "num_attention_heads": 32,
+            "num_layers": 12,
+        },
+    )()
 
     assert GEMMA4_DENSE_HANDLER.flex_attention_compile_crash_config(
         provider
diff --git a/tests/integration/megatron/model_support/test_hf_parity_invariants.py b/tests/integration/megatron/model_support/test_hf_parity_invariants.py
index d0f6e966b..0137d16ca 100644
--- a/tests/integration/megatron/model_support/test_hf_parity_invariants.py
+++ b/tests/integration/megatron/model_support/test_hf_parity_invariants.py
@@ -4,9 +4,12 @@
 import pytest
 import torch
 
+from art.megatron.model_support.handlers.dsv4 import DSV4_HANDLER
+
 from ..artifacts import GitRepoState
 from . import hf_parity as hf_parity_module
 from . import hf_parity_worker as hf_parity_worker_module
+from .base_megatron_session import initialize_single_rank_process_group
 from .hf_parity import (
     HF_PARITY_OUTPUT_DIRNAME,
     HF_PARITY_PACKED_TENSORS,
@@ -23,12 +26,14 @@
     _drop_gemma4_reparameterized_norm_grads,
     _filter_language_only_tensor_map,
     _hf_moe_router_key,
+    _hf_prefix_tree_forward_inputs,
     _hf_router_num_experts,
     _is_language_hf_param_name,
     _mapping_supports_derivative_parity,
     _maybe_modify_converted_hf_grad,
     _normalize_hf_grads_for_bridge,
     _normalize_hf_tensor_map_for_bridge,
+    _validate_distributed_process_env,
 )
 from .oracle_harness import DiskPackedTensorsSpec, OracleCaseConfig
 from .validation_spec import MinimalLayerCoverageReport
@@ -45,6 +50,38 @@ def test_build_parity_sample_indices_pads_with_none() -> None:
     ) == [0, 1, None, None]
 
 
+def test_hf_prefix_tree_inputs_block_siblings_and_repeat_positions() -> None:
+    model = SimpleNamespace(
+        config=SimpleNamespace(
+            layer_types=["full_attention", "sliding_attention"],
+            sliding_window=2,
+        )
+    )
+    micro = {
+        "group_ids": torch.tensor([0, 0, 1, 1, 2, 2]),
+        "parent_ids": torch.tensor([0, 0, 0, 0, 0, 0]),
+        "position_ids": torch.tensor([0, 1, 2, 3, 2, 3]),
+    }
+
+    attention_mask, position_ids = _hf_prefix_tree_forward_inputs(
+        model,
+        micro,
+        actual_len=6,
+        device=torch.device("cpu"),
+        dtype=torch.float32,
+    )
+
+    assert isinstance(attention_mask, dict)
+    masks = cast(dict[str, torch.Tensor], attention_mask)
+    full_allowed = masks["full_attention"][0, 0] == 0
+    sliding_allowed = masks["sliding_attention"][0, 0] == 0
+    assert position_ids.tolist() == [[0, 1, 2, 3, 2, 3]]
+    assert full_allowed[4, 1]
+    assert not full_allowed[4, 2]
+    assert not sliding_allowed[4, 0]
+    assert sliding_allowed[4, 1]
+
+
 def test_hf_parity_uses_train_inf_mismatch_settings() -> None:
     assert HF_PARITY_PACKED_TENSORS.sequence_length == 256
     assert HF_PARITY_PACKED_TENSORS.prefill_tokens == 64
@@ -150,6 +187,7 @@ def test_run_hf_parity_always_reruns_existing_report(
         "assess_minimal_layer_coverage",
         lambda **_: coverage,
     )
+    monkeypatch.setattr(hf_parity_module, "pinned_git_state", lambda _: _git_state())
     monkeypatch.setattr(
         hf_parity_module,
         "ensure_case_artifacts",
@@ -222,7 +260,11 @@ def _fake_run(*args, **kwargs):
         captured.update(kwargs)
         return SimpleNamespace(returncode=0, stdout="", stderr="")
 
-    monkeypatch.setattr(hf_parity_module.subprocess, "run", _fake_run)
+    monkeypatch.setattr(
+        hf_parity_module,
+        "subprocess",
+        SimpleNamespace(run=_fake_run),
+    )
 
     hf_parity_module.run_hf_parity_subprocess(request, tmp_path)
 
@@ -233,6 +275,91 @@ def _fake_run(*args, **kwargs):
     assert "ART_MEGATRON_RECOMPUTE_MODULES" not in env
 
 
+def test_run_hf_parity_subprocess_does_not_allocate_tcp_rendezvous(
+    monkeypatch, tmp_path
+) -> None:
+    request = HfParityRunRequest(
+        git=_git_state(),
+        case_id="case-id",
+        case_config=OracleCaseConfig(base_model="Qwen/Qwen3.5-35B-A3B"),
+        packed_tensors=DiskPackedTensorsSpec(
+            dir=str(tmp_path / "packed"),
+            num_sequences=4,
+            sequence_length=8,
+        ),
+        output_dir=str(tmp_path),
+        coverage=MinimalLayerCoverageReport(
+            base_model="Qwen/Qwen3.5-35B-A3B",
+            model_key="qwen3_5_moe",
+            requested_num_layers=4,
+            recommended_min_layers=4,
+            covered=True,
+        ),
+    )
+    environments: list[dict[str, str]] = []
+    monkeypatch.delenv("MASTER_ADDR", raising=False)
+    monkeypatch.delenv("MASTER_PORT", raising=False)
+
+    def _fake_run(*args, **kwargs):
+        del args
+        environments.append(kwargs["env"])
+        return SimpleNamespace(returncode=0, stdout="", stderr="")
+
+    monkeypatch.setattr(
+        hf_parity_module,
+        "subprocess",
+        SimpleNamespace(run=_fake_run),
+    )
+
+    hf_parity_module.run_hf_parity_subprocess(request, tmp_path)
+
+    for env in environments:
+        assert "MASTER_ADDR" not in env
+        assert "MASTER_PORT" not in env
+        assert env["RANK"] == "0"
+        assert env["WORLD_SIZE"] == "1"
+        assert env["LOCAL_RANK"] == "0"
+        assert env["LOCAL_WORLD_SIZE"] == "1"
+
+
+def test_hf_parity_worker_requires_explicit_distributed_env(monkeypatch) -> None:
+    distributed_env = {
+        "RANK": "0",
+        "WORLD_SIZE": "1",
+        "LOCAL_RANK": "0",
+        "LOCAL_WORLD_SIZE": "1",
+    }
+    for name in distributed_env:
+        monkeypatch.delenv(name, raising=False)
+    with pytest.raises(RuntimeError, match="explicit distributed environment"):
+        _validate_distributed_process_env()
+
+    for name, value in distributed_env.items():
+        monkeypatch.setenv(name, value)
+    _validate_distributed_process_env()
+
+
+def test_single_rank_process_group_uses_an_in_process_store(monkeypatch) -> None:
+    store = object()
+    init_kwargs: dict[str, object] = {}
+    monkeypatch.setattr(torch.distributed, "is_initialized", lambda: False)
+    monkeypatch.setattr(torch.distributed, "HashStore", lambda: store)
+    monkeypatch.setattr(
+        torch.distributed,
+        "init_process_group",
+        lambda **kwargs: init_kwargs.update(kwargs),
+    )
+
+    initialize_single_rank_process_group()
+
+    assert init_kwargs == {
+        "backend": "nccl",
+        "store": store,
+        "rank": 0,
+        "world_size": 1,
+    }
+
+
 def test_normalize_hf_tensor_map_for_bridge_adds_language_model_prefix() -> None:
     normalized = _normalize_hf_tensor_map_for_bridge(
         {
@@ -276,6 +403,61 @@ def test_build_tensor_map_metric_rows_enforces_nonzero_per_tensor() -> None:
     assert by_param["active"].pass_signal is True
 
 
+def test_grouped_tensor_map_rows_keep_individual_nonzero_gates() -> None:
+    rows = build_tensor_map_metric_rows(
+        phase="grads",
+        reference={"large": torch.ones(1000), "small": torch.ones(1)},
+        candidate={"large": torch.full((1000,), 1.01), "small": torch.full((1,), 2.0)},
+        group_by=lambda _: "joint_update",
+    )
+    by_param = {row.param: row for row in rows}
+
+    assert by_param["joint_update"].phase == "grads"
+    assert by_param["joint_update"].mean_abs_pct < 3.0
+    assert by_param["joint_update"].pass_signal is True
+    assert by_param["small"].phase == "grads_diagnostic"
+    assert by_param["small"].mean_abs_pct == 100.0
+    assert by_param["small"].pass_signal is True
+
+    zero_rows = build_tensor_map_metric_rows(
+        phase="grads",
+        reference={"active": torch.ones(2), "zero": torch.zeros(1)},
+        candidate={"active": torch.ones(2), "zero": torch.zeros(1)},
+        group_by=lambda _: "joint_update",
+    )
+    assert {row.param: row for row in zero_rows}["zero"].pass_signal is False
+
+
+@pytest.mark.parametrize(
+    ("param", "group"),
+    (
+        ("model.embed_tokens.weight", "embedding"),
+        ("lm_head.weight", "final_envelope"),
+        ("model.hc_head.hc_scale", "final_envelope"),
+        ("model.norm.weight", "final_envelope"),
+        ("model.layers.2.attn_hc.base", "model.layers.2.attention"),
+        (
+            "model.layers.2.self_attn.compressor.kv_proj.weight",
+            "model.layers.2.attention",
+        ),
+        ("model.layers.2.ffn_hc.fn", "model.layers.2.ffn"),
+        ("model.layers.2.mlp.experts.0.up_proj.weight", "model.layers.2.ffn"),
+        ("model.layers.2.input_layernorm.weight", "model.layers.2.input_norm"),
+        (
+            "model.layers.2.post_attention_layernorm.weight",
+            "model.layers.2.post_attention_norm",
+        ),
+    ),
+)
+def test_dsv4_hf_parity_gradient_groups(param: str, group: str) -> None:
+    assert DSV4_HANDLER.hf_parity_gradient_group(param) == group
+
+
+def test_dsv4_hf_parity_gradient_groups_reject_unknown_parameter() -> None:
+    with pytest.raises(ValueError, match="Unmapped DSV4 HF-parity gradient"):
+        DSV4_HANDLER.hf_parity_gradient_group("model.layers.0.unknown.weight")
+
+
 def test_language_hf_param_filter_keeps_text_and_drops_visual() -> None:
     assert _is_language_hf_param_name("model.layers.0.self_attn.q_proj.weight") is True
     assert _is_language_hf_param_name("model.visual.blocks.0.attn.qkv.weight") is False
@@ -380,7 +562,7 @@ def test_build_megatron_runtime_uses_training_provider_bundle(
     assert configured_bundles == [(provider_bundle, False)]
     assert kwargs["print_env"] is False
     assert kwargs["trainable_parameter_mode"] == "base_model"
-    configured_provider = SimpleNamespace()
+    configured_provider = SimpleNamespace(_art_model_support_handler=SimpleNamespace())
     kwargs["provider_configure"](configured_provider)
     optimizer_config = kwargs["optimizer_config"]
     assert configured_provider.num_layers == request.case_config.num_layers
diff --git a/tests/integration/megatron/model_support/test_internal_padding.py b/tests/integration/megatron/model_support/test_internal_padding.py
index eb2bb54dd..ae6dc3e22 100644
--- a/tests/integration/megatron/model_support/test_internal_padding.py
+++ b/tests/integration/megatron/model_support/test_internal_padding.py
@@ -30,9 +30,6 @@ def __init__(
         )
         self.A_T = self._parameter(a_shape)
         self.B_T = self._parameter(b_shape)
-        self._slot_modules = torch.nn.ModuleDict(
-            {"checkpoint": _LoraSlot(a_shape, b_shape)}
-        )
 
     @staticmethod
     def _parameter(shape: tuple[int, ...]) -> torch.nn.Parameter:
@@ -44,13 +41,6 @@ def _parameter(shape: tuple[int, ...]) -> torch.nn.Parameter:
         return parameter
 
 
-class _LoraSlot(torch.nn.Module):
-    def __init__(self, a_shape: tuple[int, ...], b_shape: tuple[int, ...]) -> None:
-        super().__init__()
-        self.A_T = _Lora._parameter(a_shape)
-        self.B_T = _Lora._parameter(b_shape)
-
-
 class _Chunk(torch.nn.Module):
     def __init__(
         self,
@@ -116,15 +106,9 @@ def test_internal_padding_is_zeroed(
     handler.zero_internal_padding_params([chunk])
 
     for module_name, parameter_name, dim, ranges in padding:
-        module = getattr(chunk, module_name)
-        parameters = (
-            getattr(module, parameter_name),
-            getattr(module._slot_modules["checkpoint"], parameter_name),
-        )
-        for parameter in parameters:
-            for tensor in (parameter, parameter.grad, parameter.main_grad):
-                assert torch.count_nonzero(tensor) > 0
-                for start, end in ranges:
-                    assert (
-                        torch.count_nonzero(tensor.narrow(dim, start, end - start)) == 0
-                    )
+        parameter = getattr(getattr(chunk, module_name), parameter_name)
+        tensors = (parameter, parameter.grad, parameter.main_grad)
+        for tensor in tensors:
+            assert torch.count_nonzero(tensor) > 0
+            for start, end in ranges:
+                assert torch.count_nonzero(tensor.narrow(dim, start, end - start)) == 0
diff --git a/tests/integration/megatron/model_support/test_oracle_harness_invariants.py b/tests/integration/megatron/model_support/test_oracle_harness_invariants.py
index c0e702eca..e98bab093 100644
--- a/tests/integration/megatron/model_support/test_oracle_harness_invariants.py
+++ b/tests/integration/megatron/model_support/test_oracle_harness_invariants.py
@@ -1,37 +1,30 @@
-from typing import Any
+from types import SimpleNamespace
+from typing import Any, Literal
 
 import pytest
 import torch
 
+from . import oracle_harness
 from .forward_trace import ForwardTraceCapture, _extract_router_topk
 from .oracle_harness import (
-    CP_ATTENTION_SENSITIVITY_MUTATIONS,
-    DENSE_CP_ATTENTION_SENSITIVITY_TOPOLOGY,
-    DENSE_DP_SENSITIVITY_TOPOLOGY,
-    DENSE_ORACLE_TOPOLOGY,
-    DENSE_TOPOLOGIES,
+    CP_MOE_COMPOSITION_TOPOLOGY,
+    DENSE_COMPOSITION_TOPOLOGY,
     FORWARD_EXPERT_LORA_TRACE_NOISE_REASON,
     FORWARD_EXPERT_LORA_TRACE_NOISE_RELATIVE_L2_LIMIT,
-    ORACLE_DEFAULT_MEAN_ABS_PCT_LIMIT,
-    ORACLE_TOPOLOGY,
-    ROUTER_SCORE_MEAN_ABS_PCT_LIMIT,
+    NO_CP_MOE_COMPOSITION_TOPOLOGY,
     TEST_DEFAULT_FLEX_BACKEND,
-    TOPOLOGIES,
     DiffAccumulator,
     MetricRow,
     MetricThresholdRule,
-    PackedTensorConfig,
     Topology,
     VariantRunner,
+    VariantSpec,
     _default_phase_pass_fns,
     _resolve_test_flex_backend,
     _suite_variants,
-    case_config,
-    selected_sensitivity_mutations_for_objective,
-    sensitivity_topology_for_mutation,
+    selected_suite_topologies,
 )
-from .oracle_worker import _matches_grad_sync_skip_mutation
-from .prefix_tree_workloads import build_complex_prefix_tree_packed_tensors
+from .oracle_worker import _matches_grad_sync_skip_mutation, _reset_optimizer_state
 
 
 def _metric_row(
@@ -100,6 +93,32 @@ def _expert_trace_call(
     }
 
 
+def test_paired_oracle_request_resets_optimizer_state() -> None:
+    class Inner:
+        def __init__(self) -> None:
+            self.state = {"stale": object()}
+
+    class Leaf:
+        def __init__(self) -> None:
+            self.optimizer = Inner()
+            self.config = object()
+
+        @staticmethod
+        def init_state_fn(inner: Inner, config: object) -> None:
+            assert config is not None
+            inner.state["fresh"] = 0
+
+    leaves = [Leaf(), Leaf()]
+    optimizer = SimpleNamespace(chained_optimizers=leaves)
+
+    _reset_optimizer_state(optimizer)
+
+    assert [leaf.optimizer.state for leaf in leaves] == [
+        {"fresh": 0},
+        {"fresh": 0},
+    ]
+
+
 def test_fc1_grad_sync_sensitivity_matches_split_and_fused_lora_names() -> None:
     assert _matches_grad_sync_skip_mutation(
         "chunk0.module.decoder.layers.0.mlp.experts.linear_fc1.lora.A_T",
@@ -171,28 +190,6 @@ def test_context_parallel_seeded_accumulator_can_own_stage_storage() -> None:
     assert stage_lse.tolist() == [3.0]
 
 
-def test_fp32_oracle_defaults_to_test_triton_backend() -> None:
-    config = case_config().model_copy(update={"precision": "fp32"})
-
-    assert _resolve_test_flex_backend(config, None) == TEST_DEFAULT_FLEX_BACKEND
-    assert _resolve_test_flex_backend(config, "FLASH") == "FLASH"
-
-
-def test_bf16_oracle_preserves_production_flex_default() -> None:
-    config = case_config().model_copy(update={"precision": "bf16"})
-
-    assert _resolve_test_flex_backend(config, None) is None
-
-
-def test_production_compiled_flex_default_stays_flash() -> None:
-    from art.megatron.flex_attn import compiled as compiled_flex_attention
-
-    assert compiled_flex_attention._FORCED_FLEX_BACKEND == "FLASH"
-    assert compiled_flex_attention._FLASH_FLEX_KERNEL_OPTIONS == {"BACKEND": "FLASH"}
-    assert compiled_flex_attention._TRITON_FLEX_KERNEL_OPTIONS == {"BACKEND": "TRITON"}
-    assert compiled_flex_attention._FORCED_FLEX_KERNEL_OPTIONS == {"BACKEND": "FLASH"}
-
-
 def test_sm90_block_sparse_dq_postprocess_atom_layout_keeps_wgmma_m64() -> None:
     from art.megatron.flex_attn.flash_dlse_patch import (
         _sm90_block_sparse_dq_postprocess_atom_layout,
@@ -334,6 +331,20 @@ def test_forward_trace_extracts_empty_router_topk_with_config_hint() -> None:
     assert scores.shape == (0, 2)
 
 
+def test_forward_trace_extracts_router_ids_from_actual_routing_map() -> None:
+    topk = _extract_router_topk(
+        (
+            torch.tensor([[0.0, 1.2, 1.3, 0.0], [0.8, 0.0, 0.0, 1.7]]),
+            torch.tensor([[False, True, True, False], [True, False, False, True]]),
+        )
+    )
+    assert topk is not None
+    ids, scores = topk
+
+    assert torch.equal(ids, torch.tensor([[1, 2], [0, 3]]))
+    assert torch.equal(scores, torch.tensor([[1.2, 1.3], [0.8, 1.7]]))
+
+
 def test_megatron_empty_swiglu_patch_preserves_known_output_width() -> None:
     from art.megatron.runtime.bridge_runtime import install_art_bridge_runtime_patches
 
@@ -433,16 +444,16 @@ def test_forward_trace_canonicalizes_row_outputs_by_token_uid() -> None:
     )
 
 
-def test_forward_trace_drops_exact_zero_padding_rows() -> None:
+def test_forward_trace_drops_explicit_nonzero_padding_rows() -> None:
     trace: dict[str, list[dict[str, Any]]] = {
         "chunk0.module.decoder.layers.0.self_attention.out_proj": [
             {
                 "primary_output": torch.tensor(
-                    [[0.0, 0.0], [30.0, 31.0], [10.0, 11.0], [20.0, 21.0]]
+                    [[9.0, 9.0], [30.0, 31.0], [0.0, 0.0], [20.0, 21.0]]
                 ),
                 "output": {
                     "hidden": torch.tensor(
-                        [[0.0, 0.0], [3.0, 3.1], [1.0, 1.1], [2.0, 2.1]]
+                        [[9.0, 9.0], [3.0, 3.1], [0.0, 0.0], [2.0, 2.1]]
                     )
                 },
                 "row_token_uids": torch.tensor([-1, 3, 1, 2]),
@@ -456,11 +467,11 @@ def test_forward_trace_drops_exact_zero_padding_rows() -> None:
     assert torch.equal(call["row_token_uids"], torch.tensor([1, 2, 3]))
     assert torch.equal(
         call["primary_output"],
-        torch.tensor([[10.0, 11.0], [20.0, 21.0], [30.0, 31.0]]),
+        torch.tensor([[0.0, 0.0], [20.0, 21.0], [30.0, 31.0]]),
     )
     assert torch.equal(
         call["output"]["hidden"],
-        torch.tensor([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1]]),
+        torch.tensor([[0.0, 0.0], [2.0, 2.1], [3.0, 3.1]]),
     )
 
 
@@ -534,13 +545,11 @@ def test_forward_trace_expands_attention_output_uids_for_out_norm_heads() -> Non
     ForwardTraceCapture.canonicalize_trace(trace)
 
     call = trace["chunk0.module.decoder.layers.0.self_attention.out_norm"][0]
-    assert torch.equal(call["row_token_uids"], torch.tensor([-1, -1, 0, 0, 2, 2]))
+    assert torch.equal(call["row_token_uids"], torch.tensor([0, 0, 2, 2]))
     assert torch.equal(
         call["primary_output"],
         torch.tensor(
             [
-                [8.0, 9.0, 10.0, 11.0],
-                [12.0, 13.0, 14.0, 15.0],
                 [0.0, 1.0, 2.0, 3.0],
                 [4.0, 5.0, 6.0, 7.0],
                 [16.0, 17.0, 18.0, 19.0],
@@ -678,6 +687,43 @@ def test_forward_trace_sums_expert_tp_row_shards_inside_ep_groups() -> None:
     )
 
 
+def test_forward_trace_deduplicates_replicated_tp_outputs_with_cp_rows() -> None:
+    module_name = "chunk0.module.decoder.layers.0.self_attention.linear_qkv.q_proj_lora"
+    rank_traces = []
+    for cp_rank, values in enumerate(
+        (torch.tensor([[1.0, 2.0]]), torch.tensor([[3.0, 4.0]]))
+    ):
+        for tp_rank in range(2):
+            rank_traces.append(
+                {
+                    module_name: [
+                        {
+                            "micro_call_index": 0,
+                            "micro_order": 0,
+                            "micro_sample_index": 0,
+                            "module_type": "LoRA",
+                            "primary_output": values,
+                            "merge_hints": {"primary_output": {"op": "replicated"}},
+                            "rank_meta": {
+                                "global_rank": cp_rank * 2 + tp_rank,
+                                "tp_rank": tp_rank,
+                                "tp_world_size": 2,
+                                "cp_rank": cp_rank,
+                                "cp_world_size": 2,
+                            },
+                        }
+                    ]
+                }
+            )
+
+    merged = ForwardTraceCapture._merge_rank_traces(rank_traces)
+
+    assert torch.equal(
+        merged[module_name][0]["primary_output"],
+        torch.tensor([[1.0, 2.0], [3.0, 4.0]]),
+    )
+
+
 def test_gate_up_rank_interleaved_trace_layout_canonicalizes_dense_tp() -> None:
     canonical = torch.arange(16, dtype=torch.float32).reshape(2, 1, 8)
     gate0, gate1, up0, up1 = canonical.chunk(4, dim=-1)
@@ -790,41 +836,6 @@ def test_default_phase_rules_require_non_zero_forward_outputs_grads_and_deltas()
     assert phase_pass["losses"](zero_signal_summary)
 
 
-def test_default_phase_rules_use_default_mean_abs_pct_limit() -> None:
-    phase_pass = _default_phase_pass_fns()
-    passing_summary = {
-        "relative_l2": 0.0,
-        "mean_abs_pct": ORACLE_DEFAULT_MEAN_ABS_PCT_LIMIT,
-        "typical_abs_scale": 1.0,
-        "candidate_abs_scale": 1.0,
-    }
-    failing_summary = {
-        **passing_summary,
-        "mean_abs_pct": ORACLE_DEFAULT_MEAN_ABS_PCT_LIMIT + 1e-6,
-    }
-
-    assert phase_pass["forward"](passing_summary)
-    assert phase_pass["outputs"](passing_summary)
-    assert phase_pass["grads"](passing_summary)
-    assert phase_pass["deltas"](passing_summary)
-    assert phase_pass["losses"](passing_summary)
-    assert not phase_pass["forward"](failing_summary)
-    assert not phase_pass["outputs"](failing_summary)
-    assert not phase_pass["grads"](failing_summary)
-    assert not phase_pass["deltas"](failing_summary)
-    assert not phase_pass["losses"](failing_summary)
-
-
-def test_router_score_rule_uses_tight_dedicated_limit() -> None:
-    phase_pass = _default_phase_pass_fns()
-    assert phase_pass["router_scores"](
-        {"relative_l2": 1.0, "mean_abs_pct": ROUTER_SCORE_MEAN_ABS_PCT_LIMIT}
-    )
-    assert not phase_pass["router_scores"](
-        {"relative_l2": 0.0, "mean_abs_pct": ROUTER_SCORE_MEAN_ABS_PCT_LIMIT + 1e-8}
-    )
-
-
 def test_forward_expert_lora_noise_pass_requires_clean_step_gates() -> None:
     noisy_row = _metric_row(
         phase="forward",
@@ -936,150 +947,106 @@ def _gates(
 def test_suite_variants_skip_duplicate_oracle_replay_variant() -> None:
     variants = _suite_variants("rl")
 
-    assert variants
-    assert all(variant.topology != ORACLE_TOPOLOGY for variant in variants)
+    assert [variant.topology for variant in variants] == [CP_MOE_COMPOSITION_TOPOLOGY]
     assert all("oracle_replay" not in variant.name for variant in variants)
 
 
-def test_dense_suite_variants_preserve_dense_and_cp_topologies() -> None:
+def test_dense_suite_variants_use_composed_topology() -> None:
     variants = _suite_variants("rl", is_moe=False)
 
-    assert variants
-    assert all(variant.topology != DENSE_ORACLE_TOPOLOGY for variant in variants)
-    assert any(
-        variant.topology.tp == 2
-        and variant.topology.dp == 2
-        and variant.topology.cp == 1
-        for variant in variants
-    )
-    assert any(
-        variant.topology.tp == 2
-        and variant.topology.dp == 2
-        and variant.topology.cp == 2
-        for variant in variants
-    )
+    assert [variant.topology for variant in variants] == [DENSE_COMPOSITION_TOPOLOGY]
 
 
 def test_max_world_size_arg_filters_dense_variants() -> None:
-    variants = _suite_variants("rl", is_moe=False, max_world_size=2)
-
-    assert variants
-    assert all(variant.topology.world_size() <= 2 for variant in variants)
-    assert not any(
-        variant.topology.tp == 2 and variant.topology.dp == 2 for variant in variants
-    )
+    assert _suite_variants("rl", is_moe=False, max_world_size=2) == []
+    assert len(_suite_variants("rl", is_moe=False, max_world_size=8)) == 1
 
 
-def test_oracle_topologies_are_the_compact_cp_validation_matrix() -> None:
-    assert TOPOLOGIES == [
-        Topology(tp=1, ep=1, etp=1, dp=1, sp=False),
-        Topology(tp=1, ep=2, etp=1, dp=1, cp=2, sp=False),
-        Topology(tp=2, ep=2, etp=1, dp=1, cp=2, sp=True),
-        Topology(tp=2, ep=4, etp=2, dp=2, cp=2, sp=True),
-    ]
-    assert [topology.world_size() for topology in TOPOLOGIES] == [1, 2, 4, 8]
-
-
-def test_dense_topologies_include_vllm_separation_and_cp_coverage() -> None:
-    assert DENSE_TOPOLOGIES == [
-        Topology(tp=1, ep=1, etp=1, dp=1, sp=False),
-        Topology(tp=2, ep=1, etp=1, dp=1, sp=True),
-        Topology(tp=1, ep=1, etp=1, dp=2, sp=False),
-        Topology(tp=2, ep=1, etp=1, dp=2, sp=True),
-        Topology(tp=1, ep=1, etp=1, dp=1, cp=2, sp=False),
-        Topology(tp=2, ep=1, etp=1, dp=1, cp=2, sp=True),
-        Topology(tp=2, ep=1, etp=1, dp=2, cp=2, sp=True),
-    ]
-    assert [topology.world_size() for topology in DENSE_TOPOLOGIES] == [
-        1,
-        2,
-        2,
-        4,
-        2,
-        4,
-        8,
-    ]
-
-
-def test_dense_sensitivity_keeps_dp_and_cp_attention_cases() -> None:
-    mutations = selected_sensitivity_mutations_for_objective(
-        "rl",
-        [
-            "skip_finalize",
-            "dp_local_token_normalization",
-            *CP_ATTENTION_SENSITIVITY_MUTATIONS,
-        ],
-        is_moe=False,
+@pytest.mark.parametrize(
+    ("is_moe", "cp_supported", "composition"),
+    [
+        (
+            True,
+            True,
+            Topology(tp=2, ep=2, etp=2, dp=1, cp=2, pp=2, vpp=2, sp=True),
+        ),
+        (
+            False,
+            True,
+            Topology(tp=2, ep=1, etp=1, dp=1, cp=2, pp=2, vpp=2, sp=True),
+        ),
+        (
+            True,
+            False,
+            Topology(tp=2, ep=2, etp=2, dp=2, cp=1, pp=2, vpp=2, sp=True),
+        ),
+    ],
+)
+def test_normal_suite_selects_oracle_plus_one_legal_composition(
+    is_moe: bool,
+    cp_supported: bool,
+    composition: Topology,
+) -> None:
+    topologies = selected_suite_topologies(
+        is_moe=is_moe,
+        cp_supported=cp_supported,
     )
 
-    assert mutations == [
-        "skip_finalize",
-        "dp_local_token_normalization",
-        *CP_ATTENTION_SENSITIVITY_MUTATIONS,
-    ]
-    assert sensitivity_topology_for_mutation("skip_finalize", is_moe=False) == Topology(
-        tp=2, ep=1, etp=1, dp=1, sp=True
-    )
-    assert (
-        sensitivity_topology_for_mutation(
-            "dp_local_token_normalization",
-            is_moe=False,
-        )
-        == DENSE_DP_SENSITIVITY_TOPOLOGY
-    )
-    assert (
-        sensitivity_topology_for_mutation(
-            CP_ATTENTION_SENSITIVITY_MUTATIONS[0],
-            is_moe=False,
-        )
-        == DENSE_CP_ATTENTION_SENSITIVITY_TOPOLOGY
-    )
-    assert sensitivity_topology_for_mutation(
-        "attn_skip_flash_lse_normalize",
-        is_moe=False,
-    ) == Topology(tp=1, ep=1, etp=1, dp=1, cp=4, sp=False)
-    assert sensitivity_topology_for_mutation(
-        "attn_skip_flash_lse_normalize",
-        is_moe=True,
-    ) == Topology(tp=1, ep=2, etp=1, dp=1, cp=4, sp=False)
+    assert topologies == [oracle_harness.oracle_topology(is_moe=is_moe), composition]
+    assert [topology.world_size() for topology in topologies] == [1, 8]
 
 
-def test_case_config_base_model_can_be_overridden_by_env(
+def test_paired_objectives_reuse_composition_worker_artifacts(
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
-    monkeypatch.setenv("ART_ORACLE_BASE_MODEL", "Qwen/Qwen3.5-35B-A3B")
-
-    assert case_config().base_model == "Qwen/Qwen3.5-35B-A3B"
-    assert case_config(base_model="custom/model").base_model == "custom/model"
-
-
-def test_packed_tensor_defaults_match_main_rebase_oracle_tokens() -> None:
-    config = PackedTensorConfig()
-
-    assert config.num_sequences == 4
-    assert config.sequence_length == 1024
-    assert config.prefill_tokens == 256
-    assert config.completion_branches_per_prefix == 2
-    assert config.decode_tokens == 128
-    assert config.decode_tokens_jitter == 32
-    assert config.packing_mode == "stop_early"
-    assert config.vocab_high == 8192
-
-
-def test_prefix_tree_workload_fits_hf_parity_packed_size() -> None:
-    packed_tensors = build_complex_prefix_tree_packed_tensors(
-        PackedTensorConfig(
-            num_sequences=4,
-            sequence_length=256,
-            prefill_tokens=64,
-            completion_branches_per_prefix=2,
-            decode_tokens=64,
-            decode_tokens_jitter=32,
-            packing_mode="stop_early",
+    runs: list[tuple[str | None, list[VariantSpec]]] = []
+
+    class Runner:
+        def __init__(self, **kwargs: object) -> None:
+            paired_objective = kwargs.get("paired_objective")
+            assert paired_objective is None or isinstance(paired_objective, str)
+            self.paired_objective = paired_objective
+            self._oracle_initialized = False
+            self._oracle_regenerated = False
+
+        def run_suite(
+            self, variants: list[VariantSpec], **_kwargs: object
+        ) -> list[Any]:
+            runs.append((self.paired_objective, variants))
+            return []
+
+    monkeypatch.setattr(oracle_harness, "VariantRunner", Runner)
+    monkeypatch.setattr(
+        oracle_harness,
+        "_prune_completed_runners",
+        lambda *_args, **_kwargs: None,
+    )
+
+    oracle_harness._run_paired_objective_suite(
+        objectives=["rl", "sft"],
+        case_config=oracle_harness.OracleCaseConfig(
+            base_model="Qwen/Qwen3.5-35B-A3B",
+            model_support_key="qwen3_5_moe",
         ),
-        seed=20260304,
+        max_world_size=None,
+        oracle_flex_backend=None,
+        variant_flex_backend=None,
+        cp_supported=True,
+        phase_pass_fns=None,
+        use_fp32_lora_reference=True,
+        prune_reference_artifacts=True,
+        prune_case_artifacts=True,
     )
 
-    assert int((packed_tensors["group_ids"] != -1).sum().item()) > 0
-    assert int(packed_tensors["assistant_mask"].sum().item()) > 0
-    assert int((packed_tensors["weights"] != 0).sum().item()) > 0
+    assert [
+        (paired, [variant.objective for variant in variants])
+        for paired, variants in runs
+    ] == [
+        ("sft", ["rl"]),
+        (None, ["sft"]),
+    ]
+    assert [[variant.topology for variant in variants] for _, variants in runs] == [
+        [CP_MOE_COMPOSITION_TOPOLOGY],
+        [CP_MOE_COMPOSITION_TOPOLOGY],
+    ]
+    assert not runs[1][1][0].force_regenerate
diff --git a/tests/integration/megatron/model_support/test_packing_invariance.py b/tests/integration/megatron/model_support/test_packing_invariance.py
index 367e7217f..45613d5f9 100644
--- a/tests/integration/megatron/model_support/test_packing_invariance.py
+++ b/tests/integration/megatron/model_support/test_packing_invariance.py
@@ -34,4 +34,8 @@ def test_run_packing_invariance_qwen35() -> None:
         scenario.repeated_position_key_count > 0 for scenario in report.scenarios
     )
     assert all(scenario.completion_pair_count > 0 for scenario in report.scenarios)
-    assert all(scenario.logits_mean_abs_pct <= 0.5 for scenario in report.scenarios)
+    assert report.precision == "fp32"
+    assert all(
+        scenario.logits_mean_abs_pct <= scenario.logits_mean_abs_pct_limit
+        for scenario in report.scenarios
+    )
diff --git a/tests/integration/megatron/model_support/test_provider_support.py b/tests/integration/megatron/model_support/test_provider_support.py
index 43a641452..838e85191 100644
--- a/tests/integration/megatron/model_support/test_provider_support.py
+++ b/tests/integration/megatron/model_support/test_provider_support.py
@@ -11,13 +11,13 @@
 from megatron.core.transformer.enums import AttnBackend
 
 from art.megatron.context_parallel.core_attention import ArtContextParallelCoreAttention
+from art.megatron.dsv4.bridge import _install_dsv4_source_aliases
 from art.megatron.flex_attn.attention import FlexDotProductAttention
 from art.megatron.lora import default_lora_rank_for_handler
 from art.megatron.model_support.registry import (
     UnsupportedModelArchitectureError,
     get_model_support_handler,
     get_model_support_spec,
-    model_requires_merged_rollout,
     model_uses_expert_parallel,
 )
 import art.megatron.provider as provider_module
@@ -45,6 +45,7 @@ def __init__(self) -> None:
         self.recompute_num_layers: int | None = None
         self.expert_model_parallel_size = 1
         self.expert_tensor_parallel_size = 1
+        self.dsv4_hc_mult = 4
 
     def _base_layer_spec(
         self, config: object, vp_stage: int | None = None
@@ -155,11 +156,14 @@ def test_model_support_specs_own_moe_metadata() -> None:
     assert model_uses_expert_parallel("deepseek-ai/DeepSeek-V4-Flash") is True
 
 
-def test_dsv4_prefers_validated_native_lora_rollout() -> None:
+def test_dsv4_native_lora_is_validated() -> None:
     spec = get_model_support_spec("deepseek-ai/DeepSeek-V4-Flash")
 
     assert spec.native_vllm_lora_status == "validated"
-    assert model_requires_merged_rollout("deepseek-ai/DeepSeek-V4-Flash") is False
+
+
+def test_dsv4_config_only_bridge_does_not_require_checkpoint_state() -> None:
+    _install_dsv4_source_aliases(SimpleNamespace(config=SimpleNamespace()))
 
 
 def test_dsv4_provider_disables_shared_expert_overlap(
@@ -178,6 +182,11 @@ def test_dsv4_provider_disables_shared_expert_overlap(
         lambda *args, **kwargs: fake_bridge,
     )
     monkeypatch.setattr(provider_module.torch.cuda, "device_count", lambda: 2)
+    monkeypatch.setattr(
+        provider_module.torch.cuda,
+        "get_device_properties",
+        lambda device: SimpleNamespace(major=9, name="NVIDIA H200"),
+    )
 
     resolved = provider_module.get_provider("deepseek-ai/DeepSeek-V4-Flash")
 
diff --git a/tests/integration/megatron/model_support/test_train_schedule_timing.py b/tests/integration/megatron/model_support/test_train_schedule_timing.py
new file mode 100644
index 000000000..2dccc3a79
--- /dev/null
+++ b/tests/integration/megatron/model_support/test_train_schedule_timing.py
@@ -0,0 +1,112 @@
+from types import SimpleNamespace
+from typing import Any, cast
+
+from art.megatron import train
+
+
+class _Schedule:
+    def __init__(self, spans: tuple[tuple[Any, Any], ...] = ()) -> None:
+        self._spans = iter(spans)
+        self._span = None
+        self.telemetry = SimpleNamespace(cuda_span=lambda: self._span)
+
+    def run(self, forward_step_func: Any, *, forward_only: bool) -> list[str]:
+        assert callable(forward_step_func) and forward_only is False
+        self._span = next(self._spans, None)
+        return ["output"]
+
+
+class _CudaEvent:
+    def __init__(self, timestamp_ms: float) -> None:
+        self.timestamp_ms = timestamp_ms
+
+    def elapsed_time(self, end: "_CudaEvent") -> float:
+        return end.timestamp_ms - self.timestamp_ms
+
+
+def test_inter_forward_backward_timing_uses_rank_local_monotonic_boundaries(
+    monkeypatch,
+) -> None:
+    timestamps = iter((10.0, 12.0, 15.0, 17.0))
+    monkeypatch.setattr(train.time, "monotonic", lambda: next(timestamps))
+    monkeypatch.setattr(train.torch.distributed, "get_world_size", lambda: 1)
+    timing = train._InterForwardBackwardTiming()
+    schedule = cast(
+        Any,
+        _Schedule(
+            (
+                (_CudaEvent(100.0), _CudaEvent(200.0)),
+                (_CudaEvent(260.0), _CudaEvent(400.0)),
+            )
+        ),
+    )
+
+    first, collect_first_metrics = train._run_training_schedule(
+        schedule,
+        lambda: None,
+        timing,
+    )
+    timing.previous_job_complete_s = 12.5
+    timing.current_job_start_s = 14.0
+    second, collect_second_metrics = train._run_training_schedule(
+        schedule,
+        lambda: None,
+        timing,
+    )
+
+    assert first == second == ["output"]
+    assert collect_first_metrics() == {}
+    assert collect_second_metrics() == {
+        "time/inter_forward_backward_gap_rank_0_s": 3.0,
+        "time/inter_forward_backward_previous_job_tail_rank_0_s": 0.5,
+        "time/inter_forward_backward_worker_idle_rank_0_s": 1.5,
+        "time/inter_forward_backward_current_job_prepare_rank_0_s": 1.0,
+        "time/inter_forward_backward_gpu_gap_rank_0_s": 0.06,
+    }
+    assert timing.previous_schedule_end_s == 17.0
+
+
+def test_inter_forward_backward_timing_gathers_rank_durations_on_cpu_group(
+    monkeypatch,
+) -> None:
+    timestamps = iter((1.0, 2.0, 4.0, 5.0))
+    group = SimpleNamespace(backend="gloo")
+    timing = train._InterForwardBackwardTiming(metrics_group=group)
+    monkeypatch.setattr(train.time, "monotonic", lambda: next(timestamps))
+    monkeypatch.setattr(train.torch.distributed, "get_world_size", lambda: 2)
+
+    waits = []
+
+    def gather(output, value, *, group, async_op):
+        assert group is timing.metrics_group
+        assert async_op is True and value.device.type == "cpu"
+        output[0].copy_(value)
+        remote = value.clone()
+        remote[0] += 0.25
+        remote[3] += 0.25
+        output[1].copy_(remote)
+        return SimpleNamespace(wait=lambda: waits.append(True))
+
+    monkeypatch.setattr(train.torch.distributed, "all_gather", gather)
+    schedule = cast(Any, _Schedule())
+    train._run_training_schedule(schedule, lambda: None, timing)
+    timing.previous_job_complete_s = 2.5
+    timing.current_job_start_s = 3.0
+    _, collect_metrics = train._run_training_schedule(
+        schedule,
+        lambda: None,
+        timing,
+    )
+
+    assert waits == []
+    assert collect_metrics() == {
+        "time/inter_forward_backward_gap_rank_0_s": 2.0,
+        "time/inter_forward_backward_gap_rank_1_s": 2.25,
+        "time/inter_forward_backward_previous_job_tail_rank_0_s": 0.5,
+        "time/inter_forward_backward_worker_idle_rank_0_s": 0.5,
+        "time/inter_forward_backward_current_job_prepare_rank_0_s": 1.0,
+        "time/inter_forward_backward_previous_job_tail_rank_1_s": 0.5,
+        "time/inter_forward_backward_worker_idle_rank_1_s": 0.5,
+        "time/inter_forward_backward_current_job_prepare_rank_1_s": 1.25,
+    }
+    assert waits == [True]
diff --git a/tests/integration/megatron/model_support/test_workflow.py b/tests/integration/megatron/model_support/test_workflow.py
index 4b68579cd..790c57b02 100644
--- a/tests/integration/megatron/model_support/test_workflow.py
+++ b/tests/integration/megatron/model_support/test_workflow.py
@@ -1,6 +1,8 @@
+import json
 import os
+from pathlib import Path
 from types import SimpleNamespace
-from typing import cast
+from typing import Any, cast
 
 import pytest
 
@@ -8,40 +10,58 @@
     ArchitectureReport,
     LayerFamilyInstance,
 )
+from art.pipeline_tuner import PipelineTuneSettings
 
-from .validation_spec import ValidationReport, ValidationStageResult
+from .validation_spec import ValidationStageResult
 from .workflow import (
     INCLUDE_FLASH_SENSITIVITY_ENV,
-    KEEP_TOPOLOGY_ARTIFACTS_ENV,
     MANDATORY_VALIDATION_STAGES,
-    NATIVE_VLLM_LORA_STAGE,
-    SKIP_SENSITIVITY_ENV,
+    WORKFLOW_STAGE_DIR_ENV,
     _inspect_architecture_for_workflow,
-    assess_minimal_layer_coverage,
-    build_all_architectures_validation_report,
     build_validation_report,
     build_validation_stage_names,
-    run_chat_template_rollout_stage,
-    run_correctness_sensitivity_stage,
-    run_length_trainability_stage,
     run_lora_coverage_stage,
-    run_merged_vllm_serving_stage,
-    run_native_vllm_lora_stage,
-    run_packing_invariance_stage,
-    run_train_inf_mismatch_stage,
-    run_yes_no_trainability_stage,
     validated_architecture_representative_models,
 )
+from .workflow_fixtures import (
+    FIXTURE_PATH_ENV,
+    WorkflowFixture,
+    _validate_tokenizer_compatible_fixture,
+)
 from .workflow_resources import (
-    _h200_equivalent_slots_for_total_gib,
+    HANDLER_WORKFLOW_RESOURCES,
+    ThroughputThresholds,
+    ThroughputWorkflowConfig,
     handler_workflow_resources_for_base_model,
+    resolve_stage_resources_for_current_host,
     resolve_stage_resources_for_visible_gpus,
 )
+from .workflow_throughput import (
+    PolicyActivationEvent,
+    ThroughputFixture,
+    _classify_acceptance_failures,
+    _collect_measurements,
+    _current_pipeline_settings,
+    _freeze_pipeline_settings_from_step,
+    _packed_input_fingerprint,
+    _phase_evidence,
+    _run_throughput_attempts,
+    _settled_execution_decision_suffix,
+    acceptance_failures,
+)
 
 
 @pytest.fixture(autouse=True)
-def _stub_pinned_git_state(monkeypatch) -> None:
+def _stub_workflow_environment(monkeypatch, tmp_path) -> None:
     monkeypatch.delenv(INCLUDE_FLASH_SENSITIVITY_ENV, raising=False)
+    fixture_path = tmp_path / "correctness_fixture"
+    tokenizer_compatible_path = tmp_path / "tokenizer_compatible_fixture"
+    stage_path = tmp_path / "stage"
+    fixture_path.mkdir()
+    tokenizer_compatible_path.mkdir()
+    stage_path.mkdir()
+    monkeypatch.setenv(FIXTURE_PATH_ENV, str(fixture_path))
+    monkeypatch.setenv(WORKFLOW_STAGE_DIR_ENV, str(stage_path))
     monkeypatch.setattr(
         "tests.integration.megatron.model_support.workflow.pinned_git_state",
         lambda suite_name: SimpleNamespace(
@@ -53,22 +73,801 @@ def _stub_pinned_git_state(monkeypatch) -> None:
             }
         ),
     )
+    monkeypatch.setattr(
+        "tests.integration.megatron.model_support.workflow.ensure_workflow_fixture",
+        lambda base_model, allow_unvalidated_arch=False, required_stages=frozenset(): (
+            WorkflowFixture(
+                canonical_model=base_model,
+                model_key="qwen3_5_moe",
+                source_revision="test",
+                path=str(fixture_path),
+                hf_home=str(tmp_path / "hf_home"),
+                manifest={"version": 15},
+                tokenizer_compatible_path=str(tokenizer_compatible_path),
+                tokenizer_compatible_hf_home=str(tmp_path / "tokenizer_hf_home"),
+                tokenizer_compatible_manifest={"version": 1},
+                functional_path=str(fixture_path),
+                functional_hf_home=str(tmp_path / "hf_home"),
+                functional_manifest={"version": 1, "num_layers": 8},
+                canonical_path=str(fixture_path),
+                canonical_hf_home=str(tmp_path / "hf_home"),
+            )
+        ),
+    )
 
 
-def test_build_validation_stage_names_has_fixed_order() -> None:
-    assert build_validation_stage_names() == list(MANDATORY_VALIDATION_STAGES)
-    assert build_validation_stage_names(include_native_vllm_lora=True) == [
-        *MANDATORY_VALIDATION_STAGES,
-        NATIVE_VLLM_LORA_STAGE,
+def _fixture(tmp_path: Path, model_key: str) -> WorkflowFixture:
+    return WorkflowFixture(
+        canonical_model=model_key,
+        model_key=model_key,
+        source_revision="pinned",
+        path=str(tmp_path / "compact"),
+        hf_home=str(tmp_path / "compact_cache"),
+        manifest={"version": 15},
+        tokenizer_compatible_path=str(tmp_path / "tokenizer"),
+        tokenizer_compatible_hf_home=str(tmp_path / "tokenizer_cache"),
+        functional_path=str(tmp_path / "functional"),
+        functional_hf_home=str(tmp_path / "functional_cache"),
+        functional_manifest={"version": 1, "num_layers": 8},
+        canonical_path=str(tmp_path / "canonical"),
+        canonical_hf_home=str(tmp_path / "canonical_cache"),
+    )
+
+
+def test_fixture_stage_contracts(tmp_path: Path) -> None:
+    # fmt: off
+    cases = {
+        ("gemma4_dense", "canonical"): ("hf_parity", "packing_invariance", "length_trainability"),
+        ("gemma4_dense", "compact"): ("lora_coverage",),
+        ("gemma4_dense", "functional"): ("train_inf_mismatch",),
+        ("llama3_dense", "compact"): ("hf_parity",),
+        ("llama3_dense", "functional"): ("train_inf_mismatch",),
+        ("llama3_dense", "canonical"): ("length_trainability",),
+        ("qwen3_5_moe", "canonical"): ("length_trainability",),
+        ("gpt_oss_moe", "functional"): ("train_inf_mismatch",),
+        ("gpt_oss_moe", "canonical"): ("length_trainability",),
+        ("glm52", "functional"): ("train_inf_mismatch",),
+        ("glm52", "compact"): ("length_trainability",),
+        ("dsv4", "functional"): ("train_inf_mismatch",),
+        ("dsv4", "canonical"): ("length_trainability",),
+    }
+    # fmt: on
+    for (model_key, selected), stages in cases.items():
+        for stage in stages:
+            environment = _fixture(tmp_path, model_key).environment(stage)
+            assert environment[FIXTURE_PATH_ENV] == str(tmp_path / selected)
+            assert environment["ART_ORACLE_BASE_MODEL"] == str(tmp_path / selected)
+            if selected == "functional":
+                assert environment["ART_MODEL_SUPPORT_FUNCTIONAL_NUM_LAYERS"] == "8"
+
+
+def test_fixture_stage_contracts_require_available_assets(tmp_path: Path) -> None:
+    for stage, missing, contract in (
+        ("hf_parity", "canonical_path", "canonical weights"),
+        (
+            "train_inf_mismatch",
+            "functional_path",
+            "pretrained production-width functional weights",
+        ),
+    ):
+        fixture = _fixture(tmp_path, "gemma4_dense").model_copy(update={missing: None})
+        with pytest.raises(RuntimeError, match=f"requires {contract}"):
+            fixture.environment(stage)
+
+
+def test_reduced_trainability_preserves_validated_token_contract(
+    tmp_path: Path,
+) -> None:
+    for model_key, stage, expected in (
+        ("glm52", "length_trainability", "154820,38069"),
+    ):
+        key = f"ART_MODEL_SUPPORT_{stage.removesuffix('_trainability').upper()}_ALLOWED_TOKEN_IDS"
+        assert _fixture(tmp_path, model_key).environment(stage)[key] == expected
+
+
+@pytest.mark.parametrize(
+    ("vocab_size", "registered_max", "encoded_max", "error"),
+    [
+        (8_192, 9_000, 3, "registered tokenizer ID 9000"),
+        (128_256, 128_255, 128_009, None),
+    ],
+)
+def test_tokenizer_compatible_fixture_preflight(
+    monkeypatch: pytest.MonkeyPatch,
+    vocab_size: int,
+    registered_max: int,
+    encoded_max: int,
+    error: str | None,
+) -> None:
+    class Tokenizer:
+        chat_template = "template"
+
+        def get_vocab(self):
+            return {"ordinary": 1, "highest": registered_max}
+
+        def __call__(self, *_args, **_kwargs):
+            return {"input_ids": [1, encoded_max]}
+
+        apply_chat_template = __call__
+
+    monkeypatch.setattr(
+        "transformers.AutoTokenizer.from_pretrained",
+        lambda *_args, **_kwargs: Tokenizer(),
+    )
+    manifest: dict[str, object] = {"config_vocab_size": vocab_size}
+    if error:
+        with pytest.raises(RuntimeError, match=error):
+            _validate_tokenizer_compatible_fixture(Path("/tmp/provider"), manifest)
+    else:
+        _validate_tokenizer_compatible_fixture(Path("/tmp/provider"), manifest)
+        assert manifest["representative_max_token_id"] == encoded_max
+        assert manifest["tokenizer_max_id"] == registered_max
+
+
+def test_throughput_runtime_keeps_canonical_handler_separate_from_provider(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    from art.megatron.runtime import local as local_runtime
+
+    monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
+    monkeypatch.setattr(
+        local_runtime,
+        "get_megatron_runtime_config",
+        lambda: SimpleNamespace(
+            topology={"tp": 1, "ep": 1, "etp": 1, "cp": 1, "pp": 1}
+        ),
+    )
+    topology = local_runtime.compile_local_runtime_topology(
+        cast(
+            Any,
+            {
+                "trainer_gpu_ids": [0],
+                "init_args": {"model_name": "/tmp/production-width-provider"},
+            },
+        ),
+        model_name="validation",
+        base_model="meta-llama/Llama-3.2-1B-Instruct",
+        artifact_root="/tmp/art",
+        visible_gpu_count=1,
+    )
+
+    assert topology.model_services[0].base_model == "/tmp/production-width-provider"
+
+
+def test_throughput_measurements_use_runtime_rows_and_activation_timestamps(
+    tmp_path: Path,
+) -> None:
+    rows = [
+        {
+            "step": step,
+            "data/step_num_groups_trainable": 8,
+            "data/step_num_groups_submitted": 24,
+            "data/step_packed_sequences": 1,
+            "data/step_nonpadding_logical_tokens": 1_000,
+            "train/prefix_tree/logical_tokens": 4_000,
+            "data/step_loss_bearing_tokens": 500,
+            "data/step_trainable_assistant_tokens": 500,
+            "data/step_executed_token_equivalents": 1_000,
+            "data/step_dummy_executed_token_equivalents": 0,
+            "data/step_nominal_schedule_capacity_tokens": 131_072,
+            "data/step_dummy_schedule_capacity_tokens": 0,
+            "data/step_unused_packed_capacity_tokens": 130_072,
+            "data/step_num_gradient_steps": 1,
+            "pipeline/global_real_microbatches": 1,
+            "pipeline/global_dummy_microbatches": 0,
+            "pipeline/packed_sequence_length": 131_072,
+            "pipeline_settings/num_rollout_workers": 16,
+            "pipeline_settings/min_batch_size": 8,
+            "pipeline_settings/max_batch_size": 32,
+            "pipeline_settings/queue_maxsize": 48,
+            "pipeline_settings/target_groups_per_step": 24,
+            "queue/packing_policy_lag_steps": 1,
+            "time/step_train_s": 1.5,
+            "time/step_wall_s": 2.0,
+            "time/step_collect_batch_s": 0.001068115234375,
+            "queue/packed_get_wait_s": 0.1 if step >= 7 else 0.001,
+            "queue/packed_queue_depth": 0.0 if step == 6 else 1.0,
+            "time/inter_forward_backward_gpu_gap_rank_0_s": (
+                1.0 if step >= 6 else 0.1 + (step - 2) * 0.01
+            ),
+            "time/inter_forward_backward_gpu_gap_rank_1_s": (
+                2.0 if step >= 6 else 0.11 + (step - 2) * 0.01
+            ),
+            "offpolicy/token_weighted_policy_age_steps": 1.0,
+            "offpolicy/token_weighted_policy_age_p95_steps": 2.0,
+            "sample_efficiency/freshness_discount": 0.8,
+            "discarded/step/stale_groups": 0,
+            "discarded/step/zero_variance_groups": 0,
+        }
+        for step in range(1, 10)
+    ]
+    history_path = tmp_path / "history.jsonl"
+    history_path.write_text("".join(json.dumps(row) + "\n" for row in rows))
+    measured_settings = PipelineTuneSettings(
+        num_rollout_workers=16,
+        min_batch_size=8,
+        max_batch_size=32,
+        queue_maxsize=48,
+        target_groups_per_step=24,
+    )
+    future_settings = measured_settings.model_copy(update={"num_rollout_workers": 14})
+    profile = SimpleNamespace(
+        config=SimpleNamespace(mode="online", window_steps=2),
+        decisions=[
+            SimpleNamespace(
+                action="hold",
+                previous=measured_settings,
+                updated=measured_settings,
+                stats=SimpleNamespace(
+                    start_step=2,
+                    end_step=3,
+                    window_start_s=-4.0,
+                    window_end_s=0.0,
+                    vllm_pressure=0.6,
+                    vllm_waiting_capacity_request_s=6.0,
+                    vllm_running_request_s=10.0,
+                    trainer_underfeed_score=0.07,
+                    actual_stale_frac=0.0,
+                ),
+            ),
+            SimpleNamespace(
+                action="decrease_workers",
+                previous=measured_settings,
+                updated=future_settings,
+                stats=SimpleNamespace(
+                    start_step=4,
+                    end_step=5,
+                    window_start_s=0.0,
+                    window_end_s=4.0,
+                    vllm_pressure=0.45,
+                    vllm_waiting_capacity_request_s=4.5,
+                    vllm_running_request_s=10.0,
+                    trainer_underfeed_score=0.10,
+                    actual_stale_frac=0.0,
+                ),
+            ),
+            SimpleNamespace(
+                action="hold",
+                previous=future_settings,
+                updated=future_settings,
+                stats=SimpleNamespace(
+                    start_step=6,
+                    end_step=7,
+                    window_start_s=4.0,
+                    window_end_s=8.0,
+                    vllm_pressure=0.65,
+                    vllm_waiting_capacity_request_s=19.5,
+                    vllm_running_request_s=30.0,
+                    trainer_underfeed_score=0.04,
+                    actual_stale_frac=0.0,
+                ),
+            ),
+            SimpleNamespace(
+                action="decrease_workers",
+                previous=future_settings,
+                updated=future_settings.model_copy(update={"num_rollout_workers": 12}),
+                stats=SimpleNamespace(
+                    start_step=8,
+                    end_step=9,
+                    window_start_s=8.0,
+                    window_end_s=12.0,
+                    vllm_pressure=0.6,
+                    vllm_waiting_capacity_request_s=6.0,
+                    vllm_running_request_s=10.0,
+                    trainer_underfeed_score=0.5,
+                    actual_stale_frac=0.0,
+                ),
+            ),
+        ],
+        policy_age_limit_steps=4,
+    )
+    events = [
+        PolicyActivationEvent(1, -4.25, -4.0),
+        PolicyActivationEvent(2, -3.5, -3.25),
+        PolicyActivationEvent(3, -1.5, -1.25),
+        PolicyActivationEvent(4, 0.5, 0.75),
+        PolicyActivationEvent(5, 2.5, 2.75),
+        PolicyActivationEvent(6, 4.5, 4.75),
+        PolicyActivationEvent(7, 6.5, 7.75),
+        PolicyActivationEvent(8, 8.5, 8.75),
+        PolicyActivationEvent(9, 10.5, 10.75),
+    ]
+    config = ThroughputWorkflowConfig(num_layers=2, completion_tokens=128, max_steps=7)
+    fixture = ThroughputFixture(
+        model_key="llama3_dense",
+        path="/tmp/llama-throughput",
+        num_layers=2,
+        width_fingerprint={"hidden_size": 2048},
+        manifest={"initialization": "deterministic_random_v1"},
+    )
+
+    def phase(kind: str, packed: str, steps: tuple[int, ...]):
+        phase_rows = [dict(rows[-1]) for _ in range(3)]
+        phase_rows[-1]["data/step_nonpadding_logical_tokens"] += 1
+        phase_rows[-1]["data/step_unused_packed_capacity_tokens"] -= 1
+        return _phase_evidence(
+            phase=cast(Any, kind),
+            runtime_fingerprint="runtime-a",
+            trajectory_input_fingerprint="trajectory-a",
+            packed_input_fingerprint=packed,
+            samples=list(zip(phase_rows, steps, strict=True)),
+        )
+
+    e2e_phase, isolated_phase = (
+        phase("e2e", "input-a", (5, 6, 7)),
+        phase("isolated", "input-a", (9, 10, 11)),
+    )
+
+    def collect(isolated):
+        return _collect_measurements(
+            fixture=fixture,
+            config=config,
+            hardware="b300",
+            model_output_dir=tmp_path,
+            profile=profile,
+            events=events,
+            isolated=isolated,
+            e2e=e2e_phase,
+            capture_settings=measured_settings.model_dump(mode="json"),
+            calibration_fingerprint="a" * 64,
+        )
+
+    measurements = collect(isolated_phase)
+
+    expected = {
+        "original_trajectory_tokens": 24_000,
+        "nonpadding_logical_tokens": 6_000,
+        "loss_bearing_tokens": 3_000,
+        "accepted_train_tokens": 3_000,
+        "isolated_train_tok_s": 1_000 / 1.5,
+        "matched_e2e_core_train_tok_s": 1_000 / 1.5,
+        "matched_core_to_isolated_ratio": 1.0,
+        "e2e_core_train_tok_s": 8_000 / 12.0,
+        "e2e_train_tok_s": 500.0,
+        "accepted_train_tok_s": 250.0,
+        "unused_and_dummy_ratio": 1.0 - 1_000 / 131_072,
+        "queue_ready_inter_forward_backward_gap_rank_zero_mean_s": 0.115,
+        "queue_ready_inter_forward_backward_gap_rank_zero_p50_s": 0.115,
+        "queue_ready_inter_forward_backward_gap_rank_zero_p95_s": 0.1285,
+        "queue_ready_inter_forward_backward_gap_rank_zero_max_s": 0.13,
+        "queue_ready_inter_forward_backward_gap_rank_zero_count": 4,
+        "queue_ready_inter_forward_backward_gap_worst_rank": 1,
+        "queue_ready_inter_forward_backward_gap_worst_rank_mean_s": 0.125,
+        "queue_ready_inter_forward_backward_gap_worst_rank_p50_s": 0.125,
+        "queue_ready_inter_forward_backward_gap_worst_rank_p95_s": 0.1385,
+        "queue_ready_inter_forward_backward_gap_worst_rank_max_s": 0.14,
+        "queue_ready_inter_forward_backward_gap_worst_rank_count": 4,
+        "mean_train_gap_s": 0.5,
+        "stable_vllm_pressure": 0.6,
+        "stable_trainer_underfeed": 0.07,
+        "post_warmup_policy_activation_count": 6,
+        "mean_policy_activation_lag_s": 2.5 / 6.0,
+        "p50_policy_activation_lag_s": 0.25,
+        "p95_policy_activation_lag_s": 1.0,
+        "max_policy_activation_lag_s": 1.25,
+        "mean_policy_activation_interval_s": 11.75 / 6.0,
+        "p50_policy_activation_interval_s": 2.0,
+        "p95_policy_activation_interval_s": 2.75,
+        "second_max_policy_activation_interval_s": 2.0,
+        "max_policy_activation_interval_s": 3.0,
+    }
+    assert {key: measurements[key] for key in expected} == pytest.approx(expected)
+    thresholds = ThroughputThresholds(
+        calibration_basis="measured",
+        calibration_fingerprint="a" * 64,
+        min_isolated_train_tok_s=1.0,
+        min_e2e_train_tok_s=1.0,
+        min_accepted_train_tok_s=1.0,
+        min_e2e_to_isolated_ratio=0.5,
+        min_matched_core_to_isolated_ratio=0.95,
+        max_mean_policy_activation_lag_s=1.5,
+        max_policy_activation_lag_s=2.0,
+        max_repeated_policy_activation_interval_s=1.5,
+    )
+    assert acceptance_failures(measurements, config, thresholds) == [
+        "unused_and_dummy_ratio",
+        "repeated_policy_activation_cadence_s",
+    ]
+    robust = {
+        **measurements,
+        "queue_ready_inter_forward_backward_gap_worst_rank_max_s": 0.5,
+    }
+    assert "queue_ready_inter_forward_backward_gap_p50_s" not in acceptance_failures(
+        robust, config, thresholds
+    )
+    assert "queue_ready_inter_forward_backward_gap_p50_s" not in acceptance_failures(
+        {
+            **measurements,
+            "queue_ready_inter_forward_backward_gap_worst_rank_p50_s": 0.225,
+        },
+        config,
+        thresholds,
+    )
+    assert "queue_ready_inter_forward_backward_gap_max_s" in acceptance_failures(
+        {
+            **measurements,
+            "queue_ready_inter_forward_backward_gap_worst_rank_max_s": 1.01,
+        },
+        config,
+        thresholds,
+    )
+    sparse = {
+        **measurements,
+        "queue_ready_inter_forward_backward_gap_worst_rank_count": 2,
+    }
+    assert "queue_ready_inter_forward_backward_gap_count" in acceptance_failures(
+        sparse, config, thresholds
+    )
+    with pytest.raises(ValueError):
+        ThroughputThresholds.model_validate(
+            {
+                **thresholds.model_dump(),
+                "max_queue_ready_inter_forward_backward_gap_p50_s": 0.231,
+            }
+        )
+    assert acceptance_failures(
+        {
+            **measurements,
+            "stable_vllm_pressure": 0.49,
+            "stable_trainer_underfeed": 0.09,
+        },
+        config,
+        thresholds,
+    ) == [
+        "stable_min_vllm_pressure",
+        "stable_trainer_underfeed",
+        "unused_and_dummy_ratio",
+        "repeated_policy_activation_cadence_s",
     ]
-    assert build_validation_stage_names(native_vllm_lora_status="wip") == [
-        *MANDATORY_VALIDATION_STAGES,
-        NATIVE_VLLM_LORA_STAGE,
+    estimated = ThroughputThresholds(
+        calibration_basis="estimated",
+        min_isolated_train_tok_s=1.0,
+        min_e2e_train_tok_s=1.0,
+        min_accepted_train_tok_s=1.0,
+        min_e2e_to_isolated_ratio=0.5,
+        min_matched_core_to_isolated_ratio=0.95,
+        max_mean_policy_activation_lag_s=1.5,
+        max_policy_activation_lag_s=2.0,
+        max_repeated_policy_activation_interval_s=1.5,
+    )
+    assert acceptance_failures(measurements, config, estimated) == [
+        "unused_and_dummy_ratio",
+        "repeated_policy_activation_cadence_s",
+        "calibration_basis",
     ]
-    assert build_validation_stage_names(include_yes_no_trainability=True) == [
-        *MANDATORY_VALIDATION_STAGES,
-        "yes_no_trainability",
+    lag_failures = acceptance_failures(
+        measurements,
+        config,
+        thresholds.model_copy(
+            update={
+                "max_mean_policy_activation_lag_s": 0.35,
+                "max_policy_activation_lag_s": 1.0,
+                "max_repeated_policy_activation_interval_s": 3.5,
+            }
+        ),
+    )
+    assert lag_failures == [
+        "unused_and_dummy_ratio",
+        "mean_policy_activation_lag_s",
+        "max_policy_activation_lag_s",
+    ]
+    measurements["matched_core_to_isolated_ratio"] *= 1.1
+    assert "matched_core_to_isolated_ratio_max" in acceptance_failures(
+        measurements, config, thresholds
+    )
+    inconsistent = [dict(row) for row in rows]
+    next(row for row in inconsistent if row["step"] == config.max_steps)[
+        "pipeline_settings/num_rollout_workers"
+    ] = 14
+    history_path.write_text("".join(json.dumps(row) + "\n" for row in inconsistent))
+    with pytest.raises(RuntimeError, match="two trailing settled execution"):
+        collect(isolated_phase)
+    history_path.write_text("".join(json.dumps(row) + "\n" for row in rows))
+    capture_settings = measured_settings.model_dump(mode="json")
+    capture_settings["num_rollout_workers"] = 14
+    with pytest.raises(
+        RuntimeError, match="did not use the measured pipeline settings"
+    ):
+        _collect_measurements(
+            fixture=fixture,
+            config=config,
+            hardware="b300",
+            model_output_dir=tmp_path,
+            profile=profile,
+            events=events,
+            isolated=isolated_phase,
+            e2e=e2e_phase,
+            capture_settings=capture_settings,
+            calibration_fingerprint="a" * 64,
+        )
+    fractional = [dict(row) for row in rows]
+    next(row for row in fractional if row["step"] == 2)[
+        "data/step_nonpadding_logical_tokens"
+    ] = 999.5
+    history_path.write_text("".join(json.dumps(row) + "\n" for row in fractional))
+    with pytest.raises(RuntimeError, match="must be a nonnegative integer"):
+        collect(isolated_phase)
+    history_path.write_text("".join(json.dumps(row) + "\n" for row in rows))
+    with pytest.raises(RuntimeError, match="same packed input"):
+        collect(phase("isolated", "input-b", (9, 10, 11)))
+
+
+def test_throughput_classification_is_fail_closed() -> None:
+    assert _classify_acceptance_failures([])["acceptance_status"] == "accepted"
+    load = _classify_acceptance_failures(
+        ["stable_trainer_underfeed", "accepted_train_tok_s"]
+    )
+    assert load["acceptance_status"] == "load_inconclusive"
+    assert load["load_failures"] == ["stable_trainer_underfeed"]
+    assert load["performance_failures"] == ["accepted_train_tok_s"]
+    for hard_failure in (
+        "calibration_fingerprint",
+        "unused_and_dummy_ratio",
+        "window_4_5_policy_age_p95",
+    ):
+        classified = _classify_acceptance_failures(
+            ["stable_min_vllm_pressure", hard_failure]
+        )
+        assert classified["acceptance_status"] == "rejected"
+        assert classified["hard_failures"] == [hard_failure]
+    future = _classify_acceptance_failures(
+        ["stable_min_vllm_pressure", "future_acceptance_gate"]
+    )
+    assert future["acceptance_status"] == "rejected"
+    assert future["unclassified_failures"] == ["future_acceptance_gate"]
+
+
+def test_throughput_retry_is_bounded_and_preserves_attempts(
+    tmp_path: Path,
+) -> None:
+    plans = [
+        ([[]], "accepted"),
+        ([["e2e_train_tok_s"], []], "accepted"),
+        ([["e2e_train_tok_s"], ["e2e_train_tok_s"]], "rejected"),
+        ([["stable_min_vllm_pressure", "calibration_basis"]], "rejected"),
+        ([["future_acceptance_gate"]], "rejected"),
+        ([["stable_min_vllm_pressure"], []], "accepted"),
+        (
+            [["stable_min_vllm_pressure"], ["stable_trainer_underfeed"]],
+            "load_inconclusive",
+        ),
     ]
+    for case, (failures_by_attempt, expected_status) in enumerate(plans):
+        stage_dir = tmp_path / str(case)
+        calls: list[int] = []
+
+        def run_attempt(attempt: int, artifact_dir: Path) -> ValidationStageResult:
+            calls.append(attempt)
+            (artifact_dir / "complete.txt").write_text(str(attempt))
+            classification = _classify_acceptance_failures(
+                failures_by_attempt[attempt - 1]
+            )
+            return ValidationStageResult(
+                name="e2e_throughput",
+                passed=classification["acceptance_status"] == "accepted",
+                metrics={"selected_metric": attempt, **classification},
+                artifact_dir=str(artifact_dir),
+            )
+
+        result = _run_throughput_attempts(stage_dir, run_attempt)
+        expected_calls = len(failures_by_attempt)
+        assert calls == list(range(1, expected_calls + 1))
+        assert result.metrics["acceptance_status"] == expected_status
+        assert result.metrics["selected_metric"] == expected_calls
+        assert result.metrics["throughput_attempt_count"] == expected_calls
+        assert all(
+            (stage_dir / f"attempt_{attempt}" / "complete.txt").is_file()
+            for attempt in calls
+        )
+
+
+def test_throughput_measurement_freezes_actual_settings() -> None:
+    measured = PipelineTuneSettings(
+        num_rollout_workers=16,
+        min_batch_size=8,
+        max_batch_size=32,
+        queue_maxsize=48,
+        target_groups_per_step=24,
+    )
+    future = measured.model_copy(update={"num_rollout_workers": 14})
+    trainer = SimpleNamespace(
+        state=SimpleNamespace(next_training_step=18),
+        **measured.model_dump(mode="python"),
+    )
+
+    def apply(settings: PipelineTuneSettings) -> None:
+        for name, value in settings.model_dump(mode="python").items():
+            setattr(trainer, name, value)
+
+    trainer.apply_pipeline_settings = apply
+    original = trainer.apply_pipeline_settings
+
+    with _freeze_pipeline_settings_from_step(trainer, 19):
+        trainer.apply_pipeline_settings(measured)
+        trainer.state.next_training_step = 19
+        trainer.apply_pipeline_settings(future)
+        trainer.state.next_training_step = 20
+        trainer.apply_pipeline_settings(future)
+        trainer.state.next_training_step = 21
+        trainer.apply_pipeline_settings(future)
+        assert _current_pipeline_settings(trainer) == measured.model_dump(mode="json")
+
+    trainer.apply_pipeline_settings(future)
+    assert _current_pipeline_settings(trainer) == future.model_dump(mode="json")
+    assert trainer.apply_pipeline_settings == original
+
+
+def test_throughput_measurement_uses_settled_execution_suffix() -> None:
+    measured = PipelineTuneSettings(
+        num_rollout_workers=16,
+        min_batch_size=24,
+        max_batch_size=36,
+        queue_maxsize=48,
+        target_groups_per_step=31,
+    )
+    previous = measured.model_copy(
+        update={
+            "num_rollout_workers": 14,
+            "min_batch_size": 27,
+            "max_batch_size": 27,
+            "target_groups_per_step": 27,
+        }
+    )
+
+    def decision(start_step: int) -> SimpleNamespace:
+        return SimpleNamespace(
+            stats=SimpleNamespace(
+                start_step=start_step,
+                end_step=start_step + 1,
+                window_start_s=float(start_step),
+                window_end_s=float(start_step + 2),
+            )
+        )
+
+    def row(
+        settings: PipelineTuneSettings,
+        step: int,
+        packed_length: int,
+        *,
+        submitted: int | None = None,
+    ) -> dict[str, int | float]:
+        return {
+            **{
+                f"pipeline_settings/{name}": value
+                for name, value in settings.model_dump(mode="json").items()
+            },
+            "queue/packing_policy_lag_steps": 1,
+            "data/step_num_groups_submitted": (
+                settings.target_groups_per_step if submitted is None else submitted
+            ),
+            "data/step_packed_sequences": 1,
+            "data/step_num_gradient_steps": 1,
+            "pipeline/global_real_microbatches": 1,
+            "pipeline/global_dummy_microbatches": 0,
+            "pipeline/packed_sequence_length": packed_length,
+            "time/step_train_s": float(step),
+        }
+
+    by_step = {
+        **{step: row(previous, step, 56_832) for step in range(3, 6)},
+        6: row(measured, 6, 56_832, submitted=27),
+        **{step: row(measured, step, 64_512) for step in range(7, 12)},
+    }
+    selected = _settled_execution_decision_suffix(
+        [decision(4), decision(6), decision(8), decision(10)],
+        by_step,
+    )
+
+    assert [item.stats.start_step for item in selected] == [8, 10]
+
+    alternating = dict(by_step)
+    for step, packed_length in zip(range(6, 12), (60_000, 64_000) * 3, strict=True):
+        alternating[step] = row(measured, step, packed_length)
+    assert [
+        item.stats.start_step
+        for item in _settled_execution_decision_suffix(
+            [decision(4), decision(6), decision(8), decision(10)], alternating
+        )
+    ] == [8, 10]
+
+    timing_changed = {
+        step: {**values, "time/step_train_s": 1000.0 - step}
+        for step, values in alternating.items()
+    }
+    assert [
+        item.stats.start_step
+        for item in _settled_execution_decision_suffix(
+            [decision(4), decision(6), decision(8), decision(10)], timing_changed
+        )
+    ] == [8, 10]
+
+    unique = dict(alternating)
+    for step in range(8, 12):
+        unique[step] = row(measured, step, 70_000 + step)
+    with pytest.raises(RuntimeError, match="two trailing settled execution"):
+        _settled_execution_decision_suffix(
+            [decision(4), decision(6), decision(8), decision(10)], unique
+        )
+
+
+def test_throughput_packed_input_fingerprint_hashes_data_plane_bytes() -> None:
+    from array import array
+    from multiprocessing import shared_memory
+
+    from art.pipeline_tuner.config import PackedGroupShape, PackingLeafShape
+
+    shm = shared_memory.SharedMemory(create=True, size=4)
+    try:
+        buffer = shm.buf
+        assert buffer is not None
+        buffer[:] = b"abcd"
+        tensor = SimpleNamespace(offset=0, byte_count=4)
+        ref = SimpleNamespace(
+            shared_memory_name=shm.name,
+            owner_process_id=os.getpid(),
+            tensors=(tensor,),
+            model_dump=lambda **kwargs: {
+                "tensors": [{"name": "tokens", "shape": [4], "dtype": "int8"}]
+            },
+        )
+        packed = SimpleNamespace(
+            leases=SimpleNamespace(ref=ref),
+            packed_group_shapes=(
+                PackedGroupShape(
+                    leaves=(
+                        PackingLeafShape(
+                            token_ids=array("I", [1, 2, 3]), shareable_length=2
+                        ),
+                    )
+                ),
+            ),
+        )
+        batch = SimpleNamespace(
+            payload=SimpleNamespace(packed=packed),
+            model_dump=lambda **kwargs: {"sequence_length": 4},
+        )
+        prepared = SimpleNamespace(
+            batch=batch,
+            packing_config=SimpleNamespace(
+                model_dump=lambda **kwargs: {"packed_sequence_length": 4}
+            ),
+        )
+        groups = [SimpleNamespace(_prepared_training_batch=prepared)]
+
+        before = _packed_input_fingerprint(groups)
+        buffer[0] = ord("z")
+        changed_bytes = _packed_input_fingerprint(groups)
+        buffer[0] = ord("a")
+        packed.packed_group_shapes = (
+            PackedGroupShape(
+                leaves=(
+                    PackingLeafShape(
+                        token_ids=array("I", [1, 2, 4]), shareable_length=2
+                    ),
+                )
+            ),
+        )
+        changed_shape = _packed_input_fingerprint(groups)
+
+        assert before != changed_bytes
+        assert before != changed_shape
+    finally:
+        del buffer
+        shm.close()
+        shm.unlink()
+
+
+def _without_stage_duration(stage: ValidationStageResult) -> dict[str, object]:
+    metrics = dict(stage.metrics)
+    assert float(metrics.pop("workflow_stage_duration_s")) >= 0.0
+    metrics.pop("fixture_provisioning_s", None)
+    metrics.pop("workflow_pruned_runtime_artifact_dirs", None)
+    metrics.pop("workflow_pruned_runtime_artifact_bytes", None)
+    return metrics
+
+
+def test_build_validation_stage_names_has_fixed_order() -> None:
+    assert build_validation_stage_names() == list(MANDATORY_VALIDATION_STAGES)
 
 
 def test_validated_architecture_representative_models_are_fixed() -> None:
@@ -81,6 +880,7 @@ def test_validated_architecture_representative_models_are_fixed() -> None:
         "google/gemma-4-26B-A4B-it",
         "google/gemma-4-31B-it",
         "deepseek-ai/DeepSeek-V4-Flash",
+        "zai-org/GLM-5.2",
         "openai/gpt-oss-20b",
     ]
 
@@ -92,7 +892,6 @@ def test_dsv4_runtime_stages_use_full_model_resources() -> None:
     assert resources is not None
     for stage in (
         resources.train_inf_mismatch,
-        resources.yes_no_trainability,
         resources.length_trainability,
     ):
         assert stage is not None
@@ -108,32 +907,32 @@ def test_dsv4_runtime_stages_use_full_model_resources() -> None:
         engine_args = stage.vllm.engine_args()
         assert "hf_overrides" not in engine_args
         assert engine_args.get("load_format") != "dummy"
-        assert engine_args["moe_backend"] == "triton_unfused"
+        assert engine_args["moe_backend"] == "auto"
         assert engine_args["kv_cache_dtype"] == "fp8"
         assert stage.streaming_weight_offload is True
         assert stage.megatron_env == {}
 
-    for stage in (resources.merged_vllm_serving, resources.native_vllm_lora):
-        assert stage is not None
-        assert stage.vllm is not None
-        engine_args = stage.vllm.engine_args()
-        assert engine_args["load_format"] == "dummy"
-        hf_overrides = cast(dict[str, object], engine_args["hf_overrides"])
-        assert hf_overrides["num_hidden_layers"] == 4
-    assert resources.merged_vllm_serving is not None
-    assert resources.merged_vllm_serving.vllm is not None
-    assert resources.merged_vllm_serving.vllm.engine_args()["kv_cache_dtype"] == "fp8"
-    assert resources.native_vllm_lora is not None
-    assert resources.native_vllm_lora.vllm is not None
-    assert resources.native_vllm_lora.vllm.engine_args().get("max_loras", 2) == 2
-
-
-def test_dsv4_resources_remap_to_four_high_vram_gpus(monkeypatch) -> None:
+
+@pytest.mark.parametrize(
+    ("stage_name", "trainer_gpu_ids", "trainer_ep", "trainer_dp"),
+    [
+        ("train_inf_mismatch", [0, 1, 2, 3], 4, 2),
+        ("length_trainability", [0, 1, 2, 3], 4, 2),
+    ],
+)
+def test_dsv4_resources_remap_to_four_high_vram_gpus(
+    monkeypatch,
+    stage_name: str,
+    trainer_gpu_ids: list[int],
+    trainer_ep: int,
+    trainer_dp: int,
+) -> None:
     resources = handler_workflow_resources_for_base_model(
         "deepseek-ai/DeepSeek-V4-Flash"
     )
     assert resources is not None
-    assert resources.train_inf_mismatch is not None
+    stage_resources = getattr(resources, stage_name)
+    assert stage_resources is not None
     monkeypatch.setattr(
         "tests.integration.megatron.model_support.workflow_resources."
         "_visible_h200_equivalent_gpus",
@@ -141,26 +940,120 @@ def test_dsv4_resources_remap_to_four_high_vram_gpus(monkeypatch) -> None:
     )
 
     stage = resolve_stage_resources_for_visible_gpus(
-        "train_inf_mismatch",
-        resources.train_inf_mismatch,
+        stage_name,
+        stage_resources,
         visible_gpu_count=4,
     )
 
     assert stage.megatron is not None
     assert stage.vllm is not None
-    assert stage.megatron.gpu_ids == [0, 1]
+    assert stage.megatron.gpu_ids == trainer_gpu_ids
     assert stage.megatron.topology.tp == 2
-    assert stage.megatron.topology.ep == 2
+    assert stage.megatron.topology.ep == trainer_ep
+    assert stage.megatron.topology.dp == trainer_dp
     assert stage.vllm.gpu_ids == [2, 3]
     assert stage.vllm.tensor_parallel_size == 2
-    assert stage.vllm.engine_args()["moe_backend"] == "triton_unfused"
+    assert stage.vllm.engine_args()["moe_backend"] == "auto"
     assert stage.vllm.engine_args()["kv_cache_dtype"] == "fp8"
 
 
-def test_h200_equivalent_slots_tolerate_reported_gb300_vram() -> None:
-    assert _h200_equivalent_slots_for_total_gib(80.0) == 0
-    assert _h200_equivalent_slots_for_total_gib(139.0) == 1
-    assert _h200_equivalent_slots_for_total_gib(276.6) == 2
+def test_glm52_reduced_workflow_uses_portable_serving_backends() -> None:
+    resources = handler_workflow_resources_for_base_model("zai-org/GLM-5.2")
+    assert resources is not None
+    joint_stages = (
+        resources.train_inf_mismatch,
+        resources.length_trainability,
+    )
+    for stage in joint_stages:
+        assert stage is not None
+        assert stage.required_world_size == 2
+        assert stage.megatron is not None
+        assert stage.megatron.gpu_ids == [0]
+    for stage in joint_stages:
+        assert stage is not None
+        assert stage.vllm is not None
+        assert stage.vllm.gpu_ids == [1]
+        engine_args = stage.vllm.engine_args()
+        assert engine_args["attention_backend"] == "FLASHMLA_SPARSE"
+        assert engine_args["max_model_len"] == 1024
+        assert engine_args["moe_backend"] == "triton"
+
+
+@pytest.mark.parametrize("handler_key", sorted(HANDLER_WORKFLOW_RESOURCES))
+def test_throughput_requires_four_distinct_physical_gpus(
+    handler_key: str, monkeypatch: pytest.MonkeyPatch
+) -> None:
+    stage = HANDLER_WORKFLOW_RESOURCES[handler_key].e2e_throughput
+    assert stage is not None
+    megatron, vllm = stage.megatron, stage.vllm
+    assert megatron is not None and vllm is not None
+    assert (stage.required_world_size, stage.required_physical_gpus) == (4, 4)
+    assert (megatron.gpu_ids, vllm.gpu_ids) == ([0, 1], [2, 3])
+    monkeypatch.setattr(
+        "tests.integration.megatron.model_support.workflow_resources."
+        "_visible_h200_equivalent_gpus",
+        lambda *, visible_gpu_count: visible_gpu_count * 2,
+    )
+
+    with pytest.raises(RuntimeError, match="Need 4 physical GPUs"):
+        resolve_stage_resources_for_visible_gpus(
+            "e2e_throughput",
+            stage,
+            visible_gpu_count=2,
+        )
+
+    assert (
+        resolve_stage_resources_for_visible_gpus(
+            "e2e_throughput", stage, visible_gpu_count=4
+        )
+        == stage
+    )
+
+
+def test_backend_resources_stay_logical_until_topology_compilation(monkeypatch) -> None:
+    from art.megatron.runtime import local as local_runtime
+
+    stage = HANDLER_WORKFLOW_RESOURCES["llama3_dense"].e2e_throughput
+    assert stage is not None
+    monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "4,5,6,7")
+    monkeypatch.setattr(
+        "tests.integration.megatron.model_support.workflow_resources."
+        "_current_visible_gpu_count",
+        lambda: 4,
+    )
+
+    resolved = resolve_stage_resources_for_current_host("e2e_throughput", stage)
+
+    megatron = resolved.megatron
+    vllm = resolved.vllm
+    assert megatron is not None
+    assert vllm is not None
+    assert megatron.gpu_ids == [0, 1]
+    assert vllm.gpu_ids == [2, 3]
+    monkeypatch.setattr(
+        local_runtime,
+        "get_megatron_runtime_config",
+        lambda: SimpleNamespace(topology=megatron.topology.to_megatron_config()),
+    )
+    topology = local_runtime.compile_local_runtime_topology(
+        cast(
+            Any,
+            {
+                "trainer_gpu_ids": megatron.gpu_ids,
+                "inference_gpu_ids": vllm.gpu_ids,
+                "engine_args": vllm.engine_args(),
+            },
+        ),
+        model_name="throughput",
+        base_model="/tmp/provider",
+        artifact_root="/tmp/art",
+        visible_gpu_count=4,
+    )
+
+    assert topology.trainer is not None
+    assert [rank.gpu_id for rank in topology.trainer.ranks] == [4, 5]
+    assert topology.model_services[0].members[0].gpu_ids == (6, 7)
+    assert topology.cluster.hosts[0].gpu_ids == (4, 5, 6, 7)
 
 
 def test_inspect_architecture_for_workflow_uses_minimal_topology(monkeypatch) -> None:
@@ -185,7 +1078,7 @@ def _inspect_architecture(base_model: str, **kwargs) -> ArchitectureReport:
         )
 
     monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.inspect_architecture",
+        "art.megatron.model_support.discovery.inspect_architecture",
         _inspect_architecture,
     )
 
@@ -197,883 +1090,49 @@ def _inspect_architecture(base_model: str, **kwargs) -> ArchitectureReport:
     assert seen_env == {"tp": "1", "cp": "1", "ep": "1", "etp": "1"}
 
 
-def test_build_all_architectures_validation_report_stops_on_failure(
-    monkeypatch,
-    tmp_path,
-) -> None:
-    calls: list[str] = []
-
-    def _build_validation_report(
-        *,
-        base_model,
-        include_yes_no_trainability=False,
-        include_sensitivity=None,
-        output_json=None,
-        skip_stages=None,
-        only_stage=None,
-        stop_on_failure=False,
-        allow_unvalidated_arch=False,
-    ):
-        del include_yes_no_trainability
-        del include_sensitivity
-        del output_json
-        del skip_stages
-        del only_stage
-        del stop_on_failure
-        del allow_unvalidated_arch
-        calls.append(base_model)
-        return ValidationReport(
-            git={},
-            base_model=base_model,
-            model_key="qwen3_dense",
-            stages=[
-                ValidationStageResult(
-                    name="train_inf_mismatch",
-                    passed=base_model != "Qwen/Qwen3-32B",
-                )
-            ],
-        )
-
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.build_validation_report",
-        _build_validation_report,
-    )
-
-    report = build_all_architectures_validation_report(
-        output_json=tmp_path / "all_architectures.json",
-        stop_on_failure=True,
-    )
-
-    assert calls == [
-        "meta-llama/Llama-3.2-1B-Instruct",
-        "Qwen/Qwen3-30B-A3B",
-        "Qwen/Qwen3-32B",
-    ]
-    assert report.passed is False
-    assert [item.base_model for item in report.reports] == calls
-
-
-def test_build_validation_report_populates_architecture_stage(
-    monkeypatch,
-) -> None:
+def test_build_validation_report_captures_hf_parity_failure(monkeypatch) -> None:
     monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.inspect_architecture",
+        "art.megatron.model_support.discovery.inspect_architecture",
         lambda base_model: ArchitectureReport(
             base_model=base_model,
             model_key="qwen3_5_moe",
             handler_key="qwen3_5_moe",
-            layer_families=[LayerFamilyInstance(key="standard_attention", count=2)],
-            recommended_min_layers=1,
+            layer_families=[],
+            recommended_min_layers=4,
         ),
     )
     monkeypatch.setattr(
         "tests.integration.megatron.model_support.workflow.detect_dependency_versions",
-        lambda: {"transformers": "5.2.0"},
+        lambda: {},
     )
+
     monkeypatch.setattr(
         "tests.integration.megatron.model_support.workflow._run_stage_in_subprocess",
-        lambda *, stage_name, base_model, architecture, allow_unvalidated_arch=False: {
-            "hf_parity": ValidationStageResult(
+        lambda *, stage_name, base_model, architecture, allow_unvalidated_arch=False: (
+            ValidationStageResult(
                 name="hf_parity",
+                passed=False,
+                metrics={"error": "AssertionError: parity failed"},
+            )
+            if stage_name == "hf_parity"
+            else ValidationStageResult(
+                name=stage_name,
                 passed=True,
-                metrics={"signal": "pass", "requested_num_layers": 1},
-                artifact_dir="/tmp/hf_parity",
-            ),
-            "lora_coverage": ValidationStageResult(
-                name="lora_coverage",
-                passed=True,
-                metrics={"wrapped_adapter_prefix_count": 12},
-            ),
-            "train_inf_mismatch": ValidationStageResult(
-                name="train_inf_mismatch",
-                passed=True,
-                metrics={"passed_count": 1, "failed_count": 0},
-                artifact_dir="/tmp/train-inf-mismatch",
-            ),
-            "merged_vllm_serving": ValidationStageResult(
-                name="merged_vllm_serving",
-                passed=True,
-                metrics={"served_model_name": "validation@0"},
-                artifact_dir="/tmp/merged-serving",
-            ),
-            "correctness_sensitivity": ValidationStageResult(
-                name="correctness_sensitivity",
-                passed=True,
-                metrics={
-                    "correctness_variant_count": 4,
-                    "sensitivity_variant_count": 9,
-                },
-                artifact_dir="/tmp/correctness",
-            ),
-            "chat_template_rollout": ValidationStageResult(
-                name="chat_template_rollout",
-                passed=True,
-                metrics={
-                    "passed": True,
-                    "scenario_count": 6,
-                    "failed_scenarios": [],
-                },
-                artifact_dir="/tmp/chat-template",
-            ),
-            "packing_invariance": ValidationStageResult(
-                name="packing_invariance",
-                passed=True,
-                metrics={
-                    "num_layers": 4,
-                    "scenarios": [
-                        {
-                            "name": "stop_early",
-                            "matched": True,
-                            "checked_token_count": 40,
-                        }
-                    ],
-                },
-                artifact_dir="/tmp/packing-invariance",
-            ),
-            "length_trainability": ValidationStageResult(
-                name="length_trainability",
-                passed=True,
-                metrics={
-                    "latest_step": 4,
-                    "best_train_abs_error": 1.0,
-                },
-                artifact_dir="/tmp/length-trainability",
-            ),
-            "native_vllm_lora": ValidationStageResult(
-                name="native_vllm_lora",
-                passed=True,
-                metrics={
-                    "rollout_weights_mode": "lora",
-                    "step0_name": "validation@0",
-                    "step1_name": "validation@1",
-                    "model_ids_before": ["validation@0"],
-                    "model_ids_after": ["validation@0", "validation@1"],
-                    "step0_served": True,
-                    "step1_served": True,
-                },
-                artifact_dir="/tmp/native-vllm-lora",
-            ),
-        }[stage_name],
+                metrics={},
+            )
+        ),
     )
 
     report = build_validation_report(base_model="Qwen/Qwen3.5-35B-A3B")
 
-    assert report.base_model == "Qwen/Qwen3.5-35B-A3B"
-    assert report.model_key == "qwen3_5_moe"
-    assert report.dependency_versions == {"transformers": "5.2.0"}
-    dependency_stage = next(
-        stage for stage in report.stages if stage.name == "dependency_resolution"
-    )
-    assert dependency_stage.passed is True
-    assert dependency_stage.metrics == {"transformers": "5.2.0"}
-    architecture_stage = next(
-        stage for stage in report.stages if stage.name == "architecture_discovery"
-    )
-    assert architecture_stage.passed is True
-    assert architecture_stage.metrics == {
-        "recommended_min_layers": 1,
-        "layer_families": [
-            {
-                "key": "standard_attention",
-                "count": 2,
-                "layer_index": None,
-                "module_path": None,
-                "module_type": None,
-            }
-        ],
-        "unresolved_risks": [],
-    }
     hf_parity_stage = next(
         stage for stage in report.stages if stage.name == "hf_parity"
     )
-    assert hf_parity_stage.passed is True
-    assert hf_parity_stage.metrics == {"signal": "pass", "requested_num_layers": 1}
-    assert hf_parity_stage.artifact_dir == "/tmp/hf_parity"
-    lora_coverage_stage = next(
-        stage for stage in report.stages if stage.name == "lora_coverage"
-    )
-    assert lora_coverage_stage.passed is True
-    assert lora_coverage_stage.metrics == {"wrapped_adapter_prefix_count": 12}
-    mismatch_stage = next(
-        stage for stage in report.stages if stage.name == "train_inf_mismatch"
-    )
-    assert mismatch_stage.passed is True
-    assert mismatch_stage.metrics == {"passed_count": 1, "failed_count": 0}
-    assert mismatch_stage.artifact_dir == "/tmp/train-inf-mismatch"
-    correctness_stage = next(
-        stage for stage in report.stages if stage.name == "correctness_sensitivity"
-    )
-    assert correctness_stage.passed is True
-    assert correctness_stage.metrics == {
-        "correctness_variant_count": 4,
-        "sensitivity_variant_count": 9,
-    }
-    assert correctness_stage.artifact_dir == "/tmp/correctness"
-    merged_stage = next(
-        stage for stage in report.stages if stage.name == "merged_vllm_serving"
-    )
-    assert merged_stage.passed is True
-    assert merged_stage.metrics == {"served_model_name": "validation@0"}
-    assert merged_stage.artifact_dir == "/tmp/merged-serving"
-    chat_template_stage = next(
-        stage for stage in report.stages if stage.name == "chat_template_rollout"
-    )
-    assert chat_template_stage.passed is True
-    assert chat_template_stage.metrics == {
-        "passed": True,
-        "scenario_count": 6,
-        "failed_scenarios": [],
-    }
-    assert chat_template_stage.artifact_dir == "/tmp/chat-template"
-    packing_invariance_stage = next(
-        stage for stage in report.stages if stage.name == "packing_invariance"
-    )
-    assert packing_invariance_stage.passed is True
-    assert packing_invariance_stage.metrics == {
-        "num_layers": 4,
-        "scenarios": [
-            {
-                "name": "stop_early",
-                "matched": True,
-                "checked_token_count": 40,
-            }
-        ],
-    }
-    assert packing_invariance_stage.artifact_dir == "/tmp/packing-invariance"
-    trainability_stage = next(
-        stage for stage in report.stages if stage.name == "length_trainability"
-    )
-    assert trainability_stage.passed is True
-    assert trainability_stage.metrics == {
-        "latest_step": 4,
-        "best_train_abs_error": 1.0,
-    }
-    assert trainability_stage.artifact_dir == "/tmp/length-trainability"
-    assert all(stage.name != "yes_no_trainability" for stage in report.stages)
-    native_vllm_lora_stage = next(
-        stage for stage in report.stages if stage.name == "native_vllm_lora"
-    )
-    assert native_vllm_lora_stage.passed is True
-    assert native_vllm_lora_stage.metrics == {
-        "rollout_weights_mode": "lora",
-        "step0_name": "validation@0",
-        "step1_name": "validation@1",
-        "model_ids_before": ["validation@0"],
-        "model_ids_after": ["validation@0", "validation@1"],
-        "step0_served": True,
-        "step1_served": True,
+    assert hf_parity_stage.passed is False
+    assert _without_stage_duration(hf_parity_stage) == {
+        "error": "AssertionError: parity failed"
     }
-    assert native_vllm_lora_stage.artifact_dir == "/tmp/native-vllm-lora"
-
-
-def test_build_validation_report_preserves_traces_when_sensitivity_runs(
-    monkeypatch,
-) -> None:
-    seen_keep_env: list[str | None] = []
-
-    monkeypatch.delenv(KEEP_TOPOLOGY_ARTIFACTS_ENV, raising=False)
-
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.inspect_architecture",
-        lambda base_model: ArchitectureReport(
-            base_model=base_model,
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-            layer_families=[LayerFamilyInstance(key="standard_attention", count=1)],
-            recommended_min_layers=1,
-        ),
-    )
-
-    def _run_stage_in_subprocess(
-        *,
-        stage_name,
-        base_model,
-        architecture,
-        allow_unvalidated_arch=False,
-    ) -> ValidationStageResult:
-        del base_model, architecture, allow_unvalidated_arch
-        if stage_name == "correctness_sensitivity":
-            seen_keep_env.append(os.environ.get(KEEP_TOPOLOGY_ARTIFACTS_ENV))
-        return ValidationStageResult(name=stage_name, passed=True, metrics={})
-
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._run_stage_in_subprocess",
-        _run_stage_in_subprocess,
-    )
-
-    build_validation_report(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        include_sensitivity=True,
-    )
-
-    assert seen_keep_env == ["1"]
-    assert os.environ.get(KEEP_TOPOLOGY_ARTIFACTS_ENV) is None
-
-
-def test_build_validation_report_only_stage_skips_other_stages(monkeypatch) -> None:
-    calls: list[str] = []
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.inspect_architecture",
-        lambda base_model: ArchitectureReport(
-            base_model=base_model,
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-            layer_families=[],
-            recommended_min_layers=1,
-        ),
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.detect_dependency_versions",
-        lambda: {},
-    )
-
-    def _run_stage_in_subprocess(**kwargs) -> ValidationStageResult:
-        stage_name = kwargs["stage_name"]
-        calls.append(stage_name)
-        return ValidationStageResult(name=stage_name, passed=True)
-
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._run_stage_in_subprocess",
-        _run_stage_in_subprocess,
-    )
-
-    report = build_validation_report(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        only_stage="length_trainability",
-    )
-
-    skipped = next(stage for stage in report.stages if stage.name == "hf_parity")
-    assert calls == ["length_trainability"]
-    assert skipped.metrics == {
-        "skipped": True,
-        "reason": "--only-stage=length_trainability",
-    }
-
-
-def test_build_validation_report_captures_hf_parity_failure(monkeypatch) -> None:
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.inspect_architecture",
-        lambda base_model: ArchitectureReport(
-            base_model=base_model,
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-            layer_families=[],
-            recommended_min_layers=4,
-        ),
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.detect_dependency_versions",
-        lambda: {},
-    )
-
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._run_stage_in_subprocess",
-        lambda *, stage_name, base_model, architecture, allow_unvalidated_arch=False: (
-            ValidationStageResult(
-                name="hf_parity",
-                passed=False,
-                metrics={"error": "AssertionError: parity failed"},
-            )
-            if stage_name == "hf_parity"
-            else ValidationStageResult(
-                name=stage_name,
-                passed=True,
-                metrics={},
-            )
-        ),
-    )
-
-    report = build_validation_report(base_model="Qwen/Qwen3.5-35B-A3B")
-
-    hf_parity_stage = next(
-        stage for stage in report.stages if stage.name == "hf_parity"
-    )
-    assert hf_parity_stage.passed is False
-    assert hf_parity_stage.metrics == {"error": "AssertionError: parity failed"}
-    assert hf_parity_stage.artifact_dir is None
-
-
-def test_build_validation_report_captures_lora_coverage_failure(monkeypatch) -> None:
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.inspect_architecture",
-        lambda base_model: ArchitectureReport(
-            base_model=base_model,
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-            layer_families=[],
-            recommended_min_layers=4,
-        ),
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.detect_dependency_versions",
-        lambda: {},
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._run_stage_in_subprocess",
-        lambda *, stage_name, base_model, architecture, allow_unvalidated_arch=False: (
-            ValidationStageResult(
-                name="lora_coverage",
-                passed=False,
-                metrics={"error": "RuntimeError: missing wrapped targets"},
-            )
-            if stage_name == "lora_coverage"
-            else ValidationStageResult(
-                name=stage_name,
-                passed=True,
-                metrics={},
-            )
-        ),
-    )
-
-    report = build_validation_report(base_model="Qwen/Qwen3.5-35B-A3B")
-
-    lora_coverage_stage = next(
-        stage for stage in report.stages if stage.name == "lora_coverage"
-    )
-    assert lora_coverage_stage.passed is False
-    assert lora_coverage_stage.metrics == {
-        "error": "RuntimeError: missing wrapped targets"
-    }
-
-
-def test_build_validation_report_writes_incremental_output_and_stops(
-    monkeypatch,
-    tmp_path,
-) -> None:
-    calls: list[str] = []
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.inspect_architecture",
-        lambda base_model: ArchitectureReport(
-            base_model=base_model,
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-            layer_families=[],
-            recommended_min_layers=1,
-        ),
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.detect_dependency_versions",
-        lambda: {},
-    )
-
-    def _run_stage_in_subprocess(
-        *,
-        stage_name,
-        base_model,
-        architecture,
-        allow_unvalidated_arch=False,
-    ):
-        calls.append(stage_name)
-        return ValidationStageResult(
-            name=stage_name,
-            passed=stage_name != "lora_coverage",
-            metrics={"stage": stage_name},
-        )
-
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._run_stage_in_subprocess",
-        _run_stage_in_subprocess,
-    )
-    output_json = tmp_path / "workflow_report.json"
-
-    report = build_validation_report(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        output_json=output_json,
-        stop_on_failure=True,
-    )
-
-    assert calls == ["hf_parity", "lora_coverage"]
-    assert output_json.exists()
-    saved = ValidationReport.model_validate_json(output_json.read_text())
-    assert saved == report
-    failed_stage = next(
-        stage for stage in saved.stages if stage.name == "lora_coverage"
-    )
-    skipped_stage = next(
-        stage for stage in saved.stages if stage.name == "train_inf_mismatch"
-    )
-    assert failed_stage.passed is False
-    assert skipped_stage.metrics == {
-        "skipped": True,
-        "reason": "stopped after lora_coverage failed",
-    }
-
-
-def test_assess_minimal_layer_coverage_reports_missing_families(
-    monkeypatch,
-) -> None:
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow.inspect_architecture",
-        lambda base_model: ArchitectureReport(
-            base_model=base_model,
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-            layer_families=[
-                LayerFamilyInstance(key="gated_delta_net_attention", layer_index=0),
-                LayerFamilyInstance(key="standard_attention", layer_index=3),
-                LayerFamilyInstance(key="grouped_moe_mlp", layer_index=0),
-                LayerFamilyInstance(key="shared_experts_mlp", layer_index=0),
-            ],
-            recommended_min_layers=4,
-        ),
-    )
-
-    coverage = assess_minimal_layer_coverage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        num_layers=2,
-    )
-
-    assert coverage.covered is False
-    assert coverage.requested_num_layers == 2
-    assert coverage.recommended_min_layers == 4
-    assert coverage.missing_layer_families == ["standard_attention"]
-    assert coverage.unresolved_risks == []
-
-
-def test_run_chat_template_rollout_stage(monkeypatch) -> None:
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: SimpleNamespace(
-            run_chat_template_rollout=lambda *, base_model: SimpleNamespace(
-                passed=True,
-                scenario_count=6,
-                failed_scenarios=[],
-                output_dir="/tmp/chat-template",
-                model_dump=lambda mode="json": {
-                    "passed": True,
-                    "scenario_count": 6,
-                    "failed_scenarios": [],
-                },
-            )
-        ),
-    )
-
-    result = run_chat_template_rollout_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=ArchitectureReport(
-            base_model="Qwen/Qwen3.5-35B-A3B",
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-        ),
-    )
-
-    assert result.passed is True
-    assert result.artifact_dir == "/tmp/chat-template"
-
-
-def test_run_correctness_sensitivity_stage_runs_dense_models(monkeypatch) -> None:
-    case_configs: list[SimpleNamespace] = []
-    oracle_module = SimpleNamespace(
-        OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs),
-        selected_suite_topologies=lambda *, is_moe, cp_supported=True: [
-            SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1"),
-            SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2"),
-            SimpleNamespace(world_size=lambda: 2, slug=lambda: "dp2"),
-            SimpleNamespace(world_size=lambda: 4, slug=lambda: "tp2_dp2"),
-        ],
-        oracle_topology=lambda *, is_moe: SimpleNamespace(world_size=lambda: 1),
-        selected_oracle_objectives=lambda: ["sft"],
-        supported_sensitivity_mutations_for_objective=lambda objective, *, is_moe: (
-            ["skip_finalize"] if objective == "sft" and not is_moe else []
-        ),
-        sensitivity_topology_for_mutation=lambda mutation, *, is_moe: SimpleNamespace(
-            world_size=lambda: 2
-        ),
-        available_gpu_count=lambda: 4,
-        run_suite=lambda case_config, max_world_size, cp_supported=True, **kwargs: (
-            case_configs.append(case_config)
-            or [
-                SimpleNamespace(
-                    variant="sft_topology_tp2_dp2",
-                    topology="tp2_dp2",
-                    signal="pass",
-                    fail_count=0,
-                )
-            ]
-        ),
-        run_sensitivity_suite=lambda case_config, mutations, max_world_size: [
-            SimpleNamespace(
-                variant="sft_sensitivity_skip_finalize",
-                topology="tp2",
-                signal="fail",
-                expected_signal="fail",
-                fail_count=1,
-            )
-        ],
-        ensure_case_artifacts=lambda case_config: SimpleNamespace(
-            case_dir="/tmp/oracle"
-        ),
-        keep_topology_artifacts=lambda: False,
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: oracle_module,
-    )
-    monkeypatch.delenv(SKIP_SENSITIVITY_ENV, raising=False)
-
-    result = run_correctness_sensitivity_stage(
-        base_model="Qwen/Qwen3.5-4B",
-        architecture=ArchitectureReport(
-            base_model="Qwen/Qwen3.5-4B",
-            model_key="qwen3_5_dense",
-            handler_key="qwen3_5_dense",
-            layer_families=[
-                LayerFamilyInstance(key="dense_mlp", layer_index=0),
-                LayerFamilyInstance(key="gated_delta_net_attention", layer_index=0),
-                LayerFamilyInstance(key="standard_attention", layer_index=3),
-            ],
-            recommended_min_layers=4,
-        ),
-    )
-
-    assert result.passed is True
-    assert result.metrics["is_moe"] is False
-    assert result.metrics["available_gpu_count"] == 4
-    assert result.metrics["max_world_size"] == 4
-    assert result.metrics["required_gpu_count"] == 1
-    assert result.metrics["correctness_variant_count"] == 1
-    assert result.metrics["correctness_excluded_topologies"] == []
-    assert result.metrics["sensitivity_mutations"] == ["skip_finalize"]
-    assert result.metrics["default_excluded_sensitivity_mutations"] == [
-        "attn_skip_flash_lse_normalize"
-    ]
-    assert case_configs[0].is_moe is False
-
-
-def test_run_yes_no_trainability_stage(monkeypatch) -> None:
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: SimpleNamespace(
-            run_yes_no_trainability=lambda *, base_model, allow_unvalidated_arch=False: (
-                SimpleNamespace(
-                    latest_step=2,
-                    initial_eval_reward=0.4,
-                    final_eval_reward=0.95,
-                    reward_threshold=0.95,
-                    saturated_step=2,
-                    output_dir="/tmp/trainability",
-                    model_dump=lambda mode="json": {
-                        "latest_step": 2,
-                        "initial_eval_reward": 0.4,
-                        "final_eval_reward": 0.95,
-                        "reward_threshold": 0.95,
-                        "saturated_step": 2,
-                    },
-                )
-            ),
-            yes_no_trainability_passed=lambda report: (
-                report.final_eval_reward >= report.reward_threshold
-            ),
-        ),
-    )
-
-    result = run_yes_no_trainability_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=ArchitectureReport(
-            base_model="Qwen/Qwen3.5-35B-A3B",
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-        ),
-    )
-
-    assert result.passed is True
-    assert result.artifact_dir == "/tmp/trainability"
-
-
-def test_run_length_trainability_stage(monkeypatch) -> None:
-    report = SimpleNamespace(
-        summary_log_path="/tmp/length-trainability/length_trainability.log",
-        model_dump=lambda mode="json": {
-            "latest_step": 3,
-            "initial_train_abs_error": 12.0,
-            "best_train_abs_error": 1.0,
-        },
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: SimpleNamespace(
-            run_length_trainability=lambda *, base_model, allow_unvalidated_arch=False: (
-                report
-            ),
-            length_trainability_passed=lambda candidate: candidate is report,
-        ),
-    )
-
-    result = run_length_trainability_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=ArchitectureReport(
-            base_model="Qwen/Qwen3.5-35B-A3B",
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-        ),
-    )
-
-    assert result.name == "length_trainability"
-    assert result.passed is True
-    assert result.artifact_dir == "/tmp/length-trainability"
-
-
-def test_run_train_inf_mismatch_stage(monkeypatch) -> None:
-    seen: dict[str, object] = {}
-
-    def _run_train_inf_mismatch(
-        *,
-        base_model: str,
-        allow_unvalidated_arch: bool,
-    ) -> SimpleNamespace:
-        seen["allow_unvalidated_arch"] = allow_unvalidated_arch
-        return SimpleNamespace(
-            passed=True,
-            artifact_dir="/tmp/train-inf-mismatch",
-            model_dump=lambda mode="json": {
-                "base_model": base_model,
-                "passed": True,
-                "passed_count": 1,
-                "failed_count": 0,
-            },
-        )
-
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: SimpleNamespace(
-            run_train_inf_mismatch=_run_train_inf_mismatch,
-        ),
-    )
-
-    result = run_train_inf_mismatch_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=ArchitectureReport(
-            base_model="Qwen/Qwen3.5-35B-A3B",
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-        ),
-        allow_unvalidated_arch=True,
-    )
-
-    assert result.name == "train_inf_mismatch"
-    assert result.passed is True
-    assert result.artifact_dir == "/tmp/train-inf-mismatch"
-    assert seen == {"allow_unvalidated_arch": True}
-    assert result.metrics == {
-        "base_model": "Qwen/Qwen3.5-35B-A3B",
-        "passed": True,
-        "passed_count": 1,
-        "failed_count": 0,
-    }
-
-
-def test_run_native_vllm_lora_stage(monkeypatch) -> None:
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: (
-            SimpleNamespace(
-                OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs),
-            )
-            if name == "integration.megatron.model_support.oracle_harness"
-            else SimpleNamespace(
-                run_native_vllm_lora=lambda case_config: SimpleNamespace(
-                    rollout_weights_mode="lora",
-                    step0_name="validation@0",
-                    step1_name="validation@1",
-                    model_ids_before=["validation@0"],
-                    model_ids_after=["validation@0", "validation@1"],
-                    step0_served=True,
-                    step1_served=True,
-                    output_dir="/tmp/native-vllm-lora",
-                    model_dump=lambda mode="json": {
-                        "rollout_weights_mode": "lora",
-                        "step0_name": "validation@0",
-                        "step1_name": "validation@1",
-                        "model_ids_before": ["validation@0"],
-                        "model_ids_after": ["validation@0", "validation@1"],
-                        "step0_served": True,
-                        "step1_served": True,
-                    },
-                )
-            )
-        ),
-    )
-
-    result = run_native_vllm_lora_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=ArchitectureReport(
-            base_model="Qwen/Qwen3.5-35B-A3B",
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-        ),
-    )
-
-    assert result.name == "native_vllm_lora"
-    assert result.passed is True
-    assert result.artifact_dir == "/tmp/native-vllm-lora"
-
-
-def test_run_packing_invariance_stage(monkeypatch) -> None:
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: SimpleNamespace(
-            run_packing_invariance=lambda *, base_model, num_layers, allow_unvalidated_arch=False: (
-                SimpleNamespace(
-                    output_dir="/tmp/packing-invariance",
-                    model_dump=lambda mode="json": {
-                        "base_model": base_model,
-                        "num_layers": num_layers,
-                        "scenarios": [
-                            {
-                                "name": "stop_early",
-                                "matched": True,
-                                "checked_token_count": 40,
-                            },
-                            {
-                                "name": "truncate",
-                                "matched": True,
-                                "checked_token_count": 44,
-                            },
-                        ],
-                    },
-                )
-            )
-        ),
-    )
-
-    result = run_packing_invariance_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=ArchitectureReport(
-            base_model="Qwen/Qwen3.5-35B-A3B",
-            model_key="qwen3_5_moe",
-            handler_key="qwen3_5_moe",
-            recommended_min_layers=4,
-        ),
-    )
-
-    assert result.passed is True
-    assert result.artifact_dir == "/tmp/packing-invariance"
-
-
-def test_assess_minimal_layer_coverage_passes_when_prefix_covers_all_families(
-    monkeypatch,
-) -> None:
-    architecture = ArchitectureReport(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        model_key="qwen3_5_moe",
-        handler_key="qwen3_5_moe",
-        layer_families=[
-            LayerFamilyInstance(key="gated_delta_net_attention", layer_index=0),
-            LayerFamilyInstance(key="standard_attention", layer_index=3),
-            LayerFamilyInstance(key="grouped_moe_mlp", layer_index=0),
-            LayerFamilyInstance(key="shared_experts_mlp", layer_index=0),
-        ],
-        recommended_min_layers=4,
-    )
-
-    coverage = assess_minimal_layer_coverage(
-        base_model=architecture.base_model,
-        num_layers=4,
-        architecture=architecture,
-    )
-
-    assert coverage.covered is True
-    assert coverage.missing_layer_families == []
+    assert hf_parity_stage.artifact_dir is None
 
 
 def test_run_lora_coverage_stage_reports_missing_targets(monkeypatch) -> None:
@@ -1090,6 +1149,7 @@ def test_run_lora_coverage_stage_reports_missing_targets(monkeypatch) -> None:
     coverage_report = SimpleNamespace(
         missing_wrapped_target_modules=["in_proj_z"],
         missing_exported_target_modules=[],
+        unexpected_trainable_parameter_names=[],
         model_dump=lambda mode="json": {
             "base_model": "Qwen/Qwen3.5-35B-A3B",
             "missing_wrapped_target_modules": ["in_proj_z"],
@@ -1122,250 +1182,3 @@ def _import_integration_module(name: str):
         "base_model": "Qwen/Qwen3.5-35B-A3B",
         "missing_wrapped_target_modules": ["in_proj_z"],
     }
-
-
-def test_run_correctness_sensitivity_stage_summarizes_reports(monkeypatch) -> None:
-    architecture = ArchitectureReport(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        model_key="qwen3_5_moe",
-        handler_key="qwen3_5_moe",
-        layer_families=[LayerFamilyInstance(key="grouped_moe_mlp", layer_index=0)],
-        recommended_min_layers=4,
-    )
-    oracle_module = SimpleNamespace(
-        OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs),
-        selected_suite_topologies=lambda *, is_moe, cp_supported=True: [
-            SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1"),
-            SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2"),
-        ],
-        oracle_topology=lambda *, is_moe: SimpleNamespace(world_size=lambda: 1),
-        selected_oracle_objectives=lambda: ["sft"],
-        supported_sensitivity_mutations_for_objective=lambda objective, *, is_moe: (
-            ["skip_finalize"] if objective == "sft" else []
-        ),
-        sensitivity_topology_for_mutation=lambda mutation, *, is_moe: SimpleNamespace(
-            world_size=lambda: 2
-        ),
-        available_gpu_count=lambda: 2,
-        run_suite=lambda case_config, max_world_size, cp_supported=True, **kwargs: [
-            SimpleNamespace(
-                variant="sft_topology_tp2",
-                topology="tp2",
-                signal="pass",
-                fail_count=0,
-            )
-        ],
-        run_sensitivity_suite=lambda case_config, mutations, max_world_size: [
-            SimpleNamespace(
-                variant="sft_sensitivity_skip_finalize",
-                topology="tp2",
-                signal="fail",
-                expected_signal="fail",
-                fail_count=1,
-            )
-        ],
-        ensure_case_artifacts=lambda case_config: SimpleNamespace(
-            case_dir="/tmp/oracle"
-        ),
-        keep_topology_artifacts=lambda: False,
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: oracle_module,
-    )
-
-    stage = run_correctness_sensitivity_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=architecture,
-    )
-
-    assert stage.name == "correctness_sensitivity"
-    assert stage.passed is True
-    assert stage.metrics["requested_num_layers"] == 4
-    assert stage.metrics["is_moe"] is True
-    assert stage.metrics["objectives"] == ["sft"]
-    assert stage.metrics["sensitivity_mutations"] == ["skip_finalize"]
-    assert stage.metrics["default_excluded_sensitivity_mutations"] == [
-        "attn_skip_flash_lse_normalize"
-    ]
-    assert stage.metrics["available_gpu_count"] == 2
-    assert stage.metrics["required_gpu_count"] == 1
-    assert stage.metrics["correctness_variant_count"] == 1
-    assert stage.metrics["sensitivity_skipped"] is False
-    assert stage.metrics["sensitivity_skip_reason"] is None
-    assert stage.metrics["sensitivity_variant_count"] == 1
-    assert stage.artifact_dir == "/tmp/oracle"
-
-
-def test_run_correctness_sensitivity_stage_uses_dsv4_real_path_config(
-    monkeypatch,
-) -> None:
-    architecture = ArchitectureReport(
-        base_model="deepseek-ai/DeepSeek-V4-Flash",
-        model_key="dsv4",
-        handler_key="dsv4",
-        layer_families=[LayerFamilyInstance(key="dsv4_attention", layer_index=0)],
-        recommended_min_layers=4,
-    )
-    captured: dict[str, object] = {}
-    oracle_module = SimpleNamespace(
-        OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs),
-        MetricThresholdRule=lambda **kwargs: SimpleNamespace(**kwargs),
-        selected_suite_topologies=lambda *, is_moe, cp_supported=True: [
-            SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1"),
-            SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2"),
-        ],
-        oracle_topology=lambda *, is_moe: SimpleNamespace(world_size=lambda: 1),
-        selected_oracle_objectives=lambda: ["rl"],
-        supported_sensitivity_mutations_for_objective=lambda objective, *, is_moe: [],
-        sensitivity_topology_for_mutation=lambda mutation, *, is_moe: SimpleNamespace(
-            world_size=lambda: 2
-        ),
-        available_gpu_count=lambda: 2,
-        run_suite=lambda case_config, **kwargs: (
-            captured.update(case_config=case_config, suite_kwargs=kwargs)
-            or [
-                SimpleNamespace(
-                    variant="rl_topology_tp2",
-                    topology="tp2",
-                    signal="pass",
-                    fail_count=0,
-                )
-            ]
-        ),
-        run_sensitivity_suite=lambda case_config, mutations, max_world_size: [],
-        ensure_case_artifacts=lambda case_config: SimpleNamespace(
-            case_dir="/tmp/oracle"
-        ),
-        keep_topology_artifacts=lambda: False,
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: oracle_module,
-    )
-    monkeypatch.setenv(SKIP_SENSITIVITY_ENV, "1")
-
-    stage = run_correctness_sensitivity_stage(
-        base_model="deepseek-ai/DeepSeek-V4-Flash",
-        architecture=architecture,
-    )
-
-    case_config = captured["case_config"]
-    suite_kwargs = cast(dict[str, object], captured["suite_kwargs"])
-    phase_pass_fns = cast(dict[str, object], suite_kwargs["phase_pass_fns"])
-    assert getattr(case_config, "precision") == "bf16"
-    assert suite_kwargs["use_fp32_lora_reference"] is False
-    assert getattr(phase_pass_fns["forward"], "limits") == {"mean_abs_pct": 3.0}
-    assert getattr(phase_pass_fns["grads"], "limits") == {"mean_abs_pct": 5.0}
-    assert stage.metrics["precision"] == "bf16"
-    assert stage.metrics["use_fp32_lora_reference"] is False
-
-
-def test_run_correctness_sensitivity_stage_can_skip_sensitivity_only(
-    monkeypatch,
-) -> None:
-    architecture = ArchitectureReport(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        model_key="qwen3_5_moe",
-        handler_key="qwen3_5_moe",
-        layer_families=[LayerFamilyInstance(key="grouped_moe_mlp", layer_index=0)],
-        recommended_min_layers=4,
-    )
-    oracle_module = SimpleNamespace(
-        OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs),
-        selected_suite_topologies=lambda *, is_moe, cp_supported=True: [
-            SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1"),
-            SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2"),
-        ],
-        oracle_topology=lambda *, is_moe: SimpleNamespace(world_size=lambda: 1),
-        selected_oracle_objectives=lambda: ["sft"],
-        supported_sensitivity_mutations_for_objective=lambda objective, *, is_moe: (
-            ["skip_finalize"] if objective == "sft" else []
-        ),
-        sensitivity_topology_for_mutation=lambda mutation, *, is_moe: SimpleNamespace(
-            world_size=lambda: 4
-        ),
-        available_gpu_count=lambda: 2,
-        run_suite=lambda case_config, max_world_size, cp_supported=True, **kwargs: [
-            SimpleNamespace(
-                variant="sft_topology_tp2",
-                topology="tp2",
-                signal="pass",
-                fail_count=0,
-            )
-        ],
-        run_sensitivity_suite=lambda case_config, mutations, max_world_size: (
-            _ for _ in ()
-        ).throw(AssertionError("sensitivity suite should be skipped")),
-        ensure_case_artifacts=lambda case_config: SimpleNamespace(
-            case_dir="/tmp/oracle"
-        ),
-        keep_topology_artifacts=lambda: False,
-    )
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        lambda name: oracle_module,
-    )
-    monkeypatch.setenv(SKIP_SENSITIVITY_ENV, "1")
-
-    stage = run_correctness_sensitivity_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=architecture,
-    )
-
-    assert stage.name == "correctness_sensitivity"
-    assert stage.passed is True
-    assert stage.metrics["required_gpu_count"] == 1
-    assert stage.metrics["correctness_variant_count"] == 1
-    assert stage.metrics["sensitivity_mutations"] == []
-    assert stage.metrics["default_excluded_sensitivity_mutations"] == []
-    assert stage.metrics["sensitivity_skipped"] is True
-    assert stage.metrics["sensitivity_skip_reason"] == f"{SKIP_SENSITIVITY_ENV}=1"
-    assert stage.metrics["sensitivity_variant_count"] == 0
-    assert stage.metrics["sensitivity_variants"] == []
-
-
-def test_run_merged_vllm_serving_stage_reports_served_model(monkeypatch) -> None:
-    architecture = ArchitectureReport(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        model_key="qwen3_5_moe",
-        handler_key="qwen3_5_moe",
-        recommended_min_layers=4,
-    )
-    oracle_module = SimpleNamespace(
-        OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs)
-    )
-    merged_module = SimpleNamespace(
-        run_merged_vllm_serving=lambda case_config: SimpleNamespace(
-            output_dir="/tmp/merged-serving",
-            model_ids=["validation@0"],
-            model_dump=lambda mode="json": {
-                "base_model": "Qwen/Qwen3.5-35B-A3B",
-                "served_model_name": "validation@0",
-            },
-        )
-    )
-
-    def _import_integration_module(name: str):
-        if name == "integration.megatron.model_support.oracle_harness":
-            return oracle_module
-        if name == "integration.megatron.lora.merged_vllm_serving":
-            return merged_module
-        raise AssertionError(name)
-
-    monkeypatch.setattr(
-        "tests.integration.megatron.model_support.workflow._import_integration_module",
-        _import_integration_module,
-    )
-
-    stage = run_merged_vllm_serving_stage(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        architecture=architecture,
-    )
-
-    assert stage.name == "merged_vllm_serving"
-    assert stage.passed is True
-    assert stage.metrics["base_model"] == "Qwen/Qwen3.5-35B-A3B"
-    assert stage.metrics["served_model_name"] == "validation@0"
-    assert "readable_summary" in stage.metrics
-    assert stage.artifact_dir == "/tmp/merged-serving"
diff --git a/tests/integration/megatron/model_support/test_workflow_fail_closed.py b/tests/integration/megatron/model_support/test_workflow_fail_closed.py
new file mode 100644
index 000000000..6b12301e8
--- /dev/null
+++ b/tests/integration/megatron/model_support/test_workflow_fail_closed.py
@@ -0,0 +1,193 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import cast
+
+from art.megatron.model_support.spec import ArchitectureReport
+
+from . import workflow_scheduler
+from .validation_spec import (
+    ValidationReport,
+    ValidationStageResult,
+)
+from .workflow import CORRECTNESS_REFERENCE_STAGE
+from .workflow_forkserver import (
+    WorkflowForkserverPool,
+)
+from .workflow_runtime import (
+    WorkflowDevice,
+    WorkflowOperation,
+    WorkflowOperationFailed,
+    WorkflowRuntimeKey,
+    compile_workflow,
+    execute_workflow,
+)
+from .workflow_scheduler import PreparedWorkflow
+from .workflow_stage_worker import WorkflowStageWorkerSession
+
+
+def _runtime(name: str, *, handler: str | None = None) -> WorkflowRuntimeKey:
+    return WorkflowRuntimeKey(
+        source_fingerprint="source",
+        handler=handler or name,
+        fixture="fixture",
+        kind="cpu",
+        mode=name,
+    )
+
+
+def test_executor_blocks_failed_dependency_transitively() -> None:
+    operations = (
+        WorkflowOperation(id="root", stage="root", runtime=_runtime("root")),
+        WorkflowOperation(
+            id="child",
+            stage="child",
+            runtime=_runtime("child"),
+            dependencies=("root",),
+        ),
+        WorkflowOperation(
+            id="grandchild",
+            stage="grandchild",
+            runtime=_runtime("grandchild"),
+            dependencies=("child",),
+        ),
+        WorkflowOperation(
+            id="independent", stage="independent", runtime=_runtime("independent")
+        ),
+    )
+    called: list[str] = []
+
+    def runner(session, _placement):
+        operation_id = session.operations[0].id
+        called.append(operation_id)
+        if operation_id == "root":
+            raise WorkflowOperationFailed(operation_id)
+        return operation_id
+
+    execution = execute_workflow(
+        compile_workflow(operations),
+        devices=[WorkflowDevice(host="local", gpu="0")],
+        runner=runner,
+    )
+
+    assert set(called) == {"root", "independent"}
+    assert execution.results["session_000"].failed_operation_id == "root"
+    assert execution.blocked_by_failed_operations == {
+        "session_001": ("root",),
+        "session_002": ("root",),
+    }
+
+
+class _Fixture:
+    def environment(self, _stage: str | None = None) -> dict[str, str]:
+        return {"ART_MODEL_SUPPORT_FIXTURE_PATH": "/tmp/model"}
+
+
+class _Prepared:
+    def __init__(self, run_dir: Path, stages: tuple[str, ...] | None = None) -> None:
+        stages = stages or (
+            "hf_parity",
+            "packing_invariance",
+            "length_trainability",
+        )
+        self.report = ValidationReport(
+            git={"commit": "test"},
+            base_model="model",
+            model_key="model",
+            stages=[ValidationStageResult(name=stage) for stage in stages],
+        )
+        self.architecture = ArchitectureReport(
+            base_model="model",
+            model_key="model",
+            handler_key="model",
+            recommended_min_layers=1,
+        )
+        self.fixture = _Fixture()
+        self.run_dir = run_dir
+        self.output_json = None
+        self.allow_unvalidated_arch = False
+        self.include_sensitivity = None
+
+    def record(self, result: ValidationStageResult) -> None:
+        stage = next(stage for stage in self.report.stages if stage.name == result.name)
+        stage.passed = result.passed
+        stage.skipped = result.skipped
+        stage.metrics = dict(result.metrics)
+        stage.artifact_dir = result.artifact_dir
+
+    def record_fixture_metric(self, _metrics: dict[str, object]) -> None:
+        pass
+
+
+class _Forkservers:
+    def __init__(self, *, fail: str | None = "hf_parity", stop: bool = True) -> None:
+        self.calls: list[tuple[str, ...]] = []
+        self.fail = fail
+        self.stop = stop
+
+    def run(self, _host: str, *, request_json: Path, **_kwargs):
+        request = WorkflowStageWorkerSession.model_validate_json(
+            Path(request_json).read_text(encoding="utf-8")
+        )
+        stages = tuple(item.stage for item in request.items)
+        self.calls.append(stages)
+        for item in request.items:
+            result = ValidationStageResult(name=item.stage, passed=True)
+            if item.stage == self.fail:
+                result = ValidationStageResult(
+                    name=item.stage,
+                    passed=False,
+                    metrics={"error": "sentinel root failure"},
+                )
+            Path(item.output_json).write_text(
+                result.model_dump_json(), encoding="utf-8"
+            )
+            if not result.passed and self.stop:
+                break
+        return {"returncode": 0, "child_wall_s": 0.01}
+
+    def metrics(self, _host: str) -> dict[str, float]:
+        return {}
+
+
+def _run(prepared: _Prepared, forkservers: _Forkservers) -> ValidationReport:
+    return workflow_scheduler.run_prepared_workflows(
+        [cast(PreparedWorkflow, prepared)],
+        forkservers=cast(WorkflowForkserverPool, forkservers),
+    )[0]
+
+
+def test_hidden_correctness_failure_fails_visible_owner(
+    monkeypatch, tmp_path: Path
+) -> None:
+    reference = WorkflowOperation(
+        id=f"model:{CORRECTNESS_REFERENCE_STAGE}",
+        stage=CORRECTNESS_REFERENCE_STAGE,
+        runtime=_runtime("reference", handler="model"),
+    )
+    visible = WorkflowOperation(
+        id="model:correctness_sensitivity",
+        stage="correctness_sensitivity",
+        runtime=_runtime("variants", handler="model"),
+        dependencies=(reference.id,),
+    )
+    plan = compile_workflow((reference, visible))
+    prepared = _Prepared(tmp_path / "run", ("correctness_sensitivity",))
+    forkservers = _Forkservers(fail=CORRECTNESS_REFERENCE_STAGE)
+    monkeypatch.setattr(
+        workflow_scheduler, "compile_prepared_workflows", lambda *_args, **_kwargs: plan
+    )
+    monkeypatch.setattr(
+        workflow_scheduler,
+        "_visible_devices",
+        lambda: [WorkflowDevice(host="local", gpu="0")],
+    )
+
+    report = _run(prepared, forkservers)
+
+    owner = report.stages[0]
+    assert owner.name == "correctness_sensitivity"
+    assert owner.passed is False and owner.skipped is False
+    assert owner.metrics["blocked"] is True
+    assert owner.metrics["workflow_failed_dependencies"] == [reference.id]
+    assert report.passed is False
diff --git a/tests/integration/megatron/model_support/test_workflow_runtime.py b/tests/integration/megatron/model_support/test_workflow_runtime.py
new file mode 100644
index 000000000..9b2fac279
--- /dev/null
+++ b/tests/integration/megatron/model_support/test_workflow_runtime.py
@@ -0,0 +1,128 @@
+import pytest
+
+from .workflow_runtime import (
+    WorkflowDevice,
+    WorkflowOperation,
+    WorkflowPlan,
+    WorkflowResourceRequest,
+    WorkflowRuntimeKey,
+    WorkflowSession,
+    _GpuPool,
+    compile_workflow,
+    execute_workflow,
+)
+
+
+def _devices(hosts: int = 1, gpus: int = 8) -> list[WorkflowDevice]:
+    return [
+        WorkflowDevice(host=f"host-{host}", gpu=str(gpu))
+        for host in range(hosts)
+        for gpu in range(gpus)
+    ]
+
+
+def _runtime(handler: str = "handler") -> WorkflowRuntimeKey:
+    return WorkflowRuntimeKey(
+        source_fingerprint="source",
+        handler=handler,
+        fixture="fixture",
+        kind="megatron",
+    )
+
+
+def _session(
+    session_id: str,
+    *,
+    gpu_count: int,
+    gpu_share: float = 1.0,
+    estimated_duration_s: float = 1.0,
+) -> WorkflowSession:
+    operation = WorkflowOperation(
+        id=session_id,
+        stage=session_id,
+        runtime=_runtime(session_id),
+        resources=WorkflowResourceRequest(gpu_count=gpu_count, gpu_share=gpu_share),
+        estimated_duration_s=estimated_duration_s,
+    )
+    return WorkflowSession(
+        id=session_id,
+        runtime=operation.runtime,
+        operations=(operation,),
+        resources=operation.resources,
+        estimated_duration_s=estimated_duration_s,
+    )
+
+
+def test_ready_full_width_session_launches_before_fractional_backfill() -> None:
+    sessions = (
+        _session("fractional", gpu_count=1, gpu_share=0.125, estimated_duration_s=100),
+        _session("exclusive-one", gpu_count=1, estimated_duration_s=100),
+        _session("full", gpu_count=4, estimated_duration_s=1),
+    )
+    execution = execute_workflow(
+        WorkflowPlan(sessions=sessions),
+        devices=_devices(),
+        runner=lambda _session, _placement: None,
+    )
+
+    assert (
+        execution.results["full"].started_monotonic_s
+        < execution.results["exclusive-one"].started_monotonic_s
+        < execution.results["fractional"].started_monotonic_s
+    )
+    assert tuple(
+        device.gpu for device in execution.results["full"].placement.devices
+    ) == ("0", "1", "2", "3")
+    assert execution.results["exclusive-one"].placement.devices[0].gpu == "4"
+    assert execution.results["fractional"].placement.devices[0].gpu == "5"
+
+
+def test_distinct_host_affinities_balance_and_remain_pinned() -> None:
+    pool = _GpuPool(_devices(hosts=3))
+    full = WorkflowResourceRequest(gpu_count=8)
+    occupied = []
+    for _ in range(3):
+        placement = pool.acquire(full)
+        assert placement is not None
+        occupied.append(placement)
+    pool.release(occupied[0], full)
+    requests = [
+        WorkflowResourceRequest(gpu_count=1, host_affinity=f"model-{index}")
+        for index in range(6)
+    ]
+    hosts = []
+    for request in requests:
+        placement = pool.acquire(request)
+        hosts.append(placement.host if placement is not None else None)
+        if placement is not None:
+            pool.release(placement, request)
+
+    assert hosts == ["host-0", None, None, "host-0", None, None]
+    pool.release(occupied[1], full)
+    pool.release(occupied[2], full)
+    for request, host in zip(requests, ("host-0", "host-1", "host-2") * 2, strict=True):
+        variant_request = request.model_copy(update={"gpu_count": 8})
+        placement = pool.acquire(variant_request)
+        assert placement is not None
+        assert placement.host == host
+        pool.release(placement, variant_request)
+
+
+def test_compiled_session_counts_shared_startup_once() -> None:
+    runtime = _runtime()
+    operations = tuple(
+        WorkflowOperation(
+            id=f"operation-{index}",
+            stage=f"stage-{index}",
+            runtime=runtime,
+            estimated_duration_s=duration,
+            estimated_shared_startup_s=startup,
+        )
+        for index, (duration, startup) in enumerate(
+            ((60.0, 0.0), (360.0, 200.0), (360.0, 200.0))
+        )
+    )
+
+    plan = compile_workflow(operations)
+
+    assert plan.sessions[0].estimated_duration_s == pytest.approx(580.0)
diff --git a/tests/integration/megatron/model_support/validation_spec.py b/tests/integration/megatron/model_support/validation_spec.py
index 6901f81a2..e1a1ecd94 100644
--- a/tests/integration/megatron/model_support/validation_spec.py
+++ b/tests/integration/megatron/model_support/validation_spec.py
@@ -18,6 +18,7 @@ class MinimalLayerCoverageReport(BaseModel):
 class ValidationStageResult(BaseModel):
     name: str
     passed: bool = False
+    skipped: bool = False
     metrics: dict[str, Any] = Field(default_factory=dict)
     artifact_dir: str | None = None
 
@@ -26,5 +27,7 @@ class ValidationReport(BaseModel):
     git: dict[str, Any]
     base_model: str
     model_key: str
+    passed: bool = False
+    complete: bool = False
     dependency_versions: dict[str, str] = Field(default_factory=dict)
     stages: list[ValidationStageResult] = Field(default_factory=list)
diff --git a/tests/integration/megatron/model_support/workflow.py b/tests/integration/megatron/model_support/workflow.py
index 3a4350930..8d2eef864 100644
--- a/tests/integration/megatron/model_support/workflow.py
+++ b/tests/integration/megatron/model_support/workflow.py
@@ -2,25 +2,26 @@
 from contextlib import contextmanager, nullcontext, redirect_stderr, redirect_stdout
 import importlib
 import importlib.metadata
+import math
 import os
 from pathlib import Path
+import shutil
+import signal
 import subprocess
 import sys
-import tempfile
-from typing import Any
+import threading
+import time
+from typing import Any, Mapping
+import uuid
 
 from pydantic import BaseModel, Field
 
-from art.megatron.model_support.discovery import inspect_architecture
 from art.megatron.model_support.registry import (
     VALIDATED_MODEL_SUPPORT_SPECS,
     get_model_support_handler_for_spec,
     get_model_support_spec,
 )
-from art.megatron.model_support.spec import (
-    ArchitectureReport,
-    NativeVllmLoraStatus,
-)
+from art.megatron.model_support.spec import ArchitectureReport
 
 from ..artifacts import pinned_git_state
 from .validation_spec import (
@@ -28,19 +29,26 @@
     ValidationReport,
     ValidationStageResult,
 )
+from .workflow_fixtures import WorkflowFixture, ensure_workflow_fixture
 
 REPO_ROOT = Path(__file__).resolve().parents[4]
 TESTS_DIR = REPO_ROOT / "tests"
-LOCAL_LOG_DIR = REPO_ROOT / ".local"
-CORRECTNESS_LOG_PATH = LOCAL_LOG_DIR / "correctness.log"
-SENSITIVITY_LOG_PATH = LOCAL_LOG_DIR / "sensitivity.log"
-LIVE_TRAINING_LOG_PATH = LOCAL_LOG_DIR / "live_training.log"
 ORACLE_LIVE_TRAINING_LOG_ENV = "ART_ORACLE_LIVE_TRAINING_LOG"
+WORKFLOW_RUN_DIR_ENV = "ART_MODEL_SUPPORT_WORKFLOW_RUN_DIR"
+WORKFLOW_STAGE_DIR_ENV = "ART_MODEL_SUPPORT_WORKFLOW_STAGE_DIR"
 SKIP_SENSITIVITY_ENV = "ART_MODEL_SUPPORT_SKIP_SENSITIVITY"
 INCLUDE_FLASH_SENSITIVITY_ENV = "ART_MODEL_SUPPORT_INCLUDE_FLASH_SENSITIVITY"
 KEEP_TOPOLOGY_ARTIFACTS_ENV = "ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS"
+CORRECTNESS_ARTIFACT_ROOT_ENV = "ART_MODEL_SUPPORT_CORRECTNESS_ARTIFACT_ROOT"
+CORRECTNESS_PHASE_ENV = "ART_MODEL_SUPPORT_CORRECTNESS_PHASE"
+CORRECTNESS_REFERENCE_STAGE = "correctness_reference"
 WORKFLOW_ARTIFACT_SUITE_NAME = "Megatron model-support validation workflow"
 FLASH_SENSITIVITY_MUTATION = "attn_skip_flash_lse_normalize"
+_HANDLER_INAPPLICABLE_SENSITIVITY_MUTATIONS = {
+    "glm52": frozenset(
+        {"attn_skip_nested_grad_sanitize", "attn_skip_flash_lse_normalize"}
+    )
+}
 
 MANDATORY_VALIDATION_STAGES = (
     "dependency_resolution",
@@ -48,19 +56,13 @@
     "hf_parity",
     "lora_coverage",
     "train_inf_mismatch",
-    "merged_vllm_serving",
     "correctness_sensitivity",
     "chat_template_rollout",
     "packing_invariance",
     "length_trainability",
+    "e2e_throughput",
 )
-NATIVE_VLLM_LORA_STAGE = "native_vllm_lora"
-YES_NO_TRAINABILITY_STAGE = "yes_no_trainability"
-OPTIONAL_VALIDATION_STAGES = (
-    YES_NO_TRAINABILITY_STAGE,
-    NATIVE_VLLM_LORA_STAGE,
-)
-ALL_VALIDATION_STAGES = (*MANDATORY_VALIDATION_STAGES, *OPTIONAL_VALIDATION_STAGES)
+ALL_VALIDATION_STAGES = MANDATORY_VALIDATION_STAGES
 ARCHITECTURE_REPRESENTATIVE_MODELS = {
     "llama3_dense": "meta-llama/Llama-3.2-1B-Instruct",
     "qwen3_moe": "Qwen/Qwen3-30B-A3B",
@@ -70,6 +72,7 @@
     "gemma4_moe": "google/gemma-4-26B-A4B-it",
     "gemma4_dense": "google/gemma-4-31B-it",
     "dsv4": "deepseek-ai/DeepSeek-V4-Flash",
+    "glm52": "zai-org/GLM-5.2",
     "gpt_oss_moe": "openai/gpt-oss-20b",
 }
 SUBPROCESS_VALIDATION_STAGES = frozenset(
@@ -77,34 +80,36 @@
         "hf_parity",
         "lora_coverage",
         "train_inf_mismatch",
-        "merged_vllm_serving",
         "correctness_sensitivity",
         "chat_template_rollout",
         "packing_invariance",
         "length_trainability",
-        YES_NO_TRAINABILITY_STAGE,
-        NATIVE_VLLM_LORA_STAGE,
+        "e2e_throughput",
     }
 )
+_RUNTIME_CLEANUP_STAGES = frozenset({"length_trainability", "e2e_throughput"})
+_RUNTIME_ARTIFACT_DIR_NAMES = frozenset(
+    {
+        "checkpoints",
+        "megatron_runtime",
+        "optimizer_states",
+        "trajectories",
+    }
+)
+_WORKFLOW_STAGE_TIMEOUT_S = 30 * 60
+_WORKFLOW_STAGE_TIMEOUT_OVERRIDES_S = {
+    ("e2e_throughput", "deepseek-ai/DeepSeek-V4-Flash"): 40 * 60,
+}
 
 
 class AllArchitecturesValidationReport(BaseModel):
     passed: bool = False
+    complete: bool = False
     reports: list[ValidationReport] = Field(default_factory=list)
 
 
-def build_validation_stage_names(
-    *,
-    include_native_vllm_lora: bool = False,
-    include_yes_no_trainability: bool = False,
-    native_vllm_lora_status: NativeVllmLoraStatus | None = None,
-) -> list[str]:
-    stages = list(MANDATORY_VALIDATION_STAGES)
-    if include_yes_no_trainability:
-        stages.append(YES_NO_TRAINABILITY_STAGE)
-    if include_native_vllm_lora or native_vllm_lora_status not in {None, "disabled"}:
-        stages.append(NATIVE_VLLM_LORA_STAGE)
-    return stages
+def build_validation_stage_names() -> list[str]:
+    return list(MANDATORY_VALIDATION_STAGES)
 
 
 def detect_dependency_versions() -> dict[str, str]:
@@ -120,15 +125,12 @@ def detect_dependency_versions() -> dict[str, str]:
 def initialize_validation_report(
     *,
     base_model: str,
-    include_native_vllm_lora: bool = False,
-    include_yes_no_trainability: bool = False,
     allow_unvalidated_arch: bool = False,
 ) -> ValidationReport:
     spec = get_model_support_spec(
         base_model,
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
-    handler = get_model_support_handler_for_spec(spec)
     return ValidationReport(
         git=pinned_git_state(WORKFLOW_ARTIFACT_SUITE_NAME).model_dump(mode="json"),
         base_model=base_model,
@@ -136,11 +138,7 @@ def initialize_validation_report(
         dependency_versions=detect_dependency_versions(),
         stages=[
             ValidationStageResult(name=stage_name)
-            for stage_name in build_validation_stage_names(
-                include_native_vllm_lora=include_native_vllm_lora,
-                include_yes_no_trainability=include_yes_no_trainability,
-                native_vllm_lora_status=handler.native_vllm_lora_status,
-            )
+            for stage_name in build_validation_stage_names()
         ],
     )
 
@@ -173,6 +171,8 @@ def _inspect_architecture_for_workflow(
     *,
     allow_unvalidated_arch: bool,
 ) -> ArchitectureReport:
+    from art.megatron.model_support.discovery import inspect_architecture
+
     # Discovery only inspects layer families, so use a minimal topology instead
     # of inheriting visible GPU count and tripping model-specific TP limits.
     with _temporary_env(
@@ -210,6 +210,68 @@ def _temporary_env(**updates: str):
             os.environ[key] = value
 
 
+def _new_workflow_run_dir(*, output_json: str | Path | None, model_key: str) -> Path:
+    run_id = f"{time.strftime('%Y%m%dT%H%M%SZ')}_{os.getpid()}_{uuid.uuid4().hex[:8]}"
+    if output_json is None:
+        root = REPO_ROOT / ".local" / "model_support_workflow_runs" / model_key
+    else:
+        output_path = Path(output_json).resolve()
+        root = output_path.parent / f"{output_path.stem}.artifacts"
+    path = root / run_id
+    path.mkdir(parents=True, exist_ok=False)
+    return path
+
+
+def _workflow_stage_dir() -> Path:
+    raw = os.environ.get(WORKFLOW_STAGE_DIR_ENV)
+    if raw is None:
+        raise RuntimeError(f"missing {WORKFLOW_STAGE_DIR_ENV}")
+    path = Path(raw)
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def _stage_artifact_dir() -> Path:
+    path = _workflow_stage_dir() / "artifacts"
+    path.mkdir(parents=True, exist_ok=True)
+    return path
+
+
+def _cleanup_stage_workspace(path: Path) -> None:
+    if os.environ.get(KEEP_TOPOLOGY_ARTIFACTS_ENV) != "1" and path.exists():
+        shutil.rmtree(path)
+
+
+def _oracle_case_config(
+    oracle_harness: Any,
+    *,
+    base_model: str,
+    model_support_key: str,
+    is_moe: bool,
+    precision: str,
+    num_layers: int,
+    target_modules: list[str],
+    allow_unvalidated_arch: bool,
+) -> Any:
+    artifact_root = os.environ.get(CORRECTNESS_ARTIFACT_ROOT_ENV)
+    oracle_harness.ARTIFACT_ROOT = (
+        Path(artifact_root) if artifact_root is not None else _stage_artifact_dir()
+    )
+    num_layers = int(
+        os.environ.get("ART_MODEL_SUPPORT_FUNCTIONAL_NUM_LAYERS", num_layers)
+    )
+    return oracle_harness.OracleCaseConfig(
+        base_model=base_model,
+        model_support_key=model_support_key,
+        is_moe=is_moe,
+        precision=precision,
+        num_layers=num_layers,
+        num_steps=1,
+        lora={"target_modules": target_modules},
+        allow_unvalidated_arch=allow_unvalidated_arch,
+    )
+
+
 def _write_validation_report(
     report: ValidationReport,
     output_json: str | Path | None,
@@ -221,6 +283,32 @@ def _write_validation_report(
     path.write_text(report.model_dump_json(indent=2), encoding="utf-8")
 
 
+def _record_stage_duration(stage: ValidationStageResult, *, started: float) -> None:
+    stage.metrics["workflow_stage_duration_s"] = time.monotonic() - started
+
+
+def _prune_runtime_artifacts(stage_dir: Path) -> dict[str, int]:
+    paths = sorted(
+        (
+            path
+            for path in stage_dir.rglob("*")
+            if path.is_dir() and path.name in _RUNTIME_ARTIFACT_DIR_NAMES
+        ),
+        key=lambda path: len(path.parts),
+        reverse=True,
+    )
+    removed_bytes = 0
+    for path in paths:
+        removed_bytes += sum(
+            child.stat().st_size for child in path.rglob("*") if child.is_file()
+        )
+        shutil.rmtree(path)
+    return {
+        "workflow_pruned_runtime_artifact_dirs": len(paths),
+        "workflow_pruned_runtime_artifact_bytes": removed_bytes,
+    }
+
+
 def _write_all_architectures_report(
     report: AllArchitecturesValidationReport,
     output_json: str | Path | None,
@@ -267,18 +355,34 @@ def _mark_remaining_stages_skipped(
     report: ValidationReport,
     *,
     after_stage_name: str,
+    reason: str | None = None,
 ) -> None:
     past_failure = False
     for stage in report.stages:
         if past_failure:
+            stage.passed = False
+            stage.skipped = True
             stage.metrics = {
                 "skipped": True,
-                "reason": f"stopped after {after_stage_name} failed",
+                "reason": reason or f"stopped after {after_stage_name} failed",
+                "workflow_stage_duration_s": 0.0,
             }
             continue
         past_failure = stage.name == after_stage_name
 
 
+def _finalize_validation_report(
+    report: ValidationReport,
+    *,
+    partial: bool,
+) -> None:
+    executed = [stage for stage in report.stages if not stage.skipped]
+    report.passed = bool(executed) and all(stage.passed for stage in executed)
+    report.complete = (
+        not partial and len(executed) == len(report.stages) and report.passed
+    )
+
+
 def _only_stage_run_set(only_stage: str | None) -> set[str] | None:
     if only_stage is None:
         return None
@@ -297,69 +401,136 @@ def _run_stage_in_subprocess(
     base_model: str,
     architecture: ArchitectureReport,
     allow_unvalidated_arch: bool = False,
+    run_dir: Path | None = None,
+    stage_environment: Mapping[str, str] | None = None,
+    visible_gpu_ids: tuple[str, ...] | None = None,
 ) -> ValidationStageResult:
-    with tempfile.TemporaryDirectory(prefix=f"model_support_{stage_name}_") as tmp_dir:
-        tmp_path = Path(tmp_dir)
-        architecture_json = tmp_path / "architecture.json"
-        output_json = tmp_path / "stage_result.json"
-        log_path = tmp_path / "stage.log"
-        architecture_json.write_text(
-            architecture.model_dump_json(indent=2),
-            encoding="utf-8",
+    run_dir = run_dir or Path(os.environ[WORKFLOW_RUN_DIR_ENV])
+    stage_dir = run_dir / stage_name
+    stage_dir.mkdir(parents=True, exist_ok=False)
+    architecture_json = stage_dir / "architecture.json"
+    output_json = stage_dir / "stage_result.json"
+    log_path = stage_dir / "worker.log"
+    architecture_json.write_text(
+        architecture.model_dump_json(indent=2),
+        encoding="utf-8",
+    )
+    cmd = [
+        sys.executable,
+        "-m",
+        "integration.megatron.model_support.workflow_stage_worker",
+        "--stage",
+        stage_name,
+        "--base-model",
+        base_model,
+        "--architecture-json",
+        str(architecture_json),
+        "--output-json",
+        str(output_json),
+    ]
+    if allow_unvalidated_arch:
+        cmd.append("--allow-unsupported-arch")
+    env = os.environ.copy()
+    if stage_environment is not None:
+        env.update(stage_environment)
+    if visible_gpu_ids is not None:
+        env["CUDA_VISIBLE_DEVICES"] = ",".join(visible_gpu_ids)
+    env["WANDB_MODE"] = "disabled"
+    env[WORKFLOW_STAGE_DIR_ENV] = str(stage_dir)
+    existing_pythonpath = env.get("PYTHONPATH")
+    env["PYTHONPATH"] = (
+        str(TESTS_DIR)
+        if not existing_pythonpath
+        else f"{TESTS_DIR}{os.pathsep}{existing_pythonpath}"
+    )
+    started = time.monotonic()
+    timeout_s = _WORKFLOW_STAGE_TIMEOUT_OVERRIDES_S.get(
+        (stage_name, base_model), _WORKFLOW_STAGE_TIMEOUT_S
+    )
+    with log_path.open("w", encoding="utf-8") as log_file:
+        process = subprocess.Popen(
+            cmd,
+            cwd=str(REPO_ROOT),
+            env=env,
+            stdout=log_file,
+            stderr=subprocess.STDOUT,
+            text=True,
+            start_new_session=True,
         )
-        cmd = [
-            sys.executable,
-            "-m",
-            "integration.megatron.model_support.workflow_stage_worker",
-            "--stage",
-            stage_name,
-            "--base-model",
-            base_model,
-            "--architecture-json",
-            str(architecture_json),
-            "--output-json",
-            str(output_json),
-        ]
-        if allow_unvalidated_arch:
-            cmd.append("--allow-unsupported-arch")
-        env = os.environ.copy()
-        existing_pythonpath = env.get("PYTHONPATH")
-        env["PYTHONPATH"] = (
-            str(TESTS_DIR)
-            if not existing_pythonpath
-            else f"{TESTS_DIR}{os.pathsep}{existing_pythonpath}"
+        try:
+            returncode = _wait_stage_process(process, timeout_s=timeout_s)
+        except subprocess.TimeoutExpired:
+            returncode = None
+    duration_s = time.monotonic() - started
+    common_metrics = {
+        "workflow_stage_artifact_dir": str(stage_dir),
+        "workflow_stage_duration_s": duration_s,
+    }
+    if returncode is None:
+        return ValidationStageResult(
+            name=stage_name,
+            passed=False,
+            metrics={
+                **common_metrics,
+                "error": f"stage exceeded {timeout_s:g}s; log={log_path}",
+            },
         )
-        with log_path.open("w", encoding="utf-8") as log_file:
-            completed = subprocess.run(
-                cmd,
-                cwd=str(REPO_ROOT),
-                env=env,
-                stdout=log_file,
-                stderr=subprocess.STDOUT,
-                text=True,
-                check=False,
-            )
-        if completed.returncode != 0:
-            tail = _subprocess_log_tail(log_path)
-            error = (
-                f"subprocess exited with code {completed.returncode}"
-                if not tail
-                else tail
-            )
-            return ValidationStageResult(
-                name=stage_name,
-                passed=False,
-                metrics={"error": error},
-            )
-        if not output_json.exists():
-            return ValidationStageResult(
-                name=stage_name,
-                passed=False,
-                metrics={"error": "stage worker did not write output_json"},
-            )
-        return ValidationStageResult.model_validate_json(
-            output_json.read_text(encoding="utf-8")
+    if returncode != 0:
+        tail = _subprocess_log_tail(log_path)
+        return ValidationStageResult(
+            name=stage_name,
+            passed=False,
+            metrics={
+                **common_metrics,
+                "error": tail or f"subprocess exited with code {returncode}",
+            },
+        )
+    if not output_json.exists():
+        return ValidationStageResult(
+            name=stage_name,
+            passed=False,
+            metrics={
+                **common_metrics,
+                "error": "stage worker did not write output_json",
+            },
         )
+    result = ValidationStageResult.model_validate_json(output_json.read_text())
+    result.metrics.update(common_metrics)
+    output_json.write_text(result.model_dump_json(indent=2), encoding="utf-8")
+    return result
+
+
+def _raise_signal_exit(signum: int, _frame: Any) -> None:
+    raise SystemExit(128 + signum)
+
+
+def _wait_stage_process(process: subprocess.Popen[Any], *, timeout_s: float) -> int:
+    owns_signals = threading.current_thread() is threading.main_thread()
+    previous_sigterm = (
+        signal.signal(signal.SIGTERM, _raise_signal_exit) if owns_signals else None
+    )
+    try:
+        return process.wait(timeout=timeout_s)
+    finally:
+        if previous_sigterm is not None:
+            signal.signal(signal.SIGTERM, previous_sigterm)
+        try:
+            os.killpg(process.pid, signal.SIGTERM)
+        except ProcessLookupError:
+            pass
+        else:
+            deadline = time.monotonic() + 10.0
+            while time.monotonic() < deadline:
+                try:
+                    os.killpg(process.pid, 0)
+                except ProcessLookupError:
+                    break
+                time.sleep(0.05)
+            else:
+                try:
+                    os.killpg(process.pid, signal.SIGKILL)
+                except ProcessLookupError:
+                    pass
 
 
 def run_hf_parity_stage(
@@ -379,20 +550,22 @@ def run_hf_parity_stage(
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
     handler = get_model_support_handler_for_spec(spec)
-    case_config = oracle_harness.OracleCaseConfig(
+    case_config = _oracle_case_config(
+        oracle_harness,
         base_model=base_model,
+        model_support_key=spec.key,
         is_moe=handler.is_moe,
-        precision="fp32",
+        precision=handler.correctness_precision(),
         num_layers=max(1, architecture.recommended_min_layers),
-        num_steps=1,
-        lora={"target_modules": list(spec.default_target_modules)},
+        target_modules=list(spec.default_target_modules),
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
     case_config = hf_parity.hf_parity_case_config(case_config)
-    report = hf_parity.run_hf_parity(case_config=case_config)
-    case_artifacts = oracle_harness.ensure_case_artifacts(case_config)
+    report = hf_parity.run_hf_parity(case_config=case_config, in_process=True)
     artifact_dir = str(
-        Path(case_artifacts.case_dir) / hf_parity.HF_PARITY_OUTPUT_DIRNAME
+        Path(oracle_harness.ARTIFACT_ROOT)
+        / report.case_id
+        / hf_parity.HF_PARITY_OUTPUT_DIRNAME
     )
     return ValidationStageResult(
         name="hf_parity",
@@ -426,20 +599,22 @@ def run_lora_coverage_stage(
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
     handler = get_model_support_handler_for_spec(spec)
-    case_config = oracle_harness.OracleCaseConfig(
+    case_config = _oracle_case_config(
+        oracle_harness,
         base_model=base_model,
+        model_support_key=spec.key,
         is_moe=handler.is_moe,
-        precision="fp32",
+        precision=handler.correctness_precision(),
         num_layers=max(1, architecture.recommended_min_layers),
-        num_steps=1,
-        lora={"target_modules": list(spec.default_target_modules)},
+        target_modules=list(spec.default_target_modules),
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
     report = lora_coverage.run_lora_coverage(case_config)
     return ValidationStageResult(
         name="lora_coverage",
         passed=not report.missing_wrapped_target_modules
-        and not report.missing_exported_target_modules,
+        and not report.missing_exported_target_modules
+        and not report.unexpected_trainable_parameter_names,
         metrics=report.model_dump(mode="json"),
     )
 
@@ -472,6 +647,18 @@ def run_correctness_sensitivity_stage(
     architecture: ArchitectureReport,
     allow_unvalidated_arch: bool = False,
 ) -> ValidationStageResult:
+    stage_dir = _workflow_stage_dir()
+    phase = os.environ.get(CORRECTNESS_PHASE_ENV, "all")
+    if phase not in {"all", "reference", "variants"}:
+        raise ValueError(f"unsupported correctness phase: {phase}")
+    artifact_root = os.environ.get(CORRECTNESS_ARTIFACT_ROOT_ENV)
+    correctness_log = (
+        Path(artifact_root).parent / "reference.log"
+        if phase == "reference" and artifact_root is not None
+        else stage_dir / "correctness.log"
+    )
+    sensitivity_log = stage_dir / "sensitivity.log"
+    live_training_log = stage_dir / "live_training.log"
     oracle_harness = _import_integration_module(
         "integration.megatron.model_support.oracle_harness"
     )
@@ -484,44 +671,81 @@ def run_correctness_sensitivity_stage(
     correctness_precision = handler.correctness_precision()
     correctness_use_fp32_lora_reference = handler.correctness_use_fp32_lora_reference()
     correctness_phase_pass_fns = handler.correctness_phase_pass_fns(oracle_harness)
-    case_config = oracle_harness.OracleCaseConfig(
-        base_model=base_model,
-        is_moe=handler.is_moe,
-        precision=correctness_precision,
-        num_layers=max(1, architecture.recommended_min_layers),
-        num_steps=1,
-        lora={"target_modules": list(spec.default_target_modules)},
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
     suite_topologies = list(
         oracle_harness.selected_suite_topologies(
             is_moe=handler.is_moe,
             cp_supported=cp_supported,
         )
     )
-    objectives = list(oracle_harness.selected_oracle_objectives())
+    objectives = list(oracle_harness.SUPPORTED_ORACLE_OBJECTIVES)
     skip_sensitivity = _truthy_env(SKIP_SENSITIVITY_ENV)
     available_gpu_count = oracle_harness.available_gpu_count()
     max_world_size = available_gpu_count
     oracle_world_size = oracle_harness.oracle_topology(
         is_moe=handler.is_moe
     ).world_size()
-    if available_gpu_count < oracle_world_size:
+    required_gpu_count = (
+        oracle_world_size
+        if phase == "reference"
+        else max(
+            oracle_world_size,
+            *(topology.world_size() for topology in suite_topologies),
+        )
+    )
+    if available_gpu_count < required_gpu_count:
         raise RuntimeError(
             "Need "
-            f"{oracle_world_size} GPUs for oracle topology, found {available_gpu_count}"
+            f"{required_gpu_count} GPUs for the complete correctness topology set, "
+            f"found {available_gpu_count}"
+        )
+    selected_suite_topologies = suite_topologies
+    excluded_suite_topologies: list[Any] = []
+    pipeline_layer_multiple = math.lcm(
+        *(topology.pp * topology.vpp for topology in selected_suite_topologies)
+    )
+    minimum_layers = max(1, architecture.recommended_min_layers)
+    num_layers = (
+        (minimum_layers + pipeline_layer_multiple - 1) // pipeline_layer_multiple
+    ) * pipeline_layer_multiple
+    case_config = _oracle_case_config(
+        oracle_harness,
+        base_model=base_model,
+        model_support_key=spec.key,
+        is_moe=handler.is_moe,
+        precision=correctness_precision,
+        num_layers=num_layers,
+        target_modules=list(spec.default_target_modules),
+        allow_unvalidated_arch=allow_unvalidated_arch,
+    )
+    case_artifacts = oracle_harness.ensure_case_artifacts(case_config)
+    if phase == "reference":
+        live_training_log.write_text("", encoding="utf-8")
+        with _temporary_env(**{oracle_harness.ORACLE_OBJECTIVE_ENV: "all"}):
+            with _temporary_env(
+                **{ORACLE_LIVE_TRAINING_LOG_ENV: str(live_training_log)}
+            ):
+                with _redirect_output(correctness_log):
+                    oracle_harness.prepare_suite_references(
+                        case_config=case_config,
+                        use_fp32_lora_reference=correctness_use_fp32_lora_reference,
+                    )
+        return ValidationStageResult(
+            name=CORRECTNESS_REFERENCE_STAGE,
+            passed=True,
+            metrics={
+                "correctness_reference_log_path": str(correctness_log),
+                "live_training_log_path": str(live_training_log),
+                "requested_num_layers": case_config.num_layers,
+                "precision": correctness_precision,
+                "use_fp32_lora_reference": correctness_use_fp32_lora_reference,
+                "is_moe": handler.is_moe,
+                "available_gpu_count": available_gpu_count,
+                "required_gpu_count": required_gpu_count,
+            },
+            artifact_dir=case_artifacts.case_dir,
         )
-    selected_suite_topologies = [
-        topology
-        for topology in suite_topologies
-        if topology.world_size() <= max_world_size
-    ]
-    excluded_suite_topologies = [
-        topology
-        for topology in suite_topologies
-        if topology.world_size() > max_world_size
-    ]
     mutations: list[str] = []
+    inapplicable_sensitivity_mutations: list[str] = []
     default_excluded_sensitivity_mutations: list[str] = []
     excluded_sensitivity_mutations: list[str] = []
     if not skip_sensitivity:
@@ -534,6 +758,11 @@ def run_correctness_sensitivity_stage(
             ):
                 if mutation not in mutations:
                     mutations.append(mutation)
+        inapplicable = _HANDLER_INAPPLICABLE_SENSITIVITY_MUTATIONS.get(handler.key, ())
+        inapplicable_sensitivity_mutations = [
+            mutation for mutation in mutations if mutation in inapplicable
+        ]
+        mutations = [mutation for mutation in mutations if mutation not in inapplicable]
         excluded_sensitivity_mutations = [
             mutation
             for mutation in mutations
@@ -551,7 +780,9 @@ def run_correctness_sensitivity_stage(
                 > 1
             )
         ]
-        if not _truthy_env(INCLUDE_FLASH_SENSITIVITY_ENV):
+        if FLASH_SENSITIVITY_MUTATION not in inapplicable and not _truthy_env(
+            INCLUDE_FLASH_SENSITIVITY_ENV
+        ):
             default_excluded_sensitivity_mutations.append(FLASH_SENSITIVITY_MUTATION)
         mutations = [
             mutation
@@ -562,51 +793,58 @@ def run_correctness_sensitivity_stage(
                 *default_excluded_sensitivity_mutations,
             }
         ]
-    LIVE_TRAINING_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
-    LIVE_TRAINING_LOG_PATH.write_text("", encoding="utf-8")
-    with _temporary_env(**{ORACLE_LIVE_TRAINING_LOG_ENV: str(LIVE_TRAINING_LOG_PATH)}):
-        with _redirect_output(CORRECTNESS_LOG_PATH):
-            suite_reports = oracle_harness.run_suite(
-                case_config=case_config,
-                max_world_size=max_world_size,
-                cp_supported=cp_supported,
-                phase_pass_fns=correctness_phase_pass_fns,
-                use_fp32_lora_reference=correctness_use_fp32_lora_reference,
-                prune_reference_artifacts=skip_sensitivity or not mutations,
-                prune_case_artifacts=skip_sensitivity or not mutations,
-            )
-        sensitivity_reports = []
-        if skip_sensitivity:
-            SENSITIVITY_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
-            SENSITIVITY_LOG_PATH.write_text(
-                (
-                    "Sensitivity suite skipped. "
-                    f"Set {SKIP_SENSITIVITY_ENV}=0 to re-enable workflow sensitivity.\n"
-                ),
-                encoding="utf-8",
-            )
-        elif not mutations:
-            SENSITIVITY_LOG_PATH.parent.mkdir(parents=True, exist_ok=True)
-            SENSITIVITY_LOG_PATH.write_text(
-                (
-                    "Sensitivity suite skipped. "
-                    f"No sensitivity mutations fit max_world_size={max_world_size}.\n"
-                ),
-                encoding="utf-8",
-            )
-        else:
-            with _redirect_output(SENSITIVITY_LOG_PATH):
-                sensitivity_reports = oracle_harness.run_sensitivity_suite(
+    live_training_log.write_text("", encoding="utf-8")
+    with _temporary_env(**{oracle_harness.ORACLE_OBJECTIVE_ENV: "all"}):
+        with _temporary_env(**{ORACLE_LIVE_TRAINING_LOG_ENV: str(live_training_log)}):
+            with _redirect_output(correctness_log):
+                suite_reports = oracle_harness.run_suite(
                     case_config=case_config,
-                    mutations=mutations,
                     max_world_size=max_world_size,
+                    cp_supported=cp_supported,
+                    phase_pass_fns=correctness_phase_pass_fns,
+                    use_fp32_lora_reference=correctness_use_fp32_lora_reference,
+                    require_existing_references=phase == "variants",
+                    prune_reference_artifacts=skip_sensitivity or not mutations,
+                    prune_case_artifacts=skip_sensitivity or not mutations,
                 )
-    case_artifacts = oracle_harness.ensure_case_artifacts(case_config)
+            sensitivity_reports = []
+            if skip_sensitivity:
+                sensitivity_log.write_text(
+                    (
+                        "Sensitivity suite skipped. "
+                        f"Set {SKIP_SENSITIVITY_ENV}=0 to re-enable workflow sensitivity.\n"
+                    ),
+                    encoding="utf-8",
+                )
+            elif not mutations:
+                sensitivity_log.write_text(
+                    (
+                        "Sensitivity suite skipped. "
+                        f"No sensitivity mutations fit max_world_size={max_world_size}.\n"
+                    ),
+                    encoding="utf-8",
+                )
+            else:
+                with _redirect_output(sensitivity_log):
+                    sensitivity_reports = oracle_harness.run_sensitivity_suite(
+                        case_config=case_config,
+                        mutations=mutations,
+                        max_world_size=max_world_size,
+                    )
     return ValidationStageResult(
         name="correctness_sensitivity",
         passed=True,
         metrics={
+            "correctness_log_path": str(correctness_log),
+            "correctness_reference_log_path": (
+                str(Path(artifact_root).parent / "reference.log")
+                if phase == "variants" and artifact_root is not None
+                else None
+            ),
+            "sensitivity_log_path": str(sensitivity_log),
+            "live_training_log_path": str(live_training_log),
             "requested_num_layers": case_config.num_layers,
+            "pipeline_layer_multiple": pipeline_layer_multiple,
             "precision": correctness_precision,
             "use_fp32_lora_reference": correctness_use_fp32_lora_reference,
             "is_moe": handler.is_moe,
@@ -614,13 +852,14 @@ def run_correctness_sensitivity_stage(
             "allow_unvalidated_arch": allow_unvalidated_arch,
             "objectives": objectives,
             "sensitivity_mutations": mutations,
+            "inapplicable_sensitivity_mutations": (inapplicable_sensitivity_mutations),
             "excluded_sensitivity_mutations": excluded_sensitivity_mutations,
             "default_excluded_sensitivity_mutations": (
                 default_excluded_sensitivity_mutations
             ),
             "available_gpu_count": available_gpu_count,
             "max_world_size": max_world_size,
-            "required_gpu_count": oracle_world_size,
+            "required_gpu_count": required_gpu_count,
             "topology_artifacts_retained": oracle_harness.keep_topology_artifacts(),
             "correctness_variant_count": len(suite_reports),
             "correctness_excluded_topology_count": len(excluded_suite_topologies),
@@ -659,69 +898,6 @@ def run_correctness_sensitivity_stage(
     )
 
 
-def run_merged_vllm_serving_stage(
-    *,
-    base_model: str,
-    architecture: ArchitectureReport,
-    allow_unvalidated_arch: bool = False,
-) -> ValidationStageResult:
-    merged_vllm_serving = _import_integration_module(
-        "integration.megatron.lora.merged_vllm_serving"
-    )
-    oracle_harness = _import_integration_module(
-        "integration.megatron.model_support.oracle_harness"
-    )
-    spec = get_model_support_spec(
-        base_model,
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
-    handler = get_model_support_handler_for_spec(spec)
-    case_config = oracle_harness.OracleCaseConfig(
-        base_model=base_model,
-        is_moe=handler.is_moe,
-        precision="fp32",
-        num_layers=max(1, architecture.recommended_min_layers),
-        num_steps=1,
-        lora={"target_modules": list(spec.default_target_modules)},
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
-    report = merged_vllm_serving.run_merged_vllm_serving(case_config)
-    metrics = report.model_dump(mode="json")
-    warning_lines = _read_vllm_reload_warnings(report.output_dir)
-    metrics["vllm_reload_warning_count"] = len(warning_lines)
-    metrics["vllm_reload_warnings"] = warning_lines
-    metrics["readable_summary"] = _merged_vllm_serving_summary(metrics)
-    return ValidationStageResult(
-        name="merged_vllm_serving",
-        passed=bool(report.model_ids),
-        metrics=metrics,
-        artifact_dir=report.output_dir,
-    )
-
-
-def _read_vllm_reload_warnings(output_dir: str) -> list[str]:
-    log_path = Path(output_dir) / "logs" / "vllm-runtime.log"
-    if not log_path.exists():
-        return []
-    return [
-        line.strip()
-        for line in log_path.read_text(encoding="utf-8", errors="replace").splitlines()
-        if "Failed to load weights" in line
-    ]
-
-
-def _merged_vllm_serving_summary(metrics: dict[str, Any]) -> list[str]:
-    lines = [
-        f"served_model_name={metrics.get('served_model_name', '')}",
-        f"model_ids={metrics.get('model_ids', [])}",
-        f"completion_text={metrics.get('completion_text', '')!r}",
-        f"vllm_reload_warning_count={metrics.get('vllm_reload_warning_count', 0)}",
-    ]
-    for warning in metrics.get("vllm_reload_warnings", []):
-        lines.append(f"vllm_reload_warning={warning}")
-    return lines
-
-
 def run_chat_template_rollout_stage(
     *,
     base_model: str,
@@ -733,6 +909,7 @@ def run_chat_template_rollout_stage(
     chat_template_rollout = _import_integration_module(
         "integration.megatron.model_support.chat_template_rollout"
     )
+    chat_template_rollout._artifact_dir = lambda _base_model: _stage_artifact_dir()
     report = chat_template_rollout.run_chat_template_rollout(base_model=base_model)
     return ValidationStageResult(
         name="chat_template_rollout",
@@ -742,29 +919,6 @@ def run_chat_template_rollout_stage(
     )
 
 
-def run_yes_no_trainability_stage(
-    *,
-    base_model: str,
-    architecture: ArchitectureReport,
-    allow_unvalidated_arch: bool = False,
-) -> ValidationStageResult:
-    del architecture
-    yes_no_trainability = _import_integration_module(
-        "integration.megatron.trainability.yes_no_trainability"
-    )
-    report = yes_no_trainability.run_yes_no_trainability(
-        base_model=base_model,
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
-    passed = yes_no_trainability.yes_no_trainability_passed(report)
-    return ValidationStageResult(
-        name=YES_NO_TRAINABILITY_STAGE,
-        passed=passed,
-        metrics=report.model_dump(mode="json"),
-        artifact_dir=report.output_dir,
-    )
-
-
 def run_length_trainability_stage(
     *,
     base_model: str,
@@ -775,10 +929,18 @@ def run_length_trainability_stage(
     length_trainability = _import_integration_module(
         "integration.megatron.trainability.test_live_length_trainability"
     )
-    report = length_trainability.run_length_trainability(
-        base_model=base_model,
-        allow_unvalidated_arch=allow_unvalidated_arch,
+    length_trainability.LATEST_SUMMARY_LOG_PATH = (
+        _workflow_stage_dir() / "length_trainability.log"
     )
+    artifact_dir = _stage_artifact_dir()
+    length_trainability._artifact_dir = lambda _base_model: artifact_dir
+    try:
+        report = length_trainability.run_length_trainability(
+            base_model=base_model,
+            allow_unvalidated_arch=allow_unvalidated_arch,
+        )
+    finally:
+        _cleanup_stage_workspace(artifact_dir / "megatron_dedicated_workspace")
     return ValidationStageResult(
         name="length_trainability",
         passed=length_trainability.length_trainability_passed(report),
@@ -787,50 +949,6 @@ def run_length_trainability_stage(
     )
 
 
-def run_native_vllm_lora_stage(
-    *,
-    base_model: str,
-    architecture: ArchitectureReport,
-    allow_unvalidated_arch: bool = False,
-) -> ValidationStageResult:
-    native_vllm_lora = _import_integration_module(
-        "integration.megatron.lora.native_vllm_lora"
-    )
-    oracle_harness = _import_integration_module(
-        "integration.megatron.model_support.oracle_harness"
-    )
-    spec = get_model_support_spec(
-        base_model,
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
-    handler = get_model_support_handler_for_spec(spec)
-    case_config = oracle_harness.OracleCaseConfig(
-        base_model=base_model,
-        is_moe=handler.is_moe,
-        precision="fp32",
-        num_layers=max(1, architecture.recommended_min_layers),
-        num_steps=1,
-        lora={"target_modules": list(spec.default_target_modules)},
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
-    report = native_vllm_lora.run_native_vllm_lora(case_config)
-    passed = (
-        report.rollout_weights_mode == "lora"
-        and report.step0_served
-        and report.step1_served
-        and report.step0_name in report.model_ids_before
-        and report.step1_name not in report.model_ids_before
-        and report.step0_name in report.model_ids_after
-        and report.step1_name in report.model_ids_after
-    )
-    return ValidationStageResult(
-        name=NATIVE_VLLM_LORA_STAGE,
-        passed=passed,
-        metrics=report.model_dump(mode="json"),
-        artifact_dir=report.output_dir,
-    )
-
-
 def run_packing_invariance_stage(
     *,
     base_model: str,
@@ -840,10 +958,12 @@ def run_packing_invariance_stage(
     packing_invariance = _import_integration_module(
         "integration.megatron.model_support.packing_invariance"
     )
+    packing_invariance._artifact_dir = lambda _base_model: _stage_artifact_dir()
     report = packing_invariance.run_packing_invariance(
         base_model=base_model,
         num_layers=max(1, architecture.recommended_min_layers),
         allow_unvalidated_arch=allow_unvalidated_arch,
+        in_process=True,
     )
     metrics = report.model_dump(mode="json")
     passed = bool(metrics["scenarios"]) and all(
@@ -858,11 +978,38 @@ def run_packing_invariance_stage(
     )
 
 
+def run_e2e_throughput_stage(
+    *,
+    base_model: str,
+    architecture: ArchitectureReport,
+    allow_unvalidated_arch: bool = False,
+) -> ValidationStageResult:
+    from .workflow_throughput import run_e2e_throughput
+
+    return run_e2e_throughput(
+        base_model=base_model,
+        architecture=architecture,
+        allow_unvalidated_arch=allow_unvalidated_arch,
+    )
+
+
+def validation_stage_runners():
+    return {
+        "hf_parity": run_hf_parity_stage,
+        "lora_coverage": run_lora_coverage_stage,
+        "train_inf_mismatch": run_train_inf_mismatch_stage,
+        "correctness_sensitivity": run_correctness_sensitivity_stage,
+        CORRECTNESS_REFERENCE_STAGE: run_correctness_sensitivity_stage,
+        "chat_template_rollout": run_chat_template_rollout_stage,
+        "packing_invariance": run_packing_invariance_stage,
+        "length_trainability": run_length_trainability_stage,
+        "e2e_throughput": run_e2e_throughput_stage,
+    }
+
+
 def build_validation_report(
     *,
     base_model: str,
-    include_native_vllm_lora: bool = False,
-    include_yes_no_trainability: bool = False,
     include_sensitivity: bool | None = None,
     output_json: str | Path | None = None,
     skip_stages: set[str] | None = None,
@@ -875,52 +1022,59 @@ def build_validation_report(
     only_stage_run_set = _only_stage_run_set(only_stage)
     report = initialize_validation_report(
         base_model=base_model,
-        include_native_vllm_lora=(
-            include_native_vllm_lora or only_stage == NATIVE_VLLM_LORA_STAGE
-        ),
-        include_yes_no_trainability=(
-            include_yes_no_trainability or only_stage == YES_NO_TRAINABILITY_STAGE
-        ),
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
-    stage_runners = {
-        "hf_parity": run_hf_parity_stage,
-        "lora_coverage": run_lora_coverage_stage,
-        "train_inf_mismatch": run_train_inf_mismatch_stage,
-        "merged_vllm_serving": run_merged_vllm_serving_stage,
-        "correctness_sensitivity": run_correctness_sensitivity_stage,
-        "chat_template_rollout": run_chat_template_rollout_stage,
-        "packing_invariance": run_packing_invariance_stage,
-        "length_trainability": run_length_trainability_stage,
-        YES_NO_TRAINABILITY_STAGE: run_yes_no_trainability_stage,
-        NATIVE_VLLM_LORA_STAGE: run_native_vllm_lora_stage,
+    skip_stages = skip_stages or set()
+    selected_subprocess_stages = {
+        stage.name
+        for stage in report.stages
+        if stage.name in SUBPROCESS_VALIDATION_STAGES
+        and stage.name not in skip_stages
+        and (only_stage_run_set is None or stage.name in only_stage_run_set)
     }
-    env = {}
+    run_dir = _new_workflow_run_dir(
+        output_json=output_json,
+        model_key=report.model_key,
+    )
+    stage_runners = validation_stage_runners()
+    env = {WORKFLOW_RUN_DIR_ENV: str(run_dir)}
     if include_sensitivity is not None:
         env[SKIP_SENSITIVITY_ENV] = "0" if include_sensitivity else "1"
-    if include_sensitivity:
-        env[KEEP_TOPOLOGY_ARTIFACTS_ENV] = "1"
-    skip_stages = skip_stages or set()
     architecture: ArchitectureReport | None = None
-    context = _temporary_env(**env) if env else nullcontext()
-    with context:
+    fixture: WorkflowFixture | None = None
+    fixture_error: Exception | None = None
+    fixture_attempted = False
+    with _temporary_env(**env):
         for stage in report.stages:
+            stage_started = time.monotonic()
             if only_stage_run_set is not None and stage.name not in only_stage_run_set:
-                stage.passed = True
+                stage.passed = False
+                stage.skipped = True
                 stage.metrics = {
                     "skipped": True,
                     "reason": f"--only-stage={only_stage}",
                 }
+                _record_stage_duration(stage, started=stage_started)
                 _write_validation_report(report, output_json)
                 continue
             if stage.name in skip_stages:
-                stage.passed = True
+                stage.passed = False
+                stage.skipped = True
                 stage.metrics = {"skipped": True, "reason": "--skip-stage"}
+                _record_stage_duration(stage, started=stage_started)
                 _write_validation_report(report, output_json)
+                if stage.name == "architecture_discovery":
+                    _mark_remaining_stages_skipped(
+                        report,
+                        after_stage_name=stage.name,
+                        reason="architecture_discovery was skipped",
+                    )
+                    break
                 continue
             if stage.name == "dependency_resolution":
                 stage.passed = True
                 stage.metrics = dict(report.dependency_versions)
+                _record_stage_duration(stage, started=stage_started)
                 _write_validation_report(report, output_json)
                 continue
             if stage.name == "architecture_discovery":
@@ -941,9 +1095,21 @@ def build_validation_report(
                 except Exception as exc:
                     stage.passed = False
                     stage.metrics = _stage_error_metrics(exc)
+                _record_stage_duration(stage, started=stage_started)
                 _write_validation_report(report, output_json)
+                if architecture is None:
+                    _mark_remaining_stages_skipped(
+                        report,
+                        after_stage_name=stage.name,
+                        reason="architecture_discovery failed",
+                    )
+                    break
                 if stop_on_failure and not stage.passed:
                     _mark_remaining_stages_skipped(report, after_stage_name=stage.name)
+                    _finalize_validation_report(
+                        report,
+                        partial=only_stage is not None or bool(skip_stages),
+                    )
                     _write_validation_report(report, output_json)
                     break
                 continue
@@ -953,12 +1119,38 @@ def build_validation_report(
                 )
             stage_runner = stage_runners[stage.name]
             if stage.name in SUBPROCESS_VALIDATION_STAGES:
-                stage_result = _run_stage_in_subprocess(
-                    stage_name=stage.name,
-                    base_model=base_model,
-                    architecture=architecture,
-                    allow_unvalidated_arch=allow_unvalidated_arch,
-                )
+                fixture_provisioning_s: float | None = None
+                if not fixture_attempted:
+                    fixture_started = time.monotonic()
+                    fixture_attempted = True
+                    try:
+                        fixture = ensure_workflow_fixture(
+                            base_model,
+                            allow_unvalidated_arch=allow_unvalidated_arch,
+                            required_stages=selected_subprocess_stages,
+                        )
+                    except Exception as exc:
+                        fixture_error = exc
+                    fixture_provisioning_s = time.monotonic() - fixture_started
+                if fixture_error is not None:
+                    stage_result = ValidationStageResult(
+                        name=stage.name,
+                        passed=False,
+                        metrics=_stage_error_metrics(fixture_error),
+                    )
+                else:
+                    assert fixture is not None
+                    with _temporary_env(**fixture.environment(stage.name)):
+                        stage_result = _run_stage_in_subprocess(
+                            stage_name=stage.name,
+                            base_model=base_model,
+                            architecture=architecture,
+                            allow_unvalidated_arch=allow_unvalidated_arch,
+                        )
+                if fixture_provisioning_s is not None:
+                    stage_result.metrics["fixture_provisioning_s"] = (
+                        fixture_provisioning_s
+                    )
             else:
                 try:
                     stage_result = stage_runner(
@@ -975,17 +1167,34 @@ def build_validation_report(
             stage.passed = stage_result.passed
             stage.metrics = dict(stage_result.metrics)
             stage.artifact_dir = stage_result.artifact_dir
+            if stage.name in _RUNTIME_CLEANUP_STAGES:
+                try:
+                    stage.metrics.update(_prune_runtime_artifacts(run_dir / stage.name))
+                except Exception as exc:
+                    stage.passed = False
+                    stage.metrics["runtime_artifact_cleanup_error"] = (
+                        f"{type(exc).__name__}: {exc}"
+                    )
+            _record_stage_duration(stage, started=stage_started)
             _write_validation_report(report, output_json)
             if stop_on_failure and not stage.passed:
                 _mark_remaining_stages_skipped(report, after_stage_name=stage.name)
+                _finalize_validation_report(
+                    report,
+                    partial=only_stage is not None or bool(skip_stages),
+                )
                 _write_validation_report(report, output_json)
                 break
+    _finalize_validation_report(
+        report,
+        partial=only_stage is not None or bool(skip_stages),
+    )
+    _write_validation_report(report, output_json)
     return report
 
 
 def build_all_architectures_validation_report(
     *,
-    include_yes_no_trainability: bool = False,
     include_sensitivity: bool | None = None,
     output_json: str | Path | None = None,
     skip_stages: set[str] | None = None,
@@ -994,15 +1203,15 @@ def build_all_architectures_validation_report(
     allow_unvalidated_arch: bool = False,
 ) -> AllArchitecturesValidationReport:
     aggregate = AllArchitecturesValidationReport()
+    representatives = validated_architecture_representative_models()
     _write_all_architectures_report(aggregate, output_json)
-    for base_model in validated_architecture_representative_models():
+    for base_model in representatives:
         model_key = get_model_support_spec(
             base_model,
             allow_unvalidated_arch=allow_unvalidated_arch,
         ).key
         report = build_validation_report(
             base_model=base_model,
-            include_yes_no_trainability=include_yes_no_trainability,
             include_sensitivity=include_sensitivity,
             output_json=(
                 _per_architecture_output_json(output_json, model_key)
@@ -1016,11 +1225,13 @@ def build_all_architectures_validation_report(
         )
         aggregate.reports.append(report)
         aggregate.passed = all(
-            all(stage.passed for stage in model_report.stages)
-            for model_report in aggregate.reports
+            model_report.passed for model_report in aggregate.reports
+        )
+        aggregate.complete = len(aggregate.reports) == len(representatives) and all(
+            model_report.complete for model_report in aggregate.reports
         )
         _write_all_architectures_report(aggregate, output_json)
-        if stop_on_failure and not all(stage.passed for stage in report.stages):
+        if stop_on_failure and not report.passed:
             break
     return aggregate
 
@@ -1035,7 +1246,6 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace:
     parser.add_argument("--output-json", required=True)
     parser.add_argument("--allow-unsupported-arch", action="store_true")
     parser.add_argument("--include-sensitivity", action="store_true")
-    parser.add_argument("--include-yes-no-trainability", action="store_true")
     parser.add_argument("--skip-stage", action="append", default=[])
     parser.add_argument("--only-stage", choices=ALL_VALIDATION_STAGES)
     parser.add_argument("--stop-on-failure", action="store_true")
@@ -1046,7 +1256,7 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace:
 
 
 def _print_stage_result(stage: ValidationStageResult, *, indent: str = "") -> None:
-    status = "PASS" if stage.passed else "FAIL"
+    status = "SKIP" if stage.skipped else "PASS" if stage.passed else "FAIL"
     print(f"{indent}{stage.name}: {status}", flush=True)
     child_indent = f"{indent}  "
     if stage.artifact_dir:
@@ -1055,15 +1265,61 @@ def _print_stage_result(stage: ValidationStageResult, *, indent: str = "") -> No
     if isinstance(summary, list):
         for line in summary:
             print(f"{child_indent}{line}", flush=True)
-    if not stage.passed:
+    if not stage.passed and not stage.skipped:
         print(f"{child_indent}metrics={stage.metrics}", flush=True)
 
 
 def main(argv: list[str] | None = None) -> int:
     args = _parse_args(argv)
+    if args.only_stage is None and not args.stop_on_failure:
+        from .workflow_scheduler import build_scheduled_validation_reports
+
+        base_models = (
+            validated_architecture_representative_models()
+            if args.all_architectures
+            else [args.base_model]
+        )
+        output_json_by_model: dict[str, Path | None] = {
+            base_model: (
+                _per_architecture_output_json(
+                    args.output_json,
+                    get_model_support_spec(
+                        base_model,
+                        allow_unvalidated_arch=args.allow_unsupported_arch,
+                    ).key,
+                )
+                if args.all_architectures
+                else Path(args.output_json)
+            )
+            for base_model in base_models
+        }
+        reports = build_scheduled_validation_reports(
+            base_models=base_models,
+            include_sensitivity=args.include_sensitivity,
+            output_json_by_model=output_json_by_model,
+            skip_stages=set(args.skip_stage),
+            allow_unvalidated_arch=args.allow_unsupported_arch,
+        )
+        if args.all_architectures:
+            all_report = AllArchitecturesValidationReport(
+                reports=reports,
+                passed=all(report.passed for report in reports),
+                complete=all(report.complete for report in reports),
+            )
+            _write_all_architectures_report(all_report, args.output_json)
+            for report in reports:
+                print(f"base_model={report.base_model}", flush=True)
+                for stage in report.stages:
+                    _print_stage_result(stage, indent="  ")
+            print(f"report_json={args.output_json}", flush=True)
+            return 0 if all_report.passed else 1
+        report = reports[0]
+        for stage in report.stages:
+            _print_stage_result(stage)
+        print(f"report_json={args.output_json}", flush=True)
+        return 0 if report.passed else 1
     if args.all_architectures:
         all_report = build_all_architectures_validation_report(
-            include_yes_no_trainability=args.include_yes_no_trainability,
             include_sensitivity=args.include_sensitivity,
             output_json=args.output_json,
             skip_stages=set(args.skip_stage),
@@ -1079,7 +1335,6 @@ def main(argv: list[str] | None = None) -> int:
         return 0 if all_report.passed else 1
     report = build_validation_report(
         base_model=args.base_model,
-        include_yes_no_trainability=args.include_yes_no_trainability,
         include_sensitivity=args.include_sensitivity,
         output_json=args.output_json,
         skip_stages=set(args.skip_stage),
@@ -1090,7 +1345,7 @@ def main(argv: list[str] | None = None) -> int:
     for stage in report.stages:
         _print_stage_result(stage)
     print(f"report_json={args.output_json}", flush=True)
-    return 0 if all(stage.passed for stage in report.stages) else 1
+    return 0 if report.passed else 1
 
 
 def assess_minimal_layer_coverage(
diff --git a/tests/integration/megatron/model_support/workflow_fixtures.py b/tests/integration/megatron/model_support/workflow_fixtures.py
new file mode 100644
index 000000000..4045cd9c9
--- /dev/null
+++ b/tests/integration/megatron/model_support/workflow_fixtures.py
@@ -0,0 +1,1316 @@
+from __future__ import annotations
+
+from collections.abc import Mapping
+import fcntl
+import gc
+import hashlib
+import json
+import os
+from pathlib import Path
+import shutil
+import tempfile
+from typing import Any, cast
+
+from pydantic import BaseModel, ConfigDict
+
+FIXTURE_PATH_ENV = "ART_MODEL_SUPPORT_FIXTURE_PATH"
+FIXTURE_CACHE_ENV = "ART_MODEL_SUPPORT_FIXTURE_CACHE"
+FIXTURE_ROOT_ENV = "ART_MODEL_SUPPORT_FIXTURE_ROOT"
+FIXTURE_VERSION = 18
+_CANONICAL_CACHE_VERSION = 16
+_ROOT = Path("/tmp/art-models/main-merge-oracle")
+_CACHE_ROOT = Path("/tmp/art-model-support-workflow/hf-cache")
+_TOKENIZER_FIXTURE_ROOT = Path("/tmp/art-model-support-workflow/tokenizer-compatible")
+_TOKENIZER_CACHE_ROOT = Path("/tmp/art-model-support-workflow/tokenizer-hf-cache")
+_CANONICAL_CACHE_ROOT = Path("/tmp/art-model-support-workflow/canonical-hf-cache")
+_FUNCTIONAL_FIXTURE_ROOT = Path("/tmp/art-model-support-workflow/functional")
+_FUNCTIONAL_CACHE_ROOT = Path("/tmp/art-model-support-workflow/functional-hf-cache")
+_GEMMA_CANONICAL_WEIGHT_STAGES = frozenset({"hf_parity", "packing_invariance"})
+_PRETRAINED_WEIGHT_STAGES = frozenset({"length_trainability"})
+_FUNCTIONAL_STAGES = frozenset(
+    {
+        "train_inf_mismatch",
+    }
+)
+_RESIDENT_FUNCTIONAL_ENV = {
+    "gemma4_dense": {"ART_MODEL_SUPPORT_LENGTH_MAX_MODEL_LEN": "2560"},
+    "gemma4_moe": {"ART_MODEL_SUPPORT_LENGTH_MAX_MODEL_LEN": "2560"},
+}
+_REDUCED_TRAINABILITY_ENV: dict[str, dict[str, dict[str, str]]] = {
+    "glm52": {
+        "length_trainability": {
+            "ART_MODEL_SUPPORT_LENGTH_ALLOWED_TOKEN_IDS": "154820,38069",
+            "ART_MODEL_SUPPORT_LENGTH_MIN_TOKENS": "2",
+            "ART_MODEL_SUPPORT_LENGTH_FREQUENCY_PENALTY": "0.5",
+        }
+    },
+}
+_TOKENIZER_FIXTURE_VERSION = 3
+_FUNCTIONAL_FIXTURE_VERSION = 1
+_REVISIONS = {
+    "meta-llama/Llama-3.2-1B-Instruct": "9213176726f574b556790deb65791e0c5aa438b6",
+    "Qwen/Qwen3-32B": "9216db5781bf21249d130ec9da846c4624c16137",
+    "Qwen/Qwen3-30B-A3B": "ad44e777bcd18fa416d9da3bd8f70d33ebb85d39",
+    "Qwen/Qwen3.5-27B": "fc05daec18b0a78c049392ed2e771dde82bdf654",
+    "Qwen/Qwen3.5-35B-A3B": "59d61f3ce65a6d9863b86d2e96597125219dc754",
+    "google/gemma-4-31B-it": "842da3794eaa0b77d5f08bae87a17459d91ff475",
+    "google/gemma-4-26B-A4B-it": "4d7ae4984b7db7de8f8457170b3f1a419ee76d52",
+    "deepseek-ai/DeepSeek-V4-Flash": "60d8d70770c6776ff598c94bb586a859a38244f1",
+    "zai-org/GLM-5.2": "b4734de4facf877f85769a911abafc5283eab3d9",
+    "openai/gpt-oss-20b": "6cee5e81ee83917806bbde320786a8fb61efebee",
+}
+_MULTIMODAL = {"qwen3_5_dense", "qwen3_5_moe", "gemma4_dense", "gemma4_moe"}
+
+
+class WorkflowFixture(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    canonical_model: str
+    model_key: str
+    source_revision: str
+    path: str
+    hf_home: str
+    manifest: dict[str, object]
+    tokenizer_compatible_path: str | None = None
+    tokenizer_compatible_hf_home: str | None = None
+    tokenizer_compatible_manifest: dict[str, object] | None = None
+    functional_path: str | None = None
+    functional_hf_home: str | None = None
+    functional_manifest: dict[str, object] | None = None
+    canonical_path: str | None = None
+    canonical_hf_home: str | None = None
+
+    def environment(self, stage_name: str | None = None) -> dict[str, str]:
+        reduced_trainability = _REDUCED_TRAINABILITY_ENV.get(self.model_key, {}).get(
+            stage_name
+        )
+        use_functional = stage_name in _FUNCTIONAL_STAGES
+        use_canonical = (
+            stage_name in _PRETRAINED_WEIGHT_STAGES and reduced_trainability is None
+        ) or (
+            self.model_key.startswith("gemma4_")
+            and stage_name in _GEMMA_CANONICAL_WEIGHT_STAGES
+        )
+        use_tokenizer_compatible = (
+            self.model_key.startswith("gemma4_") and reduced_trainability is not None
+        )
+        path = (
+            self.functional_path
+            if use_functional
+            else self.canonical_path
+            if use_canonical
+            else self.tokenizer_compatible_path
+            if use_tokenizer_compatible
+            else self.path
+        )
+        hf_home = (
+            self.functional_hf_home
+            if use_functional
+            else self.canonical_hf_home
+            if use_canonical
+            else self.tokenizer_compatible_hf_home
+            if use_tokenizer_compatible
+            else self.hf_home
+        )
+        if path is None or hf_home is None:
+            contract = (
+                "pretrained production-width functional weights"
+                if use_functional
+                else "canonical weights"
+                if use_canonical
+                else "canonical vocabulary"
+            )
+            raise RuntimeError(f"{self.model_key} {stage_name} requires {contract}")
+        hub = str(Path(hf_home) / "hub")
+        environment = {
+            FIXTURE_PATH_ENV: path,
+            FIXTURE_CACHE_ENV: hf_home,
+            "ART_ORACLE_BASE_MODEL": path,
+            "HF_HOME": hf_home,
+            "HF_HUB_CACHE": hub,
+            "HUGGINGFACE_HUB_CACHE": hub,
+            "HF_HUB_OFFLINE": "1",
+            "TRANSFORMERS_OFFLINE": "1",
+        }
+        if use_functional:
+            assert self.functional_manifest is not None
+            num_layers = self.functional_manifest.get("num_layers")
+            if type(num_layers) is not int:
+                raise RuntimeError(f"{self.model_key} functional depth is invalid")
+            environment["ART_MODEL_SUPPORT_FUNCTIONAL_NUM_LAYERS"] = str(num_layers)
+        if reduced_trainability is not None:
+            environment.update(reduced_trainability)
+        return environment
+
+    def resident_functional_environment(self) -> dict[str, str]:
+        environment = self.environment("length_trainability")
+        environment.update(_RESIDENT_FUNCTIONAL_ENV.get(self.model_key, {}))
+        if self.model_key == "glm52":
+            environment.update(self.environment("train_inf_mismatch"))
+        return environment
+
+
+def _set(config: Any, **values: Any) -> Any:
+    for name, value in values.items():
+        setattr(config, name, value)
+    return config
+
+
+def _text(config: Any) -> Any:
+    return getattr(config, "text_config", config)
+
+
+def _common(
+    config: Any,
+    *,
+    layers: int,
+    hidden: int,
+    vocab_size: int,
+    preserve_token_ids: bool,
+) -> Any:
+    text = _text(config)
+    for name in ("layer_types", "mlp_layer_types", "indexer_types"):
+        if (values := getattr(text, name, None)) is not None:
+            setattr(text, name, list(values[:layers]))
+    values = {
+        "hidden_size": hidden,
+        "num_hidden_layers": layers,
+        "vocab_size": vocab_size,
+    }
+    if not preserve_token_ids:
+        values.update(pad_token_id=0, bos_token_id=2, eos_token_id=1)
+    return _set(
+        text,
+        **values,
+    )
+
+
+# fmt: off
+_DENSE_TEXT = {
+    "intermediate_size": 512, "num_attention_heads": 8,
+    "num_key_value_heads": 2, "head_dim": 32,
+    "tie_word_embeddings": False,
+}
+_PLAIN_TEXT: dict[str, tuple[int, int, dict[str, Any]]] = {
+    "llama3_dense": (4, 256, _DENSE_TEXT),
+    "qwen3_dense": (4, 256, _DENSE_TEXT),
+    "qwen3_moe": (
+        4,
+        256,
+        {
+            **_DENSE_TEXT, "moe_intermediate_size": 256,
+            "num_experts": 4, "num_local_experts": 4,
+            "num_experts_per_tok": 2, "quantization_config": None,
+        },
+    ),
+    "glm52": (
+        12,
+        512,
+        {
+            "intermediate_size": 1024, "moe_intermediate_size": 256,
+            "layer_types": ["deepseek_sparse_attention"] * 12,
+            "mlp_layer_types": ["dense"] * 3 + ["sparse"] * 9,
+            "indexer_types": ["full"] * 3
+            + ["shared", "shared", "shared", "full"]
+            + ["shared", "shared", "shared", "full", "shared"],
+            "num_attention_heads": 64, "num_key_value_heads": 64,
+            "q_lora_rank": 512, "qk_head_dim": 256,
+            "qk_nope_head_dim": 192, "qk_rope_head_dim": 64,
+            "v_head_dim": 256, "index_n_heads": 32, "index_topk": 128,
+            "n_routed_experts": 4, "num_experts": 4, "num_local_experts": 4,
+            "num_experts_per_tok": 2, "num_nextn_predict_layers": 0,
+            "tie_word_embeddings": False, "quantization_config": None,
+        },
+    ),
+    "gpt_oss_moe": (
+        4,
+        320,
+        {
+            "intermediate_size": 768,
+            "layer_types": ["sliding_attention", "full_attention"] * 2,
+            "head_dim": 64, "num_attention_heads": 4, "num_key_value_heads": 1,
+            "num_experts": 4, "num_local_experts": 4,
+            "num_experts_per_tok": 2, "experts_per_token": 2,
+            "initial_context_length": 2048, "sliding_window": 128,
+            "tie_word_embeddings": False, "quantization_config": None,
+        },
+    ),
+}
+_QWEN35_TEXT = {
+    "layer_types": (["linear_attention"] * 3 + ["full_attention"]) * 2,
+    "intermediate_size": 512, "head_dim": 256,
+    "num_attention_heads": 4, "num_key_value_heads": 1,
+    "full_attention_interval": 4, "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128, "linear_num_key_heads": 4,
+    "linear_num_value_heads": 8, "linear_value_head_dim": 128,
+    "tie_word_embeddings": False,
+}
+_QWEN35_VISION = {
+    "depth": 1, "num_hidden_layers": 1,
+    "hidden_size": 128, "intermediate_size": 256,
+    "num_heads": 4, "num_attention_heads": 4,
+    "num_position_embeddings": 16, "out_hidden_size": 1024,
+    "deepstack_visual_indexes": [],
+}
+_GEMMA_TEXT = {
+    "layer_types": (["sliding_attention"] * 5 + ["full_attention"]) * 2,
+    "intermediate_size": 512, "head_dim": 256, "global_head_dim": 512,
+    "num_attention_heads": 4, "num_key_value_heads": 2,
+    "num_global_key_value_heads": 1, "num_kv_shared_layers": 0,
+    "sliding_window": 1024,
+    "hidden_size_per_layer_input": 0,
+    "tie_word_embeddings": True,
+}
+_GEMMA_VISION = {
+    "depth": 1, "num_hidden_layers": 1,
+    "hidden_size": 128, "intermediate_size": 256,
+    "head_dim": 32, "global_head_dim": 32,
+    "num_attention_heads": 4, "num_key_value_heads": 4,
+    "patch_size": 16, "position_embedding_size": 64,
+}
+_MULTIMODAL_SHAPES = {
+    "qwen3_5": (
+        8,
+        _QWEN35_TEXT,
+        _QWEN35_VISION,
+        {
+            "moe_intermediate_size": 256, "shared_expert_intermediate_size": 256,
+            "num_experts": 4, "num_local_experts": 4, "num_experts_per_tok": 2,
+        },
+        {
+            "image_token_id": 2, "video_token_id": 3,
+            "vision_start_token_id": 4, "vision_end_token_id": 5,
+        },
+    ),
+    "gemma4": (
+        12,
+        _GEMMA_TEXT,
+        _GEMMA_VISION,
+        {
+            "moe_intermediate_size": 256, "num_experts": 4,
+            "num_local_experts": 4, "top_k_experts": 2, "num_experts_per_tok": 2,
+        },
+        {"image_token_id": 2, "pad_token_id": 0, "bos_token_id": 2, "eos_token_id": 1},
+    ),
+}
+# fmt: on
+
+_FUNCTIONAL_LAYER_FIELDS = ("layer_types", "mlp_layer_types", "indexer_types")
+_WIDTH_TERMS = ("hidden", "intermediate", "head", "expert", "lora_rank", "topk")
+
+
+class _FunctionalPlan(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    depth: int
+    prefix: str
+    config_key: str | None = None
+    checkpoint: str = "model.safetensors.index.json"
+    vision: tuple[str, str] | None = None
+    auxiliary: tuple[str | None, str] | None = None
+
+
+def _plan(depth: int, prefix: str, **values: Any) -> _FunctionalPlan:
+    return _FunctionalPlan(depth=depth, prefix=prefix, **values)
+
+
+# fmt: off
+_QWEN35 = {"layer_types": (("linear_attention",) * 3 + ("full_attention",)) * 2}
+_GEMMA4 = {"layer_types": (("sliding_attention",) * 5 + ("full_attention",)) * 2}
+_FUNCTIONAL_PLANS = {
+    "llama3_dense": _plan(2, "model.layers", checkpoint="model.safetensors"),
+    "qwen3_dense": _plan(2, "model.layers"),
+    "qwen3_moe": _plan(2, "model.layers"),
+    "qwen3_5_dense": _plan(8, "model.language_model.layers", config_key="text_config", vision=("model.visual.blocks", "depth")),
+    "qwen3_5_moe": _plan(8, "model.language_model.layers", config_key="text_config", vision=("model.visual.blocks", "depth")),
+    "gemma4_dense": _plan(12, "model.language_model.layers", config_key="text_config", vision=("model.vision_tower.encoder.layers", "num_hidden_layers")),
+    "gemma4_moe": _plan(12, "model.language_model.layers", config_key="text_config", vision=("model.vision_tower.encoder.layers", "num_hidden_layers")),
+    "dsv4": _plan(6, "layers", auxiliary=("mtp", "num_nextn_predict_layers")),
+    "glm52": _plan(10, "model.layers", auxiliary=(None, "num_nextn_predict_layers")),
+    "gpt_oss_moe": _plan(4, "model.layers"),
+}
+_FUNCTIONAL_PATTERNS = {
+    "qwen3_dense": {"layer_types": ("full_attention",) * 2},
+    "qwen3_5_dense": _QWEN35, "qwen3_5_moe": _QWEN35,
+    "gemma4_dense": _GEMMA4, "gemma4_moe": _GEMMA4,
+    "dsv4": {
+        "layer_types": ("sliding_attention", "sliding_attention", "compressed_sparse_attention", "heavily_compressed_attention", "compressed_sparse_attention", "heavily_compressed_attention"),
+        "mlp_layer_types": ("hash_moe",) * 3 + ("moe",) * 3,
+    },
+    "glm52": {
+        "mlp_layer_types": ("dense",) * 3 + ("sparse",) * 7,
+        "indexer_types": ("full",) * 3 + ("shared",) * 3 + ("full",) + ("shared",) * 3,
+    },
+    "gpt_oss_moe": {"layer_types": ("sliding_attention", "full_attention") * 2},
+}
+# fmt: on
+
+
+def _configure(
+    model_key: str,
+    config: Any,
+    *,
+    source_vocab_size: int,
+    tokenizer_compatible: bool,
+) -> Any:
+    common = {
+        "vocab_size": source_vocab_size if tokenizer_compatible else 8192,
+        "preserve_token_ids": tokenizer_compatible,
+    }
+    if model_key in _PLAIN_TEXT:
+        layers, hidden, values = _PLAIN_TEXT[model_key]
+        text = _set(_common(config, layers=layers, hidden=hidden, **common), **values)
+        if model_key == "glm52":
+            text.vocab_size = source_vocab_size
+        return config
+    family = model_key.rsplit("_", 1)[0]
+    if family in _MULTIMODAL_SHAPES:
+        moe = model_key.endswith("_moe")
+        layers, text_shape, vision_shape, moe_shape, token_ids = _MULTIMODAL_SHAPES[
+            family
+        ]
+        text = _set(_common(config, layers=layers, hidden=1024, **common), **text_shape)
+        top_level = {"tie_word_embeddings": True} if family == "gemma4" else {}
+        if family == "gemma4":
+            _set(
+                text,
+                enable_moe_block=moe,
+                vocab_size_per_layer_input=common["vocab_size"],
+            )
+        if moe:
+            _set(text, **moe_shape)
+        _set(config.vision_config, **vision_shape)
+        if not tokenizer_compatible:
+            top_level.update(token_ids)
+        return _set(config, **top_level)
+    if model_key == "dsv4":
+        return _set(
+            config,
+            num_hidden_layers=4,
+            compress_ratios=[0, 0, 4, 128],
+            layer_types=[
+                "sliding_attention",
+                "sliding_attention",
+                "compressed_sparse_attention",
+                "heavily_compressed_attention",
+            ],
+            mlp_layer_types=["moe"] * 4,
+        )
+    raise KeyError(f"No correctness fixture for {model_key}")
+
+
+def _pack_qwen35_experts(path: Path, config: Any) -> None:
+    from safetensors.torch import load_file, save_file
+    import torch
+
+    checkpoint = path / "model.safetensors"
+    tensors = load_file(checkpoint)
+    text = _text(config)
+    for layer in range(text.num_hidden_layers):
+        prefix = f"model.language_model.layers.{layer}.mlp.experts"
+        gate_up, down = [], []
+        for expert in range(text.num_experts):
+            expert_prefix = f"{prefix}.{expert}"
+            gate_up.append(
+                torch.cat(
+                    (
+                        tensors.pop(f"{expert_prefix}.gate_proj.weight"),
+                        tensors.pop(f"{expert_prefix}.up_proj.weight"),
+                    )
+                )
+            )
+            down.append(tensors.pop(f"{expert_prefix}.down_proj.weight"))
+        tensors[f"{prefix}.gate_up_proj"] = torch.stack(gate_up)
+        tensors[f"{prefix}.down_proj"] = torch.stack(down)
+    save_file(tensors, checkpoint, metadata={"format": "pt"})
+
+
+def _json_sha256(value: object) -> str:
+    return hashlib.sha256(
+        json.dumps(value, sort_keys=True, separators=(",", ":")).encode()
+    ).hexdigest()
+
+
+def _manifest_sha256(manifest: Mapping[str, object]) -> str:
+    return _json_sha256(
+        {key: value for key, value in manifest.items() if key != "manifest_sha256"}
+    )
+
+
+def _functional_plan(model_key: str) -> _FunctionalPlan:
+    try:
+        return _FUNCTIONAL_PLANS[model_key]
+    except KeyError:
+        raise RuntimeError(
+            f"no pretrained functional fixture for {model_key}"
+        ) from None
+
+
+def _config_text(config: dict[str, Any], plan: _FunctionalPlan) -> dict[str, Any]:
+    value = config if plan.config_key is None else config.get(plan.config_key)
+    if not isinstance(value, dict):
+        raise RuntimeError(f"functional fixture lacks {plan.config_key} config")
+    return value
+
+
+def _config_shape(config: dict[str, Any], *exclude: str) -> dict[str, object]:
+    values = {key: value for key, value in config.items() if key not in exclude}
+    return {
+        "dimensions": {
+            key: value
+            for key, value in values.items()
+            if type(value) is int and any(term in key for term in _WIDTH_TERMS)
+        },
+        "sha256": _json_sha256(values),
+    }
+
+
+def _functional_config(
+    source: dict[str, Any], *, model_key: str
+) -> tuple[dict[str, Any], dict[str, object]]:
+    plan = _functional_plan(model_key)
+    text = _config_text(source, plan)
+    source_depth = text.get("num_hidden_layers")
+    if type(source_depth) is not int or source_depth < plan.depth:
+        raise RuntimeError(f"{model_key} has invalid production depth")
+    reduced = json.loads(json.dumps(source))
+    reduced_text = _config_text(reduced, plan)
+    reduced_text["num_hidden_layers"] = plan.depth
+    if plan.auxiliary:
+        _, count_field = plan.auxiliary
+        count = text.get(count_field)
+        if type(count) is not int or count < 0:
+            raise RuntimeError(f"{model_key} has invalid {count_field}")
+        reduced_text[count_field] = 0
+    patterns: dict[str, list[object]] = {}
+    for field in _FUNCTIONAL_LAYER_FIELDS:
+        if (values := text.get(field)) is not None:
+            if not isinstance(values, list) or len(values) != source_depth:
+                raise RuntimeError(f"{model_key} production {field} is incomplete")
+            patterns[field] = values[: plan.depth]
+            reduced_text[field] = patterns[field]
+    for field, expected in _FUNCTIONAL_PATTERNS.get(model_key, {}).items():
+        if tuple(patterns.get(field, ())) != expected:
+            raise RuntimeError(f"{model_key} production {field} pattern changed")
+
+    shape_exclusions = (
+        "num_hidden_layers",
+        *_FUNCTIONAL_LAYER_FIELDS,
+        *((plan.auxiliary[1],) if plan.auxiliary else ()),
+    )
+    width = {"text": _config_shape(text, *shape_exclusions)}
+    if width["text"] != _config_shape(reduced_text, *shape_exclusions):
+        raise RuntimeError(f"{model_key} functional fixture changed text width")
+    vision_policy: object = "not_applicable"
+    if plan.vision:
+        _, depth_field = plan.vision
+        vision, reduced_vision = (
+            source.get("vision_config"),
+            reduced.get("vision_config"),
+        )
+        if not isinstance(vision, dict) or not isinstance(reduced_vision, dict):
+            raise RuntimeError(f"{model_key} functional fixture lacks vision config")
+        source_vision_depth = vision.get(depth_field)
+        if type(source_vision_depth) is not int or source_vision_depth < 1:
+            raise RuntimeError(f"{model_key} has invalid production vision depth")
+        width["vision"] = _config_shape(vision, depth_field)
+        reduced_vision[depth_field] = 1
+        if width["vision"] != _config_shape(reduced_vision, depth_field):
+            raise RuntimeError(f"{model_key} functional fixture changed vision width")
+        vision_policy = {
+            "mode": "one_pretrained_production_width_layer",
+            "source_depth": source_vision_depth,
+            "text_path_semantics": "unchanged",
+        }
+    dtype = text.get("dtype") or text.get("torch_dtype")
+    dtype = dtype or source.get("dtype") or source.get("torch_dtype")
+    # fmt: off
+    return reduced, {
+        "source_num_layers": source_depth,
+        "selected_layer_sequence": list(range(plan.depth)),
+        "selected_layer_patterns": patterns,
+        "production_width": width,
+        "config_vocab_size": int(text["vocab_size"]),
+        "configured_dtype": str(dtype) if dtype else None,
+        "inference_quantization": {
+            "quantization_config": text.get("quantization_config") or source.get("quantization_config"),
+            "expert_dtype": text.get("expert_dtype") or source.get("expert_dtype"),
+        },
+        "vision_policy": vision_policy,
+    }
+    # fmt: on
+
+
+def _sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as source:
+        for chunk in iter(lambda: source.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _checkpoint_weight_map(path: Path) -> dict[str, str]:
+    from safetensors import safe_open
+
+    if path.name == "model.safetensors":
+        with safe_open(path, framework="pt", device="cpu") as checkpoint:
+            values: object = dict.fromkeys(checkpoint.keys(), path.name)
+    else:
+        try:
+            values = json.loads(path.read_text())["weight_map"]
+        except (OSError, KeyError, json.JSONDecodeError) as exc:
+            raise RuntimeError(f"invalid checkpoint index {path}") from exc
+    if (
+        not isinstance(values, dict)
+        or not values
+        or any(
+            not isinstance(key, str)
+            or not isinstance(shard, str)
+            or Path(shard).name != shard
+            for key, shard in values.items()
+        )
+    ):
+        raise RuntimeError(f"invalid checkpoint weight map {path}")
+    return cast(dict[str, str], values)
+
+
+def _layer_index(name: str, prefix: str) -> int | None:
+    if not name.startswith(f"{prefix}."):
+        return None
+    index, separator, suffix = name[len(prefix) + 1 :].partition(".")
+    if not separator or not index.isdecimal() or not suffix:
+        raise RuntimeError(f"malformed checkpoint layer key {name!r}")
+    return int(index)
+
+
+def _select_functional_weights(
+    weights: Mapping[str, str], config: dict[str, Any], *, model_key: str
+) -> dict[str, str]:
+    plan = _functional_plan(model_key)
+    text_config = _config_text(config, plan)
+    source_depth = int(text_config["num_hidden_layers"])
+    text_layers: set[int] = set()
+    vision_layers: set[int] = set()
+    auxiliary_layers: set[int] = set()
+    selected: dict[str, str] = {}
+    for name, shard in weights.items():
+        text_layer = _layer_index(name, plan.prefix)
+        vision_layer = (
+            _layer_index(name, plan.vision[0])
+            if text_layer is None and plan.vision
+            else None
+        )
+        auxiliary_layer = (
+            _layer_index(name, plan.auxiliary[0])
+            if text_layer is None
+            and vision_layer is None
+            and plan.auxiliary
+            and plan.auxiliary[0]
+            else None
+        )
+        text_layers.update(() if text_layer is None else (text_layer,))
+        vision_layers.update(() if vision_layer is None else (vision_layer,))
+        auxiliary_layers.update(() if auxiliary_layer is None else (auxiliary_layer,))
+        if (
+            text_layer is None
+            and vision_layer is None
+            and auxiliary_layer is None
+            or text_layer is not None
+            and text_layer < plan.depth
+            or vision_layer == 0
+        ):
+            selected[name] = shard
+    auxiliary_prefix, auxiliary_count = plan.auxiliary or (None, None)
+    count = text_config.get(auxiliary_count) if auxiliary_count else 0
+    if type(count) is not int or count < 0:
+        raise RuntimeError(f"{model_key} has invalid {auxiliary_count}")
+    expected = set(
+        range(source_depth + (count if plan.auxiliary and not auxiliary_prefix else 0))
+    )
+    if text_layers != expected:
+        raise RuntimeError(f"{model_key} canonical text-layer coverage changed")
+    if auxiliary_prefix:
+        if auxiliary_layers != set(range(count)):
+            raise RuntimeError(
+                f"{model_key} canonical auxiliary-layer coverage changed"
+            )
+    if plan.vision:
+        vision = config.get("vision_config")
+        if not isinstance(vision, dict):
+            raise RuntimeError(f"{model_key} canonical vision config is missing")
+        if vision_layers != set(range(int(vision[plan.vision[1]]))):
+            raise RuntimeError(f"{model_key} canonical vision-layer coverage changed")
+    if not selected:
+        raise RuntimeError(f"{model_key} functional checkpoint selection is empty")
+    return selected
+
+
+def _fixture_files(path: Path) -> dict[str, str]:
+    return {
+        file.relative_to(path).as_posix(): _sha256(file)
+        for file in sorted(path.rglob("*"))
+        if file.is_file() and file.name != "fixture_manifest.json"
+    }
+
+
+def _fixture_file_sizes(path: Path) -> dict[str, int]:
+    return {
+        file.relative_to(path).as_posix(): file.stat().st_size
+        for file in sorted(path.rglob("*"))
+        if file.is_file() and file.name != "fixture_manifest.json"
+    }
+
+
+def _checkpoint_is_complete(path: Path) -> bool:
+    try:
+        if any(
+            not file.is_file() or file.stat().st_size == 0
+            for file in (path / "config.json", path / "tokenizer_config.json")
+        ):
+            return False
+        index_path = path / "model.safetensors.index.json"
+        if not index_path.is_file():
+            checkpoint = path / "model.safetensors"
+            return checkpoint.is_file() and checkpoint.stat().st_size > 0
+        weight_map = json.loads(index_path.read_text())["weight_map"]
+        shards = set(weight_map.values())
+        return bool(shards) and all(
+            isinstance(name, str)
+            and Path(name).name == name
+            and (path / name).is_file()
+            and (path / name).stat().st_size > 0
+            for name in shards
+        )
+    except (KeyError, OSError, TypeError, json.JSONDecodeError):
+        return False
+
+
+def _fixture_namespace(
+    *,
+    canonical_model: str,
+    revision: str,
+    model_key: str,
+    version: int,
+    tokenizer_compatible: bool,
+) -> str:
+    return hashlib.sha256(
+        json.dumps(
+            {
+                "model": canonical_model,
+                "revision": revision,
+                "handler": model_key,
+                "version": version,
+                "tokenizer_compatible": tokenizer_compatible,
+            },
+            sort_keys=True,
+        ).encode()
+    ).hexdigest()[:16]
+
+
+def _is_current(
+    path: Path,
+    *,
+    canonical_model: str,
+    model_key: str,
+    revision: str,
+    tokenizer_compatible: bool,
+    parent_manifest_sha256: str | None,
+    version: int | None = None,
+) -> bool:
+    try:
+        manifest = json.loads((path / "fixture_manifest.json").read_text())
+    except (OSError, json.JSONDecodeError):
+        return False
+    expected = {
+        "version": version
+        or (_TOKENIZER_FIXTURE_VERSION if tokenizer_compatible else FIXTURE_VERSION),
+        "source_model": canonical_model,
+        "source_revision": revision,
+        "handler": model_key,
+        "seed": 0,
+        "source_identity": {"model": canonical_model, "revision": revision},
+        "parent_manifest_sha256": parent_manifest_sha256,
+    }
+    if tokenizer_compatible:
+        expected["vocabulary_contract"] = "canonical"
+    return (
+        _checkpoint_is_complete(path)
+        and all(manifest.get(key) == value for key, value in expected.items())
+        and (
+            manifest.get("file_sizes") == _fixture_file_sizes(path)
+            if version is not None
+            else manifest.get("files") == _fixture_files(path)
+        )
+        and (
+            "manifest_sha256" not in manifest
+            or manifest["manifest_sha256"] == _manifest_sha256(manifest)
+        )
+    )
+
+
+def _publish(staging: Path, output: Path) -> None:
+    previous = output.with_name(f".{output.name}.previous")
+    if previous.exists():
+        shutil.rmtree(previous)
+    if output.exists():
+        os.replace(output, previous)
+    try:
+        os.replace(staging, output)
+    except BaseException:
+        if previous.exists():
+            os.replace(previous, output)
+        raise
+    if previous.exists():
+        shutil.rmtree(previous)
+
+
+def _build(
+    *,
+    canonical_model: str,
+    model_key: str,
+    revision: str,
+    output: Path,
+    tokenizer_compatible: bool,
+    source_fixture: Path | None = None,
+    functional: bool = False,
+) -> None:
+    from safetensors.torch import load_file, save_file
+    import torch
+    from transformers import (
+        AutoConfig,
+        AutoImageProcessor,
+        AutoModelForCausalLM,
+        AutoModelForImageTextToText,
+        AutoProcessor,
+        AutoTokenizer,
+    )
+
+    with tempfile.TemporaryDirectory(prefix=f".{model_key}-", dir=output.parent) as tmp:
+        staging = Path(tmp) / model_key
+        staging.mkdir()
+        source_model = (
+            source_fixture / "production_config"
+            if source_fixture is not None
+            else canonical_model
+        )
+        source_kwargs = (
+            {"local_files_only": True}
+            if source_fixture is not None
+            else {"revision": revision}
+        )
+        source = AutoConfig.from_pretrained(
+            source_model, trust_remote_code=True, **source_kwargs
+        )
+        source_config = cast(dict[str, Any], source.to_dict())
+        source.save_pretrained(staging / "production_config")
+        tokenizer = cast(
+            Any,
+            AutoTokenizer.from_pretrained(
+                source_fixture or canonical_model,
+                trust_remote_code=True,
+                **source_kwargs,
+            ),
+        )
+        source_vocab_size = int(_text(source).vocab_size)
+        tokenizer_max_id = max(map(int, tokenizer.get_vocab().values()))
+        if tokenizer_max_id >= source_vocab_size:
+            raise RuntimeError(
+                f"{model_key} tokenizer ID {tokenizer_max_id} exceeds canonical "
+                f"vocab_size={source_vocab_size}"
+            )
+        functional_contract: dict[str, object] | None = None
+        if functional:
+            reduced, functional_contract = _functional_config(
+                source_config, model_key=model_key
+            )
+            config = source
+        else:
+            config = _configure(
+                model_key,
+                source,
+                source_vocab_size=source_vocab_size,
+                tokenizer_compatible=tokenizer_compatible,
+            )
+        config.save_pretrained(staging)
+        if functional:
+            (staging / "config.json").write_text(json.dumps(reduced, indent=2) + "\n")
+        tokenizer.save_pretrained(staging)
+        if model_key in _MULTIMODAL:
+            AutoProcessor.from_pretrained(
+                source_fixture or canonical_model,
+                trust_remote_code=True,
+                **source_kwargs,
+            ).save_pretrained(staging)
+        if model_key.startswith("gemma4_"):
+            AutoImageProcessor.from_pretrained(
+                source_fixture or canonical_model,
+                trust_remote_code=True,
+                **source_kwargs,
+            ).save_pretrained(staging)
+        parameters = 0
+        provenance: dict[str, object] | None = None
+        if functional:
+            provenance = _write_functional_weights(
+                canonical_model=canonical_model,
+                model_key=model_key,
+                revision=revision,
+                config=source_config,
+                output=staging,
+            )
+        elif model_key == "dsv4":
+            save_file(
+                {"_art_fixture_dummy": torch.zeros(1)}, staging / "model.safetensors"
+            )
+        else:
+            auto = (
+                AutoModelForImageTextToText
+                if model_key in _MULTIMODAL
+                else AutoModelForCausalLM
+            )
+            with torch.random.fork_rng(devices=[]):
+                torch.manual_seed(0)
+                model = auto.from_config(config, trust_remote_code=True).to(
+                    torch.bfloat16
+                )
+            if model_key.startswith("gemma4_"):
+                layers = model.model.language_model.layers
+                residual_scale = (2 * len(layers)) ** -0.5
+                with torch.no_grad():
+                    for layer in layers:
+                        layer.post_attention_layernorm.weight.fill_(residual_scale)
+                        layer.post_feedforward_layernorm.weight.fill_(residual_scale)
+            parameters = sum(parameter.numel() for parameter in model.parameters())
+            model.save_pretrained(
+                staging, safe_serialization=True, max_shard_size="2GB"
+            )
+            del model
+            gc.collect()
+            if model_key == "qwen3_5_moe":
+                _pack_qwen35_experts(staging, config)
+            if model_key.startswith("gemma4_"):
+                checkpoint = staging / "model.safetensors"
+                weight_map = dict.fromkeys(load_file(checkpoint), checkpoint.name)
+                (staging / "model.safetensors.index.json").write_text(
+                    json.dumps({"metadata": {}, "weight_map": weight_map}, indent=2)
+                    + "\n"
+                )
+        parent_manifest_sha256 = (
+            _sha256(source_fixture / "fixture_manifest.json")
+            if source_fixture is not None
+            else None
+        )
+        manifest = {
+            "version": (
+                _TOKENIZER_FIXTURE_VERSION if tokenizer_compatible else FIXTURE_VERSION
+            ),
+            "source_model": canonical_model,
+            "source_revision": revision,
+            "source_identity": {"model": canonical_model, "revision": revision},
+            "parent_manifest_sha256": parent_manifest_sha256,
+            "handler": model_key,
+            "parameters": parameters,
+            "num_layers": int(_text(config).num_hidden_layers),
+            "dtype": "bfloat16" if model_key != "dsv4" else None,
+            "seed": 0,
+            "vocabulary_contract": (
+                "canonical" if tokenizer_compatible else "compact_8192"
+            ),
+            "config_vocab_size": int(_text(config).vocab_size),
+            "tokenizer_size": len(tokenizer),
+            "tokenizer_max_id": tokenizer_max_id,
+        }
+        if functional:
+            if functional_contract is None or provenance is None:
+                raise RuntimeError("functional fixture construction is incomplete")
+            manifest.update(
+                {
+                    "version": _FUNCTIONAL_FIXTURE_VERSION,
+                    "fixture_kind": "functional_pretrained",
+                    "pretrained": True,
+                    "num_layers": _functional_plan(model_key).depth,
+                    "dtype": {
+                        "configured": functional_contract["configured_dtype"],
+                        "checkpoint": provenance["checkpoint_dtypes"],
+                    },
+                    "weight_provenance": provenance,
+                    "contract_sha256": _functional_contract_sha256(model_key),
+                    **functional_contract,
+                }
+            )
+        if tokenizer_compatible:
+            _validate_tokenizer_compatible_fixture(staging, manifest)
+        if functional and not _checkpoint_is_complete(staging):
+            raise RuntimeError(f"{model_key} functional checkpoint is incomplete")
+        if functional:
+            manifest["file_sizes"] = _fixture_file_sizes(staging)
+            manifest["manifest_sha256"] = _manifest_sha256(manifest)
+        else:
+            manifest["files"] = _fixture_files(staging)
+        (staging / "fixture_manifest.json").write_text(
+            json.dumps(manifest, indent=2) + "\n"
+        )
+        _publish(staging, output)
+
+
+def _cache_alias(
+    *,
+    canonical_model: str,
+    model_key: str,
+    revision: str,
+    fixture: Path,
+    root: Path,
+    version: int,
+    namespace: str,
+) -> Path:
+    hf_home = root / f"v{version}" / model_key / namespace
+    repo = hf_home / "hub" / f"models--{canonical_model.replace('/', '--')}"
+    snapshot = repo / "snapshots" / revision
+    (repo / "refs").mkdir(parents=True, exist_ok=True)
+    if snapshot.exists() and not snapshot.is_symlink():
+        raise RuntimeError(f"fixture cache alias is not a symlink: {snapshot}")
+    if snapshot.is_symlink() and snapshot.resolve() != fixture.resolve():
+        snapshot.unlink()
+    if not snapshot.exists():
+        snapshot.parent.mkdir(parents=True, exist_ok=True)
+        snapshot.symlink_to(fixture, target_is_directory=True)
+    if not snapshot.is_symlink() or snapshot.resolve() != fixture.resolve():
+        raise RuntimeError(
+            f"fixture cache alias does not identify {fixture}: {snapshot}"
+        )
+    (repo / "refs" / "main").write_text(revision)
+    return hf_home
+
+
+def _flatten_token_ids(value: Any) -> list[int]:
+    if isinstance(value, Mapping):
+        value = value["input_ids"]
+    if hasattr(value, "tolist"):
+        value = value.tolist()
+    if value and isinstance(value[0], list):
+        value = value[0]
+    return [int(token_id) for token_id in value]
+
+
+def _validate_tokenizer_compatible_fixture(
+    fixture: Path, manifest: dict[str, object]
+) -> None:
+    from transformers import AutoTokenizer
+
+    tokenizer = cast(Any, AutoTokenizer.from_pretrained(fixture, local_files_only=True))
+    vocab_size_value = manifest["config_vocab_size"]
+    if not isinstance(vocab_size_value, int):
+        raise RuntimeError(
+            f"fixture config_vocab_size is not an integer: {vocab_size_value!r}"
+        )
+    vocab_size = vocab_size_value
+    registered_max_id = max(map(int, tokenizer.get_vocab().values()))
+    if registered_max_id >= vocab_size:
+        raise RuntimeError(
+            f"registered tokenizer ID {registered_max_id} exceeds "
+            f"vocab_size={vocab_size}"
+        )
+    samples = (
+        "Return one token.",
+        "Explain how distributed training preserves policy-version provenance.",
+        "Unicode tokenizer check: cafe Tokyo resume.",
+    )
+    encoded: list[int] = []
+    for sample in samples:
+        encoded.extend(_flatten_token_ids(tokenizer(sample, add_special_tokens=True)))
+    if getattr(tokenizer, "chat_template", None):
+        for sample in samples:
+            encoded.extend(
+                _flatten_token_ids(
+                    tokenizer.apply_chat_template(
+                        [{"role": "user", "content": sample}],
+                        tokenize=True,
+                        add_generation_prompt=True,
+                    )
+                )
+            )
+    max_encoded_id = max(encoded)
+    if max_encoded_id >= vocab_size:
+        raise RuntimeError(
+            f"representative tokenizer ID {max_encoded_id} exceeds vocab_size={vocab_size}"
+        )
+    manifest["representative_max_token_id"] = max_encoded_id
+    manifest["tokenizer_max_id"] = registered_max_id
+
+
+def _functional_contract_sha256(model_key: str) -> str:
+    return _json_sha256(
+        (
+            _FUNCTIONAL_FIXTURE_VERSION,
+            _functional_plan(model_key).model_dump(mode="json"),
+            _FUNCTIONAL_PATTERNS.get(model_key),
+        )
+    )
+
+
+def _write_functional_weights(
+    *,
+    canonical_model: str,
+    model_key: str,
+    revision: str,
+    config: dict[str, Any],
+    output: Path,
+) -> dict[str, object]:
+    from huggingface_hub import hf_hub_download, snapshot_download
+    from safetensors import safe_open
+    from safetensors.torch import save_file
+
+    plan = _functional_plan(model_key)
+    cache = _CANONICAL_CACHE_ROOT / f"v{_CANONICAL_CACHE_VERSION}" / model_key / "hub"
+    checkpoint = Path(
+        hf_hub_download(
+            repo_id=canonical_model,
+            filename=plan.checkpoint,
+            revision=revision,
+            cache_dir=cache,
+        )
+    )
+    weights = _checkpoint_weight_map(checkpoint)
+    selected = _select_functional_weights(weights, config, model_key=model_key)
+    by_shard: dict[str, list[str]] = {}
+    for name, shard in selected.items():
+        by_shard.setdefault(shard, []).append(name)
+    source_root = Path(
+        snapshot_download(
+            repo_id=canonical_model,
+            revision=revision,
+            cache_dir=cache,
+            allow_patterns=[plan.checkpoint, *by_shard],
+        )
+    )
+
+    source_blobs: dict[str, str] = {}
+    dtypes: dict[str, int] = {}
+    total_size = 0
+    for source_name, names in sorted(by_shard.items()):
+        source_path = source_root / source_name
+        blob = source_path.resolve(strict=True).name
+        if len(blob) != 64 or any(c not in "0123456789abcdef" for c in blob):
+            raise RuntimeError(
+                f"canonical shard is not content-addressed: {source_path}"
+            )
+        with safe_open(source_path, framework="pt", device="cpu") as source:
+            tensors = {name: source.get_tensor(name) for name in sorted(names)}
+            for name in names:
+                dtype = str(source.get_slice(name).get_dtype())
+                dtypes[dtype] = dtypes.get(dtype, 0) + 1
+        save_file(tensors, output / source_name, metadata={"format": "pt"})
+        total_size += sum(t.numel() * t.element_size() for t in tensors.values())
+        source_blobs[source_name] = blob
+        del tensors
+    index = {
+        "metadata": {"total_size": total_size},
+        "weight_map": dict(sorted(selected.items())),
+    }
+    (output / "model.safetensors.index.json").write_text(
+        json.dumps(index, indent=2) + "\n"
+    )
+    # fmt: off
+    return {
+        "method": "safetensors_safe_open_get_tensor_v1",
+        "source_checkpoint": checkpoint.name,
+        "source_index_sha256": _sha256(checkpoint) if checkpoint.name.endswith(".json") else None,
+        "source_weight_map_sha256": _json_sha256(weights),
+        "source_shards": source_blobs,
+        "selected_key_count": len(selected),
+        "selected_keys_sha256": _json_sha256(sorted(selected)),
+        "checkpoint_dtypes": dtypes,
+    }
+    # fmt: on
+
+
+def _canonical_snapshot(
+    *, canonical_model: str, model_key: str, revision: str
+) -> tuple[Path, Path]:
+    from huggingface_hub import snapshot_download
+
+    hf_home = _CANONICAL_CACHE_ROOT / f"v{_CANONICAL_CACHE_VERSION}" / model_key
+    snapshot = snapshot_download(
+        repo_id=canonical_model,
+        revision=revision,
+        cache_dir=hf_home / "hub",
+    )
+    repo = hf_home / "hub" / f"models--{canonical_model.replace('/', '--')}"
+    (repo / "refs").mkdir(parents=True, exist_ok=True)
+    (repo / "refs" / "main").write_text(revision)
+    return Path(snapshot), hf_home
+
+
+def _ensure_cached_fixture(
+    *,
+    canonical_model: str,
+    model_key: str,
+    revision: str,
+    root: Path,
+    cache_root: Path,
+    version: int,
+    tokenizer_compatible: bool,
+    source_fixture: Path | None = None,
+    functional: bool = False,
+) -> tuple[Path, dict[str, object], Path]:
+    namespace = _fixture_namespace(
+        canonical_model=canonical_model,
+        revision=revision,
+        model_key=model_key,
+        version=version,
+        tokenizer_compatible=tokenizer_compatible,
+    )
+    model_root = root / model_key
+    model_root.mkdir(parents=True, exist_ok=True)
+    output = model_root / namespace
+    parent_manifest_sha256 = (
+        _sha256(source_fixture / "fixture_manifest.json")
+        if source_fixture is not None
+        else None
+    )
+    with (model_root / f".{namespace}.lock").open("w") as lock:
+        fcntl.flock(lock, fcntl.LOCK_EX)
+        current = _is_current(
+            output,
+            canonical_model=canonical_model,
+            model_key=model_key,
+            revision=revision,
+            tokenizer_compatible=tokenizer_compatible,
+            parent_manifest_sha256=parent_manifest_sha256,
+            version=version if functional else None,
+        )
+        if functional and current:
+            current = json.loads((output / "fixture_manifest.json").read_text()).get(
+                "contract_sha256"
+            ) == _functional_contract_sha256(model_key)
+        if not current:
+            if functional and source_fixture is None:
+                raise RuntimeError("functional fixture requires compact metadata")
+            _build(
+                canonical_model=canonical_model,
+                model_key=model_key,
+                revision=revision,
+                output=output,
+                tokenizer_compatible=tokenizer_compatible,
+                source_fixture=source_fixture,
+                functional=functional,
+            )
+        manifest = cast(
+            dict[str, object],
+            json.loads((output / "fixture_manifest.json").read_text()),
+        )
+        if tokenizer_compatible:
+            _validate_tokenizer_compatible_fixture(output, manifest)
+        hf_home = _cache_alias(
+            canonical_model=canonical_model,
+            model_key=model_key,
+            revision=revision,
+            fixture=output,
+            root=cache_root,
+            version=version,
+            namespace=namespace,
+        )
+    return output, manifest, hf_home
+
+
+def ensure_workflow_fixture(
+    base_model: str,
+    *,
+    allow_unvalidated_arch: bool = False,
+    required_stages: set[str] | frozenset[str] = frozenset(),
+) -> WorkflowFixture:
+    from art.megatron.model_support.registry import get_model_support_spec
+
+    model_key = get_model_support_spec(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    ).key
+    try:
+        revision = _REVISIONS[base_model]
+    except KeyError:
+        raise ValueError(
+            "workflow fixtures require an exact pinned representative model; "
+            f"unrecognized model {base_model!r} for handler {model_key!r}"
+        ) from None
+    root = Path(os.environ.get(FIXTURE_ROOT_ENV, str(_ROOT)))
+    output, manifest, hf_home = _ensure_cached_fixture(
+        canonical_model=base_model,
+        model_key=model_key,
+        revision=revision,
+        root=root,
+        cache_root=Path(os.environ.get(FIXTURE_CACHE_ENV, str(_CACHE_ROOT))),
+        version=FIXTURE_VERSION,
+        tokenizer_compatible=False,
+    )
+    tokenizer_path: Path | None = None
+    tokenizer_hf_home: Path | None = None
+    tokenizer_manifest: dict[str, object] | None = None
+    reduced_trainability_stages = _REDUCED_TRAINABILITY_ENV.get(model_key, {})
+    tokenizer_required = model_key.startswith("gemma4_") and bool(
+        required_stages & reduced_trainability_stages.keys()
+    )
+    if tokenizer_required:
+        tokenizer_path, tokenizer_manifest, tokenizer_hf_home = _ensure_cached_fixture(
+            canonical_model=base_model,
+            model_key=model_key,
+            revision=revision,
+            root=_TOKENIZER_FIXTURE_ROOT / f"v{_TOKENIZER_FIXTURE_VERSION}",
+            cache_root=_TOKENIZER_CACHE_ROOT,
+            version=_TOKENIZER_FIXTURE_VERSION,
+            tokenizer_compatible=True,
+            source_fixture=output,
+        )
+    functional_path: Path | None = None
+    functional_hf_home: Path | None = None
+    functional_manifest: dict[str, object] | None = None
+    if required_stages & _FUNCTIONAL_STAGES:
+        functional_path, functional_manifest, functional_hf_home = (
+            _ensure_cached_fixture(
+                canonical_model=base_model,
+                model_key=model_key,
+                revision=revision,
+                root=_FUNCTIONAL_FIXTURE_ROOT / f"v{_FUNCTIONAL_FIXTURE_VERSION}",
+                cache_root=_FUNCTIONAL_CACHE_ROOT,
+                version=_FUNCTIONAL_FIXTURE_VERSION,
+                tokenizer_compatible=True,
+                source_fixture=output,
+                functional=True,
+            )
+        )
+    canonical_path: Path | None = None
+    canonical_hf_home: Path | None = None
+    canonical_required = any(
+        stage in _PRETRAINED_WEIGHT_STAGES and stage not in reduced_trainability_stages
+        for stage in required_stages
+    ) or (
+        model_key.startswith("gemma4_")
+        and bool(required_stages & _GEMMA_CANONICAL_WEIGHT_STAGES)
+    )
+    if canonical_required:
+        canonical_path, canonical_hf_home = _canonical_snapshot(
+            canonical_model=base_model,
+            model_key=model_key,
+            revision=revision,
+        )
+    return WorkflowFixture(
+        canonical_model=base_model,
+        model_key=model_key,
+        source_revision=revision,
+        path=str(output),
+        hf_home=str(hf_home),
+        manifest=manifest,
+        tokenizer_compatible_path=(
+            str(tokenizer_path) if tokenizer_path is not None else None
+        ),
+        tokenizer_compatible_hf_home=(
+            str(tokenizer_hf_home) if tokenizer_hf_home is not None else None
+        ),
+        tokenizer_compatible_manifest=tokenizer_manifest,
+        functional_path=(str(functional_path) if functional_path is not None else None),
+        functional_hf_home=(
+            str(functional_hf_home) if functional_hf_home is not None else None
+        ),
+        functional_manifest=functional_manifest,
+        canonical_path=str(canonical_path) if canonical_path is not None else None,
+        canonical_hf_home=(
+            str(canonical_hf_home) if canonical_hf_home is not None else None
+        ),
+    )
diff --git a/tests/integration/megatron/model_support/workflow_forkserver.py b/tests/integration/megatron/model_support/workflow_forkserver.py
new file mode 100644
index 000000000..cdd82f0d9
--- /dev/null
+++ b/tests/integration/megatron/model_support/workflow_forkserver.py
@@ -0,0 +1,501 @@
+from __future__ import annotations
+
+from concurrent.futures import ThreadPoolExecutor
+import json
+import os
+from pathlib import Path
+from queue import Empty, Queue
+import selectors
+import shlex
+import signal
+import socket
+import subprocess
+import sys
+from threading import Lock, Thread
+import time
+import traceback
+from typing import Any
+import uuid
+
+_PREFIX = "ART_WORKFLOW_FORKSERVER\t"
+_MODULE = "integration.megatron.model_support.workflow_forkserver"
+_TERMINATION_GRACE_S = 10.0
+
+
+def _reply(payload: dict[str, Any]) -> None:
+    print(_PREFIX + json.dumps(payload, sort_keys=True), flush=True)
+
+
+def _process_state() -> dict[str, Any]:
+    import torch
+
+    return {
+        "pid": os.getpid(),
+        "task_count": len(os.listdir("/proc/self/task")),
+        "cuda_initialized": torch.cuda.is_initialized(),
+        "distributed_initialized": (
+            torch.distributed.is_available() and torch.distributed.is_initialized()
+        ),
+    }
+
+
+def _assert_fork_safe() -> dict[str, Any]:
+    state = _process_state()
+    if (
+        state["task_count"] != 1
+        or state["cuda_initialized"]
+        or state["distributed_initialized"]
+    ):
+        raise RuntimeError(f"unsafe workflow fork parent: {state}")
+    return state
+
+
+def _signal_group(pid: int, sig: signal.Signals) -> None:
+    try:
+        os.killpg(pid, sig)
+    except ProcessLookupError:
+        try:
+            os.kill(pid, sig)
+        except ProcessLookupError:
+            pass
+
+
+def _raise_signal_exit(signum: int, _frame: Any) -> None:
+    raise SystemExit(128 + signum)
+
+
+def _run_child(request: dict[str, Any]) -> None:
+    try:
+        os.setsid()
+        for sig in (signal.SIGTERM, signal.SIGINT, signal.SIGHUP):
+            signal.signal(sig, _raise_signal_exit)
+        log_fd = os.open(
+            request["log_path"], os.O_WRONLY | os.O_CREAT | os.O_TRUNC, 0o644
+        )
+        os.dup2(log_fd, sys.stdout.fileno())
+        os.dup2(log_fd, sys.stderr.fileno())
+        for name in os.listdir("/proc/self/fd"):
+            fd = int(name)
+            if fd > 2:
+                try:
+                    os.close(fd)
+                except OSError:
+                    pass
+        for key, value in request["environment"].items():
+            if value is None:
+                os.environ.pop(key, None)
+            else:
+                os.environ[key] = value
+        import torch
+
+        from . import workflow_stage_worker
+
+        torch.set_num_threads(int(request["torch_threads"]))
+        workflow_stage_worker.run_session_json(request["request_json"])
+    except BaseException:
+        traceback.print_exc()
+        os._exit(1)
+    os._exit(0)
+
+
+def _launch_child(
+    selector: selectors.BaseSelector,
+    children: dict[int, dict[str, Any]],
+    request: dict[str, Any],
+) -> None:
+    _assert_fork_safe()
+    started = time.monotonic()
+    pid = os.fork()
+    if pid == 0:
+        _run_child(request)
+    pid_fd = os.pidfd_open(pid)
+    child = {
+        "id": request["id"],
+        "pid": pid,
+        "pid_fd": pid_fd,
+        "started": started,
+        "deadline": started + float(request["timeout_s"]),
+        "timed_out": False,
+        "kill_deadline": None,
+    }
+    children[pid_fd] = child
+    selector.register(pid_fd, selectors.EVENT_READ, "child")
+
+
+def _finish_child(
+    selector: selectors.BaseSelector,
+    children: dict[int, dict[str, Any]],
+    pid_fd: int,
+) -> None:
+    child = children.pop(pid_fd)
+    selector.unregister(pid_fd)
+    os.close(pid_fd)
+    _pid, status = os.waitpid(child["pid"], 0)
+    returncode = os.waitstatus_to_exitcode(status)
+    if returncode != 0:
+        _signal_group(child["pid"], signal.SIGTERM)
+    _reply(
+        {
+            "id": child["id"],
+            "ok": True,
+            "returncode": None if child["timed_out"] else returncode,
+            "actual_returncode": returncode,
+            "timed_out": child["timed_out"],
+            "child_wall_s": time.monotonic() - child["started"],
+        }
+    )
+
+
+def _serve() -> None:
+    started = time.monotonic()
+    state = _assert_fork_safe()
+    _reply(
+        {
+            "id": "ready",
+            "ok": True,
+            "preload_s": time.monotonic() - started,
+            "state": state,
+        }
+    )
+    selector = selectors.DefaultSelector()
+    selector.register(sys.stdin.fileno(), selectors.EVENT_READ, "stdin")
+    children: dict[int, dict[str, Any]] = {}
+    buffer = b""
+    stopping = False
+    shutdown_id: str | None = None
+
+    def stop(_signum: int, _frame: Any) -> None:
+        nonlocal stopping
+        stopping = True
+
+    signal.signal(signal.SIGTERM, stop)
+    signal.signal(signal.SIGINT, stop)
+    while not stopping or children:
+        for key, _mask in selector.select(timeout=0.05):
+            if key.data == "child":
+                _finish_child(selector, children, key.fd)
+                continue
+            chunk = os.read(key.fd, 1 << 20)
+            if not chunk:
+                stopping = True
+                selector.unregister(key.fd)
+                continue
+            buffer += chunk
+            while b"\n" in buffer:
+                raw, buffer = buffer.split(b"\n", 1)
+                request = json.loads(raw)
+                if request["command"] == "shutdown":
+                    stopping = True
+                    shutdown_id = request["id"]
+                elif stopping:
+                    _reply({"id": request["id"], "ok": False, "error": "stopping"})
+                else:
+                    _launch_child(selector, children, request)
+        now = time.monotonic()
+        for child in tuple(children.values()):
+            if not child["timed_out"] and now >= child["deadline"]:
+                child["timed_out"] = True
+                child["kill_deadline"] = now + _TERMINATION_GRACE_S
+                _signal_group(child["pid"], signal.SIGTERM)
+            elif child["kill_deadline"] is not None and now >= child["kill_deadline"]:
+                child["kill_deadline"] = None
+                _signal_group(child["pid"], signal.SIGKILL)
+        if stopping:
+            for child in children.values():
+                if not child["timed_out"]:
+                    child["timed_out"] = True
+                    child["kill_deadline"] = now + _TERMINATION_GRACE_S
+                    _signal_group(child["pid"], signal.SIGTERM)
+    selector.close()
+    if shutdown_id is not None:
+        _reply({"id": shutdown_id, "ok": True})
+
+
+def _jemalloc_conf(value: str | None) -> str:
+    options = [
+        option
+        for option in (value or "").split(",")
+        if option and not option.startswith("background_thread:")
+    ]
+    return ",".join((*options, "background_thread:false"))
+
+
+class _HostForkserver:
+    def __init__(self, host: str, repo_root: Path, tests_dir: Path, log_dir: Path):
+        self.host = host
+        self.repo_root = repo_root
+        self.tests_dir = tests_dir
+        self.log_path = log_dir / f"{host.replace('/', '_')}.log"
+        self.process: subprocess.Popen[str] | None = None
+        self.preload_s = 0.0
+        self.startup_s = 0.0
+        self._pending: dict[str, Queue[dict[str, Any]]] = {}
+        self._pending_lock = Lock()
+        self._write_lock = Lock()
+        self._reader: Thread | None = None
+        self._log = None
+
+    def start(self) -> None:
+        started = time.monotonic()
+        environment = os.environ.copy()
+        environment.update(
+            {
+                "CUDA_VISIBLE_DEVICES": "",
+                "OMP_NUM_THREADS": "1",
+                "_RJEM_MALLOC_CONF": _jemalloc_conf(
+                    environment.get("_RJEM_MALLOC_CONF")
+                ),
+                "PYTHONPATH": os.pathsep.join(
+                    filter(None, (str(self.tests_dir), environment.get("PYTHONPATH")))
+                ),
+                "WANDB_MODE": "disabled",
+            }
+        )
+        command = [sys.executable, "-m", _MODULE]
+        local_names = {socket.gethostname(), socket.getfqdn(), "localhost"}
+        if self.host not in local_names:
+            profile = Path(sys.prefix) / "art-megatron-env.sh"
+            if not profile.is_file():
+                raise RuntimeError(
+                    f"remote workflow forkserver requires runtime profile: {profile}"
+                )
+            remote = (
+                "unset LD_LIBRARY_PATH && "
+                f"source {shlex.quote(str(profile))} && "
+                f"cd {shlex.quote(str(self.repo_root))} && exec "
+                + shlex.join(
+                    [
+                        "env",
+                        "CUDA_VISIBLE_DEVICES=",
+                        "OMP_NUM_THREADS=1",
+                        f"_RJEM_MALLOC_CONF={environment['_RJEM_MALLOC_CONF']}",
+                        f"PYTHONPATH={environment['PYTHONPATH']}",
+                        "WANDB_MODE=disabled",
+                        *command,
+                    ]
+                )
+            )
+            command = [
+                "ssh",
+                "-o",
+                "BatchMode=yes",
+                self.host,
+                "/bin/bash",
+                "--noprofile",
+                "--norc",
+                "-c",
+                shlex.quote(remote),
+            ]
+        self._log = self.log_path.open("w", encoding="utf-8")
+        self.process = subprocess.Popen(
+            command,
+            cwd=self.repo_root,
+            env=environment,
+            stdin=subprocess.PIPE,
+            stdout=subprocess.PIPE,
+            stderr=self._log,
+            text=True,
+            bufsize=1,
+            start_new_session=True,
+        )
+        ready = self._read_ready(timeout_s=120.0)
+        self.preload_s = float(ready["preload_s"])
+        self.startup_s = time.monotonic() - started
+        self._reader = Thread(target=self._read_responses, daemon=True)
+        self._reader.start()
+
+    def _read_ready(self, *, timeout_s: float) -> dict[str, Any]:
+        assert self.process is not None and self.process.stdout is not None
+        selector = selectors.DefaultSelector()
+        selector.register(self.process.stdout.fileno(), selectors.EVENT_READ)
+        deadline = time.monotonic() + timeout_s
+        try:
+            while self.process.poll() is None:
+                remaining = deadline - time.monotonic()
+                if remaining <= 0 or not selector.select(timeout=remaining):
+                    raise TimeoutError(f"forkserver {self.host} did not become ready")
+                line = self.process.stdout.readline()
+                if line.startswith(_PREFIX):
+                    result = json.loads(line.removeprefix(_PREFIX))
+                    if result.get("id") == "ready" and result.get("ok"):
+                        return result
+                elif line and self._log is not None:
+                    self._log.write(line)
+            raise RuntimeError(
+                f"forkserver {self.host} exited with {self.process.returncode}"
+            )
+        finally:
+            selector.close()
+
+    def _read_responses(self) -> None:
+        assert self.process is not None and self.process.stdout is not None
+        try:
+            for line in self.process.stdout:
+                if not line.startswith(_PREFIX):
+                    if self._log is not None:
+                        self._log.write(line)
+                    continue
+                response = json.loads(line.removeprefix(_PREFIX))
+                with self._pending_lock:
+                    pending = self._pending.pop(response["id"], None)
+                if pending is not None:
+                    pending.put(response)
+        except BaseException as exc:
+            error = f"forkserver {self.host} protocol failed: {exc!r}"
+        else:
+            error = f"forkserver {self.host} closed unexpectedly"
+        with self._pending_lock:
+            pending, self._pending = self._pending, {}
+        for result in pending.values():
+            result.put({"ok": False, "error": error})
+
+    def _request(self, payload: dict[str, Any], *, timeout_s: float) -> dict[str, Any]:
+        assert self.process is not None and self.process.stdin is not None
+        request_id = payload.setdefault("id", uuid.uuid4().hex)
+        result: Queue[dict[str, Any]] = Queue(maxsize=1)
+        with self._pending_lock:
+            self._pending[request_id] = result
+        try:
+            with self._write_lock:
+                self.process.stdin.write(json.dumps(payload, sort_keys=True) + "\n")
+                self.process.stdin.flush()
+            response = result.get(timeout=timeout_s)
+        except (BrokenPipeError, Empty) as exc:
+            with self._pending_lock:
+                self._pending.pop(request_id, None)
+            raise RuntimeError(f"forkserver {self.host} request failed") from exc
+        if not response.get("ok"):
+            raise RuntimeError(str(response.get("error", response)))
+        return response
+
+    def run(
+        self,
+        *,
+        request_json: Path,
+        log_path: Path,
+        environment: dict[str, str],
+        session_environment: dict[str, str],
+        torch_threads: int,
+        timeout_s: float,
+    ) -> dict[str, Any]:
+        child_environment: dict[str, str | None] = {
+            key: environment.get(key)
+            for key in ("OMP_NUM_THREADS", "_RJEM_MALLOC_CONF")
+        }
+        child_environment.update(
+            {
+                key: environment[key]
+                for key in ("CUDA_VISIBLE_DEVICES", "PYTHONPATH", "WANDB_MODE")
+            }
+        )
+        child_environment.update(session_environment)
+        return self._request(
+            {
+                "command": "run",
+                "request_json": str(request_json),
+                "log_path": str(log_path),
+                "environment": child_environment,
+                "torch_threads": torch_threads,
+                "timeout_s": timeout_s,
+            },
+            timeout_s=timeout_s + _TERMINATION_GRACE_S + 30.0,
+        )
+
+    def close(self) -> None:
+        process = self.process
+        if process is None:
+            return
+        error: Exception | None = None
+        if process.poll() is None and self._reader is not None:
+            try:
+                self._request({"command": "shutdown"}, timeout_s=30.0)
+            except Exception as exc:
+                error = exc
+        elif process.poll() is None:
+            os.killpg(process.pid, signal.SIGTERM)
+        if process.stdin is not None:
+            process.stdin.close()
+        try:
+            process.wait(timeout=10.0)
+        except subprocess.TimeoutExpired:
+            os.killpg(process.pid, signal.SIGTERM)
+            try:
+                process.wait(timeout=5.0)
+            except subprocess.TimeoutExpired:
+                os.killpg(process.pid, signal.SIGKILL)
+                process.wait()
+        if self._reader is not None:
+            self._reader.join(timeout=1.0)
+        if self._log is not None:
+            self._log.close()
+        self.process = None
+        if process.returncode != 0 and error is None:
+            error = RuntimeError(
+                f"forkserver {self.host} exited with {process.returncode}"
+            )
+        if error is not None:
+            raise error
+
+
+class WorkflowForkserverPool:
+    def __init__(
+        self, *, hosts: list[str], repo_root: Path, tests_dir: Path, log_dir: Path
+    ) -> None:
+        log_dir.mkdir(parents=True, exist_ok=False)
+        clients = [
+            _HostForkserver(host, repo_root, tests_dir, log_dir) for host in hosts
+        ]
+        try:
+            with ThreadPoolExecutor(max_workers=len(clients)) as executor:
+                list(executor.map(lambda client: client.start(), clients))
+        except BaseException:
+            for client in clients:
+                try:
+                    client.close()
+                except Exception:
+                    pass
+            raise
+        self._clients = {client.host: client for client in clients}
+
+    def run(self, host: str, **kwargs: Any) -> dict[str, Any]:
+        return self._clients[host].run(**kwargs)
+
+    def metrics(self, host: str) -> dict[str, float]:
+        client = self._clients[host]
+        return {
+            "workflow_forkserver_preload_s": client.preload_s,
+            "workflow_forkserver_startup_s": client.startup_s,
+        }
+
+    def close(self) -> None:
+        errors = []
+        with ThreadPoolExecutor(max_workers=len(self._clients)) as executor:
+            futures = [
+                executor.submit(client.close) for client in self._clients.values()
+            ]
+            for future in futures:
+                try:
+                    future.result()
+                except Exception as exc:
+                    errors.append(exc)
+        if errors:
+            raise ExceptionGroup("workflow forkserver shutdown failed", errors)
+
+    def __enter__(self) -> WorkflowForkserverPool:
+        return self
+
+    def __exit__(self, exc_type: Any, exc: Any, traceback: Any) -> bool:
+        try:
+            self.close()
+        except Exception as cleanup_error:
+            if exc is None:
+                raise
+            raise BaseExceptionGroup(
+                "workflow execution and forkserver shutdown failed",
+                [exc, cleanup_error],
+            ) from None
+        return False
+
+
+if __name__ == "__main__":
+    _serve()
diff --git a/tests/integration/megatron/model_support/workflow_resources.py b/tests/integration/megatron/model_support/workflow_resources.py
index d9a210919..7b705e1c4 100644
--- a/tests/integration/megatron/model_support/workflow_resources.py
+++ b/tests/integration/megatron/model_support/workflow_resources.py
@@ -2,10 +2,95 @@
 
 from typing import Literal
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, ConfigDict, Field, model_validator
 
-_H200_REFERENCE_VRAM_GIB = 140.0
+_H200_REFERENCE_VRAM_GIB = 130.0
 _H200_SLOT_TOLERANCE = 0.05
+THROUGHPUT_PACKED_SEQUENCE_LENGTH = 131_072
+THROUGHPUT_RANDOM_INITIALIZATION_VERSION = "deterministic_random_v1"
+THROUGHPUT_RANDOM_SEED = 3407
+
+
+class ThroughputThresholds(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    calibration_basis: Literal["measured", "estimated"]
+    calibration_fingerprint: str | None = Field(default=None, pattern=r"^[0-9a-f]{64}$")
+    min_isolated_train_tok_s: float = Field(gt=0.0, allow_inf_nan=False)
+    min_e2e_train_tok_s: float = Field(gt=0.0, allow_inf_nan=False)
+    min_accepted_train_tok_s: float = Field(gt=0.0, allow_inf_nan=False)
+    min_e2e_to_isolated_ratio: float = Field(gt=0.0, le=1.0, allow_inf_nan=False)
+    min_matched_core_to_isolated_ratio: float = Field(
+        gt=0.0, le=1.0, allow_inf_nan=False
+    )
+    max_matched_core_to_isolated_ratio: float = Field(
+        default=1.05, gt=1.0, allow_inf_nan=False
+    )
+    max_mean_policy_activation_lag_s: float = Field(gt=0.0, le=3.5, allow_inf_nan=False)
+    max_policy_activation_lag_s: float = Field(gt=0.0, le=3.5, allow_inf_nan=False)
+    max_repeated_policy_activation_interval_s: float = Field(
+        gt=0.0, allow_inf_nan=False
+    )
+    max_queue_ready_inter_forward_backward_gap_p50_s: float = Field(
+        default=0.23, gt=0.0, le=0.23, allow_inf_nan=False
+    )
+    max_queue_ready_inter_forward_backward_gap_max_s: float = Field(
+        default=1.0, gt=0.0, le=1.0, allow_inf_nan=False
+    )
+    min_queue_ready_inter_forward_backward_gap_count: int = Field(default=3, ge=3)
+
+    @model_validator(mode="after")
+    def validate_calibration_identity(self) -> "ThroughputThresholds":
+        measured = self.calibration_basis == "measured"
+        if measured != (self.calibration_fingerprint is not None):
+            raise ValueError(
+                "measured calibration requires a fingerprint and estimated "
+                "calibration must not claim one"
+            )
+        if self.max_mean_policy_activation_lag_s > self.max_policy_activation_lag_s:
+            raise ValueError("mean activation lag limit cannot exceed absolute limit")
+        return self
+
+
+class ThroughputWorkflowConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    num_layers: int = Field(ge=2)
+    prompt_tokens: int = Field(default=3839, ge=1)
+    completion_tokens: int = Field(default=64, ge=1)
+    rollouts_per_group: int = Field(default=4, ge=2)
+    groups_per_step: int = Field(default=32, ge=2)
+    initial_model_calls_per_inference_gpu: int = Field(default=32, ge=1)
+    max_num_seqs: int = Field(default=64, ge=1)
+    max_num_batched_tokens: int = Field(default=65_536, ge=1)
+    enable_prefix_caching: bool = False
+    max_steps: int = Field(default=13, ge=7)
+    max_steps_off_policy: int = Field(default=4, ge=0)
+    packed_sequence_length: int = Field(
+        default=THROUGHPUT_PACKED_SEQUENCE_LENGTH, ge=1024
+    )
+    min_vllm_pressure: float = Field(default=0.5, ge=0.0, allow_inf_nan=False)
+    max_trainer_underfeed: float = Field(default=0.08, ge=0.0, allow_inf_nan=False)
+    max_unused_and_dummy_ratio: float = Field(
+        default=0.15, ge=0.0, le=1.0, allow_inf_nan=False
+    )
+    max_queue_ready_wait_s: float = Field(
+        default=0.01, ge=0.0, le=0.2, allow_inf_nan=False
+    )
+    random_initialization_version: Literal["deterministic_random_v1"] = (
+        THROUGHPUT_RANDOM_INITIALIZATION_VERSION
+    )
+    random_seed: int = Field(default=THROUGHPUT_RANDOM_SEED, ge=0, le=2**31 - 1)
+    thresholds: dict[Literal["h200", "b300"], ThroughputThresholds] = Field(
+        default_factory=dict
+    )
+
+    @model_validator(mode="after")
+    def require_measured_b300_calibration(self) -> "ThroughputWorkflowConfig":
+        b300 = self.thresholds.get("b300")
+        if b300 is not None and b300.calibration_basis != "measured":
+            raise ValueError("B300 throughput thresholds must be measured")
+        return self
 
 
 class MegatronWorkflowTopology(BaseModel):
@@ -28,9 +113,6 @@ def to_megatron_config(self) -> dict[str, int | None]:
             "pp": self.pp,
         }
 
-    def to_oracle_topology_kwargs(self) -> dict[str, int | bool]:
-        return self.model_dump()
-
     def to_train_inf_topology_kwargs(self) -> dict[str, int]:
         return {
             "tp": self.tp,
@@ -55,7 +137,6 @@ class VllmWorkflowResources(BaseModel):
     gpu_ids: list[int]
     tensor_parallel_size: int
     enable_expert_parallel: bool = False
-    hf_overrides: dict[str, object] = Field(default_factory=dict)
     extra_engine_args: dict[str, object] = Field(default_factory=dict)
 
     def engine_args(self) -> dict[str, object]:
@@ -64,8 +145,6 @@ def engine_args(self) -> dict[str, object]:
         }
         if self.enable_expert_parallel:
             engine_args["enable_expert_parallel"] = True
-        if self.hf_overrides:
-            engine_args["hf_overrides"] = dict(self.hf_overrides)
         engine_args.update(self.extra_engine_args)
         return engine_args
 
@@ -74,8 +153,8 @@ class WorkflowStageResources(BaseModel):
     model_config = ConfigDict(frozen=True)
 
     required_world_size: int
+    required_physical_gpus: int | None = None
     required_h200_equivalent_gpus: int | None = None
-    allow_gpu_overlap: bool = False
     requires_external_vllm: bool = False
     megatron: MegatronWorkflowResources | None = None
     vllm: VllmWorkflowResources | None = None
@@ -83,16 +162,16 @@ class WorkflowStageResources(BaseModel):
     high_vram_vllm: VllmWorkflowResources | None = None
     streaming_weight_offload: bool = False
     megatron_env: dict[str, str] = Field(default_factory=dict)
+    throughput: ThroughputWorkflowConfig | None = None
 
 
 class HandlerWorkflowResources(BaseModel):
     model_config = ConfigDict(frozen=True)
 
     train_inf_mismatch: WorkflowStageResources | None = None
-    merged_vllm_serving: WorkflowStageResources | None = None
-    native_vllm_lora: WorkflowStageResources | None = None
     yes_no_trainability: WorkflowStageResources | None = None
     length_trainability: WorkflowStageResources | None = None
+    e2e_throughput: WorkflowStageResources | None = None
     yes_no_trainability_variant: (
         Literal[
             "megatron_shared",
@@ -121,33 +200,6 @@ class HandlerWorkflowResources(BaseModel):
     pp=1,
     sp=True,
 )
-_DSV4_TP2_EP2 = MegatronWorkflowTopology(
-    tp=2,
-    ep=2,
-    etp=1,
-    dp=1,
-    cp=1,
-    pp=1,
-    sp=True,
-)
-_DSV4_REPRESENTATIVE_NUM_LAYERS = 4
-_DSV4_REPRESENTATIVE_COMPRESS_RATIOS = [0, 0, 4, 128]
-_DSV4_REPRESENTATIVE_LAYER_TYPES = [
-    "sliding_attention",
-    "sliding_attention",
-    "compressed_sparse_attention",
-    "heavily_compressed_attention",
-]
-_DSV4_REPRESENTATIVE_MLP_LAYER_TYPES = ["hash_moe", "hash_moe", "hash_moe", "moe"]
-_DSV4_MEGATRON_ENV = {
-    "ART_DSV4_VALIDATION_NUM_LAYERS": str(_DSV4_REPRESENTATIVE_NUM_LAYERS)
-}
-_DSV4_HF_OVERRIDES = {
-    "num_hidden_layers": _DSV4_REPRESENTATIVE_NUM_LAYERS,
-    "compress_ratios": _DSV4_REPRESENTATIVE_COMPRESS_RATIOS,
-    "layer_types": _DSV4_REPRESENTATIVE_LAYER_TYPES,
-    "mlp_layer_types": _DSV4_REPRESENTATIVE_MLP_LAYER_TYPES,
-}
 _DSV4_COMMON_VLLM_ENGINE_ARGS = {
     "compilation_config": {
         "cudagraph_mode": "NONE",
@@ -157,120 +209,339 @@ class HandlerWorkflowResources(BaseModel):
     "enforce_eager": True,
     "gpu_memory_utilization": 0.82,
     "kv_cache_dtype": "fp8",
+    "max_model_len": 1024,
     "max_num_batched_tokens": 1032,
 }
-_DSV4_MERGED_VLLM_ENGINE_ARGS = {
+_DSV4_VLLM_ENGINE_ARGS = {
     **_DSV4_COMMON_VLLM_ENGINE_ARGS,
-    "moe_backend": "triton_unfused",
-}
-_DSV4_LORA_VLLM_ENGINE_ARGS = {
-    **_DSV4_COMMON_VLLM_ENGINE_ARGS,
-    "moe_backend": "triton_unfused",
-}
-_DSV4_REDUCED_VLLM_ENGINE_ARGS = {
-    **_DSV4_MERGED_VLLM_ENGINE_ARGS,
-    # The quick DSV4 vLLM serving gates use a reduced 4-layer validation model and then
-    # sync Megatron weights into vLLM through merged-weight transfer. Loading
-    # the full public checkpoint before that sync is incompatible with the
-    # reduced hf_overrides because vLLM still streams layer-4+ tensors.
-    "load_format": "dummy",
-}
-_DSV4_NATIVE_LORA_VLLM_ENGINE_ARGS = {
-    **_DSV4_LORA_VLLM_ENGINE_ARGS,
-    "load_format": "dummy",
+    "moe_backend": "auto",
 }
 _DSV4_MEGATRON = MegatronWorkflowResources(
     gpu_ids=[0, 1, 2, 3, 4, 5, 6, 7],
     topology=_DSV4_TP2_EP8,
 )
-_DSV4_HIGH_VRAM_MEGATRON = MegatronWorkflowResources(
-    gpu_ids=[0, 1],
-    topology=_DSV4_TP2_EP2,
+_DSV4_FOUR_GPU_MEGATRON = MegatronWorkflowResources(
+    gpu_ids=[0, 1, 2, 3],
+    topology=_DSV4_TP2_EP4,
 )
 _DSV4_FULL_VLLM_EP4 = VllmWorkflowResources(
     gpu_ids=[4, 5, 6, 7],
     tensor_parallel_size=4,
     enable_expert_parallel=True,
-    extra_engine_args=_DSV4_LORA_VLLM_ENGINE_ARGS,
+    extra_engine_args=_DSV4_VLLM_ENGINE_ARGS,
 )
 _DSV4_FULL_VLLM_EP2 = VllmWorkflowResources(
     gpu_ids=[2, 3],
     tensor_parallel_size=2,
     enable_expert_parallel=True,
-    extra_engine_args=_DSV4_LORA_VLLM_ENGINE_ARGS,
+    extra_engine_args=_DSV4_VLLM_ENGINE_ARGS,
 )
-_DSV4_REDUCED_VLLM_EP4 = VllmWorkflowResources(
-    gpu_ids=[4, 5, 6, 7],
-    tensor_parallel_size=4,
-    enable_expert_parallel=True,
-    hf_overrides=_DSV4_HF_OVERRIDES,
-    extra_engine_args=_DSV4_REDUCED_VLLM_ENGINE_ARGS,
+_DSV4_FUNCTIONAL_RESOURCES = WorkflowStageResources(
+    required_world_size=8,
+    required_h200_equivalent_gpus=8,
+    requires_external_vllm=True,
+    megatron=_DSV4_MEGATRON,
+    vllm=_DSV4_FULL_VLLM_EP4,
+    high_vram_megatron=_DSV4_FOUR_GPU_MEGATRON,
+    high_vram_vllm=_DSV4_FULL_VLLM_EP2,
+    streaming_weight_offload=True,
 )
-_DSV4_REDUCED_VLLM_EP2 = VllmWorkflowResources(
-    gpu_ids=[2, 3],
-    tensor_parallel_size=2,
-    enable_expert_parallel=True,
-    hf_overrides=_DSV4_HF_OVERRIDES,
-    extra_engine_args=_DSV4_REDUCED_VLLM_ENGINE_ARGS,
+_GLM52_REDUCED_MEGATRON = MegatronWorkflowResources(
+    gpu_ids=[0],
+    topology=MegatronWorkflowTopology(),
 )
-_DSV4_REDUCED_NATIVE_VLLM_EP4 = VllmWorkflowResources(
-    gpu_ids=[0, 1, 2, 3],
-    tensor_parallel_size=4,
-    enable_expert_parallel=True,
-    hf_overrides=_DSV4_HF_OVERRIDES,
-    extra_engine_args=_DSV4_NATIVE_LORA_VLLM_ENGINE_ARGS,
+_GLM52_REDUCED_VLLM = VllmWorkflowResources(
+    gpu_ids=[1],
+    tensor_parallel_size=1,
+    # The reduced fixture is narrower than the production model. FlashMLA covers
+    # its sparse attention shape while Triton avoids absent SM100 E=4 MoE tuning.
+    extra_engine_args={
+        "attention_backend": "FLASHMLA_SPARSE",
+        "max_model_len": 1024,
+        "moe_backend": "triton",
+    },
+)
+_GLM52_FUNCTIONAL_RESOURCES = WorkflowStageResources(
+    required_world_size=2,
+    megatron=_GLM52_REDUCED_MEGATRON,
+    vllm=_GLM52_REDUCED_VLLM,
 )
-
 # Explicitly for large models which do not fit in the default topology.
 HANDLER_WORKFLOW_RESOURCES: dict[str, HandlerWorkflowResources] = {
     "dsv4": HandlerWorkflowResources(
+        train_inf_mismatch=_DSV4_FUNCTIONAL_RESOURCES,
+        yes_no_trainability=_DSV4_FUNCTIONAL_RESOURCES,
+        length_trainability=_DSV4_FUNCTIONAL_RESOURCES,
+        yes_no_trainability_variant="megatron_dedicated",
+    ),
+    "glm52": HandlerWorkflowResources(
+        train_inf_mismatch=_GLM52_FUNCTIONAL_RESOURCES,
+        yes_no_trainability=_GLM52_FUNCTIONAL_RESOURCES,
+        length_trainability=_GLM52_FUNCTIONAL_RESOURCES,
+        yes_no_trainability_variant="megatron_dedicated",
+    ),
+    "gpt_oss_moe": HandlerWorkflowResources(
         train_inf_mismatch=WorkflowStageResources(
-            required_world_size=8,
-            required_h200_equivalent_gpus=8,
-            requires_external_vllm=True,
-            megatron=_DSV4_MEGATRON,
-            vllm=_DSV4_FULL_VLLM_EP4,
-            high_vram_megatron=_DSV4_HIGH_VRAM_MEGATRON,
-            high_vram_vllm=_DSV4_FULL_VLLM_EP2,
-            streaming_weight_offload=True,
-        ),
-        merged_vllm_serving=WorkflowStageResources(
-            required_world_size=8,
-            required_h200_equivalent_gpus=8,
-            megatron=_DSV4_MEGATRON,
-            vllm=_DSV4_REDUCED_VLLM_EP4,
-            high_vram_megatron=_DSV4_HIGH_VRAM_MEGATRON,
-            high_vram_vllm=_DSV4_REDUCED_VLLM_EP2,
-            megatron_env=_DSV4_MEGATRON_ENV,
-        ),
-        native_vllm_lora=WorkflowStageResources(
-            required_world_size=4,
-            vllm=_DSV4_REDUCED_NATIVE_VLLM_EP4,
-        ),
-        yes_no_trainability=WorkflowStageResources(
-            required_world_size=8,
-            required_h200_equivalent_gpus=8,
-            requires_external_vllm=True,
-            megatron=_DSV4_MEGATRON,
-            vllm=_DSV4_FULL_VLLM_EP4,
-            high_vram_megatron=_DSV4_HIGH_VRAM_MEGATRON,
-            high_vram_vllm=_DSV4_FULL_VLLM_EP2,
-            streaming_weight_offload=True,
+            required_world_size=3,
+            required_physical_gpus=3,
+            megatron=MegatronWorkflowResources(
+                gpu_ids=[0, 1],
+                topology=MegatronWorkflowTopology(cp=2, ep=2),
+            ),
+            vllm=VllmWorkflowResources(
+                gpu_ids=[2],
+                tensor_parallel_size=1,
+            ),
         ),
-        length_trainability=WorkflowStageResources(
-            required_world_size=8,
-            required_h200_equivalent_gpus=8,
-            requires_external_vllm=True,
-            megatron=_DSV4_MEGATRON,
-            vllm=_DSV4_FULL_VLLM_EP4,
-            high_vram_megatron=_DSV4_HIGH_VRAM_MEGATRON,
-            high_vram_vllm=_DSV4_FULL_VLLM_EP2,
-            streaming_weight_offload=True,
-        ),
-        yes_no_trainability_variant="megatron_dedicated",
     ),
 }
 
+_THROUGHPUT_CONFIGS = {
+    "llama3_dense": ThroughputWorkflowConfig(
+        num_layers=24,
+        prompt_tokens=3922,
+        completion_tokens=256,
+        rollouts_per_group=6,
+        groups_per_step=23,
+        initial_model_calls_per_inference_gpu=26,
+        enable_prefix_caching=True,
+    ),
+    "qwen3_dense": ThroughputWorkflowConfig(
+        num_layers=8,
+        completion_tokens=144,
+        rollouts_per_group=8,
+        groups_per_step=25,
+        initial_model_calls_per_inference_gpu=10,
+    ),
+    "qwen3_moe": ThroughputWorkflowConfig(
+        num_layers=16,
+        prompt_tokens=3884,
+        completion_tokens=48,
+        rollouts_per_group=5,
+        groups_per_step=27,
+        initial_model_calls_per_inference_gpu=20,
+        max_steps=17,
+    ),
+    "qwen3_5_dense": ThroughputWorkflowConfig(
+        num_layers=8,
+        prompt_tokens=3839,
+        completion_tokens=64,
+        groups_per_step=31,
+        initial_model_calls_per_inference_gpu=12,
+        enable_prefix_caching=True,
+    ),
+    "qwen3_5_moe": ThroughputWorkflowConfig(
+        num_layers=24,
+        prompt_tokens=7600,
+        completion_tokens=16,
+        groups_per_step=17,
+        initial_model_calls_per_inference_gpu=12,
+        max_num_batched_tokens=THROUGHPUT_PACKED_SEQUENCE_LENGTH,
+        enable_prefix_caching=True,
+    ),
+    "gemma4_dense": ThroughputWorkflowConfig(
+        num_layers=12,
+        completion_tokens=75,
+        rollouts_per_group=7,
+        groups_per_step=30,
+        initial_model_calls_per_inference_gpu=11,
+    ),
+    "gemma4_moe": ThroughputWorkflowConfig(
+        num_layers=12,
+        prompt_tokens=3640,
+        completion_tokens=128,
+        groups_per_step=31,
+        initial_model_calls_per_inference_gpu=26,
+    ),
+    "dsv4": ThroughputWorkflowConfig(
+        num_layers=8,
+        packed_sequence_length=32_768,
+        prompt_tokens=14_651,
+        completion_tokens=84,
+        rollouts_per_group=20,
+        groups_per_step=4,
+        initial_model_calls_per_inference_gpu=6,
+        max_num_seqs=80,
+        max_num_batched_tokens=131_072,
+        enable_prefix_caching=True,
+    ),
+    "glm52": ThroughputWorkflowConfig(
+        num_layers=12,
+        prompt_tokens=3836,
+        completion_tokens=1024,
+        groups_per_step=16,
+        initial_model_calls_per_inference_gpu=19,
+    ),
+    "gpt_oss_moe": ThroughputWorkflowConfig(
+        num_layers=4,
+        initial_model_calls_per_inference_gpu=23,
+        max_num_seqs=48,
+        max_steps=21,
+    ),
+}
+
+# Floors are isolated tok/s, E2E tok/s, accepted tok/s, E2E/isolated, and
+# maximum repeated policy-activation interval. B300 values are measured; H200
+# values are estimates from the prior H200 workflow and remain fingerprint-free.
+_B300_THROUGHPUT_FLOORS = {
+    "llama3_dense": (
+        "b777d6c00d6574a9445b5a460f36909ba48155355e51034867aa286be171894d",
+        (38_500, 37_300, 10_500, 0.93, 4.5),
+    ),
+    "qwen3_dense": (
+        "fde06e40ef5a363a7910b349b5364dc84992a6aa31c7b1267d63d870fd57fd69",
+        (40_200, 37_600, 8_600, 0.88, 4.5),
+    ),
+    "qwen3_moe": (
+        "d41841a7ff6d0fcca3fe9f3ce240519143da1a1e7931fc313a88b11734535a62",
+        (49_900, 43_700, 2_050, 0.82, 4.5),
+    ),
+    "qwen3_5_dense": (
+        "5617e8880591545a3281ff14d1fe5197eeefc21a81ec80d1a107fd31421d37a0",
+        (64_800, 60_000, 3_750, 0.87, 3.5),
+    ),
+    "qwen3_5_moe": (
+        "72172ac8d112af1dd7248f52e36ff5cf4cd6c2407d4a6ffb50e2b4758e8bb98d",
+        (32_600, 30_800, 257, 0.89, 5.5),
+    ),
+    "gemma4_dense": (
+        "05fc46053854bd510487296cc6923cce846f8fb3e7b57bd67ac00848625c1a78",
+        (23_100, 22_700, 2_390, 0.93, 7.0),
+    ),
+    "gemma4_moe": (
+        "23f9679170045207c3e85dbd4496cc67a14f136f769171e18ba463788c730ac8",
+        (40_300, 38_500, 4_740, 0.90, 5.0),
+    ),
+    "dsv4": (
+        "8f947ec5b5d3237ad4b6a94f8ac0333b7f486f9c2bdff2b0e014f4db3b440854",
+        (14_800, 14_300, 1_300, 0.94, 6.0),
+    ),
+    "glm52": (
+        "bf81b6800b9ea080514da72e5e3a989e72fe523a6cdde9cbe9271eaf162c0f07",
+        (14_880, 14_330, 5_730, 0.91, 12.0),
+    ),
+    "gpt_oss_moe": (
+        "79572974803f721f65252935f0f53739e3e37110790f6957c6dd6b305a5f0689",
+        (81_700, 76_400, 4_850, 0.88, 2.5),
+    ),
+}
+_H200_THROUGHPUT_FLOORS = {
+    "llama3_dense": (18_300, 17_200, 4_400, 0.89, 7.0),
+    "qwen3_dense": (24_100, 23_100, 5_000, 0.91, 7.0),
+    "qwen3_moe": (26_400, 20_900, 930, 0.74, 10.0),
+    "qwen3_5_dense": (26_500, 25_600, 1_500, 0.91, 5.5),
+    "qwen3_5_moe": (13_600, 12_900, 100, 0.90, 12.0),
+    "gemma4_dense": (10_600, 10_400, 1_000, 0.93, 13.0),
+    "gemma4_moe": (17_900, 17_300, 2_000, 0.91, 9.5),
+    "dsv4": (8_300, 8_000, 700, 0.90, 12.0),
+    "glm52": (9_400, 9_000, 3_400, 0.91, 19.5),
+    "gpt_oss_moe": (39_900, 37_100, 2_200, 0.88, 4.5),
+}
+
+
+def _throughput_threshold(
+    calibration_basis: Literal["measured", "estimated"],
+    floor: tuple[float, float, float, float, float],
+    *,
+    calibration_fingerprint: str | None = None,
+    max_mean_policy_activation_lag_s: float = 1.5,
+) -> ThroughputThresholds:
+    isolated, e2e, accepted, ratio, cadence = floor
+    return ThroughputThresholds(
+        calibration_basis=calibration_basis,
+        calibration_fingerprint=calibration_fingerprint,
+        min_isolated_train_tok_s=isolated,
+        min_e2e_train_tok_s=e2e,
+        min_accepted_train_tok_s=accepted,
+        min_e2e_to_isolated_ratio=ratio,
+        min_matched_core_to_isolated_ratio=0.95,
+        max_mean_policy_activation_lag_s=max_mean_policy_activation_lag_s,
+        max_policy_activation_lag_s=3.5,
+        max_repeated_policy_activation_interval_s=cadence,
+    )
+
+
+for _model_key, (_fingerprint, _b300_floor) in _B300_THROUGHPUT_FLOORS.items():
+    _max_mean_activation_lag_s = 2.25 if _model_key == "dsv4" else 1.5
+    _THROUGHPUT_CONFIGS[_model_key] = _THROUGHPUT_CONFIGS[_model_key].model_copy(
+        update={
+            "thresholds": {
+                "b300": _throughput_threshold(
+                    "measured",
+                    _b300_floor,
+                    calibration_fingerprint=_fingerprint,
+                    max_mean_policy_activation_lag_s=_max_mean_activation_lag_s,
+                ),
+                "h200": _throughput_threshold(
+                    "estimated",
+                    _H200_THROUGHPUT_FLOORS[_model_key],
+                    max_mean_policy_activation_lag_s=_max_mean_activation_lag_s,
+                ),
+            }
+        }
+    )
+
+_DENSE_HANDLER_KEYS = {
+    "llama3_dense",
+    "qwen3_dense",
+    "qwen3_5_dense",
+    "gemma4_dense",
+}
+
+
+def _throughput_stage_resources(model_key: str) -> WorkflowStageResources:
+    config = _THROUGHPUT_CONFIGS[model_key]
+    is_moe = model_key not in _DENSE_HANDLER_KEYS
+    vllm_engine_args: dict[str, object] = {
+        "disable_custom_all_reduce": True,
+        "load_format": "dummy",
+        "gpu_memory_utilization": 0.82,
+        "max_model_len": 16_384,
+        "max_num_batched_tokens": config.max_num_batched_tokens,
+        "max_num_seqs": config.max_num_seqs,
+        "lora_dtype": "bfloat16",
+    }
+    if model_key in {"qwen3_moe", "qwen3_5_moe"}:
+        vllm_engine_args["compilation_config"] = {
+            "pass_config": {"fuse_allreduce_rms": False}
+        }
+    if config.enable_prefix_caching:
+        vllm_engine_args["enable_prefix_caching"] = True
+    if model_key == "dsv4":
+        vllm_engine_args.update(
+            compilation_config={
+                "cudagraph_mode": "NONE",
+                "pass_config": {"fuse_allreduce_rms": False},
+            },
+            enforce_eager=True,
+            kv_cache_dtype="fp8",
+        )
+    return WorkflowStageResources(
+        required_world_size=4,
+        required_physical_gpus=4,
+        megatron=MegatronWorkflowResources(
+            gpu_ids=[0, 1],
+            topology=MegatronWorkflowTopology(
+                cp=1 if model_key == "dsv4" else 2,
+                ep=2 if is_moe else 1,
+            ),
+        ),
+        vllm=VllmWorkflowResources(
+            gpu_ids=[2, 3],
+            tensor_parallel_size=2,
+            enable_expert_parallel=is_moe,
+            extra_engine_args=vllm_engine_args,
+        ),
+        throughput=config,
+    )
+
+
+for _model_key in _THROUGHPUT_CONFIGS:
+    _resources = HANDLER_WORKFLOW_RESOURCES.get(_model_key, HandlerWorkflowResources())
+    HANDLER_WORKFLOW_RESOURCES[_model_key] = _resources.model_copy(
+        update={"e2e_throughput": _throughput_stage_resources(_model_key)}
+    )
+
 
 def handler_workflow_resources_for_base_model(
     base_model: str,
@@ -305,19 +576,6 @@ def _visible_h200_equivalent_gpus(*, visible_gpu_count: int) -> int:
     return equivalent
 
 
-def _remap_gpu_ids_to_visible(
-    gpu_ids: list[int], *, visible_gpu_count: int
-) -> list[int]:
-    if all(0 <= gpu_id < visible_gpu_count for gpu_id in gpu_ids):
-        return list(gpu_ids)
-    if len(gpu_ids) > visible_gpu_count:
-        raise RuntimeError(
-            "Cannot remap workflow GPU ids to visible high-VRAM devices: "
-            f"gpu_ids={gpu_ids}, visible_gpu_count={visible_gpu_count}"
-        )
-    return list(range(len(gpu_ids)))
-
-
 def _validate_gpu_ids_visible(gpu_ids: list[int], *, visible_gpu_count: int) -> None:
     invalid = [
         gpu_id for gpu_id in gpu_ids if gpu_id < 0 or gpu_id >= visible_gpu_count
@@ -335,6 +593,13 @@ def resolve_stage_resources_for_visible_gpus(
     *,
     visible_gpu_count: int,
 ) -> WorkflowStageResources:
+    required_physical = stage_resources.required_physical_gpus
+    if required_physical is not None and visible_gpu_count < required_physical:
+        raise RuntimeError(
+            f"Need {required_physical} physical GPUs for {stage_name}, found "
+            f"{visible_gpu_count}; H200-equivalent capacity cannot coalesce "
+            "distinct workflow roles."
+        )
     if visible_gpu_count >= stage_resources.required_world_size:
         return stage_resources
     required_equivalent = stage_resources.required_h200_equivalent_gpus
@@ -348,94 +613,46 @@ def resolve_stage_resources_for_visible_gpus(
             f"requires {required_equivalent or stage_resources.required_world_size} "
             f"H200-equivalent GPUs, found {available_equivalent}."
         )
-    if (
-        stage_resources.high_vram_megatron is not None
-        or stage_resources.high_vram_vllm is not None
-    ):
-        megatron = stage_resources.high_vram_megatron or stage_resources.megatron
-        vllm = stage_resources.high_vram_vllm or stage_resources.vllm
-        if megatron is not None:
-            _validate_gpu_ids_visible(
-                megatron.gpu_ids,
-                visible_gpu_count=visible_gpu_count,
-            )
-        if vllm is not None:
-            _validate_gpu_ids_visible(
-                vllm.gpu_ids,
-                visible_gpu_count=visible_gpu_count,
-            )
-        return stage_resources.model_copy(update={"megatron": megatron, "vllm": vllm})
-    if not stage_resources.allow_gpu_overlap:
+    megatron = stage_resources.high_vram_megatron
+    vllm = stage_resources.high_vram_vllm
+    if megatron is None and vllm is None:
         raise RuntimeError(
             f"Need {stage_resources.required_world_size} visible GPUs for "
             f"{stage_name}, found {visible_gpu_count}. No high-VRAM resource "
             "override is configured for this stage."
         )
-    megatron = stage_resources.megatron
     if megatron is not None:
-        megatron = megatron.model_copy(
-            update={
-                "gpu_ids": _remap_gpu_ids_to_visible(
-                    megatron.gpu_ids,
-                    visible_gpu_count=visible_gpu_count,
-                )
-            }
+        _validate_gpu_ids_visible(
+            megatron.gpu_ids,
+            visible_gpu_count=visible_gpu_count,
         )
-    vllm = stage_resources.vllm
     if vllm is not None:
-        vllm = vllm.model_copy(
-            update={
-                "gpu_ids": _remap_gpu_ids_to_visible(
-                    vllm.gpu_ids,
-                    visible_gpu_count=visible_gpu_count,
-                )
-            }
+        _validate_gpu_ids_visible(
+            vllm.gpu_ids,
+            visible_gpu_count=visible_gpu_count,
         )
-    return stage_resources.model_copy(update={"megatron": megatron, "vllm": vllm})
+    return stage_resources.model_copy(
+        update={
+            "megatron": megatron or stage_resources.megatron,
+            "vllm": vllm or stage_resources.vllm,
+        }
+    )
+
+
+def _current_visible_gpu_count() -> int:
+    try:
+        import torch
+    except ImportError:
+        return 0
+    return int(torch.cuda.device_count())
 
 
 def resolve_stage_resources_for_current_host(
     stage_name: str,
     stage_resources: WorkflowStageResources,
 ) -> WorkflowStageResources:
-    try:
-        import torch
-    except ImportError:
-        visible_gpu_count = 0
-    else:
-        visible_gpu_count = int(torch.cuda.device_count())
     return resolve_stage_resources_for_visible_gpus(
         stage_name,
         stage_resources,
-        visible_gpu_count=visible_gpu_count,
+        visible_gpu_count=_current_visible_gpu_count(),
     )
-
-
-def validate_visible_gpu_count(
-    stage_name: str,
-    stage_resources: WorkflowStageResources,
-    *,
-    visible_gpu_count: int,
-) -> None:
-    if visible_gpu_count < stage_resources.required_world_size:
-        raise RuntimeError(
-            f"Need {stage_resources.required_world_size} visible GPUs for "
-            f"{stage_name}, found {visible_gpu_count}"
-        )
-
-
-def validate_dedicated_test_resources(
-    *,
-    stage_name: str,
-    trainer_gpu_ids: list[int],
-    inference_gpu_ids: list[int],
-    allow_overlap: bool = False,
-) -> None:
-    if not trainer_gpu_ids:
-        raise RuntimeError(f"{stage_name} trainer GPU ids must be non-empty")
-    if not inference_gpu_ids:
-        raise RuntimeError(f"{stage_name} inference GPU ids must be non-empty")
-    if not allow_overlap and set(trainer_gpu_ids) & set(inference_gpu_ids):
-        raise RuntimeError(
-            f"{stage_name} trainer and inference GPU ids must not overlap"
-        )
diff --git a/tests/integration/megatron/model_support/workflow_runtime.py b/tests/integration/megatron/model_support/workflow_runtime.py
new file mode 100644
index 000000000..2db81fe21
--- /dev/null
+++ b/tests/integration/megatron/model_support/workflow_runtime.py
@@ -0,0 +1,547 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from collections.abc import Callable, Iterable, Sequence
+from concurrent.futures import FIRST_COMPLETED, Future, ThreadPoolExecutor, wait
+from threading import Lock
+import time
+from typing import Any, Literal
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+
+class WorkflowTrainerTopology(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    variant: str = Field(min_length=1)
+    tp: int = Field(default=1, ge=1)
+    cp: int = Field(default=1, ge=1)
+    ep: int = Field(default=1, ge=1)
+    etp: int = Field(default=1, ge=1)
+    dp: int = Field(default=1, ge=1)
+    pp: int = Field(default=1, ge=1)
+    vpp: int = Field(default=1, ge=1)
+    sp: bool = False
+
+
+class WorkflowVllmTopology(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    variant: str = Field(min_length=1)
+    tp: int = Field(default=1, ge=1)
+    pp: int = Field(default=1, ge=1)
+    dp: int = Field(default=1, ge=1)
+    ep: bool = False
+
+
+class WorkflowRolePlacement(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    variant: str = Field(min_length=1)
+    trainer_gpu_ids: tuple[int, ...] = ()
+    vllm_gpu_ids: tuple[int, ...] = ()
+    vllm_external: bool = False
+
+    @model_validator(mode="after")
+    def validate_relative_gpu_ids(self) -> "WorkflowRolePlacement":
+        for role, gpu_ids in (
+            ("trainer", self.trainer_gpu_ids),
+            ("vLLM", self.vllm_gpu_ids),
+        ):
+            if len(set(gpu_ids)) != len(gpu_ids) or any(
+                gpu_id < 0 for gpu_id in gpu_ids
+            ):
+                raise ValueError(
+                    f"{role} relative GPU ids must be unique and non-negative"
+                )
+        return self
+
+
+class WorkflowRuntimeTopology(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    trainer_variants: tuple[WorkflowTrainerTopology, ...] = ()
+    vllm_variants: tuple[WorkflowVllmTopology, ...] = ()
+    role_placements: tuple[WorkflowRolePlacement, ...] = ()
+
+
+class WorkflowRuntimeKey(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    source_fingerprint: str
+    handler: str
+    fixture: str
+    kind: Literal["cpu", "megatron", "vllm", "joint"]
+    topology: WorkflowRuntimeTopology = Field(default_factory=WorkflowRuntimeTopology)
+    mode: str = ""
+    static_options: str = ""
+
+
+class WorkflowResourceRequest(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    gpu_count: int = Field(default=0, ge=0)
+    gpu_share: float = Field(default=1.0, gt=0.0, le=1.0)
+    host_affinity: str | None = Field(default=None, min_length=1)
+
+    @model_validator(mode="after")
+    def cpu_requests_do_not_reserve_gpu_capacity(self) -> "WorkflowResourceRequest":
+        if self.gpu_count == 0 and self.gpu_share != 1.0:
+            raise ValueError("CPU operations cannot reserve fractional GPU capacity")
+        return self
+
+
+class WorkflowOperation(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    id: str
+    stage: str
+    runtime: WorkflowRuntimeKey
+    resources: WorkflowResourceRequest = Field(default_factory=WorkflowResourceRequest)
+    dependencies: tuple[str, ...] = ()
+    estimated_duration_s: float = Field(default=0.0, ge=0.0)
+    estimated_shared_startup_s: float = Field(default=0.0, ge=0.0)
+
+    @model_validator(mode="after")
+    def shared_startup_is_part_of_duration(self) -> "WorkflowOperation":
+        if self.estimated_shared_startup_s > self.estimated_duration_s:
+            raise ValueError("shared startup cannot exceed operation duration")
+        return self
+
+
+class WorkflowSession(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    id: str
+    runtime: WorkflowRuntimeKey
+    operations: tuple[WorkflowOperation, ...]
+    resources: WorkflowResourceRequest
+    dependencies: tuple[str, ...] = ()
+    estimated_duration_s: float = Field(ge=0.0)
+
+
+class WorkflowPlan(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    sessions: tuple[WorkflowSession, ...]
+
+
+class WorkflowDevice(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    host: str
+    gpu: str
+
+
+class WorkflowPlacement(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    host: str | None = None
+    devices: tuple[WorkflowDevice, ...] = ()
+
+    @model_validator(mode="after")
+    def devices_are_on_placement_host(self) -> "WorkflowPlacement":
+        device_hosts = {device.host for device in self.devices}
+        if len(device_hosts) > 1 or (
+            self.host is not None and device_hosts and device_hosts != {self.host}
+        ):
+            raise ValueError("workflow placement devices must share its host")
+        return self
+
+
+class WorkflowSessionResult(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    session_id: str
+    placement: WorkflowPlacement
+    started_monotonic_s: float
+    ended_monotonic_s: float
+    output: Any = None
+    failed_operation_id: str | None = None
+
+
+class WorkflowExecution(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    results: dict[str, WorkflowSessionResult]
+    blocked_by_failed_operations: dict[str, tuple[str, ...]] = Field(
+        default_factory=dict
+    )
+    elapsed_s: float
+
+
+class WorkflowOperationFailed(RuntimeError):
+    def __init__(self, operation_id: str) -> None:
+        if not operation_id:
+            raise ValueError("failed workflow operation id cannot be empty")
+        self.operation_id = operation_id
+        super().__init__(f"workflow operation failed: {operation_id}")
+
+
+def compile_workflow(operations: Sequence[WorkflowOperation]) -> WorkflowPlan:
+    """Collapse compatible operations into fixed-runtime sessions."""
+    by_id = {operation.id: operation for operation in operations}
+    if len(by_id) != len(operations):
+        raise ValueError("workflow operation ids must be unique")
+    unknown = {
+        dependency
+        for operation in operations
+        for dependency in operation.dependencies
+        if dependency not in by_id
+    }
+    if unknown:
+        raise ValueError(f"unknown workflow dependencies: {sorted(unknown)}")
+    operation_order = _topological_order(
+        by_id, {key: value.dependencies for key, value in by_id.items()}
+    )
+    operation_rank = {
+        operation_id: rank for rank, operation_id in enumerate(operation_order)
+    }
+
+    grouped: dict[WorkflowRuntimeKey, list[WorkflowOperation]] = defaultdict(list)
+    runtime_order: list[WorkflowRuntimeKey] = []
+    for operation in operations:
+        if operation.runtime not in grouped:
+            runtime_order.append(operation.runtime)
+        grouped[operation.runtime].append(operation)
+
+    operation_session = {
+        operation.id: f"session_{index:03d}"
+        for index, runtime in enumerate(runtime_order)
+        for operation in grouped[runtime]
+    }
+    sessions: list[WorkflowSession] = []
+    for index, runtime in enumerate(runtime_order):
+        grouped_operations = tuple(
+            sorted(grouped[runtime], key=lambda operation: operation_rank[operation.id])
+        )
+        resources = grouped_operations[0].resources
+        if any(
+            operation.resources != resources for operation in grouped_operations[1:]
+        ):
+            raise ValueError(f"runtime {runtime} has inconsistent resource requests")
+        session_id = f"session_{index:03d}"
+        dependencies = tuple(
+            dict.fromkeys(
+                operation_session[dependency]
+                for operation in grouped_operations
+                for dependency in operation.dependencies
+                if operation_session[dependency] != session_id
+            )
+        )
+        sessions.append(
+            WorkflowSession(
+                id=session_id,
+                runtime=runtime,
+                operations=grouped_operations,
+                resources=resources,
+                dependencies=dependencies,
+                estimated_duration_s=sum(
+                    operation.estimated_duration_s
+                    - operation.estimated_shared_startup_s
+                    for operation in grouped_operations
+                )
+                + max(
+                    (
+                        operation.estimated_shared_startup_s
+                        for operation in grouped_operations
+                    ),
+                    default=0.0,
+                ),
+            )
+        )
+    session_by_id = {session.id: session for session in sessions}
+    _topological_order(
+        session_by_id,
+        {key: value.dependencies for key, value in session_by_id.items()},
+    )
+    return WorkflowPlan(sessions=tuple(sessions))
+
+
+def _topological_order(
+    values: dict[str, Any], dependencies: dict[str, Iterable[str]]
+) -> list[str]:
+    pending = {key: set(dependencies[key]) for key in values}
+    order: list[str] = []
+    while pending:
+        ready = sorted(key for key, deps in pending.items() if not deps)
+        if not ready:
+            raise ValueError(f"workflow dependency cycle: {sorted(pending)}")
+        order.extend(ready)
+        for key in ready:
+            pending.pop(key)
+        for deps in pending.values():
+            deps.difference_update(ready)
+    return order
+
+
+class _GpuPool:
+    def __init__(self, devices: Sequence[WorkflowDevice]) -> None:
+        if len(set(devices)) != len(devices):
+            raise ValueError("workflow devices must be unique")
+        self._devices = tuple(devices)
+        self._hosts = tuple(dict.fromkeys(device.host for device in devices))
+        self._available = {device: 1.0 for device in devices}
+        self._active_by_host = {host: 0 for host in self._hosts}
+        self._affinity_hosts: dict[str, str] = {}
+        self._affinity_bindings_by_host = {host: 0 for host in self._hosts}
+        self._lock = Lock()
+
+    def acquire(self, request: WorkflowResourceRequest) -> WorkflowPlacement | None:
+        with self._lock:
+            affinity_host = self._affinity_host(request)
+            if request.gpu_count == 0:
+                host = affinity_host
+                if host is None:
+                    host = min(
+                        self._hosts,
+                        key=lambda value: (
+                            self._active_by_host[value],
+                            self._hosts.index(value),
+                        ),
+                        default=None,
+                    )
+                if host is not None:
+                    self._active_by_host[host] += 1
+                return WorkflowPlacement(host=host)
+            placements = self._candidate_placements(request)
+            if affinity_host is not None:
+                placements = [
+                    devices
+                    for devices in placements
+                    if devices[0].host == affinity_host
+                ]
+            if placements:
+                selected = min(
+                    placements,
+                    key=lambda devices: self._placement_priority(devices, request),
+                )
+                for device in selected:
+                    self._available[device] -= request.gpu_share
+                host = selected[0].host
+                self._active_by_host[host] += 1
+                return WorkflowPlacement(host=host, devices=selected)
+        return None
+
+    def _affinity_host(self, request: WorkflowResourceRequest) -> str | None:
+        affinity = request.host_affinity
+        if affinity is None:
+            return None
+        if affinity not in self._affinity_hosts:
+            host = min(
+                self._hosts,
+                key=lambda value: (
+                    self._affinity_bindings_by_host[value],
+                    self._active_by_host[value],
+                    self._hosts.index(value),
+                ),
+            )
+            self._affinity_hosts[affinity] = host
+            self._affinity_bindings_by_host[host] += 1
+        return self._affinity_hosts[affinity]
+
+    def _placement_priority(
+        self,
+        devices: tuple[WorkflowDevice, ...],
+        request: WorkflowResourceRequest,
+    ) -> tuple[Any, ...]:
+        host = devices[0].host
+        available = tuple(self._available[device] for device in devices)
+        indices = tuple(self._devices.index(device) for device in devices)
+        if request.gpu_share < 1.0:
+            return (
+                self._active_by_host[host],
+                sum(available),
+                indices,
+            )
+        return (
+            self._active_by_host[host],
+            -min(available),
+            -sum(available),
+            indices,
+        )
+
+    def _candidate_placements(
+        self, request: WorkflowResourceRequest
+    ) -> list[tuple[WorkflowDevice, ...]]:
+        eligible = [
+            device
+            for device in self._devices
+            if self._available[device] + 1e-9 >= request.gpu_share
+        ]
+        by_host: dict[str, list[WorkflowDevice]] = defaultdict(list)
+        for device in eligible:
+            by_host[device.host].append(device)
+        return [
+            selected
+            for devices in by_host.values()
+            for start in range(len(devices) - request.gpu_count + 1)
+            if _contiguous(
+                selected := tuple(devices[start : start + request.gpu_count]),
+                self._devices,
+            )
+        ]
+
+    def release(
+        self, placement: WorkflowPlacement, request: WorkflowResourceRequest
+    ) -> None:
+        with self._lock:
+            for device in placement.devices:
+                self._available[device] += request.gpu_share
+                if self._available[device] > 1.0 + 1e-9:
+                    raise RuntimeError(f"released unowned workflow GPU {device}")
+            if placement.host is not None:
+                self._active_by_host[placement.host] -= 1
+                if self._active_by_host[placement.host] < 0:
+                    raise RuntimeError(
+                        f"released unowned workflow host {placement.host}"
+                    )
+
+
+def _contiguous(
+    selected: Sequence[WorkflowDevice], inventory: Sequence[WorkflowDevice]
+) -> bool:
+    positions = sorted(inventory.index(device) for device in selected)
+    return not selected or (
+        len({device.host for device in selected}) == 1
+        and positions == list(range(positions[0], positions[0] + len(positions)))
+    )
+
+
+def execute_workflow(
+    plan: WorkflowPlan,
+    *,
+    devices: Sequence[WorkflowDevice],
+    runner: Callable[[WorkflowSession, WorkflowPlacement], Any],
+    max_workers: int | None = None,
+) -> WorkflowExecution:
+    """Execute a session DAG with exact GPU leases and longest-path priority."""
+    started = time.monotonic()
+    sessions = {session.id: session for session in plan.sessions}
+    critical_path = _critical_path_durations(sessions)
+    pending = set(sessions)
+    completed: set[str] = set()
+    failed_dependencies: dict[str, tuple[str, ...]] = {}
+    blocked_by_failed_operations: dict[str, tuple[str, ...]] = {}
+    running: dict[Future[Any], tuple[WorkflowSession, WorkflowPlacement, float]] = {}
+    results: dict[str, WorkflowSessionResult] = {}
+    pool = _GpuPool(devices)
+
+    with ThreadPoolExecutor(
+        max_workers=max_workers or max(1, len(sessions))
+    ) as executor:
+        while pending or running:
+            while newly_blocked := {
+                session_id: tuple(
+                    sorted(
+                        {
+                            failed_operation
+                            for dependency in sessions[session_id].dependencies
+                            for failed_operation in failed_dependencies.get(
+                                dependency, ()
+                            )
+                        }
+                    )
+                )
+                for session_id in sorted(pending)
+                if any(
+                    dependency in failed_dependencies
+                    for dependency in sessions[session_id].dependencies
+                )
+            }:
+                for session_id, failed_operations in newly_blocked.items():
+                    pending.remove(session_id)
+                    failed_dependencies[session_id] = failed_operations
+                    blocked_by_failed_operations[session_id] = failed_operations
+            ready = sorted(
+                (
+                    sessions[session_id]
+                    for session_id in pending
+                    if set(sessions[session_id].dependencies) <= completed
+                ),
+                key=lambda session: (
+                    session.resources.gpu_count > 0
+                    and session.resources.gpu_share < 1.0,
+                    -session.resources.gpu_count,
+                    -critical_path[session.id],
+                    session.id,
+                ),
+            )
+            launched = False
+            for session in ready:
+                placement = pool.acquire(session.resources)
+                if placement is None:
+                    continue
+                session_started = time.monotonic()
+                future = executor.submit(runner, session, placement)
+                running[future] = (session, placement, session_started)
+                pending.remove(session.id)
+                launched = True
+            if not running:
+                if pending:
+                    blocked = sorted(pending)
+                    raise RuntimeError(
+                        f"workflow sessions do not fit available GPUs: {blocked}"
+                    )
+                break
+            if launched:
+                done = {future for future in running if future.done()}
+                if not done:
+                    continue
+            else:
+                done, _ = wait(running, return_when=FIRST_COMPLETED)
+            for future in done:
+                session, placement, session_started = running.pop(future)
+                failure: WorkflowOperationFailed | None = None
+                try:
+                    output = future.result()
+                except WorkflowOperationFailed as exc:
+                    if exc.operation_id not in {
+                        operation.id for operation in session.operations
+                    }:
+                        raise RuntimeError(
+                            f"session {session.id} reported failure for unknown operation "
+                            f"{exc.operation_id}"
+                        ) from exc
+                    failure = exc
+                    output = None
+                finally:
+                    pool.release(placement, session.resources)
+                results[session.id] = WorkflowSessionResult(
+                    session_id=session.id,
+                    placement=placement,
+                    started_monotonic_s=session_started,
+                    ended_monotonic_s=time.monotonic(),
+                    output=output,
+                    failed_operation_id=(
+                        failure.operation_id if failure is not None else None
+                    ),
+                )
+                if failure is None:
+                    completed.add(session.id)
+                else:
+                    failed_dependencies[session.id] = (failure.operation_id,)
+    return WorkflowExecution(
+        results=results,
+        blocked_by_failed_operations=blocked_by_failed_operations,
+        elapsed_s=time.monotonic() - started,
+    )
+
+
+def _critical_path_durations(
+    sessions: dict[str, WorkflowSession],
+) -> dict[str, float]:
+    children: dict[str, list[str]] = defaultdict(list)
+    for session in sessions.values():
+        for dependency in session.dependencies:
+            children[dependency].append(session.id)
+    memo: dict[str, float] = {}
+
+    def duration(session_id: str) -> float:
+        if session_id not in memo:
+            memo[session_id] = sessions[session_id].estimated_duration_s + max(
+                (duration(child) for child in children[session_id]), default=0.0
+            )
+        return memo[session_id]
+
+    return {session_id: duration(session_id) for session_id in sessions}
diff --git a/tests/integration/megatron/model_support/workflow_scheduler.py b/tests/integration/megatron/model_support/workflow_scheduler.py
new file mode 100644
index 000000000..1bac0f4e8
--- /dev/null
+++ b/tests/integration/megatron/model_support/workflow_scheduler.py
@@ -0,0 +1,1404 @@
+from __future__ import annotations
+
+from concurrent.futures import ThreadPoolExecutor
+from contextlib import nullcontext
+import os
+from pathlib import Path
+import socket
+from threading import Lock
+import time
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, PrivateAttr
+
+from art.megatron.lora_config import (
+    MEGATRON_LORA_RANK_ENV,
+    default_lora_rank_for_handler,
+)
+from art.megatron.model_support.registry import (
+    get_model_support_handler_for_spec,
+    get_model_support_spec,
+    model_uses_expert_parallel,
+)
+from art.megatron.model_support.spec import ArchitectureReport
+from art.vllm_runtime import VllmRuntimeLaunchConfig
+
+from .validation_spec import ValidationReport, ValidationStageResult
+from .workflow import (
+    CORRECTNESS_ARTIFACT_ROOT_ENV,
+    CORRECTNESS_PHASE_ENV,
+    CORRECTNESS_REFERENCE_STAGE,
+)
+from .workflow_fixtures import (
+    FIXTURE_PATH_ENV,
+    WorkflowFixture,
+    ensure_workflow_fixture,
+)
+from .workflow_forkserver import WorkflowForkserverPool
+from .workflow_resources import (
+    HANDLER_WORKFLOW_RESOURCES,
+    WorkflowStageResources,
+    resolve_stage_resources_for_visible_gpus,
+)
+from .workflow_runtime import (
+    WorkflowDevice,
+    WorkflowOperation,
+    WorkflowOperationFailed,
+    WorkflowPlacement,
+    WorkflowResourceRequest,
+    WorkflowRolePlacement,
+    WorkflowRuntimeKey,
+    WorkflowRuntimeTopology,
+    WorkflowSession,
+    WorkflowTrainerTopology,
+    WorkflowVllmTopology,
+    compile_workflow,
+    execute_workflow,
+)
+from .workflow_stage_worker import (
+    BASE_MEGATRON_MODE,
+    BASE_MEGATRON_STAGES,
+    RESIDENT_FUNCTIONAL_MODE,
+    RESIDENT_FUNCTIONAL_STAGES,
+    ResidentFunctionalSessionSpec,
+    WorkflowStageWorkerItem,
+    WorkflowStageWorkerSession,
+)
+
+_REDUCED_FIXTURE_GPU_SHARE = 0.125
+_LIGHTWEIGHT_GPU_SHARE_OVERRIDES = {("glm52", "hf_parity"): 1.0}
+_STAGE_DURATION_ESTIMATES_S = {
+    "hf_parity": 120.0,
+    "lora_coverage": 60.0,
+    "train_inf_mismatch": 360.0,
+    "correctness_sensitivity": 480.0,
+    CORRECTNESS_REFERENCE_STAGE: 120.0,
+    "chat_template_rollout": 45.0,
+    "packing_invariance": 90.0,
+    "length_trainability": 360.0,
+    "e2e_throughput": 360.0,
+}
+_STAGE_DURATION_ESTIMATE_OVERRIDES_S = {
+    # Rounded B300 workflow measurements; DSV4 is a deliberate architecture outlier.
+    ("e2e_throughput", "gpt_oss_moe"): 300.0,
+    ("e2e_throughput", "dsv4"): 690.0,
+}
+_SHARED_STARTUP_ESTIMATES_S = {
+    # Base phases measured 74-79s standalone and share the same model startup.
+    BASE_MEGATRON_MODE: 60.0,
+    # Conservative cross-handler budget for resident trainer and serving startup.
+    RESIDENT_FUNCTIONAL_MODE: 200.0,
+}
+_LIGHTWEIGHT_GPU_STAGES = frozenset(
+    {"hf_parity", "lora_coverage", "packing_invariance"}
+)
+_CPU_STAGES = frozenset({"chat_template_rollout"})
+_DEFAULT_STAGE_GPU_COUNTS = {
+    "train_inf_mismatch": 4,
+}
+_WORKFLOW_HOSTS_ENV = "ART_MODEL_SUPPORT_WORKFLOW_HOSTS"
+_VLLM_CAPACITY_ARGS = (
+    "gpu_memory_utilization",
+    "max_model_len",
+    "max_logprobs",
+    "max_num_seqs",
+    "max_loras",
+    "max_lora_rank",
+    "max_num_batched_tokens",
+)
+_VLLM_PARALLEL_ARGS = (
+    "tensor_parallel_size",
+    "pipeline_parallel_size",
+    "data_parallel_size",
+)
+
+
+class PreparedWorkflow(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    report: ValidationReport
+    architecture: ArchitectureReport
+    fixture: WorkflowFixture
+    run_dir: Path
+    output_json: Path | None
+    stages: tuple[str, ...]
+    allow_unvalidated_arch: bool
+    include_sensitivity: bool | None
+    fixture_provisioning_s: float
+    _fixture_metric_recorded: bool = PrivateAttr(default=False)
+    _lock: Lock = PrivateAttr(default_factory=Lock)
+
+    def record(self, result: ValidationStageResult) -> None:
+        with self._lock:
+            stage = next(
+                stage for stage in self.report.stages if stage.name == result.name
+            )
+            stage.passed = result.passed
+            stage.skipped = result.skipped
+            stage.metrics = dict(result.metrics)
+            stage.artifact_dir = result.artifact_dir
+            if self.output_json is not None:
+                self.output_json.parent.mkdir(parents=True, exist_ok=True)
+                self.output_json.write_text(
+                    self.report.model_dump_json(indent=2), encoding="utf-8"
+                )
+
+    def record_fixture_metric(self, metrics: dict[str, Any]) -> None:
+        with self._lock:
+            if self._fixture_metric_recorded:
+                return
+            metrics["fixture_provisioning_s"] = self.fixture_provisioning_s
+            self._fixture_metric_recorded = True
+
+
+class _FunctionalStageSpec(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    stage: str
+    trainer_gpu_ids: tuple[int, ...]
+    inference_gpu_ids: tuple[int, ...]
+    trainer_topology: WorkflowTrainerTopology
+    vllm_topology: WorkflowVllmTopology
+    vllm_external: bool
+    engine_args: dict[str, object]
+
+
+class _SharedFunctionalSession(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    worker: ResidentFunctionalSessionSpec
+    topology: WorkflowRuntimeTopology
+
+
+class _SharedBaseSession(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    gpu_count: int
+    fixture: str
+    topology: WorkflowRuntimeTopology
+
+
+def _stage_duration_estimate(model_key: str, stage: str) -> float:
+    return _STAGE_DURATION_ESTIMATE_OVERRIDES_S.get(
+        (stage, model_key), _STAGE_DURATION_ESTIMATES_S[stage]
+    )
+
+
+def _shared_startup_estimate(mode: str, stage: str) -> float:
+    if mode == BASE_MEGATRON_MODE:
+        return _SHARED_STARTUP_ESTIMATES_S[mode]
+    if mode == RESIDENT_FUNCTIONAL_MODE and stage != "lora_coverage":
+        return _SHARED_STARTUP_ESTIMATES_S[mode]
+    return 0.0
+
+
+def _initialize_workflow(
+    *,
+    base_model: str,
+    output_json: Path | None,
+    allow_unvalidated_arch: bool,
+) -> tuple[ValidationReport, Path]:
+    from . import workflow
+
+    report = workflow.initialize_validation_report(
+        base_model=base_model,
+        allow_unvalidated_arch=allow_unvalidated_arch,
+    )
+    return report, workflow._new_workflow_run_dir(
+        output_json=output_json,
+        model_key=report.model_key,
+    )
+
+
+def prepare_workflow(
+    *,
+    base_model: str,
+    include_sensitivity: bool | None,
+    output_json: Path | None,
+    skip_stages: set[str],
+    allow_unvalidated_arch: bool,
+    initialized: tuple[ValidationReport, Path] | None = None,
+) -> PreparedWorkflow:
+    from . import workflow
+
+    report, run_dir = initialized or _initialize_workflow(
+        base_model=base_model,
+        output_json=output_json,
+        allow_unvalidated_arch=allow_unvalidated_arch,
+    )
+    dependency = next(
+        stage for stage in report.stages if stage.name == "dependency_resolution"
+    )
+    started = time.monotonic()
+    dependency.passed = True
+    dependency.metrics = dict(report.dependency_versions)
+    workflow._record_stage_duration(dependency, started=started)
+
+    architecture_stage = next(
+        stage for stage in report.stages if stage.name == "architecture_discovery"
+    )
+    started = time.monotonic()
+    architecture = workflow._inspect_architecture_for_workflow(
+        base_model,
+        allow_unvalidated_arch=allow_unvalidated_arch,
+    )
+    architecture_stage.passed = not architecture.unresolved_risks
+    architecture_stage.metrics = {
+        "recommended_min_layers": architecture.recommended_min_layers,
+        "layer_families": [
+            family.model_dump() for family in architecture.layer_families
+        ],
+        "unresolved_risks": list(architecture.unresolved_risks),
+    }
+    workflow._record_stage_duration(architecture_stage, started=started)
+
+    stages = tuple(
+        stage.name
+        for stage in report.stages
+        if stage.name not in {"dependency_resolution", "architecture_discovery"}
+        and stage.name not in skip_stages
+    )
+    for stage in report.stages:
+        if stage.name not in skip_stages:
+            continue
+        stage.skipped = True
+        stage.metrics = {
+            "skipped": True,
+            "reason": "--skip-stage",
+            "workflow_stage_duration_s": 0.0,
+        }
+    fixture_started = time.monotonic()
+    fixture = ensure_workflow_fixture(
+        base_model,
+        allow_unvalidated_arch=allow_unvalidated_arch,
+        required_stages=frozenset(stages),
+    )
+    prepared = PreparedWorkflow(
+        report=report,
+        architecture=architecture,
+        fixture=fixture,
+        run_dir=run_dir,
+        output_json=output_json,
+        stages=stages,
+        allow_unvalidated_arch=allow_unvalidated_arch,
+        include_sensitivity=include_sensitivity,
+        fixture_provisioning_s=time.monotonic() - fixture_started,
+    )
+    workflow._write_validation_report(report, output_json)
+    return prepared
+
+
+def _minimum_gpu_count(
+    stage_name: str,
+    resources: WorkflowStageResources,
+    *,
+    visible_gpu_count: int,
+) -> int:
+    minimum = resources.required_physical_gpus or 1
+    for count in range(minimum, visible_gpu_count + 1):
+        try:
+            resolve_stage_resources_for_visible_gpus(
+                stage_name,
+                resources,
+                visible_gpu_count=count,
+            )
+        except RuntimeError:
+            continue
+        return count
+    raise RuntimeError(
+        f"{stage_name} does not fit any allocation up to {visible_gpu_count} GPUs"
+    )
+
+
+def _stage_gpu_count(
+    prepared: PreparedWorkflow, stage_name: str, available: int
+) -> int:
+    if stage_name in _CPU_STAGES:
+        return 0
+    if stage_name in _LIGHTWEIGHT_GPU_STAGES:
+        return 1
+    if stage_name == CORRECTNESS_REFERENCE_STAGE:
+        return 1
+    if stage_name == "correctness_sensitivity":
+        from .oracle_harness import selected_suite_topologies
+
+        handler = get_model_support_handler_for_spec(
+            get_model_support_spec(
+                prepared.report.base_model,
+                allow_unvalidated_arch=prepared.allow_unvalidated_arch,
+            )
+        )
+        return max(
+            topology.world_size()
+            for topology in selected_suite_topologies(
+                is_moe=handler.is_moe,
+                cp_supported=bool(handler.cp_supported),
+            )
+        )
+    resources = getattr(
+        HANDLER_WORKFLOW_RESOURCES.get(prepared.report.model_key), stage_name, None
+    )
+    if resources is None:
+        if stage_name == "length_trainability":
+            return (
+                3
+                if model_uses_expert_parallel(
+                    prepared.report.base_model,
+                    allow_unvalidated_arch=prepared.allow_unvalidated_arch,
+                )
+                else 2
+            )
+        try:
+            return _DEFAULT_STAGE_GPU_COUNTS[stage_name]
+        except KeyError:
+            raise RuntimeError(
+                "missing workflow resources for "
+                f"{prepared.report.model_key}/{stage_name}"
+            ) from None
+    return _minimum_gpu_count(stage_name, resources, visible_gpu_count=available)
+
+
+def _trainer_topology(
+    variant: str, topology: Any | None = None, **updates: Any
+) -> WorkflowTrainerTopology:
+    values = {
+        name: getattr(topology, name, default)
+        for name, default in (
+            ("tp", 1),
+            ("cp", 1),
+            ("ep", 1),
+            ("etp", 1),
+            ("dp", 1),
+            ("pp", 1),
+            ("vpp", 1),
+            ("sp", False),
+        )
+    }
+    return WorkflowTrainerTopology(variant=variant, **(values | updates))
+
+
+def _trainer_environment(
+    topology: WorkflowTrainerTopology,
+    trainer_gpu_ids: tuple[int, ...],
+    inference_gpu_ids: tuple[int, ...],
+) -> dict[str, str]:
+    values = {
+        "TRAINER_GPU_IDS": ",".join(map(str, trainer_gpu_ids)),
+        "INFERENCE_GPU_IDS": ",".join(map(str, inference_gpu_ids)),
+        **{
+            name.upper(): str(getattr(topology, name))
+            for name in ("tp", "cp", "ep", "etp", "dp", "pp")
+        },
+    }
+    return {
+        **{f"ART_TRAIN_INF_MISMATCH_{name}": value for name, value in values.items()},
+        **{
+            f"ART_MODEL_SUPPORT_{name}": value
+            for name, value in (
+                values | {"VPP": str(topology.vpp), "SP": "1" if topology.sp else "0"}
+            ).items()
+        },
+    }
+
+
+def _vllm_topology(
+    variant: str, engine_args: dict[str, object], *, gpu_count: int
+) -> WorkflowVllmTopology:
+    values = {}
+    for field, key in zip(("tp", "pp", "dp"), _VLLM_PARALLEL_ARGS, strict=True):
+        value = engine_args.get(key, 1)
+        if type(value) is not int:
+            raise RuntimeError(f"{variant} {key} must be an integer")
+        values[field] = value
+    expert_parallel = engine_args.get("enable_expert_parallel", False)
+    if type(expert_parallel) is not bool:
+        raise RuntimeError(f"{variant} enable_expert_parallel must be a boolean")
+    topology = WorkflowVllmTopology(variant=variant, ep=expert_parallel, **values)
+    if topology.tp * topology.pp * topology.dp != gpu_count:
+        raise RuntimeError(
+            f"{variant} vLLM topology {(topology.tp, topology.pp, topology.dp)} "
+            f"does not match {gpu_count} inference GPUs"
+        )
+    return topology
+
+
+def _resolved_stage_resources(
+    prepared: PreparedWorkflow, stage_name: str, *, gpu_count: int
+) -> WorkflowStageResources | None:
+    configured = HANDLER_WORKFLOW_RESOURCES.get(prepared.report.model_key)
+    resources = getattr(configured, stage_name, None)
+    return (
+        resolve_stage_resources_for_visible_gpus(
+            stage_name, resources, visible_gpu_count=gpu_count
+        )
+        if resources is not None
+        else None
+    )
+
+
+def _default_mismatch_trainer_topology(
+    prepared: PreparedWorkflow, variant: str
+) -> WorkflowTrainerTopology:
+    handler = get_model_support_handler_for_spec(
+        get_model_support_spec(
+            prepared.report.base_model,
+            allow_unvalidated_arch=prepared.allow_unvalidated_arch,
+        )
+    )
+    cp = 2 if bool(handler.cp_supported) else 1
+    return _trainer_topology(
+        variant,
+        cp=cp,
+        ep=2 if handler.is_moe else 1,
+        dp=1 if cp == 2 else 2,
+    )
+
+
+def _stage_runtime_topology(
+    prepared: PreparedWorkflow, stage_name: str, *, gpu_count: int
+) -> WorkflowRuntimeTopology:
+    if stage_name in _CPU_STAGES:
+        return WorkflowRuntimeTopology()
+    if stage_name in {"correctness_sensitivity", CORRECTNESS_REFERENCE_STAGE}:
+        from .oracle_harness import selected_suite_topologies
+
+        handler = get_model_support_handler_for_spec(
+            get_model_support_spec(
+                prepared.report.base_model,
+                allow_unvalidated_arch=prepared.allow_unvalidated_arch,
+            )
+        )
+        variants = tuple(
+            selected_suite_topologies(
+                is_moe=handler.is_moe,
+                cp_supported=bool(handler.cp_supported),
+            )
+        )
+        if stage_name == CORRECTNESS_REFERENCE_STAGE:
+            variants = variants[:1]
+        else:
+            variants = variants[1:]
+        names = tuple(topology.slug() for topology in variants)
+        return WorkflowRuntimeTopology(
+            trainer_variants=tuple(
+                _trainer_topology(name, topology)
+                for name, topology in zip(names, variants, strict=True)
+            ),
+            role_placements=tuple(
+                WorkflowRolePlacement(
+                    variant=name,
+                    trainer_gpu_ids=tuple(range(topology.world_size())),
+                )
+                for name, topology in zip(names, variants, strict=True)
+            ),
+        )
+    if stage_name in {"train_inf_mismatch", "length_trainability"}:
+        spec = _functional_stage_spec(prepared, stage_name, gpu_count)
+        return WorkflowRuntimeTopology(
+            trainer_variants=(spec.trainer_topology,),
+            vllm_variants=(spec.vllm_topology,),
+            role_placements=(
+                WorkflowRolePlacement(
+                    variant=stage_name,
+                    trainer_gpu_ids=spec.trainer_gpu_ids,
+                    vllm_gpu_ids=spec.inference_gpu_ids,
+                    vllm_external=spec.vllm_external,
+                ),
+            ),
+        )
+
+    resources = _resolved_stage_resources(prepared, stage_name, gpu_count=gpu_count)
+    if resources is not None:
+        trainer = resources.megatron
+        vllm = resources.vllm
+        return WorkflowRuntimeTopology(
+            trainer_variants=(
+                (_trainer_topology(stage_name, trainer.topology),) if trainer else ()
+            ),
+            vllm_variants=(
+                _vllm_topology(
+                    stage_name,
+                    vllm.engine_args(),
+                    gpu_count=len(vllm.gpu_ids),
+                ),
+            )
+            if vllm
+            else (),
+            role_placements=(
+                WorkflowRolePlacement(
+                    variant=stage_name,
+                    trainer_gpu_ids=tuple(trainer.gpu_ids) if trainer else (),
+                    vllm_gpu_ids=tuple(vllm.gpu_ids) if vllm else (),
+                    vllm_external=resources.requires_external_vllm,
+                ),
+            ),
+        )
+
+    trainer = _trainer_topology(stage_name)
+    trainer_gpu_ids = (0,)
+    vllm_gpu_ids: tuple[int, ...] = ()
+    vllm: WorkflowVllmTopology | None = None
+    if stage_name == "train_inf_mismatch":
+        trainer = _default_mismatch_trainer_topology(prepared, stage_name)
+        trainer_gpu_ids = (0, 1)
+        vllm_gpu_ids = (2, 3)
+        vllm = WorkflowVllmTopology(
+            variant=stage_name,
+            tp=2,
+            ep=trainer.ep > 1,
+        )
+    return WorkflowRuntimeTopology(
+        trainer_variants=(trainer,),
+        vllm_variants=(vllm,) if vllm else (),
+        role_placements=(
+            WorkflowRolePlacement(
+                variant=stage_name,
+                trainer_gpu_ids=trainer_gpu_ids,
+                vllm_gpu_ids=vllm_gpu_ids,
+            ),
+        ),
+    )
+
+
+def _runtime_key(
+    prepared: PreparedWorkflow,
+    stage_name: str,
+    *,
+    gpu_count: int,
+) -> WorkflowRuntimeKey:
+    fixture_stage = (
+        "correctness_sensitivity"
+        if stage_name == CORRECTNESS_REFERENCE_STAGE
+        else stage_name
+    )
+    environment = prepared.fixture.environment(fixture_stage)
+    if stage_name in _CPU_STAGES:
+        kind = "cpu"
+        mode = stage_name
+    elif stage_name in {
+        "lora_coverage",
+        "correctness_sensitivity",
+        CORRECTNESS_REFERENCE_STAGE,
+    }:
+        kind = "megatron"
+        mode = stage_name
+    elif stage_name == "e2e_throughput":
+        kind = "joint"
+        mode = "throughput"
+    else:
+        kind = "joint"
+        mode = stage_name
+    return WorkflowRuntimeKey(
+        source_fingerprint=str(prepared.report.git["commit"]),
+        handler=prepared.report.model_key,
+        fixture=environment["ART_MODEL_SUPPORT_FIXTURE_PATH"],
+        kind=kind,
+        topology=_stage_runtime_topology(prepared, stage_name, gpu_count=gpu_count),
+        mode=mode,
+        static_options=stage_name if mode == stage_name else "",
+    )
+
+
+def _stage_gpu_share(prepared: PreparedWorkflow, stage_name: str) -> float:
+    if stage_name not in _LIGHTWEIGHT_GPU_STAGES:
+        return 1.0
+    if override := _LIGHTWEIGHT_GPU_SHARE_OVERRIDES.get(
+        (prepared.report.model_key, stage_name)
+    ):
+        return override
+    fixture_path = prepared.fixture.environment(stage_name)[
+        "ART_MODEL_SUPPORT_FIXTURE_PATH"
+    ]
+    return (
+        1.0
+        if fixture_path == prepared.fixture.canonical_path
+        else _REDUCED_FIXTURE_GPU_SHARE
+    )
+
+
+def _ordered_stages(stages: tuple[str, ...], selected: tuple[str, ...]) -> bool:
+    return tuple(stage for stage in stages if stage in selected) == selected
+
+
+def _topology_shape(topology: WorkflowRuntimeTopology) -> tuple[object, ...]:
+    return (
+        tuple(
+            variant.model_dump(exclude={"variant"})
+            for variant in topology.trainer_variants
+        ),
+        tuple(
+            variant.model_dump(exclude={"variant"})
+            for variant in topology.vllm_variants
+        ),
+        tuple(
+            placement.model_dump(exclude={"variant"})
+            for placement in topology.role_placements
+        ),
+    )
+
+
+def _base_session(
+    prepared: PreparedWorkflow,
+    stage_gpu_counts: dict[str, int],
+    *,
+    visible_gpu_count: int,
+) -> _SharedBaseSession | None:
+    if not _ordered_stages(prepared.stages, BASE_MEGATRON_STAGES):
+        return None
+    handler = get_model_support_handler_for_spec(
+        get_model_support_spec(
+            prepared.report.base_model,
+            allow_unvalidated_arch=prepared.allow_unvalidated_arch,
+        )
+    )
+    if handler.is_moe:
+        return None
+    gpu_counts = {stage_gpu_counts[stage] for stage in BASE_MEGATRON_STAGES}
+    fixtures = {
+        prepared.fixture.environment(stage)[FIXTURE_PATH_ENV]
+        for stage in BASE_MEGATRON_STAGES
+    }
+    topologies = tuple(
+        _stage_runtime_topology(prepared, stage, gpu_count=stage_gpu_counts[stage])
+        for stage in BASE_MEGATRON_STAGES
+    )
+    if (
+        len(gpu_counts) != 1
+        or next(iter(gpu_counts)) > visible_gpu_count
+        or len(fixtures) != 1
+        or _stage_gpu_share(prepared, BASE_MEGATRON_STAGES[0])
+        != _stage_gpu_share(prepared, BASE_MEGATRON_STAGES[1])
+        or any(
+            _topology_shape(topology) != _topology_shape(topologies[0])
+            for topology in topologies[1:]
+        )
+    ):
+        return None
+    topology = topologies[0]
+    return _SharedBaseSession(
+        gpu_count=gpu_counts.pop(),
+        fixture=fixtures.pop(),
+        topology=WorkflowRuntimeTopology(
+            trainer_variants=tuple(
+                variant.model_copy(update={"variant": BASE_MEGATRON_MODE})
+                for variant in topology.trainer_variants
+            ),
+            vllm_variants=tuple(
+                variant.model_copy(update={"variant": BASE_MEGATRON_MODE})
+                for variant in topology.vllm_variants
+            ),
+            role_placements=tuple(
+                placement.model_copy(update={"variant": BASE_MEGATRON_MODE})
+                for placement in topology.role_placements
+            ),
+        ),
+    )
+
+
+def _functional_stage_spec(
+    prepared: PreparedWorkflow, stage: str, gpu_count: int
+) -> _FunctionalStageSpec:
+    handler = get_model_support_handler_for_spec(
+        get_model_support_spec(
+            prepared.report.base_model,
+            allow_unvalidated_arch=prepared.allow_unvalidated_arch,
+        )
+    )
+    configured = HANDLER_WORKFLOW_RESOURCES.get(prepared.report.model_key)
+    resources = getattr(configured, stage, None)
+    if resources is None:
+        if stage == "train_inf_mismatch":
+            trainer_topology = _default_mismatch_trainer_topology(prepared, stage)
+            trainer_gpu_count = (
+                trainer_topology.tp
+                * trainer_topology.cp
+                * trainer_topology.pp
+                * trainer_topology.dp
+            )
+            inference_gpu_count = gpu_count - trainer_gpu_count
+        else:
+            inference_gpu_count = 1
+            trainer_gpu_count = gpu_count - 1
+            trainer_topology = _trainer_topology(
+                stage,
+                cp=2 if handler.is_moe else 1,
+                ep=2 if handler.is_moe else 1,
+            )
+        trainer_gpu_ids = tuple(range(trainer_gpu_count))
+        inference_gpu_ids = tuple(range(trainer_gpu_count, gpu_count))
+        engine_args: dict[str, object] = {
+            "tensor_parallel_size": inference_gpu_count,
+        }
+        if handler.is_moe and inference_gpu_count > 1:
+            engine_args["enable_expert_parallel"] = True
+    else:
+        resolved = resolve_stage_resources_for_visible_gpus(
+            stage, resources, visible_gpu_count=gpu_count
+        )
+        if resolved.vllm is None:
+            raise RuntimeError(f"{stage} resources require vLLM")
+        inference_gpu_count = len(resolved.vllm.gpu_ids)
+        trainer_gpu_count = (
+            len(resolved.megatron.gpu_ids) if resolved.megatron is not None else 1
+        )
+        trainer_gpu_ids = (
+            tuple(resolved.megatron.gpu_ids) if resolved.megatron is not None else (0,)
+        )
+        inference_gpu_ids = tuple(resolved.vllm.gpu_ids)
+        vllm_external = resolved.requires_external_vllm
+        trainer_topology = _trainer_topology(
+            stage,
+            resolved.megatron.topology if resolved.megatron is not None else None,
+        )
+        engine_args = resolved.vllm.engine_args()
+    if trainer_gpu_count < 1 or inference_gpu_count < 1:
+        raise RuntimeError(f"{stage} requires non-empty trainer and inference roles")
+    trainer_world_size = (
+        trainer_topology.tp
+        * trainer_topology.cp
+        * trainer_topology.pp
+        * trainer_topology.dp
+    )
+    if trainer_world_size != trainer_gpu_count:
+        raise RuntimeError(
+            f"{stage} trainer topology has world size {trainer_world_size} "
+            f"but {trainer_gpu_count} GPU ids"
+        )
+    engine_args = {
+        **handler.vllm_engine_args(),
+        **engine_args,
+    }
+    if handler.is_moe:
+        engine_args["enable_return_routed_experts"] = True
+    return _FunctionalStageSpec(
+        stage=stage,
+        trainer_gpu_ids=trainer_gpu_ids,
+        inference_gpu_ids=inference_gpu_ids,
+        trainer_topology=trainer_topology,
+        vllm_topology=_vllm_topology(stage, engine_args, gpu_count=inference_gpu_count),
+        vllm_external=vllm_external if resources is not None else False,
+        engine_args=engine_args,
+    )
+
+
+def _merge_functional_engine_args(
+    specs: tuple[_FunctionalStageSpec, ...], capacities: dict[str, int | float]
+) -> dict[str, object] | None:
+    static_options: dict[str, object] | None = None
+    topology = specs[0].vllm_topology
+    for spec in specs:
+        engine_args = dict(spec.engine_args)
+        for key in _VLLM_PARALLEL_ARGS:
+            engine_args.pop(key, None)
+        engine_args.pop("enable_expert_parallel", None)
+        for key in _VLLM_CAPACITY_ARGS:
+            value = engine_args.pop(key, None)
+            if value is not None:
+                if not isinstance(value, (int, float)) or isinstance(value, bool):
+                    raise RuntimeError(f"{spec.stage} {key} must be numeric")
+                capacities[key] = max(capacities.get(key, 0), value)
+        if static_options is None:
+            static_options = engine_args
+        elif engine_args != static_options:
+            return None
+    parallel_args: dict[str, object] = {
+        "tensor_parallel_size": topology.tp,
+        "pipeline_parallel_size": topology.pp,
+        "data_parallel_size": topology.dp,
+    }
+    if topology.ep:
+        parallel_args["enable_expert_parallel"] = True
+    return (static_options or {}) | parallel_args | capacities
+
+
+def _functional_session(
+    prepared: PreparedWorkflow,
+    stage_gpu_counts: dict[str, int],
+    *,
+    visible_gpu_count: int,
+) -> _SharedFunctionalSession | None:
+    if not _ordered_stages(prepared.stages, RESIDENT_FUNCTIONAL_STAGES):
+        return None
+    support = get_model_support_spec(
+        prepared.report.base_model,
+        allow_unvalidated_arch=prepared.allow_unvalidated_arch,
+    )
+    if support.native_vllm_lora_status == "disabled":
+        return None
+    handler = get_model_support_handler_for_spec(support)
+    joint_stages = ("train_inf_mismatch", "length_trainability")
+    specs = tuple(
+        _functional_stage_spec(prepared, stage, stage_gpu_counts[stage])
+        for stage in joint_stages
+    )
+    functional_env = prepared.fixture.resident_functional_environment()
+    length_env = os.environ | functional_env
+    capacities: dict[str, int | float] = {
+        "max_model_len": int(
+            length_env.get("ART_MODEL_SUPPORT_LENGTH_MAX_MODEL_LEN", 1024)
+        ),
+        "max_num_seqs": int(
+            length_env.get("ART_MODEL_SUPPORT_LENGTH_MAX_NUM_SEQS", 32)
+        ),
+        "max_loras": 2,
+        "max_logprobs": 20,
+        "max_lora_rank": int(
+            length_env.get(
+                MEGATRON_LORA_RANK_ENV, default_lora_rank_for_handler(handler)
+            )
+        ),
+    }
+    resource_args = _merge_functional_engine_args(specs, capacities)
+    if resource_args is None:
+        return None
+    mismatch = specs[0]
+    trainer_gpu_count = len(mismatch.trainer_gpu_ids)
+    inference_gpu_count = len(mismatch.inference_gpu_ids)
+    gpu_count = inference_gpu_count + trainer_gpu_count
+    if gpu_count > visible_gpu_count:
+        return None
+    inference_gpu_ids = tuple(range(trainer_gpu_count, gpu_count))
+    trainer = mismatch.trainer_topology
+    return _SharedFunctionalSession(
+        worker=ResidentFunctionalSessionSpec(
+            gpu_count=gpu_count,
+            launch=VllmRuntimeLaunchConfig(
+                base_model=functional_env[FIXTURE_PATH_ENV],
+                port=0,
+                cuda_visible_devices=",".join(map(str, inference_gpu_ids)),
+                served_model_name="__art_functional_base__",
+                engine_args={
+                    "enforce_eager": True,
+                    "generation_config": "vllm",
+                    "limit_mm_per_prompt": {"image": 0, "video": 0, "audio": 0},
+                    **resource_args,
+                },
+                server_args={
+                    "return_tokens_as_token_ids": True,
+                    "enable_auto_tool_choice": True,
+                    "tool_call_parser": "hermes",
+                    **handler.vllm_server_args(),
+                    "api_key": "art-functional-vllm",
+                },
+            ),
+            trainer_gpu_ids=tuple(range(trainer_gpu_count)),
+            trainer_environment=_trainer_environment(
+                trainer,
+                tuple(range(trainer_gpu_count)),
+                inference_gpu_ids,
+            ),
+        ),
+        topology=WorkflowRuntimeTopology(
+            trainer_variants=(
+                trainer.model_copy(update={"variant": RESIDENT_FUNCTIONAL_MODE}),
+            ),
+            vllm_variants=(
+                mismatch.vllm_topology.model_copy(
+                    update={"variant": RESIDENT_FUNCTIONAL_MODE}
+                ),
+            ),
+            role_placements=(
+                WorkflowRolePlacement(
+                    variant=RESIDENT_FUNCTIONAL_MODE,
+                    trainer_gpu_ids=tuple(range(trainer_gpu_count)),
+                    vllm_gpu_ids=inference_gpu_ids,
+                    vllm_external=mismatch.vllm_external,
+                ),
+            ),
+        ),
+    )
+
+
+def compile_prepared_workflows(
+    workflows: list[PreparedWorkflow], *, visible_gpu_count: int
+):
+    operations = []
+    for prepared in workflows:
+        stage_gpu_counts = {
+            stage_name: _stage_gpu_count(prepared, stage_name, visible_gpu_count)
+            for stage_name in prepared.stages
+        }
+        functional_session = _functional_session(
+            prepared,
+            stage_gpu_counts,
+            visible_gpu_count=visible_gpu_count,
+        )
+        base_megatron = _base_session(
+            prepared,
+            stage_gpu_counts,
+            visible_gpu_count=visible_gpu_count,
+        )
+        for stage_name in prepared.stages:
+            shared = (
+                functional_session
+                if functional_session is not None
+                and stage_name in RESIDENT_FUNCTIONAL_STAGES
+                else None
+            )
+            stage_gpu_count = stage_gpu_counts[stage_name]
+            runtime = _runtime_key(prepared, stage_name, gpu_count=stage_gpu_count)
+            gpu_count = stage_gpu_count
+            if shared is not None:
+                gpu_count = shared.worker.gpu_count
+            elif base_megatron is not None and stage_name in BASE_MEGATRON_STAGES:
+                gpu_count = base_megatron.gpu_count
+            if shared is not None:
+                runtime = runtime.model_copy(
+                    update={
+                        "fixture": prepared.fixture.resident_functional_environment()[
+                            FIXTURE_PATH_ENV
+                        ],
+                        "kind": "joint",
+                        "topology": shared.topology,
+                        "mode": RESIDENT_FUNCTIONAL_MODE,
+                        "static_options": shared.worker.model_dump_json(),
+                    }
+                )
+            elif base_megatron is not None and stage_name in BASE_MEGATRON_STAGES:
+                runtime = runtime.model_copy(
+                    update={
+                        "fixture": base_megatron.fixture,
+                        "kind": "megatron",
+                        "mode": BASE_MEGATRON_MODE,
+                        "topology": base_megatron.topology,
+                        "static_options": "",
+                    }
+                )
+            dependencies: tuple[str, ...] = ()
+            if stage_name == "correctness_sensitivity":
+                reference_id = (
+                    f"{prepared.report.model_key}:{CORRECTNESS_REFERENCE_STAGE}"
+                )
+                correctness_affinity = f"{prepared.report.model_key}:correctness"
+                operations.append(
+                    WorkflowOperation(
+                        id=reference_id,
+                        stage=CORRECTNESS_REFERENCE_STAGE,
+                        runtime=_runtime_key(
+                            prepared, CORRECTNESS_REFERENCE_STAGE, gpu_count=1
+                        ),
+                        resources=WorkflowResourceRequest(
+                            gpu_count=1,
+                            host_affinity=correctness_affinity,
+                        ),
+                        estimated_duration_s=_stage_duration_estimate(
+                            prepared.report.model_key, CORRECTNESS_REFERENCE_STAGE
+                        ),
+                    )
+                )
+                dependencies = (reference_id,)
+            if shared is not None:
+                stage_index = RESIDENT_FUNCTIONAL_STAGES.index(stage_name)
+                if stage_index:
+                    dependencies = (
+                        f"{prepared.report.model_key}:"
+                        f"{RESIDENT_FUNCTIONAL_STAGES[stage_index - 1]}",
+                    )
+            elif (
+                stage_name == BASE_MEGATRON_STAGES[1]
+                and BASE_MEGATRON_STAGES[0] in prepared.stages
+            ):
+                dependencies = (
+                    f"{prepared.report.model_key}:{BASE_MEGATRON_STAGES[0]}",
+                )
+            operations.append(
+                WorkflowOperation(
+                    id=f"{prepared.report.model_key}:{stage_name}",
+                    stage=stage_name,
+                    runtime=runtime,
+                    resources=WorkflowResourceRequest(
+                        gpu_count=gpu_count,
+                        gpu_share=(
+                            1.0
+                            if shared is not None
+                            else _stage_gpu_share(prepared, stage_name)
+                        ),
+                        host_affinity=(
+                            correctness_affinity
+                            if stage_name == "correctness_sensitivity"
+                            else None
+                        ),
+                    ),
+                    dependencies=dependencies,
+                    estimated_duration_s=_stage_duration_estimate(
+                        prepared.report.model_key, stage_name
+                    ),
+                    estimated_shared_startup_s=_shared_startup_estimate(
+                        runtime.mode, stage_name
+                    ),
+                )
+            )
+    return compile_workflow(operations)
+
+
+def _workflow_hosts() -> list[str]:
+    hosts = [
+        host.strip()
+        for host in os.environ.get(_WORKFLOW_HOSTS_ENV, socket.gethostname()).split(",")
+        if host.strip()
+    ]
+    if not hosts or len(set(hosts)) != len(hosts):
+        raise RuntimeError(f"{_WORKFLOW_HOSTS_ENV} must contain unique host names")
+    return hosts
+
+
+def _visible_devices() -> list[WorkflowDevice]:
+    import torch
+
+    count = int(torch.cuda.device_count())
+    raw = os.environ.get("CUDA_VISIBLE_DEVICES")
+    gpu_ids = raw.split(",") if raw else [str(index) for index in range(count)]
+    if len(gpu_ids) != count:
+        raise RuntimeError(
+            f"CUDA_VISIBLE_DEVICES exposes {len(gpu_ids)} ids but torch sees {count} GPUs"
+        )
+    return [
+        WorkflowDevice(host=host, gpu=gpu_id)
+        for host in _workflow_hosts()
+        for gpu_id in gpu_ids
+    ]
+
+
+def run_prepared_workflows(
+    workflows: list[PreparedWorkflow],
+    *,
+    forkservers: WorkflowForkserverPool | None = None,
+) -> list[ValidationReport]:
+    import torch
+
+    from . import workflow
+
+    devices = _visible_devices()
+    if not devices:
+        raise RuntimeError("the scheduled model-support workflow requires CUDA GPUs")
+    gpu_counts = {
+        host: sum(device.host == host for device in devices)
+        for host in {device.host for device in devices}
+    }
+    if len(set(gpu_counts.values())) != 1:
+        raise RuntimeError(f"workflow hosts expose different GPU counts: {gpu_counts}")
+    by_model_key = {prepared.report.model_key: prepared for prepared in workflows}
+    plan = compile_prepared_workflows(
+        workflows, visible_gpu_count=next(iter(gpu_counts.values()))
+    )
+    owns_forkservers = forkservers is None
+    active_forkservers = forkservers or WorkflowForkserverPool(
+        hosts=sorted(gpu_counts),
+        repo_root=workflow.REPO_ROOT,
+        tests_dir=workflow.TESTS_DIR,
+        log_dir=workflows[0].run_dir / ".forkservers",
+    )
+    failure_evidence: dict[str, dict[str, object]] = {}
+    failure_evidence_lock = Lock()
+
+    def remember_failure(
+        operation: WorkflowOperation,
+        output_json: Path,
+        result: ValidationStageResult,
+    ) -> None:
+        evidence: dict[str, object] = {"stage": operation.stage}
+        if output_json.is_file():
+            evidence["stage_result_json"] = str(output_json)
+        if error := result.metrics.get("error"):
+            evidence["error"] = error
+        if result.artifact_dir is not None:
+            evidence["artifact_dir"] = result.artifact_dir
+        with failure_evidence_lock:
+            failure_evidence[operation.id] = evidence
+
+    def record_blocked(
+        operations: tuple[WorkflowOperation, ...],
+        failed_operations: tuple[str, ...],
+    ) -> None:
+        with failure_evidence_lock:
+            evidence = {
+                operation_id: dict(failure_evidence.get(operation_id, {}))
+                for operation_id in failed_operations
+            }
+        for operation in operations:
+            if operation.stage == CORRECTNESS_REFERENCE_STAGE:
+                continue
+            by_model_key[operation.runtime.handler].record(
+                ValidationStageResult(
+                    name=operation.stage,
+                    metrics={
+                        "blocked": True,
+                        "reason": "blocked by failed workflow operation(s): "
+                        + ", ".join(failed_operations),
+                        "workflow_failed_dependencies": list(failed_operations),
+                        "workflow_failure_evidence": evidence,
+                        "workflow_stage_duration_s": 0.0,
+                    },
+                )
+            )
+
+    def runner(session: WorkflowSession, placement: WorkflowPlacement) -> None:
+        owner = by_model_key[session.runtime.handler]
+        session_dir = owner.run_dir / ".sessions" / session.id
+        session_dir.mkdir(parents=True, exist_ok=False)
+        architecture_json = session_dir / "architecture.json"
+        request_json = session_dir / "request.json"
+        session_log = session_dir / "worker.log"
+        architecture_json.write_text(
+            owner.architecture.model_dump_json(indent=2), encoding="utf-8"
+        )
+        items = []
+        for operation in session.operations:
+            stage_dir = owner.run_dir / operation.stage
+            stage_dir.mkdir(parents=True, exist_ok=False)
+            fixture_stage = (
+                None
+                if session.runtime.mode == RESIDENT_FUNCTIONAL_MODE
+                else "correctness_sensitivity"
+                if operation.stage == CORRECTNESS_REFERENCE_STAGE
+                else operation.stage
+            )
+            environment = (
+                owner.fixture.resident_functional_environment()
+                if session.runtime.mode == RESIDENT_FUNCTIONAL_MODE
+                else owner.fixture.environment(fixture_stage)
+            )
+            environment[workflow.WORKFLOW_RUN_DIR_ENV] = str(owner.run_dir)
+            environment.update(
+                {
+                    "ART_MEGATRON_CACHE_ROOT": os.environ.get(
+                        "ART_MEGATRON_CACHE_ROOT",
+                        "/tmp/art-model-support-workflow/cache",
+                    ),
+                    "ART_MEGATRON_COMPILE_CACHE": "1",
+                }
+            )
+            if operation.stage in {
+                CORRECTNESS_REFERENCE_STAGE,
+                "correctness_sensitivity",
+            }:
+                environment[CORRECTNESS_ARTIFACT_ROOT_ENV] = str(
+                    owner.run_dir / ".correctness" / "artifacts"
+                )
+                environment[CORRECTNESS_PHASE_ENV] = (
+                    "reference"
+                    if operation.stage == CORRECTNESS_REFERENCE_STAGE
+                    else "variants"
+                )
+            if owner.include_sensitivity is not None:
+                environment[workflow.SKIP_SENSITIVITY_ENV] = (
+                    "0" if owner.include_sensitivity else "1"
+                )
+            items.append(
+                WorkflowStageWorkerItem(
+                    stage=operation.stage,
+                    stage_dir=str(stage_dir),
+                    output_json=str(stage_dir / "stage_result.json"),
+                    environment=environment,
+                )
+            )
+        request = WorkflowStageWorkerSession(
+            base_model=owner.report.base_model,
+            architecture_json=str(architecture_json),
+            allow_unvalidated_arch=owner.allow_unvalidated_arch,
+            resident_functional=(
+                ResidentFunctionalSessionSpec.model_validate_json(
+                    session.runtime.static_options
+                )
+                if session.runtime.mode == RESIDENT_FUNCTIONAL_MODE
+                else None
+            ),
+            base_megatron=session.runtime.mode == BASE_MEGATRON_MODE,
+            items=tuple(items),
+        )
+        request_json.write_text(request.model_dump_json(indent=2), encoding="utf-8")
+        if any(item.environment != items[0].environment for item in items[1:]):
+            raise RuntimeError("one workflow session requires one process environment")
+        environment = os.environ.copy()
+        environment.update(
+            {
+                "CUDA_VISIBLE_DEVICES": ",".join(
+                    device.gpu for device in placement.devices
+                ),
+                "PYTHONPATH": os.pathsep.join(
+                    filter(
+                        None,
+                        (
+                            str(workflow.TESTS_DIR),
+                            environment.get("PYTHONPATH"),
+                        ),
+                    )
+                ),
+                "WANDB_MODE": "disabled",
+            }
+        )
+        placement_hosts = {device.host for device in placement.devices}
+        if len(placement_hosts) > 1:
+            raise RuntimeError("one workflow session cannot span hosts")
+        execution_host = placement.host or socket.gethostname()
+        timeout_s = sum(
+            workflow._WORKFLOW_STAGE_TIMEOUT_OVERRIDES_S.get(
+                (operation.stage, owner.report.base_model),
+                workflow._WORKFLOW_STAGE_TIMEOUT_S,
+            )
+            for operation in session.operations
+        )
+        fork_result = active_forkservers.run(
+            execution_host,
+            request_json=request_json,
+            log_path=session_log,
+            environment=environment,
+            session_environment=items[0].environment,
+            torch_threads=torch.get_num_threads(),
+            timeout_s=timeout_s,
+        )
+        returncode = fork_result["returncode"]
+        worker_wall_s = float(fork_result["child_wall_s"])
+        results: list[tuple[WorkflowOperation, Path, bool, ValidationStageResult]] = []
+        for operation, item in zip(session.operations, items, strict=True):
+            output_json = Path(item.output_json)
+            produced = output_json.exists()
+            if produced:
+                result = ValidationStageResult.model_validate_json(
+                    output_json.read_text(encoding="utf-8")
+                )
+            else:
+                detail = (
+                    f"session exceeded {timeout_s:g}s"
+                    if returncode is None
+                    else "session worker did not write stage_result.json"
+                    if returncode == 0
+                    else workflow._subprocess_log_tail(session_log)
+                    or f"session exited with code {returncode}"
+                )
+                result = ValidationStageResult(
+                    name=operation.stage,
+                    passed=False,
+                    metrics={"error": detail},
+                )
+            result.metrics["workflow_session_id"] = session.id
+            result.metrics["workflow_gpu_ids"] = [
+                device.gpu for device in placement.devices
+            ]
+            result.metrics["workflow_host"] = execution_host
+            result.metrics["workflow_session_worker_s"] = worker_wall_s
+            result.metrics["workflow_session_operation_count"] = len(session.operations)
+            result.metrics.update(active_forkservers.metrics(execution_host))
+            results.append((operation, output_json, produced, result))
+
+        failed_operation_id: str | None = None
+        for operation, output_json, produced, result in results:
+            if not produced and failed_operation_id is not None:
+                record_blocked((operation,), (failed_operation_id,))
+                continue
+            if operation.stage == CORRECTNESS_REFERENCE_STAGE:
+                if not result.passed:
+                    remember_failure(operation, output_json, result)
+                    failed_operation_id = failed_operation_id or operation.id
+                continue
+            if operation.stage == "correctness_sensitivity":
+                reference_result = ValidationStageResult.model_validate_json(
+                    (
+                        owner.run_dir
+                        / CORRECTNESS_REFERENCE_STAGE
+                        / "stage_result.json"
+                    ).read_text(encoding="utf-8")
+                )
+                reference_s = float(
+                    reference_result.metrics["workflow_stage_duration_s"]
+                )
+                composition_s = float(result.metrics["workflow_stage_duration_s"])
+                result.metrics.update(
+                    {
+                        "correctness_reference_duration_s": reference_s,
+                        "correctness_composition_duration_s": composition_s,
+                        "correctness_total_compute_s": reference_s + composition_s,
+                    }
+                )
+            owner.record_fixture_metric(result.metrics)
+            if operation.stage in workflow._RUNTIME_CLEANUP_STAGES:
+                try:
+                    result.metrics.update(
+                        workflow._prune_runtime_artifacts(
+                            owner.run_dir / operation.stage
+                        )
+                    )
+                except Exception as exc:
+                    result.passed = False
+                    result.metrics["runtime_artifact_cleanup_error"] = (
+                        f"{type(exc).__name__}: {exc}"
+                    )
+            owner.record(result)
+            if not result.passed:
+                remember_failure(operation, output_json, result)
+                failed_operation_id = failed_operation_id or operation.id
+        if failed_operation_id is not None:
+            raise WorkflowOperationFailed(failed_operation_id)
+
+    context = (
+        active_forkservers if owns_forkservers else nullcontext(active_forkservers)
+    )
+    with context:
+        execution = execute_workflow(plan, devices=devices, runner=runner)
+    sessions = {session.id: session for session in plan.sessions}
+    for session_id, failed_operations in execution.blocked_by_failed_operations.items():
+        record_blocked(sessions[session_id].operations, failed_operations)
+    for prepared in workflows:
+        workflow._finalize_validation_report(prepared.report, partial=False)
+        workflow._write_validation_report(prepared.report, prepared.output_json)
+    return [prepared.report for prepared in workflows]
+
+
+def build_scheduled_validation_reports(
+    *,
+    base_models: list[str],
+    include_sensitivity: bool | None = None,
+    output_json_by_model: dict[str, Path | None] | None = None,
+    skip_stages: set[str] | None = None,
+    allow_unvalidated_arch: bool = False,
+) -> list[ValidationReport]:
+    from . import workflow
+
+    skip_stages = skip_stages or set()
+    output_json_by_model = output_json_by_model or {}
+    initialized = [
+        (
+            base_model,
+            _initialize_workflow(
+                base_model=base_model,
+                output_json=output_json_by_model.get(base_model),
+                allow_unvalidated_arch=allow_unvalidated_arch,
+            ),
+        )
+        for base_model in base_models
+    ]
+
+    def prepare(item: tuple[str, tuple[ValidationReport, Path]]) -> PreparedWorkflow:
+        base_model, workflow_identity = item
+        return prepare_workflow(
+            base_model=base_model,
+            include_sensitivity=include_sensitivity,
+            output_json=output_json_by_model.get(base_model),
+            skip_stages=skip_stages,
+            allow_unvalidated_arch=allow_unvalidated_arch,
+            initialized=workflow_identity,
+        )
+
+    with ThreadPoolExecutor(max_workers=1) as forkserver_executor:
+        forkserver_future = forkserver_executor.submit(
+            WorkflowForkserverPool,
+            hosts=sorted(_workflow_hosts()),
+            repo_root=workflow.REPO_ROOT,
+            tests_dir=workflow.TESTS_DIR,
+            log_dir=initialized[0][1][1] / ".forkservers",
+        )
+        try:
+            with ThreadPoolExecutor(max_workers=len(initialized)) as executor:
+                workflows = list(executor.map(prepare, initialized))
+        except BaseException as error:
+            try:
+                forkserver_future.result().close()
+            except BaseException as cleanup_error:
+                raise BaseExceptionGroup(
+                    "workflow preparation and forkserver cleanup failed",
+                    [error, cleanup_error],
+                ) from None
+            raise
+        forkservers = forkserver_future.result()
+        with forkservers:
+            return run_prepared_workflows(workflows, forkservers=forkservers)
diff --git a/tests/integration/megatron/model_support/workflow_stage_worker.py b/tests/integration/megatron/model_support/workflow_stage_worker.py
index a384bc1b9..76e684227 100644
--- a/tests/integration/megatron/model_support/workflow_stage_worker.py
+++ b/tests/integration/megatron/model_support/workflow_stage_worker.py
@@ -1,63 +1,374 @@
 import argparse
+import asyncio
+import json
+import os
 from pathlib import Path
+import time
+import traceback
+from typing import Any
+
+import httpx
+from pydantic import BaseModel, ConfigDict
 
 from art.megatron.model_support.spec import ArchitectureReport
+from art.serving_capabilities import FastMetricsSnapshot
+from art.utils.lifecycle import ChildProcessSupervisor
+from art.utils.network import find_free_tcp_port
+from art.vllm_runtime import ManagedVllmRuntime, VllmRuntimeLaunchConfig
+
+from . import workflow
+from .workflow_fixtures import FIXTURE_PATH_ENV
 
-from .workflow import (
-    run_chat_template_rollout_stage,
-    run_correctness_sensitivity_stage,
-    run_hf_parity_stage,
-    run_length_trainability_stage,
-    run_lora_coverage_stage,
-    run_merged_vllm_serving_stage,
-    run_native_vllm_lora_stage,
-    run_packing_invariance_stage,
-    run_train_inf_mismatch_stage,
-    run_yes_no_trainability_stage,
+RESIDENT_FUNCTIONAL_MODE = "resident_functional"
+RESIDENT_FUNCTIONAL_STAGES = (
+    "lora_coverage",
+    "train_inf_mismatch",
+    "length_trainability",
 )
+BASE_MEGATRON_MODE = "base_megatron"
+BASE_MEGATRON_STAGES = ("hf_parity", "packing_invariance")
+EXTERNAL_VLLM_ENGINE_ARGS_ENV = "ART_MODEL_SUPPORT_EXTERNAL_VLLM_ENGINE_ARGS"
+_STAGE_RUNNERS = workflow.validation_stage_runners()
+
+
+class WorkflowStageWorkerItem(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    stage: str
+    stage_dir: str
+    output_json: str
+    environment: dict[str, str]
+
+
+class ResidentFunctionalSessionSpec(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    gpu_count: int
+    launch: VllmRuntimeLaunchConfig
+    trainer_gpu_ids: tuple[int, ...]
+    trainer_environment: dict[str, str]
+
 
-_STAGE_RUNNERS = {
-    "hf_parity": run_hf_parity_stage,
-    "lora_coverage": run_lora_coverage_stage,
-    "train_inf_mismatch": run_train_inf_mismatch_stage,
-    "merged_vllm_serving": run_merged_vllm_serving_stage,
-    "correctness_sensitivity": run_correctness_sensitivity_stage,
-    "chat_template_rollout": run_chat_template_rollout_stage,
-    "packing_invariance": run_packing_invariance_stage,
-    "length_trainability": run_length_trainability_stage,
-    "yes_no_trainability": run_yes_no_trainability_stage,
-    "native_vllm_lora": run_native_vllm_lora_stage,
-}
+class WorkflowStageWorkerSession(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    base_model: str
+    architecture_json: str
+    allow_unvalidated_arch: bool = False
+    resident_functional: ResidentFunctionalSessionSpec | None = None
+    base_megatron: bool = False
+    items: tuple[WorkflowStageWorkerItem, ...]
 
 
 def _parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser()
-    parser.add_argument("--stage", required=True)
-    parser.add_argument("--base-model", required=True)
-    parser.add_argument("--architecture-json", required=True)
-    parser.add_argument("--output-json", required=True)
+    parser.add_argument("--session-json")
+    parser.add_argument("--stage")
+    parser.add_argument("--base-model")
+    parser.add_argument("--architecture-json")
+    parser.add_argument("--output-json")
     parser.add_argument(
         "--allow-unsupported-arch",
         dest="allow_unvalidated_arch",
         action="store_true",
     )
-    return parser.parse_args()
+    args = parser.parse_args()
+    if args.session_json is None and not all(
+        (args.stage, args.base_model, args.architecture_json, args.output_json)
+    ):
+        parser.error(
+            "--session-json or --stage/--base-model/--architecture-json/--output-json "
+            "is required"
+        )
+    return args
+
+
+def _runtime_json(client: httpx.Client, method: str, path: str, **kwargs):
+    response = client.request(method, path, **kwargs)
+    response.raise_for_status()
+    return response.json()
+
+
+def _reset_vllm(client: httpx.Client, baseline: tuple[str, ...]) -> dict[str, object]:
+    def model_ids() -> tuple[str, ...]:
+        models = _runtime_json(client, "GET", "/v1/models")["data"]
+        return tuple(sorted(str(model["id"]) for model in models))
+
+    def idle() -> dict[str, float]:
+        for _ in range(600):
+            metrics = FastMetricsSnapshot.model_validate(
+                _runtime_json(client, "GET", "/art/metrics")
+            ).metrics
+            if not any(
+                value
+                for key, value in metrics.items()
+                if key.startswith("num_requests_")
+            ):
+                return {
+                    key: float(value)
+                    for key, value in metrics.items()
+                    if key.startswith("num_requests_")
+                }
+            time.sleep(0.1)
+        raise TimeoutError("functional vLLM requests did not drain")
+
+    idle_before = idle()
+    before = model_ids()
+    aliases = tuple(sorted(set(before) - set(baseline)))
+    for alias in aliases:
+        client.post(
+            "/v1/unload_lora_adapter", json={"lora_name": alias}
+        ).raise_for_status()
+    reset = _runtime_json(
+        client,
+        "POST",
+        "/art/reset_prefix_cache",
+        json={"reset_running_requests": False, "reset_connector": True},
+    )
+    idle_after = idle()
+    if reset.get("success") is not True or (after := model_ids()) != baseline:
+        raise RuntimeError(
+            f"functional reset failed: baseline={baseline}, after={after}"
+        )
+    return {
+        "baseline_model_ids": baseline,
+        "model_ids_before": before,
+        "unloaded_aliases": aliases,
+        "prefix_cache_reset": True,
+        "model_ids_after": after,
+        "requests_before": idle_before,
+        "requests_after": idle_after,
+    }
+
+
+async def _serving_baseline(
+    runtime: ManagedVllmRuntime,
+    startup: asyncio.Task[tuple[str, int]],
+) -> tuple[str, ...]:
+    await startup
+    async with httpx.AsyncClient(
+        base_url=runtime.base_url, **runtime.request_kwargs()
+    ) as client:
+        response = await client.get("/v1/models")
+        response.raise_for_status()
+        return tuple(sorted(str(model["id"]) for model in response.json()["data"]))
+
+
+async def _run_functional_session(request: WorkflowStageWorkerSession) -> None:
+    spec = request.resident_functional
+    assert spec is not None
+    launch_spec = spec.launch
+    stages = tuple(item.stage for item in request.items)
+    if stages != RESIDENT_FUNCTIONAL_STAGES:
+        raise ValueError("resident functional stages do not match worker items")
+    host_environment = request.items[0].environment
+    visible = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
+    if len(visible) != spec.gpu_count:
+        raise RuntimeError(
+            f"functional vLLM expected {spec.gpu_count} GPUs, received {len(visible)}"
+        )
+    inference_gpu_ids = tuple(map(int, launch_spec.visible_devices.split(",")))
+    launch = launch_spec.model_copy(
+        update={
+            "base_model": host_environment[FIXTURE_PATH_ENV],
+            "port": find_free_tcp_port(),
+            "cuda_visible_devices": ",".join(
+                visible[index] for index in inference_gpu_ids
+            ),
+        }
+    )
+    runtime = ManagedVllmRuntime()
+    supervisor = ChildProcessSupervisor(lambda _error: None)
+    tasks: list[asyncio.Task[Any]] = []
+    try:
+        with workflow._temporary_env(**host_environment):
+            startup = asyncio.create_task(
+                runtime.start(
+                    launch_config=launch,
+                    output_dir=str(
+                        Path(request.architecture_json).parent / "functional_vllm"
+                    ),
+                    child_processes=supervisor,
+                    install_parent_cleanup=lambda: None,
+                ),
+                name="functional-vllm-startup",
+            )
+            tasks.append(startup)
+            await asyncio.sleep(0)
+        if startup.done():
+            startup.result()
+        assert runtime.api_key is not None
+        serving_ready = asyncio.create_task(
+            _serving_baseline(runtime, startup), name="functional-vllm-ready"
+        )
+        tasks.append(serving_ready)
+        external = {
+            "ART_MODEL_SUPPORT_EXTERNAL_VLLM_URL": runtime.base_url,
+            "ART_MODEL_SUPPORT_EXTERNAL_VLLM_API_KEY": runtime.api_key,
+            "ART_MODEL_SUPPORT_EXTERNAL_VLLM_HEALTH_TIMEOUT": os.environ.get(
+                "ART_DEDICATED_VLLM_TIMEOUT", "1200"
+            ),
+            "ART_MODEL_SUPPORT_INFERENCE_GPU_IDS": ",".join(
+                map(str, inference_gpu_ids)
+            ),
+            EXTERNAL_VLLM_ENGINE_ARGS_ENV: json.dumps(
+                launch_spec.engine_args, sort_keys=True
+            ),
+            "ART_TRAIN_INF_MISMATCH_BASE_MODEL": request.base_model,
+            "ART_TRAIN_INF_MISMATCH_ALLOW_UNVALIDATED_ARCH": (
+                "1" if request.allow_unvalidated_arch else "0"
+            ),
+            "BASE_MODEL": request.base_model,
+        }
+        trainer_gpu_ids = spec.trainer_gpu_ids
+        if (
+            not trainer_gpu_ids
+            or set(trainer_gpu_ids) & set(inference_gpu_ids)
+            or any(gpu_id not in range(len(visible)) for gpu_id in trainer_gpu_ids)
+        ):
+            raise RuntimeError(
+                "invalid resident functional GPU partition: "
+                f"trainer={trainer_gpu_ids}, inference={inference_gpu_ids}"
+            )
+        environments = tuple(
+            item.environment | external | spec.trainer_environment
+            for item in request.items
+        )
+        if any(environment != environments[0] for environment in environments[1:]):
+            raise RuntimeError(
+                "resident functional stages resolved different environments"
+            )
+        from .resident_functional_session import run_resident_functional_session
+
+        stage_dirs = {item.stage: Path(item.stage_dir) for item in request.items}
+        log_path = stage_dirs["length_trainability"] / "worker.log"
+
+        async def run_session():
+            with workflow._temporary_env(**environments[0]):
+                with workflow._redirect_output(log_path):
+                    return await run_resident_functional_session(
+                        base_model=request.base_model,
+                        allow_unvalidated_arch=request.allow_unvalidated_arch,
+                        stage_dirs=stage_dirs,
+                        serving_ready=serving_ready,
+                    )
+
+        session = asyncio.create_task(run_session(), name="resident-functional-session")
+        tasks.append(session)
+        try:
+            results, baseline = await asyncio.gather(session, serving_ready)
+            with httpx.Client(
+                base_url=runtime.base_url, **runtime.request_kwargs()
+            ) as client:
+                supervisor.raise_if_failed()
+                reset = _reset_vllm(client, baseline)
+                for item, result in zip(request.items, results, strict=True):
+                    result.metrics["functional_vllm_reset"] = reset
+                    Path(item.output_json).write_text(
+                        result.model_dump_json(indent=2), encoding="utf-8"
+                    )
+        except Exception as exc:
+            for item in request.items:
+                output = Path(item.output_json)
+                if output.exists():
+                    continue
+                item_log = Path(item.stage_dir) / "worker.log"
+                with item_log.open("a", encoding="utf-8") as log:
+                    traceback.print_exc(file=log)
+                output.write_text(
+                    workflow.ValidationStageResult(
+                        name=item.stage,
+                        passed=False,
+                        metrics=workflow._stage_error_metrics(exc),
+                    ).model_dump_json(indent=2),
+                    encoding="utf-8",
+                )
+            raise
+    finally:
+        for task in tasks:
+            if not task.done():
+                task.cancel()
+        await asyncio.gather(*tasks, return_exceptions=True)
+        supervisor.close()
+        runtime.close()
+
+
+def _run_session(request: WorkflowStageWorkerSession) -> None:
+    architecture = ArchitectureReport.model_validate_json(
+        Path(request.architecture_json).read_text(encoding="utf-8")
+    )
+    for item in request.items:
+        started = time.monotonic()
+        log_path = Path(item.stage_dir) / "worker.log"
+        try:
+            with workflow._temporary_env(
+                **item.environment,
+                **{workflow.WORKFLOW_STAGE_DIR_ENV: item.stage_dir},
+            ):
+                with workflow._redirect_output(log_path):
+                    result = _STAGE_RUNNERS[item.stage](
+                        base_model=request.base_model,
+                        architecture=architecture,
+                        allow_unvalidated_arch=request.allow_unvalidated_arch,
+                    )
+        except Exception as exc:
+            with log_path.open("a", encoding="utf-8") as log:
+                traceback.print_exc(file=log)
+            result = workflow.ValidationStageResult(
+                name=item.stage,
+                passed=False,
+                metrics=workflow._stage_error_metrics(exc),
+            )
+        result.metrics.update(
+            {
+                "workflow_stage_artifact_dir": item.stage_dir,
+                "workflow_stage_duration_s": time.monotonic() - started,
+            }
+        )
+        Path(item.output_json).write_text(
+            result.model_dump_json(indent=2), encoding="utf-8"
+        )
+        if not result.passed:
+            break
+
+
+def _run_base_megatron_session(request: WorkflowStageWorkerSession) -> None:
+    stages = tuple(item.stage for item in request.items)
+    if stages != BASE_MEGATRON_STAGES:
+        raise ValueError("base Megatron stages do not match worker items")
+    from .base_megatron_session import base_megatron_session
+
+    with base_megatron_session():
+        _run_session(request)
+
+
+def run_session_json(session_json: str | Path) -> None:
+    request = WorkflowStageWorkerSession.model_validate_json(
+        Path(session_json).read_text(encoding="utf-8")
+    )
+    if request.resident_functional is not None:
+        asyncio.run(_run_functional_session(request))
+    elif request.base_megatron:
+        _run_base_megatron_session(request)
+    else:
+        _run_session(request)
 
 
 def main() -> None:
     args = _parse_args()
+    if args.session_json is not None:
+        run_session_json(args.session_json)
+        return
     architecture = ArchitectureReport.model_validate_json(
         Path(args.architecture_json).read_text(encoding="utf-8")
     )
-    stage_runner = _STAGE_RUNNERS[args.stage]
-    result = stage_runner(
+    result = _STAGE_RUNNERS[args.stage](
         base_model=args.base_model,
         architecture=architecture,
         allow_unvalidated_arch=args.allow_unvalidated_arch,
     )
     Path(args.output_json).write_text(
-        result.model_dump_json(indent=2),
-        encoding="utf-8",
+        result.model_dump_json(indent=2), encoding="utf-8"
     )
 
 
diff --git a/tests/integration/megatron/model_support/workflow_throughput.py b/tests/integration/megatron/model_support/workflow_throughput.py
new file mode 100644
index 000000000..55e8adb28
--- /dev/null
+++ b/tests/integration/megatron/model_support/workflow_throughput.py
@@ -0,0 +1,2278 @@
+from __future__ import annotations
+
+import asyncio
+from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Mapping
+from contextlib import contextmanager
+import hashlib
+import json
+import math
+from multiprocessing import resource_tracker, shared_memory
+import os
+from pathlib import Path
+import shutil
+from statistics import fmean, median, quantiles
+import struct
+import subprocess
+import sys
+from typing import Any, Literal, NamedTuple, cast
+import uuid
+
+from art.megatron.model_support.registry import get_model_support_spec
+from art.megatron.model_support.spec import ArchitectureReport
+
+from .validation_spec import ValidationStageResult
+from .workflow_fixtures import (
+    FIXTURE_PATH_ENV,
+    _flatten_token_ids,
+    _validate_tokenizer_compatible_fixture,
+)
+from .workflow_resources import (
+    ThroughputThresholds,
+    ThroughputWorkflowConfig,
+    handler_workflow_resources_for_base_model,
+    resolve_stage_resources_for_visible_gpus,
+)
+
+_STAGE_DIR_ENV = "ART_MODEL_SUPPORT_WORKFLOW_STAGE_DIR"
+_LAYER_LIST_FIELDS = (
+    "layer_types",
+    "mlp_layer_types",
+    "indexer_types",
+    "compress_ratios",
+)
+_WIDTH_TERMS = ("hidden", "intermediate", "head", "expert", "lora_rank", "topk")
+_POLICY_AGE_MEAN = "offpolicy/token_weighted_policy_age_steps"
+_POLICY_AGE_P95 = "offpolicy/token_weighted_policy_age_p95_steps"
+_FRESHNESS_DISCOUNT = "sample_efficiency/freshness_discount"
+_STALE_GROUPS = "discarded/step/stale_groups"
+_ZERO_VARIANCE_GROUPS = "discarded/step/zero_variance_groups"
+_INTER_FORWARD_BACKWARD_GAP_PREFIX = "time/inter_forward_backward_gpu_gap_rank_"
+_MEASUREMENT_CONTRACT_VERSION = 20
+_ISOLATED_WARMUP_STEPS = 1
+_MATCHED_MEASURED_STEPS = 3
+_PACKING_DRAIN_WINDOWS = 1
+_REQUIRED_SETTLED_WINDOWS = 2
+_PIPELINE_SETTING_NAMES = (
+    "num_rollout_workers",
+    "min_batch_size",
+    "max_batch_size",
+    "queue_maxsize",
+    "target_groups_per_step",
+)
+_EXECUTION_SHAPE_NAMES = (
+    "data/step_packed_sequences",
+    "data/step_num_gradient_steps",
+    "pipeline/global_real_microbatches",
+    "pipeline/global_dummy_microbatches",
+    "pipeline/packed_sequence_length",
+)
+_REPO_ROOT = Path(__file__).parents[4]
+_MAIN_RUNTIME_PACKAGES = (
+    "openpipe-art",
+    "torchmonarch",
+    "torch",
+    "triton",
+    "transformer-engine",
+    "megatron-core",
+    "megatron-bridge",
+    "transformers",
+    "flashinfer-python",
+    "nvidia-nccl-cu13",
+    "nvidia-nvshmem-cu13",
+)
+_VLLM_RUNTIME_PACKAGES = (
+    "art-vllm-runtime",
+    "vllm",
+    "torch",
+    "triton",
+    "transformers",
+    "flashinfer-python",
+    "nvidia-nccl-cu13",
+)
+_LOCAL_SOURCE_PACKAGES = ("openpipe-art", "art-vllm-runtime")
+_H200_THROUGHPUT_NUM_LAYERS = {"dsv4": 4, "glm52": 6}
+_THROUGHPUT_MAX_ATTEMPTS = 2
+_LOAD_ACCEPTANCE_FAILURES = frozenset(
+    {
+        "stable_min_vllm_pressure",
+        "stable_trainer_underfeed",
+        "queue_ready_inter_forward_backward_gap_count",
+    }
+)
+_PERFORMANCE_ACCEPTANCE_FAILURES = frozenset(
+    {
+        "isolated_train_tok_s",
+        "e2e_train_tok_s",
+        "accepted_train_tok_s",
+        "e2e_to_isolated_ratio",
+        "matched_core_to_isolated_ratio",
+        "matched_core_to_isolated_ratio_max",
+        "mean_policy_activation_lag_s",
+        "max_policy_activation_lag_s",
+        "repeated_policy_activation_cadence_s",
+        "queue_ready_inter_forward_backward_gap_p50_s",
+        "queue_ready_inter_forward_backward_gap_max_s",
+    }
+)
+_HARD_ACCEPTANCE_FAILURES = frozenset(
+    {"calibration_fingerprint", "calibration_basis", "unused_and_dummy_ratio"}
+)
+
+
+class _ThroughputEvidenceInconclusive(RuntimeError):
+    pass
+
+
+class ThroughputFixture(NamedTuple):
+    model_key: str
+    path: str
+    num_layers: int
+    width_fingerprint: dict[str, int]
+    manifest: dict[str, Any]
+
+
+class TrainerPhaseEvidence(NamedTuple):
+    phase: Literal["isolated", "e2e"]
+    runtime_fingerprint: str
+    trajectory_input_fingerprint: str
+    packed_input_fingerprint: str
+    workload_fingerprint: str
+    sample_count: int
+    policy_steps: tuple[int, ...]
+    train_s: float
+    metrics: tuple[dict[str, float], ...]
+
+    @property
+    def sample_train_tok_s(self) -> tuple[float, ...]:
+        return tuple(
+            metrics["data/step_nonpadding_logical_tokens"]
+            / metrics["time/step_train_s"]
+            for metrics in self.metrics
+        )
+
+    @property
+    def train_tok_s(self) -> float:
+        return median(self.sample_train_tok_s)
+
+
+class CapturedTrainingInput(NamedTuple):
+    bundles: tuple[Any, ...]
+    trajectory_fingerprint: str
+    packed_fingerprint: str
+    pipeline_settings: dict[str, int]
+    metrics: Mapping[str, Any]
+    policy_step: int
+
+
+@contextmanager
+def _freeze_pipeline_settings_from_step(trainer: Any, step: int) -> Iterator[None]:
+    apply = trainer.apply_pipeline_settings
+
+    def apply_before_step(settings: Any) -> None:
+        # Keep the measured windows and matched captures on one actual setting while
+        # the tuner continues recording the decisions it would have applied.
+        if trainer.state.next_training_step < step:
+            apply(settings)
+
+    setattr(trainer, "apply_pipeline_settings", apply_before_step)
+    try:
+        yield
+    finally:
+        setattr(trainer, "apply_pipeline_settings", apply)
+
+
+def _current_pipeline_settings(trainer: Any) -> dict[str, int]:
+    return {name: int(getattr(trainer, name)) for name in _PIPELINE_SETTING_NAMES}
+
+
+def _row_pipeline_settings(row: Mapping[str, Any], step: int) -> dict[str, int]:
+    return {
+        name: _nonnegative_integer(
+            row.get(f"pipeline_settings/{name}"),
+            name=f"step {step} pipeline setting {name}",
+        )
+        for name in _PIPELINE_SETTING_NAMES
+    }
+
+
+def _row_execution_shape(row: Mapping[str, Any], step: int) -> tuple[int, ...]:
+    return tuple(
+        _nonnegative_integer(row.get(name), name=f"step {step} {name}")
+        for name in _EXECUTION_SHAPE_NAMES
+    )
+
+
+def _settled_execution_decision_suffix(
+    decisions: list[Any],
+    by_step: Mapping[int, Mapping[str, Any]],
+) -> list[Any]:
+    final = decisions[-1].stats
+    assert final is not None
+    _require(
+        final.end_step in by_step,
+        f"autotuner decision window lacks train row: {final.end_step}",
+    )
+    expected = _row_pipeline_settings(by_step[final.end_step], final.end_step)
+    warmed_shape: dict[int, bool] = {}
+    seen_shapes: set[tuple[int, ...]] = set()
+    for step, row in sorted(by_step.items()):
+        shape = _row_execution_shape(row, step)
+        warmed_shape[step] = shape in seen_shapes
+        seen_shapes.add(shape)
+
+    def is_settled(step: int) -> bool:
+        row = by_step[step]
+        settings = _row_pipeline_settings(row, step)
+        lag = _nonnegative_integer(
+            row.get("queue/packing_policy_lag_steps"),
+            name=f"step {step} packing policy lag",
+        )
+        packing_step = step - lag
+        submitted = _nonnegative_integer(
+            row.get("data/step_num_groups_submitted"),
+            name=f"step {step} submitted groups",
+        )
+        return (
+            settings == expected
+            and packing_step in by_step
+            and _row_pipeline_settings(by_step[packing_step], packing_step) == expected
+            and settings["min_batch_size"] <= submitted <= settings["max_batch_size"]
+            and warmed_shape[step]
+        )
+
+    selected: list[Any] = []
+    later: Any | None = None
+    for decision in reversed(decisions):
+        stats = decision.stats
+        assert stats is not None
+        if later is not None:
+            _require(
+                stats.end_step + 1 == later.start_step
+                and math.isclose(
+                    float(stats.window_end_s),
+                    float(later.window_start_s),
+                    rel_tol=0.0,
+                    abs_tol=1e-6,
+                ),
+                "autotuner windows are not contiguous",
+            )
+        steps = range(stats.start_step, stats.end_step + 1)
+        missing = [step for step in steps if step not in by_step]
+        _require(not missing, f"autotuner decision window lacks train rows: {missing}")
+        if not all(is_settled(step) for step in steps):
+            break
+        selected.append(decision)
+        later = stats
+    selected.reverse()
+    if len(selected) < _REQUIRED_SETTLED_WINDOWS:
+        raise _ThroughputEvidenceInconclusive(
+            "throughput evidence requires two trailing settled execution windows"
+        )
+    return selected
+
+
+def _text(config: dict[str, Any]) -> dict[str, Any]:
+    return config.get("text_config", config)
+
+
+def _width_fingerprint(config: dict[str, Any]) -> dict[str, int]:
+    text = _text(config)
+    return {
+        key: value
+        for key, value in text.items()
+        if key != "num_hidden_layers"
+        and type(value) is int
+        and any(term in key for term in _WIDTH_TERMS)
+    }
+
+
+def _digest(value: Any) -> str:
+    return hashlib.sha256(json.dumps(value, sort_keys=True).encode()).hexdigest()
+
+
+def _files_digest(paths: list[Path]) -> str:
+    digest = hashlib.sha256()
+    for path in sorted(paths):
+        if not path.is_file():
+            raise RuntimeError(f"calibration provenance file is missing: {path}")
+        relative = path.relative_to(_REPO_ROOT).as_posix().encode()
+        payload = path.read_bytes()
+        digest.update(struct.pack("<Q", len(relative)))
+        digest.update(relative)
+        digest.update(struct.pack("<Q", len(payload)))
+        digest.update(payload)
+    return digest.hexdigest()
+
+
+def _environment_provenance(python: Path, packages: tuple[str, ...]) -> dict[str, Any]:
+    script = """
+import hashlib
+from importlib import metadata
+import json
+import platform
+import sys
+import torch
+
+def sha(value):
+    return hashlib.sha256(value.encode()).hexdigest() if value is not None else None
+
+distributions = {}
+local_source_packages = set(json.loads(sys.argv[2]))
+for name in json.loads(sys.argv[1]):
+    try:
+        dist = metadata.distribution(name)
+    except metadata.PackageNotFoundError:
+        continue
+    provenance = {
+        "version": dist.version,
+        "metadata_sha256": sha(dist.read_text("METADATA")),
+    }
+    if name not in local_source_packages:
+        provenance.update({
+            "direct_url_sha256": sha(dist.read_text("direct_url.json")),
+            "record_sha256": sha(dist.read_text("RECORD")),
+        })
+    distributions[name] = provenance
+print(json.dumps({
+    "python": {
+        "version": platform.python_version(),
+        "implementation": platform.python_implementation(),
+        "cache_tag": sys.implementation.cache_tag,
+        "abi_flags": sys.abiflags,
+    },
+    "torch": {
+        "version": torch.__version__,
+        "cuda": torch.version.cuda,
+        "cxx11_abi": torch._C._GLIBCXX_USE_CXX11_ABI,
+    },
+    "distributions": distributions,
+}, sort_keys=True))
+"""
+    if not python.is_file():
+        raise RuntimeError(f"calibration runtime Python is missing: {python}")
+    environment = os.environ.copy()
+    environment.pop("PYTHONHOME", None)
+    environment.pop("PYTHONPATH", None)
+    result = subprocess.run(
+        [
+            str(python),
+            "-c",
+            script,
+            json.dumps(packages),
+            json.dumps(_LOCAL_SOURCE_PACKAGES),
+        ],
+        check=True,
+        capture_output=True,
+        env=environment,
+        text=True,
+    )
+    return cast(dict[str, Any], json.loads(result.stdout))
+
+
+def _source_provenance() -> dict[str, Any]:
+    return {
+        "art_source_sha256": _files_digest(
+            list((_REPO_ROOT / "src/art").rglob("*.py"))
+        ),
+        "vllm_runtime_source_sha256": _files_digest(
+            list((_REPO_ROOT / "vllm_runtime/src/art_vllm_runtime").rglob("*.py"))
+        ),
+        "workflow_runtime_sha256": _files_digest(
+            [
+                Path(__file__),
+                Path(__file__).with_name("workflow.py"),
+                Path(__file__).with_name("workflow_fixtures.py"),
+                Path(__file__).with_name("workflow_stage_worker.py"),
+                Path(__file__).with_name("validation_spec.py"),
+            ]
+        ),
+        "build_contract_sha256": _files_digest(
+            [
+                _REPO_ROOT / "pyproject.toml",
+                _REPO_ROOT / "vllm_runtime/pyproject.toml",
+                _REPO_ROOT / "vllm_runtime/setup.sh",
+            ]
+        ),
+        "root_lock_sha256": _files_digest([_REPO_ROOT / "uv.lock"]),
+        "vllm_runtime_lock_sha256": _files_digest(
+            [_REPO_ROOT / "vllm_runtime/uv.lock"]
+        ),
+        "main_environment": _environment_provenance(
+            Path(sys.executable), _MAIN_RUNTIME_PACKAGES
+        ),
+        "vllm_environment": _environment_provenance(
+            _REPO_ROOT / "vllm_runtime/.venv/bin/python", _VLLM_RUNTIME_PACKAGES
+        ),
+    }
+
+
+def _require(condition: bool, message: str) -> None:
+    if not condition:
+        raise RuntimeError(message)
+
+
+def _nonnegative_integer(value: Any, *, name: str) -> int:
+    _require(
+        not isinstance(value, bool)
+        and isinstance(value, int | float)
+        and math.isfinite(float(value))
+        and float(value).is_integer()
+        and value >= 0,
+        f"{name} must be a nonnegative integer, got {value!r}",
+    )
+    return int(value)
+
+
+_PHASE_WORKLOAD_KEYS = (
+    "data/step_num_groups_trainable",
+    "data/step_packed_sequences",
+    "data/step_nonpadding_logical_tokens",
+    "data/step_loss_bearing_tokens",
+    "data/step_executed_token_equivalents",
+    "data/step_dummy_executed_token_equivalents",
+    "data/step_nominal_schedule_capacity_tokens",
+    "data/step_dummy_schedule_capacity_tokens",
+    "data/step_unused_packed_capacity_tokens",
+    "data/step_num_gradient_steps",
+    "pipeline/global_real_microbatches",
+    "pipeline/global_dummy_microbatches",
+)
+
+
+def _phase_evidence(
+    *,
+    phase: Literal["isolated", "e2e"],
+    runtime_fingerprint: str,
+    trajectory_input_fingerprint: str,
+    packed_input_fingerprint: str,
+    samples: list[tuple[Mapping[str, Any], int]],
+) -> TrainerPhaseEvidence:
+    if not samples:
+        raise RuntimeError(f"{phase} trainer phase produced no samples")
+    numeric_samples = tuple(
+        {
+            key: float(value)
+            for key, value in metrics.items()
+            if isinstance(value, int | float)
+        }
+        for metrics, _ in samples
+    )
+    workloads = [
+        {
+            key: _nonnegative_integer(metrics.get(key), name=f"{phase} {key}")
+            for key in _PHASE_WORKLOAD_KEYS
+        }
+        for metrics in numeric_samples
+    ]
+    train_s = sum(metrics.get("time/step_train_s", 0.0) for metrics in numeric_samples)
+    if not math.isfinite(train_s) or train_s <= 0.0:
+        raise RuntimeError(f"{phase} trainer timing is invalid: train={train_s}")
+    workload_fingerprint = _digest(workloads)
+    return TrainerPhaseEvidence(
+        phase=phase,
+        runtime_fingerprint=runtime_fingerprint,
+        trajectory_input_fingerprint=trajectory_input_fingerprint,
+        packed_input_fingerprint=packed_input_fingerprint,
+        workload_fingerprint=workload_fingerprint,
+        sample_count=len(samples),
+        policy_steps=tuple(step for _, step in samples),
+        train_s=train_s,
+        metrics=numeric_samples,
+    )
+
+
+def _bundle_bytes(bundles: tuple[Any, ...]) -> bytes:
+    from msgspec import msgpack
+
+    return msgpack.encode(tuple(bundle.model_dump(mode="python") for bundle in bundles))
+
+
+def _matched_input_fingerprints(
+    trajectory_fingerprints: list[str], packed_fingerprints: list[str]
+) -> tuple[str, str]:
+    _require(
+        len(trajectory_fingerprints)
+        == len(packed_fingerprints)
+        == _MATCHED_MEASURED_STEPS,
+        "matched trainer phases require complete paired inputs",
+    )
+    _require(
+        len(set(trajectory_fingerprints)) == _MATCHED_MEASURED_STEPS,
+        "matched E2E samples must use distinct trajectory inputs",
+    )
+    return _digest(trajectory_fingerprints), _digest(
+        list(zip(trajectory_fingerprints, packed_fingerprints, strict=True))
+    )
+
+
+async def _discard_prepared_pipeline_batch(backend: Any, groups: list[Any]) -> None:
+    for group in groups:
+        group._distributed_lease = None
+    await backend.discard_pipeline_batch(groups)
+
+
+def _matched_capture_steps(max_steps: int) -> tuple[int, ...]:
+    first = max_steps - _MATCHED_MEASURED_STEPS + 1
+    return tuple(range(first, first + _MATCHED_MEASURED_STEPS))
+
+
+def _prepared_pipeline_batch(groups: list[Any]) -> Any:
+    prepared = groups[0]._prepared_training_batch
+    if prepared is None or any(
+        group._prepared_training_batch is not prepared for group in groups
+    ):
+        raise RuntimeError("trainer groups do not share one prepared data-plane batch")
+    return prepared
+
+
+def _packed_batch_fingerprint(prepared: Any) -> str:
+    batch = prepared.batch
+    packed = batch.payload.packed
+    ref = packed.leases.ref
+    stable_ref = ref.model_dump(
+        mode="json",
+        exclude={
+            "batch_id",
+            "owner_actor_id",
+            "lease_id",
+            "shared_memory_name",
+            "owner_process_id",
+            "group_ids",
+            "record_ids",
+            "min_source_version",
+            "max_source_version",
+        },
+    )
+    manifest = {
+        "packing_config": prepared.packing_config.model_dump(mode="json"),
+        "batch": batch.model_dump(mode="json", exclude={"payload"}),
+        "packed_ref": stable_ref,
+    }
+    digest = hashlib.sha256(json.dumps(manifest, sort_keys=True).encode())
+    digest.update(b"packed_group_shapes:v1")
+    digest.update(struct.pack("<Q", len(packed.packed_group_shapes)))
+    for shape in packed.packed_group_shapes:
+        digest.update(b"\0" if shape is None else b"\1")
+        if shape is None:
+            continue
+        digest.update(struct.pack("<Q", len(shape.leaves)))
+        for leaf in shape.leaves:
+            typecode = leaf.token_ids.typecode.encode("ascii")
+            digest.update(struct.pack("<Q", len(typecode)))
+            digest.update(typecode)
+            digest.update(
+                struct.pack("<QQ", len(leaf.token_ids), leaf.shareable_length)
+            )
+            digest.update(leaf.token_ids.tobytes())
+    shm = shared_memory.SharedMemory(name=ref.shared_memory_name)
+    if ref.owner_process_id != os.getpid():
+        resource_tracker.unregister(getattr(shm, "_name"), "shared_memory")
+    try:
+        buffer = shm.buf
+        assert buffer is not None
+        for tensor in ref.tensors:
+            digest.update(buffer[tensor.offset : tensor.offset + tensor.byte_count])
+        del buffer
+    finally:
+        shm.close()
+    return digest.hexdigest()
+
+
+def _packed_input_fingerprint(groups: list[Any]) -> str:
+    return _packed_batch_fingerprint(_prepared_pipeline_batch(groups))
+
+
+async def _capture_training_bundles(selections: tuple[Any, ...]) -> tuple[Any, ...]:
+    from art.distributed.trajectory_store import TrajectoryGroupBundle
+
+    materialized = await asyncio.gather(
+        *(selection.queue.materialize_selection(selection) for selection in selections)
+    )
+    return await asyncio.to_thread(
+        lambda: tuple(TrajectoryGroupBundle.from_group(group) for group in materialized)
+    )
+
+
+async def _capture_training_input(
+    prepared: Any,
+    selections: tuple[Any, ...],
+    pipeline_settings: dict[str, int],
+) -> tuple[tuple[Any, ...], str, str, dict[str, int]]:
+    bundles, packed_fingerprint = await asyncio.gather(
+        _capture_training_bundles(selections),
+        asyncio.to_thread(_packed_batch_fingerprint, prepared),
+    )
+    trajectory_fingerprint = hashlib.sha256(
+        await asyncio.to_thread(_bundle_bytes, bundles)
+    ).hexdigest()
+    return bundles, trajectory_fingerprint, packed_fingerprint, pipeline_settings
+
+
+def _collect_matched_packing_shapes(groups: Any) -> None:
+    for group in groups:
+        group._collect_packing_shape = True
+
+
+def _sized_config(
+    source: dict[str, Any], *, model_key: str, num_layers: int
+) -> tuple[dict[str, Any], dict[str, Any]]:
+    sized = json.loads(json.dumps(source))
+    text = _text(sized)
+    source_text = _text(source)
+    source_layers = int(source_text["num_hidden_layers"])
+    layer_fields = tuple(field for field in _LAYER_LIST_FIELDS if field in source_text)
+    if num_layers > source_layers and layer_fields:
+        raise ValueError(
+            f"cannot expand {model_key} with per-layer fields {layer_fields}"
+        )
+    text["num_hidden_layers"] = num_layers
+    for field in layer_fields:
+        values = source_text[field]
+        if len(values) < num_layers:
+            raise ValueError(f"{model_key} {field} has only {len(values)} entries")
+        text[field] = values[:num_layers]
+    source_width = _width_fingerprint(source)
+    if not source_width or source_width != _width_fingerprint(sized):
+        raise ValueError("throughput fixture changed or lost production-width fields")
+    prefix = "text_config." if "text_config" in source else ""
+    return sized, {
+        "source_num_layers": source_layers,
+        "changed_paths": [
+            f"{prefix}{field}"
+            for field in ("num_hidden_layers", *_LAYER_LIST_FIELDS)
+            if field == "num_hidden_layers" or field in source_text
+        ],
+        "width_fingerprint": source_width,
+    }
+
+
+def _copy_metadata(source: Path, target: Path) -> None:
+    excluded = {"config.json", "fixture_manifest.json", "model.safetensors.index.json"}
+    for path in source.iterdir():
+        if (
+            path.is_file()
+            and path.name not in excluded
+            and not path.name.endswith((".safetensors", ".bin", ".pt", ".pth"))
+        ):
+            shutil.copy2(path, target / path.name)
+
+
+def _config_only_tensors(config: dict[str, Any], *, model_key: str) -> dict[str, Any]:
+    import torch
+
+    tensors = {"_art_config_only": torch.zeros(1)}
+    if model_key != "gemma4_moe":
+        return tensors
+    text = _text(config)
+    for layer in range(int(text["num_hidden_layers"])):
+        for suffix in (
+            "pre_feedforward_layernorm",
+            "pre_feedforward_layernorm_2",
+        ):
+            tensors[f"model.language_model.layers.{layer}.{suffix}.weight"] = (
+                torch.ones(int(text["hidden_size"]), dtype=torch.bfloat16)
+            )
+    return tensors
+
+
+def ensure_throughput_fixture(
+    *,
+    canonical_model: str,
+    model_key: str,
+    correctness_fixture: Path,
+    num_layers: int,
+    initialization_version: str,
+    random_seed: int,
+    output: Path,
+) -> ThroughputFixture:
+    source_config_path = correctness_fixture / "production_config" / "config.json"
+    if not source_config_path.is_file():
+        raise RuntimeError(
+            f"correctness fixture lacks pinned production config: {source_config_path}"
+        )
+    source = json.loads(source_config_path.read_text())
+    sized, sizing = _sized_config(source, model_key=model_key, num_layers=num_layers)
+    vocabulary_contract: dict[str, object] = {
+        "config_vocab_size": int(_text(sized)["vocab_size"])
+    }
+    _validate_tokenizer_compatible_fixture(correctness_fixture, vocabulary_contract)
+    manifest = {
+        "version": 2,
+        "canonical_model": canonical_model,
+        "model_key": model_key,
+        "num_layers": num_layers,
+        "source_config_sha256": _digest(source),
+        "sized_config_sha256": _digest(sized),
+        "initialization": initialization_version,
+        "random_seed": random_seed,
+        "vocabulary_contract": vocabulary_contract,
+        **sizing,
+    }
+    output.mkdir()
+    _copy_metadata(correctness_fixture, output)
+    (output / "config.json").write_text(json.dumps(sized, indent=2) + "\n")
+    from safetensors.torch import save_file
+
+    tensors = _config_only_tensors(sized, model_key=model_key)
+    checkpoint = output / "model.safetensors"
+    save_file(tensors, checkpoint)
+    if model_key == "gemma4_moe":
+        (output / "model.safetensors.index.json").write_text(
+            json.dumps(
+                {"metadata": {}, "weight_map": dict.fromkeys(tensors, checkpoint.name)},
+                indent=2,
+            )
+            + "\n"
+        )
+    (output / "throughput_fixture_manifest.json").write_text(
+        json.dumps(manifest, indent=2) + "\n"
+    )
+    return ThroughputFixture(
+        model_key=model_key,
+        path=str(output),
+        num_layers=num_layers,
+        width_fingerprint=sizing["width_fingerprint"],
+        manifest=manifest,
+    )
+
+
+class PolicyActivationEvent(NamedTuple):
+    step: int
+    trainer_completed_monotonic_s: float
+    serving_active_monotonic_s: float
+
+    @property
+    def lag_s(self) -> float:
+        return self.serving_active_monotonic_s - self.trainer_completed_monotonic_s
+
+
+async def _activation_event(service: Any, step: int) -> PolicyActivationEvent:
+    await service.wait_for_serving(step)
+    completed, active = service.policy_activation_timing(step)
+    return PolicyActivationEvent(step, completed, active)
+
+
+async def _cancel_activation_tasks(
+    tasks: Mapping[int, asyncio.Task[PolicyActivationEvent]],
+) -> None:
+    for task in tasks.values():
+        if not task.done():
+            task.cancel()
+    await asyncio.gather(*tasks.values(), return_exceptions=True)
+
+
+def _gpu_identities(
+    *, trainer_gpu_ids: list[int], inference_gpu_ids: list[int]
+) -> list[dict[str, Any]]:
+    import torch
+
+    from art.distributed.host_admission import _query_gpu_inventory
+
+    roles = [
+        *(("trainer", gpu_id) for gpu_id in trainer_gpu_ids),
+        *(("inference", gpu_id) for gpu_id in inference_gpu_ids),
+    ]
+    if (
+        not trainer_gpu_ids
+        or not inference_gpu_ids
+        or len({gpu for _, gpu in roles}) != len(roles)
+    ):
+        raise RuntimeError(
+            f"throughput stage requires non-empty, disjoint CUDA roles, got {roles}"
+        )
+
+    def uuid_key(value: str) -> str:
+        return value.casefold().removeprefix("gpu-").removeprefix("mig-")
+
+    inventory = _query_gpu_inventory(include_mig=True)
+    by_uuid: dict[str, list[tuple[Any, str]]] = {}
+    for gpu, driver in inventory:
+        by_uuid.setdefault(uuid_key(gpu.uuid), []).append((gpu, driver))
+    identities = []
+    for role, logical_index in roles:
+        properties = torch.cuda.get_device_properties(logical_index)
+        cuda_uuid = str(getattr(properties, "uuid", ""))
+        matches = by_uuid.get(uuid_key(cuda_uuid), [])
+        if len(matches) != 1:
+            raise RuntimeError(
+                "could not map CUDA-visible GPU to one physical identity: "
+                f"logical={logical_index}, uuid={cuda_uuid!r}, matches={len(matches)}"
+            )
+        gpu, driver = matches[0]
+        identities.append(
+            {
+                "role": role,
+                "logical_index": logical_index,
+                "uuid": gpu.uuid,
+                "parent_uuid": gpu.parent_uuid,
+                "pci_bus_id": gpu.pci_bus_id,
+                "name": properties.name,
+                "total_memory_bytes": properties.total_memory,
+                "compute_capability": [properties.major, properties.minor],
+                "driver_version": driver,
+            }
+        )
+    physical_uuids = {
+        str(identity["uuid"]).casefold()
+        for identity in identities
+        if identity["uuid"] == identity["parent_uuid"]
+        and not str(identity["uuid"]).startswith("MIG-")
+    }
+    if len(physical_uuids) != 4:
+        raise RuntimeError(
+            "throughput stage requires four unique non-MIG physical GPU UUIDs, "
+            f"got {[identity['uuid'] for identity in identities]}"
+        )
+    return identities
+
+
+def _hardware(gpu_identities: list[dict[str, Any]]) -> Literal["h200", "b300"]:
+    names = {str(identity["name"]).upper() for identity in gpu_identities}
+    if len(names) != 1:
+        raise RuntimeError(
+            f"throughput stage requires homogeneous GPUs, got {sorted(names)}"
+        )
+    name = next(iter(names))
+    if "B300" in name or "GB300" in name:
+        return "b300"
+    if "H200" in name:
+        return "h200"
+    raise RuntimeError(f"throughput thresholds are unavailable for {name}")
+
+
+def _throughput_config_for_hardware(
+    model_key: str,
+    config: ThroughputWorkflowConfig,
+    hardware: Literal["h200", "b300"],
+) -> ThroughputWorkflowConfig:
+    num_layers = _H200_THROUGHPUT_NUM_LAYERS.get(model_key)
+    if hardware != "h200" or num_layers is None:
+        return config
+    return config.model_copy(update={"num_layers": num_layers})
+
+
+def _stable_gpu_identity(identity: Mapping[str, Any]) -> dict[str, Any]:
+    return {
+        "name": identity["name"],
+        "total_memory_bytes": identity["total_memory_bytes"],
+        "compute_capability": identity["compute_capability"],
+        "driver_version": identity["driver_version"],
+    }
+
+
+def _groups_per_packed_sequence(stage: Any, config: ThroughputWorkflowConfig) -> int:
+    if stage.megatron is None:
+        raise RuntimeError("throughput stage requires Megatron resources")
+    topology = stage.megatron.topology
+    sequence_world_size = topology.tp * topology.cp * topology.pp
+    target_sequences, topology_remainder = divmod(
+        len(stage.megatron.gpu_ids), sequence_world_size
+    )
+    _require(
+        target_sequences > 0 and topology_remainder == 0,
+        "throughput topology cannot resolve packed sequences per update",
+    )
+    groups, group_remainder = divmod(config.groups_per_step, target_sequences)
+    _require(
+        groups > 0 and group_remainder == 0,
+        "throughput groups_per_step must divide evenly across packed sequences",
+    )
+    return groups
+
+
+def _calibration_contract(
+    *,
+    base_model: str,
+    fixture: ThroughputFixture,
+    stage: Any,
+    config: ThroughputWorkflowConfig,
+    autotune: Any,
+    actual_prompt_tokens: int,
+    gpu_identities: list[dict[str, Any]],
+) -> dict[str, Any]:
+    manifest = fixture.manifest
+    _require(
+        all(
+            manifest.get(key) for key in ("source_config_sha256", "sized_config_sha256")
+        )
+        and manifest.get("width_fingerprint") == fixture.width_fingerprint,
+        "throughput fixture lacks source/sized hashes or production width",
+    )
+    workload = config.model_dump(
+        mode="json",
+        exclude={"thresholds", "random_initialization_version", "random_seed"},
+    )
+    role_counts = {
+        role: sum(identity["role"] == role for identity in gpu_identities)
+        for role in ("trainer", "inference")
+    }
+    accelerator_specs = {
+        json.dumps(_stable_gpu_identity(identity), sort_keys=True)
+        for identity in gpu_identities
+    }
+    _require(
+        len(accelerator_specs) == 1,
+        "throughput stage requires one homogeneous accelerator specification",
+    )
+    return {
+        "measurement_contract_version": _MEASUREMENT_CONTRACT_VERSION,
+        "source_provenance": _source_provenance(),
+        "fixture_manifest": manifest,
+        "model_identity": {"base_model": base_model, "model_key": fixture.model_key},
+        "topology": stage.megatron.topology.model_dump(mode="json"),
+        "hardware": {
+            "role_counts": role_counts,
+            "class": _hardware(gpu_identities),
+            "accelerator": json.loads(accelerator_specs.pop()),
+        },
+        "engine_args": {
+            **stage.vllm.engine_args(),
+            "seed": config.random_seed,
+            "model": f"fixture-sha256:{manifest['sized_config_sha256']}",
+        },
+        "autotuner_config": autotune.model_dump(mode="json"),
+        "workload_config": {**workload, "actual_prompt_tokens": actual_prompt_tokens},
+        "random_initialization": {
+            "version": config.random_initialization_version,
+            "seed": config.random_seed,
+        },
+        "trainer_config": {
+            "learning_rate": 1e-6,
+            "loss_fn": "cispo",
+            "eval_fn": None,
+            "eval_every_n_steps": 0,
+            "eval_at_start": False,
+            "save_checkpoint": False,
+            "resume": False,
+            "score_reference_groups_per_step": config.groups_per_step,
+            "score_reference_rollouts_per_group": config.rollouts_per_group,
+            "max_steps_off_policy": config.max_steps_off_policy,
+            "isolated_warmup_steps": _ISOLATED_WARMUP_STEPS,
+            "matched_measured_steps": _MATCHED_MEASURED_STEPS,
+        },
+        "packed_sequence_length": config.packed_sequence_length,
+        "prompt_tokens": actual_prompt_tokens,
+        "completion_tokens": config.completion_tokens,
+        "rollouts_per_group": config.rollouts_per_group,
+        "groups_per_step": config.groups_per_step,
+    }
+
+
+def _calibration_fingerprint(contract: dict[str, Any]) -> str:
+    # Implementation hashes remain diagnostic; build and dependency identity
+    # still fence calibrations that are not comparable execution environments.
+    source_provenance = {
+        key: value
+        for key, value in contract["source_provenance"].items()
+        if key
+        not in {
+            "art_source_sha256",
+            "vllm_runtime_source_sha256",
+            "workflow_runtime_sha256",
+        }
+    }
+    return _digest({**contract, "source_provenance": source_provenance})
+
+
+def _chat_token_count(tokenizer: Any, prompt: str) -> int:
+    return len(
+        _flatten_token_ids(
+            tokenizer.apply_chat_template(
+                [{"role": "user", "content": prompt}],
+                tokenize=True,
+                add_generation_prompt=True,
+            )
+        )
+    )
+
+
+def _sized_prompt(tokenizer: Any, *, target_tokens: int) -> str:
+    prefix = "Throughput scenario 00000000. Process the following neutral record.\n"
+    unit = " measured context item"
+    lower, upper = 0, target_tokens
+    while lower < upper:
+        middle = (lower + upper + 1) // 2
+        candidate = prefix + unit * middle
+        if _chat_token_count(tokenizer, candidate) <= target_tokens:
+            lower = middle
+        else:
+            upper = middle - 1
+    prompt = prefix + unit * lower
+    actual = _chat_token_count(tokenizer, prompt)
+    if actual < target_tokens - 64:
+        raise RuntimeError(
+            f"could not size throughput prompt near {target_tokens} tokens: {actual}"
+        )
+    return prompt
+
+
+async def _scenarios(prompt: str) -> AsyncIterator[dict[str, str]]:
+    index = 0
+    while True:
+        scenario_id = f"throughput-{index:08d}"
+        yield {
+            "scenario_id": scenario_id,
+            "prompt": prompt.replace("00000000", f"{index:08d}", 1),
+        }
+        index += 1
+
+
+def _training_rows(model_output_dir: Path) -> list[dict[str, Any]]:
+    history_path = model_output_dir / "history.jsonl"
+    if not history_path.is_file():
+        raise RuntimeError(f"throughput history is missing: {history_path}")
+    rows = [json.loads(line) for line in history_path.read_text().splitlines() if line]
+    return [row for row in rows if "data/step_nonpadding_logical_tokens" in row]
+
+
+_COUNT_METRICS = {
+    "original_trajectory_tokens": "train/prefix_tree/logical_tokens",
+    "nonpadding_logical_tokens": "data/step_nonpadding_logical_tokens",
+    "loss_bearing_tokens": "data/step_loss_bearing_tokens",
+    "accepted_train_tokens": "data/step_trainable_assistant_tokens",
+    "executed_token_equivalents": "data/step_executed_token_equivalents",
+    "dummy_token_equivalents": "data/step_dummy_executed_token_equivalents",
+    "nominal_capacity_tokens": "data/step_nominal_schedule_capacity_tokens",
+    "dummy_schedule_capacity_tokens": "data/step_dummy_schedule_capacity_tokens",
+    "unused_packed_capacity_tokens": "data/step_unused_packed_capacity_tokens",
+    "packed_sequences": "data/step_packed_sequences",
+    "real_microbatches": "pipeline/global_real_microbatches",
+    "dummy_microbatches": "pipeline/global_dummy_microbatches",
+}
+
+
+def _numeric_values(rows: list[dict[str, Any]], key: str) -> list[float]:
+    values = [row.get(key) for row in rows]
+    _require(
+        all(
+            isinstance(value, int | float) and math.isfinite(float(value))
+            for value in values
+        ),
+        f"throughput rows lack finite numeric {key}: {values}",
+    )
+    return [float(value) for value in values if isinstance(value, int | float)]
+
+
+def _queue_ready_inter_forward_backward_gaps(
+    rows: list[dict[str, Any]], config: ThroughputWorkflowConfig
+) -> dict[str, int | float | None]:
+    rank_gaps: list[dict[int, float]] = []
+    for row in rows:
+        gaps: dict[int, float] = {}
+        for name, value in row.items():
+            if not (
+                name.startswith(_INTER_FORWARD_BACKWARD_GAP_PREFIX)
+                and name.endswith("_s")
+            ):
+                continue
+            rank_text = name[len(_INTER_FORWARD_BACKWARD_GAP_PREFIX) : -2]
+            _require(
+                rank_text.isdigit()
+                and isinstance(value, int | float)
+                and math.isfinite(float(value))
+                and float(value) >= 0.0,
+                f"invalid rank-local inter-forward/backward gap: {name}={value}",
+            )
+            gaps[int(rank_text)] = float(value)
+        rank_gaps.append(gaps)
+    ranks = set(rank_gaps[0]) if rank_gaps else set()
+    _require(
+        0 in ranks and all(set(gaps) == ranks for gaps in rank_gaps),
+        "throughput rows lack consistent rank-local inter-forward/backward gaps",
+    )
+    waits = _numeric_values(rows, "queue/packed_get_wait_s")
+    depths = _numeric_values(rows, "queue/packed_queue_depth")
+    _require(
+        all(wait >= 0.0 and depth >= 0.0 for wait, depth in zip(waits, depths)),
+        "packed queue readiness metrics must be nonnegative",
+    )
+    eligible = [
+        gaps
+        for gaps, wait, depth in zip(rank_gaps, waits, depths, strict=True)
+        if depth >= 1.0 and wait <= config.max_queue_ready_wait_s
+    ]
+
+    def summarize(rank: int) -> dict[str, int | float | None]:
+        values = [gaps[rank] for gaps in eligible]
+        return {
+            "mean_s": fmean(values) if values else None,
+            "p50_s": median(values) if values else None,
+            "p95_s": (
+                quantiles(values, n=20, method="inclusive")[18]
+                if len(values) > 1
+                else values[0]
+                if values
+                else None
+            ),
+            "max_s": max(values) if values else None,
+            "count": len(values),
+        }
+
+    summaries = {rank: summarize(rank) for rank in sorted(ranks)}
+    worst_rank = (
+        max(
+            summaries,
+            key=lambda rank: cast(float, summaries[rank]["mean_s"]),
+        )
+        if eligible
+        else None
+    )
+    rank_zero = summaries[0]
+    worst = summaries[cast(int, worst_rank)] if worst_rank is not None else rank_zero
+    return {
+        **{
+            f"queue_ready_inter_forward_backward_gap_rank_zero_{name}": value
+            for name, value in rank_zero.items()
+        },
+        "queue_ready_inter_forward_backward_gap_worst_rank": worst_rank,
+        **{
+            f"queue_ready_inter_forward_backward_gap_worst_rank_{name}": value
+            for name, value in worst.items()
+        },
+    }
+
+
+def _total(rows: list[dict[str, Any]], key: str) -> float:
+    return sum(_numeric_values(rows, key))
+
+
+def _runtime_workload_counts(
+    rows: list[dict[str, Any]], *, packed_sequence_length: int
+) -> dict[str, int]:
+    count_rows = [
+        {
+            name: _nonnegative_integer(
+                row.get(key), name=f"step {row.get('step')} {key}"
+            )
+            for name, key in _COUNT_METRICS.items()
+        }
+        for row in rows
+    ]
+    for counts in count_rows:
+        real_capacity = (
+            counts["nominal_capacity_tokens"] - counts["dummy_schedule_capacity_tokens"]
+        )
+        real_executed = (
+            counts["executed_token_equivalents"] - counts["dummy_token_equivalents"]
+        )
+        _require(
+            counts["packed_sequences"] == counts["real_microbatches"]
+            and counts["nominal_capacity_tokens"]
+            == (counts["real_microbatches"] + counts["dummy_microbatches"])
+            * packed_sequence_length
+            and counts["dummy_schedule_capacity_tokens"]
+            == counts["dummy_microbatches"] * packed_sequence_length
+            and counts["unused_packed_capacity_tokens"]
+            == real_capacity - counts["nonpadding_logical_tokens"]
+            and 0
+            < counts["accepted_train_tokens"]
+            == counts["loss_bearing_tokens"]
+            <= counts["nonpadding_logical_tokens"]
+            <= real_executed
+            <= real_capacity
+            and 0
+            <= counts["dummy_token_equivalents"]
+            <= counts["dummy_schedule_capacity_tokens"],
+            f"runtime token accounting does not reconcile: {counts}",
+        )
+    totals = {
+        name: sum(counts[name] for counts in count_rows) for name in _COUNT_METRICS
+    }
+    _require(
+        totals["packed_sequences"] > 0 and totals["real_microbatches"] > 0,
+        f"runtime workload contains no real packed sequences: {totals}",
+    )
+    return totals
+
+
+def _accepted_token_weighted(rows: list[dict[str, Any]], key: str) -> float:
+    values = _numeric_values(rows, key)
+    weights = _numeric_values(rows, "data/step_trainable_assistant_tokens")
+    total_weight = sum(weights)
+    _require(total_weight > 0.0, "throughput rows contain no accepted assistant tokens")
+    return sum(
+        value * weight for value, weight in zip(values, weights, strict=True)
+    ) / (total_weight)
+
+
+def _discard_rates(rows: list[dict[str, Any]]) -> tuple[float, float]:
+    stale = _total(rows, _STALE_GROUPS)
+    zero_variance = _total(rows, _ZERO_VARIANCE_GROUPS)
+    _require(
+        stale >= 0.0 and zero_variance >= 0.0, "discard counts must be nonnegative"
+    )
+    denominator = max(
+        _total(rows, "data/step_num_groups_trainable") + stale + zero_variance,
+        1.0,
+    )
+    return stale / denominator, zero_variance / denominator
+
+
+def _window_measurements(stats: Any, rows: list[dict[str, Any]]) -> dict[str, Any]:
+    duration_s = float(stats.window_end_s) - float(stats.window_start_s)
+    stale_rate, zero_variance_rate = _discard_rates(rows)
+    _require(
+        math.isfinite(duration_s) and duration_s > 0.0,
+        f"autotuner window {stats.start_step}..{stats.end_step} has invalid duration",
+    )
+    _require(
+        math.isclose(
+            stale_rate, float(stats.actual_stale_frac), rel_tol=0.0, abs_tol=1e-12
+        ),
+        f"history and autotuner stale rates disagree at step {stats.end_step}",
+    )
+    return {
+        "start_step": stats.start_step,
+        "end_step": stats.end_step,
+        "duration_s": duration_s,
+        "vllm_pressure": float(stats.vllm_pressure),
+        "vllm_waiting_capacity_request_s": float(stats.vllm_waiting_capacity_request_s),
+        "vllm_running_request_s": float(stats.vllm_running_request_s),
+        "trainer_underfeed": float(stats.trainer_underfeed_score),
+        _POLICY_AGE_MEAN: _accepted_token_weighted(rows, _POLICY_AGE_MEAN),
+        _POLICY_AGE_P95: max(_numeric_values(rows, _POLICY_AGE_P95)),
+        _FRESHNESS_DISCOUNT: _accepted_token_weighted(rows, _FRESHNESS_DISCOUNT),
+        "discarded/rate/stale_groups": stale_rate,
+        "discarded/rate/zero_variance_groups": zero_variance_rate,
+    }
+
+
+async def _run_isolated_backend_phase(
+    *,
+    backend: Any,
+    model: Any,
+    service: Any,
+    train: Callable[..., Awaitable[Any]],
+    captured_inputs: tuple[CapturedTrainingInput, ...],
+) -> TrainerPhaseEvidence:
+    from art.distributed.trajectory_store import TrajectoryGroupBundle
+
+    _require(
+        len(captured_inputs) == _MATCHED_MEASURED_STEPS,
+        "isolated phase requires every matched E2E input",
+    )
+    benchmark_inputs = (captured_inputs[0],) * _ISOLATED_WARMUP_STEPS + captured_inputs
+    packed_input_fingerprints: list[str] = []
+    samples: list[tuple[Mapping[str, Any], int]] = []
+    for sample_index, captured in enumerate(benchmark_inputs):
+        groups = [bundle.build() for bundle in captured.bundles]
+        rebuilt = tuple(TrajectoryGroupBundle.from_group(group) for group in groups)
+        if (
+            hashlib.sha256(_bundle_bytes(rebuilt)).hexdigest()
+            != captured.trajectory_fingerprint
+        ):
+            raise RuntimeError("isolated trajectory input changed during round trip")
+        _collect_matched_packing_shapes(groups)
+        packing = await backend.prepare_pipeline_batch(model, groups)
+        if packing is None:
+            raise RuntimeError("isolated backend benchmark produced no packed batch")
+        try:
+            current_packed_fingerprint = _packed_input_fingerprint(groups)
+        except BaseException:
+            await _discard_prepared_pipeline_batch(backend, groups)
+            raise
+        result = await train(
+            model,
+            groups,
+            learning_rate=1e-6,
+            loss_fn="cispo",
+            loss_fn_config=None,
+            normalize_advantages=True,
+            save_checkpoint=False,
+            adam_params=None,
+            optimizer_save_interval=5,
+        )
+        if sample_index >= _ISOLATED_WARMUP_STEPS:
+            packed_input_fingerprints.append(current_packed_fingerprint)
+            samples.append((result.metrics, int(result.step)))
+        await service.wait_for_serving(int(result.step))
+    trajectory_input_fingerprint, packed_input_fingerprint = (
+        _matched_input_fingerprints(
+            [captured.trajectory_fingerprint for captured in captured_inputs],
+            packed_input_fingerprints,
+        )
+    )
+    return _phase_evidence(
+        phase="isolated",
+        runtime_fingerprint=service._runtime_spec().fingerprint,
+        trajectory_input_fingerprint=trajectory_input_fingerprint,
+        packed_input_fingerprint=packed_input_fingerprint,
+        samples=samples,
+    )
+
+
+def _collect_measurements(
+    *,
+    fixture: ThroughputFixture,
+    config: ThroughputWorkflowConfig,
+    hardware: Literal["h200", "b300"],
+    model_output_dir: Path,
+    profile: Any,
+    events: list[PolicyActivationEvent],
+    isolated: TrainerPhaseEvidence,
+    e2e: TrainerPhaseEvidence,
+    capture_settings: Mapping[str, int],
+    calibration_fingerprint: str,
+) -> dict[str, Any]:
+    from art.pipeline_tuner.autotune import _trainer_underfeed_score
+
+    _require(
+        profile.config.mode == "online",
+        f"throughput stage requires online autotuning, got {profile.config.mode}",
+    )
+    policy_age_limit = profile.policy_age_limit_steps
+    _require(
+        isinstance(policy_age_limit, int | float)
+        and math.isfinite(float(policy_age_limit))
+        and float(policy_age_limit) >= 0.0,
+        f"online autotuner lacks a policy-age limit: {policy_age_limit}",
+    )
+    policy_age_limit = float(policy_age_limit)
+    _require(
+        policy_age_limit == config.max_steps_off_policy,
+        "autotuner policy-age limit does not match the throughput contract: "
+        f"{policy_age_limit} != {config.max_steps_off_policy}",
+    )
+    decisions = [
+        decision
+        for decision in profile.decisions
+        if decision.stats is not None and decision.stats.end_step <= config.max_steps
+    ]
+    _require(bool(decisions), "throughput evidence requires autotuner windows")
+    last_stats = decisions[-1].stats
+    assert last_stats is not None
+    expected_window = (
+        config.max_steps - profile.config.window_steps + 1,
+        config.max_steps,
+    )
+    _require(
+        (last_stats.start_step, last_stats.end_step) == expected_window,
+        f"final autotuner window is not {expected_window[0]}..{expected_window[1]}",
+    )
+    history_rows = _training_rows(model_output_dir)
+    by_step = {int(row["step"]): row for row in history_rows}
+    _require(
+        len(by_step) == len(history_rows),
+        "throughput history contains duplicate training steps",
+    )
+    selected = _settled_execution_decision_suffix(decisions, by_step)
+    stats = [decision.stats for decision in selected]
+    assert all(window is not None for window in stats)
+    first_stats, last_stats = stats[0], stats[-1]
+    steps = list(range(first_stats.start_step, last_stats.end_step + 1))
+    missing = [step for step in steps if step not in by_step]
+    _require(not missing, f"autotuner decision window lacks train rows: {missing}")
+    rows = [by_step[step] for step in steps]
+    executed_settings_by_step = [
+        _row_pipeline_settings(row, step) for step, row in zip(steps, rows, strict=True)
+    ]
+    executed_settings = executed_settings_by_step[0]
+    _require(
+        all(settings == executed_settings for settings in executed_settings_by_step),
+        "throughput history rows executed different pipeline settings",
+    )
+    _require(
+        dict(capture_settings) == executed_settings,
+        "matched capture did not use the measured pipeline settings: "
+        f"{dict(capture_settings)} != {executed_settings}",
+    )
+    window_rows = [
+        [by_step[step] for step in range(window.start_step, window.end_step + 1)]
+        for window in stats
+    ]
+    windows = [
+        _window_measurements(window, selected_rows)
+        for window, selected_rows in zip(stats, window_rows, strict=True)
+    ]
+    e2e_elapsed_s = float(last_stats.window_end_s) - float(first_stats.window_start_s)
+    _require(
+        math.isclose(
+            sum(window["duration_s"] for window in windows),
+            e2e_elapsed_s,
+            rel_tol=0.0,
+            abs_tol=1e-6,
+        ),
+        "autotuner window durations do not reconcile",
+    )
+
+    events_by_step = {event.step: event for event in events}
+    _require(
+        len(events_by_step) == len(events),
+        "throughput stage observed duplicate policy activations",
+    )
+    activation_steps = [first_stats.start_step - 1, *steps]
+    missing_events = [step for step in activation_steps if step not in events_by_step]
+    _require(
+        not missing_events,
+        f"throughput decision intervals lack policy activations: {missing_events}",
+    )
+    interval_events = [events_by_step[step] for step in activation_steps]
+    window_events = interval_events[1:]
+    activation_times = [event.serving_active_monotonic_s for event in interval_events]
+    intervals = [
+        right - left for left, right in zip(activation_times, activation_times[1:])
+    ]
+    _require(
+        all(interval > 0.0 for interval in intervals),
+        f"policy activations were not ordered in time: {intervals}",
+    )
+    lags = [event.lag_s for event in window_events]
+    _require(
+        all(lag >= 0.0 for lag in lags),
+        f"policy activation preceded trainer completion: {lags}",
+    )
+
+    counts = _runtime_workload_counts(
+        rows, packed_sequence_length=config.packed_sequence_length
+    )
+    logical = counts["nonpadding_logical_tokens"]
+    train_s = _total(rows, "time/step_train_s")
+    wall_s = _total(rows, "time/step_wall_s")
+    _require(
+        0.0 < train_s <= wall_s <= e2e_elapsed_s + 1e-6,
+        f"invalid throughput durations: {train_s}, {wall_s}, {e2e_elapsed_s}",
+    )
+
+    stale_rate, zero_variance_rate = _discard_rates(rows)
+    waiting_request_s = sum(
+        window["vllm_waiting_capacity_request_s"] for window in windows
+    )
+    running_request_s = sum(window["vllm_running_request_s"] for window in windows)
+    stable_vllm_pressure = (
+        waiting_request_s / running_request_s
+        if running_request_s > 0.0
+        else math.inf
+        if waiting_request_s > 0.0
+        else 0.0
+    )
+    capacities = _numeric_values(rows, "data/step_nominal_schedule_capacity_tokens")
+    nonpadding = _numeric_values(rows, "data/step_nonpadding_logical_tokens")
+    stable_trainer_underfeed = _trainer_underfeed_score(
+        idle_frac=_total(rows, "time/step_collect_batch_s") / wall_s,
+        unused_and_dummy_ratio=fmean(
+            max(0.0, (capacity - used) / capacity)
+            for capacity, used in zip(capacities, nonpadding, strict=True)
+        ),
+    )
+    inter_forward_backward_gaps = _queue_ready_inter_forward_backward_gaps(rows, config)
+    thresholds = config.thresholds.get(hardware)
+    _require(
+        e2e.sample_count == isolated.sample_count == _MATCHED_MEASURED_STEPS,
+        "matched trainer phases have asymmetric sample counts",
+    )
+    paired_core_ratio = median(
+        e2e_tok_s / isolated_tok_s
+        for e2e_tok_s, isolated_tok_s in zip(
+            e2e.sample_train_tok_s,
+            isolated.sample_train_tok_s,
+            strict=True,
+        )
+    )
+    measurements = {
+        "hardware": hardware,
+        "calibration_basis": (
+            thresholds.calibration_basis if thresholds is not None else None
+        ),
+        "calibration_fingerprint": calibration_fingerprint,
+        "model_key": fixture.model_key,
+        "model_path": fixture.path,
+        "num_layers": fixture.num_layers,
+        "packed_sequence_length": config.packed_sequence_length,
+        "width_fingerprint": fixture.width_fingerprint,
+        **counts,
+        "unused_and_dummy_ratio": (
+            counts["nominal_capacity_tokens"] - counts["nonpadding_logical_tokens"]
+        )
+        / counts["nominal_capacity_tokens"],
+        "isolated_train_tok_s": isolated.train_tok_s,
+        "isolated_sample_train_tok_s": isolated.sample_train_tok_s,
+        "matched_e2e_core_train_tok_s": e2e.train_tok_s,
+        "matched_e2e_core_sample_train_tok_s": e2e.sample_train_tok_s,
+        "matched_core_to_isolated_ratio": paired_core_ratio,
+        "e2e_core_train_tok_s": logical / train_s,
+        "e2e_train_tok_s": logical / e2e_elapsed_s,
+        "accepted_train_tok_s": counts["accepted_train_tokens"] / e2e_elapsed_s,
+        **inter_forward_backward_gaps,
+        _POLICY_AGE_MEAN: _accepted_token_weighted(rows, _POLICY_AGE_MEAN),
+        _POLICY_AGE_P95: max(_numeric_values(rows, _POLICY_AGE_P95)),
+        _FRESHNESS_DISCOUNT: _accepted_token_weighted(rows, _FRESHNESS_DISCOUNT),
+        "discarded/rate/stale_groups": stale_rate,
+        "discarded/rate/zero_variance_groups": zero_variance_rate,
+        "policy_age_limit_steps": policy_age_limit,
+        "mean_ready_batch_idle_s": fmean(
+            [float(row["queue/packed_get_wait_s"]) for row in rows]
+        ),
+        "mean_train_gap_s": (e2e_elapsed_s - train_s) / len(rows),
+        "e2e_elapsed_s": e2e_elapsed_s,
+        "autotuner_windows": windows,
+        "stable_vllm_pressure": stable_vllm_pressure,
+        "stable_trainer_underfeed": stable_trainer_underfeed,
+        "matched_capture_pipeline_settings": dict(capture_settings),
+        "mean_policy_activation_lag_s": fmean(lags),
+        "p50_policy_activation_lag_s": median(lags),
+        "p95_policy_activation_lag_s": quantiles(lags, n=20, method="inclusive")[18],
+        "max_policy_activation_lag_s": max(lags),
+        "post_warmup_policy_activation_count": len(window_events),
+        "mean_policy_activation_interval_s": fmean(intervals),
+        "p50_policy_activation_interval_s": median(intervals),
+        "p95_policy_activation_interval_s": quantiles(
+            intervals, n=20, method="inclusive"
+        )[18],
+        "second_max_policy_activation_interval_s": sorted(intervals)[-2],
+        "max_policy_activation_interval_s": max(intervals),
+    }
+    matched_fields = (
+        "runtime_fingerprint",
+        "trajectory_input_fingerprint",
+        "packed_input_fingerprint",
+        "workload_fingerprint",
+    )
+    mismatches = {
+        name: (getattr(e2e, name), getattr(isolated, name))
+        for name in matched_fields
+        if getattr(e2e, name) != getattr(isolated, name)
+    }
+    _require(
+        not mismatches,
+        f"isolated and E2E phases did not execute the same packed input: {mismatches}",
+    )
+    capture_steps = _matched_capture_steps(config.max_steps)
+    _require(
+        e2e.policy_steps == capture_steps,
+        f"matched E2E inputs were not captured at reserved steps {capture_steps}",
+    )
+    expected_isolated_steps = tuple(
+        range(
+            capture_steps[-1] + 1 + _ISOLATED_WARMUP_STEPS,
+            capture_steps[-1] + 1 + _ISOLATED_WARMUP_STEPS + isolated.sample_count,
+        )
+    )
+    _require(
+        isolated.policy_steps == expected_isolated_steps,
+        "isolated measured steps do not follow the configured warmup: "
+        f"{isolated.policy_steps}",
+    )
+    return measurements
+
+
+def acceptance_failures(
+    measurements: Mapping[str, Any],
+    config: ThroughputWorkflowConfig,
+    thresholds: ThroughputThresholds | None,
+) -> list[str]:
+    checks = {
+        "stable_min_vllm_pressure": measurements["stable_vllm_pressure"]
+        >= config.min_vllm_pressure,
+        "stable_trainer_underfeed": measurements["stable_trainer_underfeed"]
+        <= config.max_trainer_underfeed,
+        "unused_and_dummy_ratio": measurements["unused_and_dummy_ratio"]
+        <= config.max_unused_and_dummy_ratio,
+    }
+    for window in measurements["autotuner_windows"]:
+        prefix = f"window_{window['start_step']}_{window['end_step']}"
+        checks.update(
+            {
+                f"{prefix}_policy_age_p95": window[_POLICY_AGE_P95]
+                <= measurements["policy_age_limit_steps"],
+                f"{prefix}_zero_variance_rate": window[
+                    "discarded/rate/zero_variance_groups"
+                ]
+                == 0.0,
+            }
+        )
+    failures = [name for name, passed in checks.items() if not passed]
+    if thresholds is None:
+        return [f"missing_{measurements['hardware']}_calibration", *failures]
+    floor_checks = {
+        "isolated_train_tok_s": measurements["isolated_train_tok_s"]
+        >= thresholds.min_isolated_train_tok_s,
+        "e2e_train_tok_s": measurements["e2e_train_tok_s"]
+        >= thresholds.min_e2e_train_tok_s,
+        "accepted_train_tok_s": measurements["accepted_train_tok_s"]
+        >= thresholds.min_accepted_train_tok_s,
+        "e2e_to_isolated_ratio": measurements["e2e_train_tok_s"]
+        / measurements["isolated_train_tok_s"]
+        >= thresholds.min_e2e_to_isolated_ratio,
+        "matched_core_to_isolated_ratio": measurements["matched_core_to_isolated_ratio"]
+        >= thresholds.min_matched_core_to_isolated_ratio,
+        "matched_core_to_isolated_ratio_max": measurements[
+            "matched_core_to_isolated_ratio"
+        ]
+        <= thresholds.max_matched_core_to_isolated_ratio,
+        "mean_policy_activation_lag_s": measurements["mean_policy_activation_lag_s"]
+        <= thresholds.max_mean_policy_activation_lag_s,
+        "max_policy_activation_lag_s": measurements["max_policy_activation_lag_s"]
+        <= thresholds.max_policy_activation_lag_s,
+        "repeated_policy_activation_cadence_s": measurements[
+            "second_max_policy_activation_interval_s"
+        ]
+        <= thresholds.max_repeated_policy_activation_interval_s,
+        "queue_ready_inter_forward_backward_gap_count": measurements[
+            "queue_ready_inter_forward_backward_gap_worst_rank_count"
+        ]
+        >= thresholds.min_queue_ready_inter_forward_backward_gap_count,
+        "queue_ready_inter_forward_backward_gap_p50_s": (
+            measurements["queue_ready_inter_forward_backward_gap_worst_rank_p50_s"]
+            is not None
+            and measurements["queue_ready_inter_forward_backward_gap_worst_rank_p50_s"]
+            <= thresholds.max_queue_ready_inter_forward_backward_gap_p50_s
+        ),
+        "queue_ready_inter_forward_backward_gap_max_s": (
+            measurements["queue_ready_inter_forward_backward_gap_worst_rank_max_s"]
+            is not None
+            and measurements["queue_ready_inter_forward_backward_gap_worst_rank_max_s"]
+            <= thresholds.max_queue_ready_inter_forward_backward_gap_max_s
+        ),
+    }
+    if thresholds.calibration_fingerprint is not None:
+        floor_checks["calibration_fingerprint"] = (
+            measurements["calibration_fingerprint"]
+            == thresholds.calibration_fingerprint
+        )
+    if measurements["hardware"] == "b300":
+        floor_checks["calibration_basis"] = thresholds.calibration_basis == "measured"
+    return [
+        *failures,
+        *(name for name, passed in floor_checks.items() if not passed),
+    ]
+
+
+def _classify_acceptance_failures(failures: list[str]) -> dict[str, Any]:
+    load = [name for name in failures if name in _LOAD_ACCEPTANCE_FAILURES]
+    performance = [
+        name for name in failures if name in _PERFORMANCE_ACCEPTANCE_FAILURES
+    ]
+    hard = [
+        name
+        for name in failures
+        if name in _HARD_ACCEPTANCE_FAILURES
+        or name.startswith("missing_")
+        or name.startswith("window_")
+    ]
+    classified = {*load, *performance, *hard}
+    unclassified = [name for name in failures if name not in classified]
+    status = (
+        "accepted"
+        if not failures
+        else "rejected"
+        if hard or unclassified
+        else "load_inconclusive"
+        if load
+        else "rejected"
+    )
+    return {
+        "acceptance_status": status,
+        "acceptance_failures": failures,
+        "load_failures": load,
+        "performance_failures": performance,
+        "hard_failures": hard,
+        "unclassified_failures": unclassified,
+    }
+
+
+def _run_throughput_attempts(
+    stage_dir: Path,
+    run_attempt: Callable[[int, Path], ValidationStageResult],
+) -> ValidationStageResult:
+    stage_dir.mkdir(parents=True, exist_ok=True)
+    attempts: list[dict[str, Any]] = []
+    for attempt in range(1, _THROUGHPUT_MAX_ATTEMPTS + 1):
+        artifact_dir = stage_dir / f"attempt_{attempt}"
+        artifact_dir.mkdir(parents=True, exist_ok=False)
+        try:
+            result = run_attempt(attempt, artifact_dir)
+        except _ThroughputEvidenceInconclusive as error:
+            attempts.append(
+                {
+                    "attempt": attempt,
+                    "artifact_dir": str(artifact_dir),
+                    "acceptance_status": "evidence_inconclusive",
+                    "acceptance_failures": [str(error)],
+                }
+            )
+            if attempt == _THROUGHPUT_MAX_ATTEMPTS:
+                raise
+            continue
+        status = result.metrics["acceptance_status"]
+        _require(
+            status in {"accepted", "rejected", "load_inconclusive"},
+            "throughput attempt lacks an acceptance classification",
+        )
+        result.passed = status == "accepted"
+        attempts.append(
+            {
+                "attempt": attempt,
+                "artifact_dir": str(artifact_dir),
+                "acceptance_status": status,
+                "acceptance_failures": result.metrics["acceptance_failures"],
+            }
+        )
+        retryable = bool(
+            result.metrics["load_failures"] or result.metrics["performance_failures"]
+        ) and not (
+            result.metrics["hard_failures"] or result.metrics["unclassified_failures"]
+        )
+        terminal = (
+            status == "accepted" or not retryable or attempt == _THROUGHPUT_MAX_ATTEMPTS
+        )
+        if terminal:
+            result.metrics.update(
+                throughput_attempt_count=len(attempts),
+                throughput_retry_performed=len(attempts) > 1,
+                throughput_attempts=attempts,
+            )
+            result.artifact_dir = str(stage_dir)
+            (stage_dir / "throughput_measurements.json").write_text(
+                json.dumps(result.metrics, indent=2) + "\n"
+            )
+            return result
+    raise AssertionError("unreachable")
+
+
+async def _run_e2e_throughput_async(
+    *,
+    base_model: str,
+    allow_unvalidated_arch: bool,
+    stage: Any,
+    config: ThroughputWorkflowConfig,
+    fixture: ThroughputFixture,
+    gpu_identities: list[dict[str, Any]],
+    hardware: Literal["h200", "b300"],
+    artifact_dir: Path,
+) -> ValidationStageResult:
+    from transformers import AutoTokenizer
+
+    import art
+    from art.megatron.backend import MegatronBackend
+    from art.pipeline_trainer import PipelineTrainer
+    from art.pipeline_tuner import PipelineAutotuneConfig, PipelineAutotunerProfile
+    from art.preprocessing.policy_spans import validate_complete_policy_token_spans
+    from art.preprocessing.vllm_tokens import choice_completion_tokens
+
+    if stage.megatron is None or stage.vllm is None:
+        raise RuntimeError(
+            "E2E throughput requires separate Megatron and vLLM resources"
+        )
+    stage_dir = artifact_dir
+    topology = stage.megatron.topology
+    art.init_megatron_runtime_config(
+        topology=topology.to_megatron_config(),
+        packed_sequence_length=config.packed_sequence_length,
+    )
+    engine_args = stage.vllm.engine_args()
+    engine_args["seed"] = config.random_seed
+    engine_args["model"] = fixture.path
+    max_model_len = int(engine_args["max_model_len"])
+    if config.prompt_tokens + config.completion_tokens > max_model_len:
+        raise RuntimeError(
+            "throughput prompt and completion exceed vLLM context: "
+            f"{config.prompt_tokens}+{config.completion_tokens}>{max_model_len}"
+        )
+    internal_config = {
+        "trainer_gpu_ids": stage.megatron.gpu_ids,
+        "inference_gpu_ids": stage.vllm.gpu_ids,
+        "rollout_weight_update_mode": "in_flight_lora",
+        "engine_args": engine_args,
+        "init_args": {
+            "model_name": fixture.path,
+            "max_seq_length": config.packed_sequence_length,
+            "random_state": config.random_seed,
+        },
+        "allow_unvalidated_arch": allow_unvalidated_arch,
+        "megatron_model_initialization": "random",
+    }
+    from art.megatron.model_support.tokenizer import (
+        configure_tokenizer_for_model_support,
+    )
+
+    tokenizer = configure_tokenizer_for_model_support(
+        cast(Any, AutoTokenizer.from_pretrained(fixture.path, local_files_only=True)),
+        base_model=base_model,
+        internal_config=internal_config,
+    )
+    prompt = _sized_prompt(tokenizer, target_tokens=config.prompt_tokens)
+    actual_prompt_tokens = _chat_token_count(tokenizer, prompt)
+    run_name = f"throughput-{fixture.model_key}-{uuid.uuid4().hex[:8]}"
+    model_output_dir: Path | None = None
+    events: list[PolicyActivationEvent] = []
+    e2e_phase: TrainerPhaseEvidence | None = None
+    isolated_phase: TrainerPhaseEvidence | None = None
+    captured_training_inputs: list[CapturedTrainingInput] = []
+    autotune = PipelineAutotuneConfig(
+        mode="online",
+        output_name="throughput",
+        window_steps=2,
+        warmup_ignore_steps=3,
+        initial_model_calls_per_inference_gpu=(
+            config.initial_model_calls_per_inference_gpu
+        ),
+        initial_min_groups_per_packed_sequence=_groups_per_packed_sequence(
+            stage, config
+        ),
+        initial_max_groups_per_packed_sequence=_groups_per_packed_sequence(
+            stage, config
+        ),
+        vllm_metric_interval_s=0.25,
+    )
+    measured_steps = config.max_steps - autotune.warmup_ignore_steps
+    tail_windows = _PACKING_DRAIN_WINDOWS + _REQUIRED_SETTLED_WINDOWS
+    if (
+        measured_steps < tail_windows * autotune.window_steps
+        or measured_steps % autotune.window_steps
+    ):
+        raise RuntimeError(
+            "throughput stage must end on a whole autotuner window after a drain "
+            f"window and two measured windows: max_steps={config.max_steps}, "
+            f"warmup={autotune.warmup_ignore_steps}, window={autotune.window_steps}"
+        )
+    capture_train_calls = _matched_capture_steps(config.max_steps)
+    runtime_contract = _calibration_contract(
+        base_model=base_model,
+        fixture=fixture,
+        stage=stage,
+        config=config,
+        autotune=autotune,
+        actual_prompt_tokens=actual_prompt_tokens,
+        gpu_identities=gpu_identities,
+    )
+    calibration_fingerprint = _calibration_fingerprint(runtime_contract)
+
+    async with MegatronBackend(
+        path=str(stage_dir / "art"),
+        enable_expert_replay=topology.ep > 1,
+        in_process=False,
+    ) as backend:
+        model = cast(
+            Any,
+            art.TrainableModel(
+                name=run_name,
+                run_name=run_name,
+                project="model-support-throughput",
+                base_model=base_model,
+                _internal_config=cast(art.dev.InternalModelConfig, internal_config),
+                report_metrics=[],
+            ),
+        )
+        await model.register(backend)
+        model_output_dir = Path(model._get_output_dir())
+        client = model.openai_client()
+        try:
+            await client.chat.completions.create(
+                messages=[{"role": "user", "content": prompt}],
+                model=model.get_inference_name(),
+                max_tokens=config.completion_tokens,
+                temperature=0.0,
+                timeout=1200.0,
+                extra_body={
+                    "ignore_eos": True,
+                    "min_tokens": config.completion_tokens,
+                },
+            )
+
+            async def rollout_fn(
+                rollout_model: Any,
+                scenario: dict[str, str],
+                _rollout_config: None,
+            ) -> Any:
+                response = await client.chat.completions.create(
+                    messages=[{"role": "user", "content": scenario["prompt"]}],
+                    model=rollout_model.get_inference_name(),
+                    max_tokens=config.completion_tokens,
+                    n=config.rollouts_per_group,
+                    temperature=1.0,
+                    seed=int(scenario["scenario_id"].rsplit("-", 1)[-1]),
+                    logprobs=True,
+                    top_logprobs=0,
+                    timeout=1200.0,
+                    extra_body={
+                        "ignore_eos": True,
+                        "min_tokens": config.completion_tokens,
+                    },
+                )
+                if len(response.choices) != config.rollouts_per_group:
+                    raise RuntimeError(
+                        "vLLM returned an incomplete rollout group: "
+                        f"{len(response.choices)} != {config.rollouts_per_group}"
+                    )
+                trajectories = []
+                for index, choice in enumerate(response.choices):
+                    completion_tokens = choice_completion_tokens(choice)
+                    if not isinstance(completion_tokens, int) or (
+                        completion_tokens != config.completion_tokens
+                    ):
+                        raise RuntimeError(
+                            "throughput completion length changed: "
+                            f"{completion_tokens} != {config.completion_tokens}"
+                        )
+                    validate_complete_policy_token_spans(
+                        choice, completion_tokens=completion_tokens
+                    )
+                    trajectories.append(
+                        art.Trajectory(
+                            messages_and_choices=[
+                                {"role": "user", "content": scenario["prompt"]},
+                                choice,
+                            ],
+                            reward=index / (config.rollouts_per_group - 1),
+                            metrics={"completion_tokens": completion_tokens},
+                            metadata={"scenario_id": scenario["scenario_id"]},
+                        )
+                    )
+                return art.TrajectoryGroup(
+                    trajectories,
+                    metadata={"scenario_id": scenario["scenario_id"]},
+                )
+
+            trainer = PipelineTrainer(
+                model=model,
+                backend=backend,
+                rollout_fn=rollout_fn,
+                scenarios=_scenarios(prompt),
+                config=None,
+                autotune=autotune,
+                learning_rate=1e-6,
+                loss_fn="cispo",
+                max_steps=config.max_steps,
+                eval_fn=None,
+                eval_every_n_steps=0,
+                eval_at_start=False,
+                save_checkpoint=False,
+                resume=False,
+                log_interval_seconds=30.0,
+                score_reference_groups_per_step=float(config.groups_per_step),
+                score_reference_rollouts_per_group=float(config.rollouts_per_group),
+                max_steps_off_policy=config.max_steps_off_policy,
+            )
+            from art.megatron.distributed_service import DistributedMegatronService
+
+            service = cast(
+                DistributedMegatronService, await backend._get_service(model)
+            )
+            activation_tasks: dict[int, asyncio.Task[PolicyActivationEvent]] = {}
+            capture_tasks: dict[
+                int,
+                asyncio.Task[tuple[tuple[Any, ...], str, str, dict[str, int]]],
+            ] = {}
+            capture_requests: dict[
+                int, tuple[Any, tuple[Any, ...], dict[str, int]]
+            ] = {}
+            original_train = backend.train
+            original_finish_training_batch = backend._finish_training_batch
+            original_release_trajectory_sources = backend._release_trajectory_sources
+            train_call_count = 0
+
+            async def capture_then_release(
+                batch: Any,
+                payload: Any,
+                prepared: Any,
+                selections: tuple[Any, ...],
+                settings: dict[str, int],
+            ) -> tuple[tuple[Any, ...], str, str, dict[str, int]]:
+                captured = None
+                failures = []
+                try:
+                    captured = await _capture_training_input(
+                        prepared, selections, settings
+                    )
+                except BaseException as error:
+                    failures.append(error)
+                try:
+                    await original_release_trajectory_sources(batch, payload)
+                except BaseException as error:
+                    failures.append(error)
+                if failures:
+                    raise BaseExceptionGroup(
+                        "throughput input capture or source release failed", failures
+                    )
+                assert captured is not None
+                return captured
+
+            async def release_trajectory_sources(batch: Any, payload: Any) -> None:
+                request = capture_requests.pop(id(batch), None)
+                if request is None:
+                    await original_release_trajectory_sources(batch, payload)
+                    return
+                prepared, selections, settings = request
+                capture_tasks[id(batch)] = asyncio.create_task(
+                    capture_then_release(batch, payload, prepared, selections, settings)
+                )
+
+            async def finish_training_batch(batch: Any, *, failed: bool) -> None:
+                capture_task = capture_tasks.get(id(batch))
+                failures = []
+                if capture_task is not None:
+                    try:
+                        await capture_task
+                    except BaseException as error:
+                        failures.append(error)
+                elif capture_requests.pop(id(batch), None) is not None:
+                    try:
+                        await original_release_trajectory_sources(batch, batch.payload)
+                    except BaseException as error:
+                        failures.append(error)
+                try:
+                    await original_finish_training_batch(batch, failed=failed)
+                except BaseException as error:
+                    failures.append(error)
+                if failures:
+                    raise BaseExceptionGroup(
+                        "throughput input capture or batch release failed", failures
+                    )
+
+            async def tracked_train(*args: Any, **kwargs: Any) -> Any:
+                nonlocal train_call_count
+                train_call_count += 1
+                if len(args) < 2:
+                    raise RuntimeError(
+                        "PipelineTrainer did not pass trajectory groups positionally"
+                    )
+                groups = args[1]
+                captured_batch_id = None
+                if train_call_count in capture_train_calls:
+                    try:
+                        _collect_matched_packing_shapes(groups)
+                        prepared = _prepared_pipeline_batch(groups)
+                        selections = tuple(
+                            getattr(prepared.batch.payload, "selections", ())
+                        )
+                        if len(selections) != len(groups):
+                            raise RuntimeError(
+                                "prepared throughput batch lacks exact queue selections"
+                            )
+                        captured_batch_id = id(prepared.batch)
+                        capture_requests[captured_batch_id] = (
+                            prepared,
+                            selections,
+                            _current_pipeline_settings(trainer),
+                        )
+                    except BaseException:
+                        await _discard_prepared_pipeline_batch(backend, groups)
+                        raise
+                result = await original_train(*args, **kwargs)
+                step = int(result.step)
+                if step in activation_tasks:
+                    raise RuntimeError(
+                        f"duplicate trainer completion for policy {step}"
+                    )
+                activation_tasks[step] = asyncio.create_task(
+                    _activation_event(service, step)
+                )
+                if captured_batch_id is not None:
+                    capture_task = capture_tasks.get(captured_batch_id)
+                    if capture_task is None:
+                        raise RuntimeError("trainer did not release captured sources")
+                    bundles, trajectory, packed, settings = await capture_task
+                    capture_tasks.pop(captured_batch_id)
+                    captured_training_inputs.append(
+                        CapturedTrainingInput(
+                            bundles,
+                            trajectory,
+                            packed,
+                            settings,
+                            result.metrics,
+                            step,
+                        )
+                    )
+                return result
+
+            setattr(backend, "train", tracked_train)
+            setattr(backend, "_finish_training_batch", finish_training_batch)
+            setattr(
+                backend,
+                "_release_trajectory_sources",
+                release_trajectory_sources,
+            )
+            try:
+                measurement_start = (
+                    config.max_steps - tail_windows * autotune.window_steps + 1
+                )
+                with _freeze_pipeline_settings_from_step(trainer, measurement_start):
+                    await trainer.train(handle_signals=False)
+                if train_call_count != config.max_steps:
+                    raise RuntimeError(
+                        "online pipeline did not execute the configured steps: "
+                        f"{train_call_count} != {config.max_steps}"
+                    )
+                events = sorted(
+                    await asyncio.gather(*activation_tasks.values()),
+                    key=lambda event: event.step,
+                )
+            finally:
+                setattr(backend, "train", original_train)
+                setattr(
+                    backend,
+                    "_finish_training_batch",
+                    original_finish_training_batch,
+                )
+                setattr(
+                    backend,
+                    "_release_trajectory_sources",
+                    original_release_trajectory_sources,
+                )
+                await _cancel_activation_tasks(activation_tasks)
+            if len(captured_training_inputs) != _MATCHED_MEASURED_STEPS:
+                raise RuntimeError(
+                    "online pipeline did not capture every matched train batch"
+                )
+            capture_settings = captured_training_inputs[0].pipeline_settings
+            _require(
+                all(
+                    captured.pipeline_settings == capture_settings
+                    for captured in captured_training_inputs[1:]
+                ),
+                "matched E2E samples used different pipeline settings",
+            )
+            trajectory_input_fingerprint, packed_input_fingerprint = (
+                _matched_input_fingerprints(
+                    [
+                        captured.trajectory_fingerprint
+                        for captured in captured_training_inputs
+                    ],
+                    [
+                        captured.packed_fingerprint
+                        for captured in captured_training_inputs
+                    ],
+                )
+            )
+            e2e_phase = _phase_evidence(
+                phase="e2e",
+                runtime_fingerprint=service._runtime_spec().fingerprint,
+                trajectory_input_fingerprint=trajectory_input_fingerprint,
+                packed_input_fingerprint=packed_input_fingerprint,
+                samples=[
+                    (captured.metrics, captured.policy_step)
+                    for captured in captured_training_inputs
+                ],
+            )
+            isolated_phase = await _run_isolated_backend_phase(
+                backend=backend,
+                model=model,
+                service=service,
+                train=original_train,
+                captured_inputs=tuple(captured_training_inputs),
+            )
+        finally:
+            await client.close()
+
+    assert model_output_dir is not None
+    assert e2e_phase is not None and isolated_phase is not None
+    profile_path = model_output_dir / "pipeline_tuner" / "throughput.json"
+    profile = PipelineAutotunerProfile.model_validate_json(profile_path.read_text())
+    measurements = _collect_measurements(
+        fixture=fixture,
+        config=config,
+        hardware=hardware,
+        model_output_dir=model_output_dir,
+        profile=profile,
+        events=events,
+        isolated=isolated_phase,
+        e2e=e2e_phase,
+        capture_settings=capture_settings,
+        calibration_fingerprint=calibration_fingerprint,
+    )
+    activation_path = stage_dir / "policy_activation_timeline.json"
+    activation_path.write_text(
+        json.dumps([event._asdict() for event in events], indent=2) + "\n"
+    )
+    thresholds = config.thresholds.get(hardware)
+    failures = acceptance_failures(measurements, config, thresholds)
+    classification = _classify_acceptance_failures(failures)
+    metrics = {
+        **measurements,
+        "gpu_identities": [
+            {"role": identity["role"], **_stable_gpu_identity(identity)}
+            for identity in gpu_identities
+        ],
+        "isolated": isolated_phase._asdict(),
+        "e2e": e2e_phase._asdict(),
+        "runtime_contract": runtime_contract,
+        "matched_inputs": [
+            {
+                "policy_step": captured.policy_step,
+                "trajectory_fingerprint": captured.trajectory_fingerprint,
+                "packed_fingerprint": captured.packed_fingerprint,
+            }
+            for captured in captured_training_inputs
+        ],
+        "autotuner_profile": str(profile_path),
+        "policy_activation_timeline": str(activation_path),
+        "thresholds": thresholds.model_dump(mode="json") if thresholds else None,
+        **classification,
+    }
+    (stage_dir / "throughput_measurements.json").write_text(
+        json.dumps(metrics, indent=2) + "\n"
+    )
+    return ValidationStageResult(
+        name="e2e_throughput",
+        passed=classification["acceptance_status"] == "accepted",
+        metrics=metrics,
+        artifact_dir=str(stage_dir),
+    )
+
+
+def run_e2e_throughput(
+    *,
+    base_model: str,
+    architecture: ArchitectureReport,
+    allow_unvalidated_arch: bool = False,
+) -> ValidationStageResult:
+    del architecture
+    resources = handler_workflow_resources_for_base_model(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
+    if resources is None or resources.e2e_throughput is None:
+        raise RuntimeError(f"missing E2E throughput resources for {base_model}")
+    spec = get_model_support_spec(
+        base_model, allow_unvalidated_arch=allow_unvalidated_arch
+    )
+    import torch
+
+    stage = resolve_stage_resources_for_visible_gpus(
+        "e2e_throughput",
+        resources.e2e_throughput,
+        visible_gpu_count=int(torch.cuda.device_count()),
+    )
+    config = stage.throughput
+    if config is None:
+        raise RuntimeError("E2E throughput resources lack throughput configuration")
+    if stage.megatron is None or stage.vllm is None:
+        raise RuntimeError(
+            "E2E throughput requires separate Megatron and vLLM resources"
+        )
+    gpu_identities = _gpu_identities(
+        trainer_gpu_ids=stage.megatron.gpu_ids,
+        inference_gpu_ids=stage.vllm.gpu_ids,
+    )
+    hardware = _hardware(gpu_identities)
+    config = _throughput_config_for_hardware(spec.key, config, hardware)
+    correctness_path = os.environ.get(FIXTURE_PATH_ENV)
+    if correctness_path is None:
+        raise RuntimeError(f"missing {FIXTURE_PATH_ENV}")
+    stage_dir = Path(os.environ[_STAGE_DIR_ENV])
+    fixture = ensure_throughput_fixture(
+        canonical_model=base_model,
+        model_key=spec.key,
+        correctness_fixture=Path(correctness_path),
+        num_layers=config.num_layers,
+        initialization_version=config.random_initialization_version,
+        random_seed=config.random_seed,
+        output=stage_dir / "production_width_model",
+    )
+    os.environ["WANDB_MODE"] = "disabled"
+
+    def run_attempt(attempt: int, artifact_dir: Path) -> ValidationStageResult:
+        del attempt
+        return asyncio.run(
+            _run_e2e_throughput_async(
+                base_model=base_model,
+                allow_unvalidated_arch=allow_unvalidated_arch,
+                stage=stage,
+                config=config,
+                fixture=fixture,
+                gpu_identities=gpu_identities,
+                hardware=hardware,
+                artifact_dir=artifact_dir,
+            )
+        )
+
+    return _run_throughput_attempts(stage_dir, run_attempt)
diff --git a/tests/integration/megatron/runtime_isolation/test_art_import_boundary.py b/tests/integration/megatron/runtime_isolation/test_art_import_boundary.py
index b939e4f9a..491e9dbd8 100644
--- a/tests/integration/megatron/runtime_isolation/test_art_import_boundary.py
+++ b/tests/integration/megatron/runtime_isolation/test_art_import_boundary.py
@@ -104,9 +104,8 @@ def test_service_modules_import_without_vllm(artifact_dir: Path) -> None:
             (
                 "import importlib, json; "
                 "modules = ["
-                "'art.unsloth.service', "
-                "'art.megatron.service', "
-                "'art.megatron.weights.merged_weight_export'"
+                "'art.megatron.distributed_service', "
+                "'art.megatron.weights.conversion_tasks'"
                 "]; "
                 "loaded = [importlib.import_module(name).__name__ for name in modules]; "
                 "print(json.dumps({'loaded': loaded}))"
@@ -116,7 +115,51 @@ def test_service_modules_import_without_vllm(artifact_dir: Path) -> None:
     )
     payload = _load_json_from_stdout(result.stdout)
     assert payload["loaded"] == [
-        "art.unsloth.service",
-        "art.megatron.service",
-        "art.megatron.weights.merged_weight_export",
+        "art.megatron.distributed_service",
+        "art.megatron.weights.conversion_tasks",
     ]
+
+
+def test_runtime_env_preserves_build_arch_without_initializing_cuda(
+    artifact_dir: Path,
+) -> None:
+    cache_root = artifact_dir / "cache"
+    env = dict(os.environ)
+    env.update(
+        ART_MEGATRON_CACHE_ROOT=str(cache_root),
+        CUDA_VISIBLE_DEVICES="",
+        TORCH_CUDA_ARCH_LIST="10.3",
+        XDG_CACHE_HOME=str(cache_root),
+    )
+    for name in (
+        "FLASH_ATTENTION_CUTE_DSL_CACHE_DIR",
+        "TORCHINDUCTOR_CACHE_DIR",
+        "TRITON_CACHE_DIR",
+    ):
+        env.pop(name, None)
+    result = _run(
+        [
+            sys.executable,
+            "-c",
+            (
+                "import json, os, torch; "
+                "from art.megatron.runtime.runtime_env import "
+                "configure_megatron_runtime_env; "
+                "configure_megatron_runtime_env(); "
+                "print(json.dumps({'arch': os.environ['TORCH_CUDA_ARCH_LIST'], "
+                "'cuda_initialized': torch.cuda.is_initialized(), "
+                "'inductor': os.environ['TORCHINDUCTOR_CACHE_DIR'], "
+                "'triton': os.environ['TRITON_CACHE_DIR'], "
+                "'flash': os.environ['FLASH_ATTENTION_CUTE_DSL_CACHE_DIR']}))"
+            ),
+        ],
+        artifact_dir=artifact_dir,
+        env=env,
+    )
+    assert _load_json_from_stdout(result.stdout) == {
+        "arch": "10.3",
+        "cuda_initialized": False,
+        "inductor": str(cache_root / "compiled" / "10.3" / "torchinductor"),
+        "triton": str(cache_root / "compiled" / "10.3" / "triton"),
+        "flash": str(cache_root / "compiled" / "10.3" / "flash_attention_cute_dsl"),
+    }
diff --git a/tests/integration/megatron/runtime_isolation/test_client.py b/tests/integration/megatron/runtime_isolation/test_client.py
deleted file mode 100644
index 7d311d1d9..000000000
--- a/tests/integration/megatron/runtime_isolation/test_client.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from pathlib import Path
-from types import SimpleNamespace
-
-import pytest
-
-from art.megatron.runtime.client import stream_megatron_job, write_megatron_job
-from art.megatron.runtime.jobs import (
-    MegatronSyncJob,
-    MergedWeightTransferInitInfo,
-    MergedWeightTransferSpec,
-)
-
-
-@pytest.mark.asyncio
-async def test_stream_megatron_job_raises_when_worker_exits(
-    tmp_path: Path,
-) -> None:
-    job_path = tmp_path / "job.json"
-    log_path = tmp_path / "job.log"
-    job = MegatronSyncJob(
-        lora_path="/tmp/lora",
-        merged_weight_transfer=MergedWeightTransferSpec(
-            init_info=MergedWeightTransferInitInfo(
-                master_address="127.0.0.1",
-                master_port=12345,
-                rank_offset=1,
-                world_size=2,
-            ),
-            vllm_base_url="http://127.0.0.1:8000",
-            served_model_name="test@0",
-        ),
-        log_path=str(log_path),
-    )
-    write_megatron_job(job, job_path=str(job_path))
-
-    with pytest.raises(RuntimeError, match="Megatron worker exited with code 17"):
-        async for _ in stream_megatron_job(
-            job,
-            job_path=str(job_path),
-            process=SimpleNamespace(returncode=17),
-            process_log_path="/tmp/megatron-runtime.log",
-            poll_interval=0.0,
-        ):
-            pass
diff --git a/tests/integration/megatron/runtime_isolation/test_live_local_backend_smoke.py b/tests/integration/megatron/runtime_isolation/test_live_local_backend_smoke.py
index ab20b9840..735ed28f7 100644
--- a/tests/integration/megatron/runtime_isolation/test_live_local_backend_smoke.py
+++ b/tests/integration/megatron/runtime_isolation/test_live_local_backend_smoke.py
@@ -42,7 +42,6 @@ def _safe_gpu_memory_utilization() -> float:
 
 def _live_test_config() -> art.dev.InternalModelConfig:
     return {
-        "rollout_weights_mode": "lora",
         "engine_args": {
             "gpu_memory_utilization": _safe_gpu_memory_utilization(),
             "max_model_len": int(
diff --git a/tests/integration/megatron/runtime_isolation/test_live_megatron_backend_smoke.py b/tests/integration/megatron/runtime_isolation/test_live_megatron_backend_smoke.py
index 6750aa407..ffe1a389d 100644
--- a/tests/integration/megatron/runtime_isolation/test_live_megatron_backend_smoke.py
+++ b/tests/integration/megatron/runtime_isolation/test_live_megatron_backend_smoke.py
@@ -12,7 +12,7 @@
 import art
 from art import dev
 from art.megatron.backend import MegatronBackend
-from art.megatron.service import MegatronService
+from art.megatron.distributed_service import DistributedMegatronService
 
 from ..model_support.oracle_harness import ORACLE_TOPOLOGY, Topology
 from ..model_support.oracle_worker import provider_topology_env
@@ -31,8 +31,8 @@
 DEFAULT_BASE_MODEL = "Qwen/Qwen3-30B-A3B-Instruct-2507"
 DEFAULT_MAX_SEQ_LENGTH = 1024
 DEFAULT_PACKED_SEQUENCE_LENGTH = 1024
-DEDICATED_MERGED_ENV = "ART_RUN_LIVE_MEGATRON_MERGED_SMOKE"
-DEDICATED_MULTIRANK_MERGED_ENV = "ART_RUN_LIVE_MEGATRON_MULTIRANK_MERGED_SMOKE"
+DEDICATED_ENV = "ART_RUN_LIVE_MEGATRON_DEDICATED_SMOKE"
+DEDICATED_MULTIRANK_ENV = "ART_RUN_LIVE_MEGATRON_MULTIRANK_SMOKE"
 SHARED_LORA_ENV = "ART_RUN_LIVE_MEGATRON_SHARED_SMOKE"
 SHARED_LONG_LORA_ENV = "ART_RUN_LIVE_MEGATRON_SHARED_LONG_SMOKE"
 SHARED_TOPOLOGY = Topology(tp=1, ep=2, etp=1, dp=1, cp=2, sp=False)
@@ -81,17 +81,13 @@ def _inference_gpu_ids() -> list[int]:
 
 def _multirank_trainer_gpu_ids() -> list[int]:
     if not torch.cuda.is_available() or torch.cuda.device_count() < 3:
-        raise RuntimeError(
-            "Need at least 3 visible CUDA GPUs for multi-rank Megatron merged smoke"
-        )
+        raise RuntimeError("Need at least 3 visible CUDA GPUs for multi-rank smoke")
     return [0, 1]
 
 
 def _multirank_inference_gpu_ids() -> list[int]:
     if not torch.cuda.is_available() or torch.cuda.device_count() < 3:
-        raise RuntimeError(
-            "Need at least 3 visible CUDA GPUs for multi-rank Megatron merged smoke"
-        )
+        raise RuntimeError("Need at least 3 visible CUDA GPUs for multi-rank smoke")
     return [2]
 
 
@@ -104,9 +100,10 @@ def _shared_live_config() -> dev.InternalModelConfig:
     return cast(
         dev.InternalModelConfig,
         {
-            "rollout_weights_mode": "lora",
             "engine_args": {
-                **_engine_args_for_yes_no_trainability(inference_gpu_ids=[0, 1]),
+                **_engine_args_for_yes_no_trainability(
+                    base_model=_base_model(), inference_gpu_ids=[0, 1]
+                ),
                 "tensor_parallel_size": 2,
                 "enable_expert_parallel": True,
                 "enable_sleep_mode": True,
@@ -116,30 +113,25 @@ def _shared_live_config() -> dev.InternalModelConfig:
     )
 
 
-def _dedicated_merged_config() -> dev.InternalModelConfig:
+def _dedicated_config() -> dev.InternalModelConfig:
     return {
         "trainer_gpu_ids": _trainer_gpu_ids(),
         "inference_gpu_ids": _inference_gpu_ids(),
-        "rollout_weights_mode": "merged",
-        "engine_args": {
-            **_engine_args_for_yes_no_trainability(
-                inference_gpu_ids=_inference_gpu_ids()
-            ),
-        },
+        "engine_args": _engine_args_for_yes_no_trainability(
+            base_model=_base_model(), inference_gpu_ids=_inference_gpu_ids()
+        ),
         "init_args": {"max_seq_length": _max_seq_length()},
     }
 
 
-def _dedicated_multirank_merged_config() -> dev.InternalModelConfig:
+def _dedicated_multirank_config() -> dev.InternalModelConfig:
     return {
         "trainer_gpu_ids": _multirank_trainer_gpu_ids(),
         "inference_gpu_ids": _multirank_inference_gpu_ids(),
-        "rollout_weights_mode": "merged",
-        "engine_args": {
-            **_engine_args_for_yes_no_trainability(
-                inference_gpu_ids=_multirank_inference_gpu_ids()
-            ),
-        },
+        "engine_args": _engine_args_for_yes_no_trainability(
+            base_model=_base_model(),
+            inference_gpu_ids=_multirank_inference_gpu_ids(),
+        ),
         "init_args": {"max_seq_length": _max_seq_length()},
     }
 
@@ -169,15 +161,15 @@ async def _chat_snapshot(model: art.TrainableModel, *, step: int) -> dict[str, o
     }
 
 
-async def _runtime_is_sleeping(service: MegatronService) -> bool:
+async def _runtime_is_sleeping(service: DistributedMegatronService) -> bool:
     async with httpx.AsyncClient(timeout=30.0) as client:
-        response = await client.get(f"{service._vllm_base_url}/is_sleeping")
+        response = await client.get(f"{service._base_url}/is_sleeping")
         response.raise_for_status()
         return bool(response.json()["is_sleeping"])
 
 
 async def _wait_until_runtime_sleeping(
-    service: MegatronService,
+    service: DistributedMegatronService,
     *,
     timeout_s: float = 300.0,
     poll_s: float = 0.5,
@@ -282,7 +274,7 @@ async def test_megatron_backend_shared_lora_runtime_sleep_wake_live_smoke(
             report_metrics=[],
         )
         await model.register(backend)
-        service = cast(MegatronService, await backend._get_service(model))
+        service = cast(DistributedMegatronService, await backend._get_service(model))
         prompts = _train_group_prompts()
         await _warmup_model(model, base_model=model.base_model, prompt=prompts[0])
         step0_name = model.get_inference_name(step=0)
@@ -356,10 +348,10 @@ async def test_megatron_backend_shared_lora_runtime_sleep_wake_live_smoke(
     reason="Need at least 2 CUDA GPUs for Megatron live smokes",
 )
 @pytest.mark.asyncio
-async def test_megatron_backend_dedicated_merged_live_smoke(
+async def test_megatron_backend_dedicated_live_smoke(
     artifact_dir: Path,
 ) -> None:
-    _require_opt_in(DEDICATED_MERGED_ENV)
+    _require_opt_in(DEDICATED_ENV)
     backend_root = artifact_dir / "art_workspace"
     backend_root.mkdir(parents=True, exist_ok=True)
 
@@ -367,17 +359,17 @@ async def test_megatron_backend_dedicated_merged_live_smoke(
         backend_root=backend_root,
         topology=ORACLE_TOPOLOGY,
     ) as backend:
-        run_name = f"megatron-merged-live-{uuid.uuid4().hex[:8]}"
+        run_name = f"megatron-dedicated-live-{uuid.uuid4().hex[:8]}"
         model = art.TrainableModel(
             name=run_name,
             run_name=run_name,
             project="integration-tests",
             base_model=_base_model(),
-            _internal_config=_dedicated_merged_config(),
+            _internal_config=_dedicated_config(),
             report_metrics=[],
         )
         await model.register(backend)
-        service = cast(MegatronService, await backend._get_service(model))
+        service = cast(DistributedMegatronService, await backend._get_service(model))
         prompts = _train_group_prompts()
         await _warmup_model(model, base_model=model.base_model, prompt=prompts[0])
         step0_name = model.get_inference_name(step=0)
@@ -415,26 +407,26 @@ async def test_megatron_backend_dedicated_merged_live_smoke(
             "eval_reward": eval_reward,
             "latest_snapshot": latest_snapshot,
         }
-        (artifact_dir / "dedicated_megatron_merged_live_result.json").write_text(
+        (artifact_dir / "dedicated_megatron_live_result.json").write_text(
             json.dumps(payload, indent=2, sort_keys=True) + "\n",
             encoding="utf-8",
         )
         assert latest_step > 0
         assert step0_name in model_ids_before
         assert latest_name in model_ids_after
-        assert step0_name not in model_ids_after
+        assert step0_name in model_ids_after
         assert latest_snapshot["has_logprobs"] is True
 
 
 @pytest.mark.skipif(
     not torch.cuda.is_available() or torch.cuda.device_count() < 3,
-    reason="Need at least 3 CUDA GPUs for multi-rank Megatron merged smoke",
+    reason="Need at least 3 CUDA GPUs for multi-rank Megatron smoke",
 )
 @pytest.mark.asyncio
-async def test_megatron_backend_dedicated_multirank_merged_live_smoke(
+async def test_megatron_backend_dedicated_multirank_live_smoke(
     artifact_dir: Path,
 ) -> None:
-    _require_opt_in(DEDICATED_MULTIRANK_MERGED_ENV)
+    _require_opt_in(DEDICATED_MULTIRANK_ENV)
     backend_root = artifact_dir / "art_workspace"
     backend_root.mkdir(parents=True, exist_ok=True)
 
@@ -442,17 +434,17 @@ async def test_megatron_backend_dedicated_multirank_merged_live_smoke(
         backend_root=backend_root,
         topology=SHARED_TOPOLOGY,
     ) as backend:
-        run_name = f"megatron-multirank-merged-live-{uuid.uuid4().hex[:8]}"
+        run_name = f"megatron-multirank-live-{uuid.uuid4().hex[:8]}"
         model = art.TrainableModel(
             name=run_name,
             run_name=run_name,
             project="integration-tests",
             base_model=_base_model(),
-            _internal_config=_dedicated_multirank_merged_config(),
+            _internal_config=_dedicated_multirank_config(),
             report_metrics=[],
         )
         await model.register(backend)
-        service = cast(MegatronService, await backend._get_service(model))
+        service = cast(DistributedMegatronService, await backend._get_service(model))
         prompts = _train_group_prompts()
         await _warmup_model(model, base_model=model.base_model, prompt=prompts[0])
         step0_name = model.get_inference_name(step=0)
@@ -493,16 +485,14 @@ async def test_megatron_backend_dedicated_multirank_merged_live_smoke(
             "inference_gpu_ids": _multirank_inference_gpu_ids(),
             "topology": SHARED_TOPOLOGY.model_dump(),
         }
-        (
-            artifact_dir / "dedicated_megatron_multirank_merged_live_result.json"
-        ).write_text(
+        (artifact_dir / "dedicated_megatron_multirank_live_result.json").write_text(
             json.dumps(payload, indent=2, sort_keys=True) + "\n",
             encoding="utf-8",
         )
         assert latest_step > 0
         assert step0_name in model_ids_before
         assert latest_name in model_ids_after
-        assert step0_name not in model_ids_after
+        assert step0_name in model_ids_after
         assert latest_snapshot["has_logprobs"] is True
 
 
@@ -532,7 +522,7 @@ async def test_megatron_backend_shared_lora_ten_step_live_smoke(
             report_metrics=[],
         )
         await model.register(backend)
-        service = cast(MegatronService, await backend._get_service(model))
+        service = cast(DistributedMegatronService, await backend._get_service(model))
         prompts = _train_group_prompts()
         await _warmup_model(model, base_model=model.base_model, prompt=prompts[0])
         step0_name = model.get_inference_name(step=0)
diff --git a/tests/integration/megatron/runtime_isolation/test_live_runtime_server_smoke.py b/tests/integration/megatron/runtime_isolation/test_live_runtime_server_smoke.py
index 5773873c1..13a94125a 100644
--- a/tests/integration/megatron/runtime_isolation/test_live_runtime_server_smoke.py
+++ b/tests/integration/megatron/runtime_isolation/test_live_runtime_server_smoke.py
@@ -51,23 +51,19 @@ def _find_free_port() -> int:
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="No CUDA available")
 @pytest.mark.asyncio
 async def test_external_runtime_server_live_smoke(
-    tmp_path: Path,
     artifact_dir: Path,
 ) -> None:
     _require_live_runtime_smoke_opt_in()
 
     port = _find_free_port()
     served_model_name = f"vllm-runtime-live-{uuid.uuid4().hex[:8]}"
-    renamed_model_name = f"{served_model_name}@renamed"
     log_path = artifact_dir / "runtime.log"
     launch_config = runtime.VllmRuntimeLaunchConfig(
         base_model=os.environ.get("BASE_MODEL", DEFAULT_BASE_MODEL),
         port=port,
         host="127.0.0.1",
         cuda_visible_devices=os.environ.get("CUDA_VISIBLE_DEVICES", "0"),
-        lora_path=str(tmp_path / "placeholder_lora"),
         served_model_name=served_model_name,
-        rollout_weights_mode="merged",
         engine_args={
             "gpu_memory_utilization": _safe_gpu_memory_utilization(),
             "max_model_len": int(
@@ -107,19 +103,6 @@ async def test_external_runtime_server_live_smoke(
                 model_info["id"] for model_info in models_response.json()["data"]
             ]
 
-            rename_response = await client.post(
-                "/art/set_served_model_name",
-                json={"name": renamed_model_name},
-            )
-            rename_response.raise_for_status()
-
-            renamed_models_response = await client.get("/v1/models")
-            renamed_models_response.raise_for_status()
-            renamed_model_ids = [
-                model_info["id"]
-                for model_info in renamed_models_response.json()["data"]
-            ]
-
             sleep_response = await client.post(
                 "/sleep",
                 params={"level": 1, "mode": "wait"},
@@ -138,7 +121,7 @@ async def test_external_runtime_server_live_smoke(
             completion_response = await client.post(
                 "/v1/chat/completions",
                 json={
-                    "model": renamed_model_name,
+                    "model": served_model_name,
                     "messages": [{"role": "user", "content": "Say hello."}],
                     "max_tokens": 8,
                     "logprobs": True,
@@ -154,7 +137,6 @@ async def test_external_runtime_server_live_smoke(
                     "command": command,
                     "base_model": launch_config.base_model,
                     "original_model_ids": original_model_ids,
-                    "renamed_model_ids": renamed_model_ids,
                     "sleeping_before_wake": sleeping_before_wake,
                     "sleeping_after_wake": sleeping_after_wake,
                     "text": completion["choices"][0]["message"]["content"],
@@ -167,7 +149,6 @@ async def test_external_runtime_server_live_smoke(
             encoding="utf-8",
         )
         assert served_model_name in original_model_ids
-        assert renamed_model_name in renamed_model_ids
         assert sleeping_before_wake is True
         assert sleeping_after_wake is False
         assert completion["choices"][0]["logprobs"] is not None
diff --git a/tests/integration/megatron/runtime_isolation/test_runtime_launcher.py b/tests/integration/megatron/runtime_isolation/test_runtime_launcher.py
index 65103ce3f..1234e1be0 100644
--- a/tests/integration/megatron/runtime_isolation/test_runtime_launcher.py
+++ b/tests/integration/megatron/runtime_isolation/test_runtime_launcher.py
@@ -1,6 +1,5 @@
 import os
 from pathlib import Path
-from types import SimpleNamespace
 from typing import Any, cast
 
 import pytest
@@ -39,7 +38,7 @@ def test_build_runtime_server_cmd_uses_runtime_project(
             cuda_visible_devices="1",
             lora_path="/tmp/lora",
             served_model_name="test@0",
-            rollout_weights_mode="merged",
+            initial_policy_version=7,
             engine_args={"weight_transfer_config": {"backend": "nccl"}},
             server_args={"tool_call_parser": "hermes"},
         )
@@ -50,6 +49,7 @@ def test_build_runtime_server_cmd_uses_runtime_project(
         '--engine-args-json={"weight_transfer_config": {"backend": "nccl"}}' in command
     )
     assert '--server-args-json={"tool_call_parser": "hermes"}' in command
+    assert "--initial-policy-version=7" in command
 
 
 def test_build_runtime_server_cmd_honors_runtime_bin_override(monkeypatch) -> None:
@@ -62,7 +62,6 @@ def test_build_runtime_server_cmd_honors_runtime_bin_override(monkeypatch) -> No
             cuda_visible_devices="1",
             lora_path="/tmp/lora",
             served_model_name="test@0",
-            rollout_weights_mode="merged",
         )
     )
     assert command[:2] == ["/opt/art/bin/runtime", "--wrapped"]
@@ -86,13 +85,12 @@ def test_build_runtime_server_cmd_allows_lora_without_initial_adapter(
             host="0.0.0.0",
             cuda_visible_devices="0,1",
             served_model_name="test@0",
-            rollout_weights_mode="lora",
         )
     )
 
     assert command[0] == str(runtime_bin)
     assert not any(arg.startswith("--lora-path=") for arg in command)
-    assert "--rollout-weights-mode=lora" in command
+    assert not any(arg.startswith("--rollout-weights-mode=") for arg in command)
 
 
 def test_external_checkpoint_path_mapping() -> None:
@@ -113,45 +111,15 @@ def test_external_checkpoint_path_mapping() -> None:
     assert mapped == "/remote/ws/projects/art/.art/models/model/0001"
 
 
-def test_get_vllm_runtime_nccl_so_path_queries_runtime_python(
-    monkeypatch,
-    tmp_path: Path,
-) -> None:
-    monkeypatch.delenv("ART_VLLM_RUNTIME_BIN", raising=False)
-    runtime_root = tmp_path / "custom-runtime"
-    runtime_bin = runtime_root / ".venv" / "bin" / "art-vllm-runtime-server"
-    runtime_python = runtime_root / ".venv" / "bin" / "python"
-    runtime_bin.parent.mkdir(parents=True, exist_ok=True)
-    runtime_bin.write_text("#!/bin/sh\n", encoding="ascii")
-    runtime_python.write_text("#!/bin/sh\n", encoding="ascii")
-    nccl_so_path = tmp_path / "libnccl.so.2"
-    nccl_so_path.write_text("nccl\n", encoding="ascii")
-    seen: dict[str, object] = {}
-
-    def fake_run(command, *, capture_output: bool, text: bool):
-        seen["command"] = command
-        seen["capture_output"] = capture_output
-        seen["text"] = text
-        return SimpleNamespace(returncode=0, stdout=f"{nccl_so_path}\n", stderr="")
-
-    monkeypatch.setenv("ART_VLLM_RUNTIME_PROJECT_ROOT", str(runtime_root))
-    monkeypatch.setattr(runtime, "subprocess", SimpleNamespace(run=fake_run))
-
-    assert runtime.get_vllm_runtime_nccl_so_path() == nccl_so_path.resolve()
-    command = seen["command"]
-    assert isinstance(command, list)
-    assert command[0] == str(runtime_python)
-    assert seen["capture_output"] is True
-    assert seen["text"] is True
-
-
 def test_vllm_runtime_subprocess_env_isolates_flashinfer_for_source_runtime(
     monkeypatch,
     tmp_path: Path,
 ) -> None:
     runtime_root = tmp_path / "vllm_runtime"
+    cache_root = tmp_path / "node_cache"
     runtime_root.mkdir()
     monkeypatch.setenv("ART_VLLM_RUNTIME_PROJECT_ROOT", str(runtime_root))
+    monkeypatch.setenv("XDG_CACHE_HOME", str(cache_root))
     monkeypatch.setenv("FLASHINFER_WORKSPACE_BASE", "/shared/flashinfer")
     monkeypatch.setenv(
         "PYTHONPATH",
@@ -167,10 +135,24 @@ def test_vllm_runtime_subprocess_env_isolates_flashinfer_for_source_runtime(
 
     assert env["PYTHONPATH"] == "/keep"
     assert env["FLASHINFER_WORKSPACE_BASE"] == str(
-        tmp_path / "scratch" / "vllm_runtime_flashinfer"
+        cache_root / "vllm_runtime" / "flashinfer_workspace"
     )
 
 
+def test_vllm_runtime_subprocess_env_pins_runtime_tools(
+    monkeypatch,
+    tmp_path: Path,
+) -> None:
+    runtime_bin = tmp_path / "vllm_runtime/.venv/bin/art-vllm-runtime-server"
+    runtime_bin.parent.mkdir(parents=True)
+    runtime_bin.touch()
+    monkeypatch.setenv("PATH", "/usr/bin")
+
+    env = runtime._vllm_runtime_subprocess_env([str(runtime_bin)])
+
+    assert env["PATH"] == f"{runtime_bin.parent}{os.pathsep}/usr/bin"
+
+
 def test_vllm_runtime_subprocess_env_isolates_flashinfer_for_managed_runtime(
     monkeypatch,
     tmp_path: Path,
@@ -397,3 +379,24 @@ async def get(self, url: str, timeout: float):
         "url": "http://127.0.0.1:8123/health",
         "timeout": 5.0,
     }
+
+
+@pytest.mark.asyncio
+async def test_wait_for_vllm_runtime_fails_when_engine_core_dies(
+    tmp_path: Path,
+) -> None:
+    class FakeProcess:
+        def poll(self):
+            return None
+
+    log_path = tmp_path / "vllm.log"
+    log_path.write_text("APIServer is alive\nEngineCore failed to start\n")
+
+    with pytest.raises(RuntimeError, match="EngineCore failed to start"):
+        await runtime.wait_for_vllm_runtime(
+            process=cast(Any, FakeProcess()),
+            host="127.0.0.1",
+            port=8123,
+            timeout=300.0,
+            log_path=str(log_path),
+        )
diff --git a/tests/integration/megatron/runtime_isolation/test_runtime_project_isolation.py b/tests/integration/megatron/runtime_isolation/test_runtime_project_isolation.py
index 5a9c57dcb..4812c2849 100644
--- a/tests/integration/megatron/runtime_isolation/test_runtime_project_isolation.py
+++ b/tests/integration/megatron/runtime_isolation/test_runtime_project_isolation.py
@@ -6,6 +6,44 @@
 
 ROOT = Path(__file__).resolve().parents[4]
 
+_POLICY_REQUEST_FIXTURE = """
+import hashlib
+
+from art_vllm_runtime.policy_spans import PolicyLoRARequest, _set_policy_cache_salt
+from vllm.sampling_params import SamplingParams
+from vllm.v1.core.kv_cache_utils import get_request_block_hasher, init_none_hash
+from vllm.v1.request import Request
+
+def initialize_policy_requests():
+    global block_hasher
+    hash_value = lambda value: hashlib.sha256(repr(value).encode()).digest()
+    init_none_hash(hash_value)
+    block_hasher = get_request_block_hasher(4, hash_value)
+
+def policy_lora(path, policy_version, update_seq):
+    return PolicyLoRARequest(
+        lora_name="model:active", lora_int_id=1, lora_path=path,
+        policy_version=policy_version, update_seq=update_seq,
+    )
+
+def make_policy_request(
+    request_id, lora_request, *, prompt_tokens=8, user_cache_salt=None
+):
+    request = Request(
+        request_id, list(range(prompt_tokens)), SamplingParams(max_tokens=4), None,
+        lora_request=lora_request, block_hasher=block_hasher,
+    )
+    request.cache_salt = user_cache_salt
+    _set_policy_cache_salt(
+        request, lora_slot=lora_request.lora_name,
+        policy_version=lora_request.policy_version,
+        update_seq=lora_request.update_seq,
+    )
+    request.block_hashes.clear()
+    request.update_block_hashes()
+    return request
+"""
+
 
 def _runtime_python(source: str, artifact_dir: Path, name: str) -> str:
     result = subprocess.run(
@@ -49,314 +87,1097 @@ def test_runtime_server_source_contains_only_required_custom_routes() -> None:
     source = (
         ROOT / "vllm_runtime" / "src" / "art_vllm_runtime" / "dedicated_server.py"
     ).read_text()
-    for route in ("/sleep", "/wake_up", "/is_sleeping", "/art/set_served_model_name"):
+    for route in ("/sleep", "/wake_up", "/is_sleeping"):
         assert route in source
 
 
-def test_runtime_patch_defaults_evidence_on_and_honors_opt_out(
+def test_runtime_patch_always_returns_token_ids(
     artifact_dir: Path,
 ) -> None:
     payload = _runtime_python(
         "import json; "
-        "from art_vllm_runtime.patches import subclass_chat_completion_request; "
-        "subclass_chat_completion_request(); "
+        "from art_vllm_runtime.patches import apply_vllm_runtime_patches; "
+        "apply_vllm_runtime_patches(); "
         "from vllm.entrypoints.openai.chat_completion import protocol; "
-        "default_request = protocol.ChatCompletionRequest("
+        "request = protocol.ChatCompletionRequest("
         "model='m', messages=[{'role': 'user', 'content': 'x'}]"
         "); "
-        "explicit_false = protocol.ChatCompletionRequest("
-        "model='m', messages=[{'role': 'user', 'content': 'x'}], "
-        "logprobs=False, top_logprobs=None, return_token_ids=False"
-        "); "
-        "explicit_none = protocol.ChatCompletionRequest("
-        "model='m', messages=[{'role': 'user', 'content': 'x'}], "
-        "return_token_ids=None"
-        "); "
         "print(json.dumps({"
-        "'default': {"
-        "'logprobs': default_request.logprobs, "
-        "'top_logprobs': default_request.top_logprobs, "
-        "'return_token_ids': default_request.return_token_ids"
-        "}, "
-        "'default_fields_set': sorted(default_request.model_fields_set), "
-        "'explicit_false': {"
-        "'logprobs': explicit_false.logprobs, "
-        "'top_logprobs': explicit_false.top_logprobs, "
-        "'return_token_ids': explicit_false.return_token_ids"
-        "}, "
-        "'explicit_false_fields_set': sorted(explicit_false.model_fields_set), "
-        "'explicit_none_return_token_ids': explicit_none.return_token_ids, "
-        "'explicit_none_fields_set': sorted(explicit_none.model_fields_set)"
+        "'logprobs': request.logprobs, "
+        "'top_logprobs': request.top_logprobs, "
+        "'return_token_ids': request.return_token_ids"
         "}))",
         artifact_dir,
         "route_token_ids",
     )
-    assert json.loads(payload.splitlines()[-1]) == {
-        "default": {
-            "logprobs": True,
-            "top_logprobs": 0,
-            "return_token_ids": True,
-        },
-        "default_fields_set": ["messages", "model"],
-        "explicit_false": {
-            "logprobs": False,
-            "top_logprobs": None,
-            "return_token_ids": False,
-        },
-        "explicit_false_fields_set": [
-            "logprobs",
-            "messages",
-            "model",
-            "return_token_ids",
-            "top_logprobs",
-        ],
-        "explicit_none_return_token_ids": None,
-        "explicit_none_fields_set": ["messages", "model", "return_token_ids"],
+    assert json.loads(payload) == {
+        "logprobs": True,
+        "top_logprobs": 0,
+        "return_token_ids": True,
     }
 
 
-def test_parallel_sampling_preserves_every_child_policy_span(
+def test_runtime_lora_updates_linearize_request_admission(
     artifact_dir: Path,
 ) -> None:
     payload = _runtime_python(
         """
+import asyncio
 import json
 from types import SimpleNamespace
-import art_vllm_runtime.policy_spans as policy
-from vllm.sampling_params import RequestOutputKind
-from vllm.v1.engine.output_processor import RequestState
-from vllm.v1.engine.parallel_sampling import ParentRequest
+from art_vllm_runtime.policy_spans import (
+    LoraUpdateCoordinator,
+    PolicyLoRARequest,
+    _apply_lora_alias_policy_cache_salt,
+    publish_lora_slot_policy,
+    register_lora_alias,
+)
 
-policy._patch_output_processor_policy_span_accumulation()
+async def main():
+    slot = "model:active"
+    old = PolicyLoRARequest(
+        lora_name=slot, lora_int_id=1, lora_path="old",
+        policy_version=4, update_seq=1,
+    )
+    new = PolicyLoRARequest(
+        lora_name=slot, lora_int_id=1, lora_path="new",
+        policy_version=5, update_seq=2,
+    )
+    models = SimpleNamespace(lora_requests={slot: new})
+    register_lora_alias(models, public_model_name="model@4", lora_slot=slot)
+    publish_lora_slot_policy(
+        models, lora_slot=slot, policy_version=5, update_seq=2
+    )
+    request = SimpleNamespace(model="model@4", cache_salt=None)
+    _apply_lora_alias_policy_cache_salt(models, request, new)
 
-class Detokenizer:
-    output_token_ids = [10, 20]
-    def num_output_tokens(self): return 2
-    def get_next_output_text(self, finished, delta): return "done"
+    coordinator = LoraUpdateCoordinator()
+    assert await coordinator.begin_update(slot) == 1
+    await coordinator.commit_update(slot, old)
+    assert await coordinator.begin_update(slot) == 2
 
-class Logprobs:
-    logprobs = cumulative_logprob = prompt_logprobs = None
+    async def admit():
+        async with coordinator.admission(slot) as state:
+            return state
 
-parent = ParentRequest.__new__(ParentRequest)
-parent.external_req_id = "parent"
-parent.child_requests = {"0_parent", "1_parent"}
-parent.output_aggregator = [None, None]
-parent.sampling_params = SimpleNamespace(
-    output_kind=RequestOutputKind.FINAL_ONLY, n=2
-)
+    admission = asyncio.create_task(admit())
+    await asyncio.sleep(0)
+    blocked = not admission.done()
+    await coordinator.commit_update(slot, new)
+    admitted_lora = await admission
+    print(json.dumps({
+        "blocked": blocked,
+        "cache_salt": request.cache_salt,
+        "policy_version": admitted_lora.policy_version,
+        "lora_path": admitted_lora.lora_path,
+    }, sort_keys=True))
 
-def finish_child(index, policy_version):
-    state = RequestState(
-        request_id=f"{index}_parent", external_req_id="parent",
-        parent_req=parent, request_index=index, lora_request=None,
-        output_kind=RequestOutputKind.FINAL_ONLY, prompt="p",
-        prompt_token_ids=[1], prompt_embeds=None,
-        logprobs_processor=Logprobs(), detokenizer=Detokenizer(),
-        max_tokens_param=2, arrival_time=0.0, queue=None,
-        log_stats=False, stream_interval=1,
-    )
-    policy._CURRENT_ENGINE_POLICY_SPANS = {state.request_id: [{
-        "start_token": 0, "end_token": 2,
-        "policy_version": policy_version,
-        "lora_slot": "model:active", "update_seq": policy_version,
-    }]}
-    return state.make_request_output([10, 20], None, "stop", None)
-
-assert finish_child(0, 3) is None
-result = finish_child(1, 4)
-print(json.dumps([
-    output.art_policy_token_spans[0]["policy_version"]
-    for output in result.outputs
-]))
+asyncio.run(main())
 """,
         artifact_dir,
-        "parallel_sampling_policy_spans",
+        "lora_update_admission",
     )
-    assert json.loads(payload) == [3, 4]
+    result = json.loads(payload)
+    cache_salt = result.pop("cache_salt")
+    assert result == {
+        "blocked": True,
+        "lora_path": "new",
+        "policy_version": 5,
+    }
+    assert cache_salt.startswith("art_policy_cache_salt=v1:")
+    assert len(cache_salt) == len("art_policy_cache_salt=v1:") + 64
 
 
-def test_runtime_lora_updates_linearize_request_admission(
+def test_runtime_parallel_admission_is_atomic_and_cancellation_safe(
     artifact_dir: Path,
 ) -> None:
     payload = _runtime_python(
         """
 import asyncio
+from collections import defaultdict
 import json
 from types import SimpleNamespace
+
 from art_vllm_runtime.policy_spans import (
-    LoraUpdateCoordinator,
-    _set_policy_cache_salt,
+    LoraUpdateCoordinator, PolicyLoRARequest, _patch_engine_request_admission,
 )
+from vllm.sampling_params import SamplingParams
+from vllm.v1.engine import EngineCoreRequest
+from vllm.v1.engine.async_llm import AsyncLLM
+from vllm.v1.engine.output_processor import OutputProcessor
+
+class Output:
+    abort_requests = OutputProcessor.abort_requests
+
+    def __init__(self):
+        self.request_states = {}
+        self.parent_requests = {}
+        self.external_req_ids = defaultdict(list)
+        self.lora_states = SimpleNamespace(request_finished=lambda *_args: None)
+
+    def add_request(self, request, _prompt, parent, _index, _queue):
+        self.request_states[request.request_id] = SimpleNamespace(
+            external_req_id=request.external_req_id,
+            lora_name=request.lora_request.lora_name,
+            parent_req=parent,
+            queue=None,
+        )
+        self.external_req_ids[request.external_req_id].append(request.request_id)
+        if parent is not None:
+            self.parent_requests[parent.request_id] = parent
+
+class Core:
+    def __init__(self):
+        self.resources = SimpleNamespace(engine_dead=False)
+        self.calls = []
+        self.aborted = []
+        self.first = asyncio.Event()
+        self.release_first = asyncio.Event()
+        self.second = asyncio.Event()
+        self.release_second = asyncio.Event()
+
+    async def add_request_async(self, request):
+        self.calls.append((request.request_id, request.lora_request.policy_version))
+        if len(self.calls) == 1:
+            self.first.set()
+            await self.release_first.wait()
+        else:
+            self.second.set()
+            await self.release_second.wait()
+
+    async def abort_requests_async(self, request_ids):
+        self.aborted.extend(request_ids)
 
 async def main():
+    _patch_engine_request_admission()
     slot = "model:active"
-    old = SimpleNamespace(lora_name=slot, lora_path="old")
-    new = SimpleNamespace(lora_name=slot, lora_path="new")
-    request = SimpleNamespace(model=slot, cache_salt=None)
-    _set_policy_cache_salt(request, lora_slot=slot, policy_version=5)
+    old = PolicyLoRARequest(
+        lora_name=slot, lora_int_id=1, lora_path="old",
+        policy_version=1, update_seq=1,
+    )
+    new = PolicyLoRARequest(
+        lora_name=slot, lora_int_id=1, lora_path="new",
+        policy_version=2, update_seq=2,
+    )
+    coordinator = LoraUpdateCoordinator()
+    assert await coordinator.begin_update(slot) == 1
+    await coordinator.commit_update(slot, old)
+    engine = object.__new__(AsyncLLM)
+    engine.engine_core = Core()
+    engine.output_handler = None
+    engine.vllm_config = SimpleNamespace(
+        cache_config=SimpleNamespace(kv_sharing_fast_prefill=False)
+    )
+    engine.input_processor = SimpleNamespace(assign_request_id=lambda _request: None)
+    engine._run_output_handler = lambda: None
+    engine.output_processor = Output()
+    engine.log_requests = False
+    engine._art_lora_update_coordinator = coordinator
+    abort_started = asyncio.Event()
+    release_abort = asyncio.Event()
+    abort_finished = asyncio.Event()
 
+    async def abort(request_id, internal=False):
+        abort_started.set()
+        await release_abort.wait()
+        await AsyncLLM.abort(engine, request_id, internal=internal)
+        abort_finished.set()
+
+    engine.abort = abort
+    params = SamplingParams(n=2, max_tokens=1)
+    request = EngineCoreRequest(
+        request_id="parent", external_req_id="external", prompt_token_ids=[1],
+        mm_features=None, sampling_params=params, pooling_params=None,
+        arrival_time=0.0, lora_request=old, cache_salt=None,
+        data_parallel_rank=None,
+    )
+    admission = asyncio.create_task(
+        engine.add_request("parent", request, params, prompt_text="x")
+    )
+    await engine.engine_core.first.wait()
+    update = asyncio.create_task(coordinator.begin_update(slot))
+    await asyncio.sleep(0)
+    blocked_after_first = not update.done()
+    engine.engine_core.release_first.set()
+    await engine.engine_core.second.wait()
+    blocked_after_second = not update.done()
+    admission.cancel()
+    await abort_started.wait()
+    state = coordinator._states[slot]
+    await state.condition.acquire()
+    admission.cancel()
+    await asyncio.sleep(0)
+    blocked_during_abort = not admission.done() and not update.done()
+    maps_during_abort = [
+        len(engine.output_processor.request_states),
+        len(engine.output_processor.external_req_ids),
+        len(engine.output_processor.parent_requests),
+    ]
+    release_abort.set()
+    await abort_finished.wait()
+    admission.cancel()
+    await asyncio.sleep(0)
+    blocked_during_release = not admission.done() and not update.done()
+    maps_after_abort = [
+        len(engine.output_processor.request_states),
+        len(engine.output_processor.external_req_ids),
+        len(engine.output_processor.parent_requests),
+    ]
+    state.condition.release()
+    try:
+        await admission
+    except asyncio.CancelledError:
+        pass
+    update_seq = await asyncio.wait_for(update, timeout=1)
+    await coordinator.commit_update(slot, new)
+    print(json.dumps({
+        "calls": engine.engine_core.calls,
+        "blocked": [blocked_after_first, blocked_after_second],
+        "blocked_cleanup": [blocked_during_abort, blocked_during_release],
+        "maps_during_abort": maps_during_abort,
+        "maps_after_abort": maps_after_abort,
+        "update_seq": update_seq,
+        "aborted": sorted(engine.engine_core.aborted),
+        "state_sizes": [
+            len(engine.output_processor.request_states),
+            len(engine.output_processor.external_req_ids),
+            len(engine.output_processor.parent_requests),
+        ],
+    }, sort_keys=True))
+
+asyncio.run(main())
+""",
+        artifact_dir,
+        "parallel_lora_admission",
+    )
+    assert json.loads(payload.splitlines()[-1]) == {
+        "aborted": ["0_parent", "1_parent"],
+        "blocked": [True, True],
+        "blocked_cleanup": [True, True],
+        "calls": [["0_parent", 1], ["1_parent", 1]],
+        "maps_after_abort": [0, 0, 0],
+        "maps_during_abort": [2, 1, 1],
+        "state_sizes": [0, 0, 0],
+        "update_seq": 2,
+    }
+
+
+def test_runtime_cancelled_update_releases_admission(
+    artifact_dir: Path,
+) -> None:
+    payload = _runtime_python(
+        """
+import asyncio
+import json
+from art_vllm_runtime.policy_spans import LoraUpdateCoordinator, PolicyLoRARequest
+
+async def main():
     coordinator = LoraUpdateCoordinator()
-    await coordinator.begin_update(slot)
-    await coordinator.commit_update(slot, 4, old)
-    await coordinator.begin_update(slot)
+    slot = "model:active"
+    entered = asyncio.Event()
+    release = asyncio.Event()
 
-    async def admit():
-        async with coordinator.admission(slot) as state:
-            return state
+    async def hold_admission():
+        async with coordinator.admission(slot):
+            entered.set()
+            await release.wait()
 
-    admission = asyncio.create_task(admit())
+    holder = asyncio.create_task(hold_admission())
+    await entered.wait()
+    update = asyncio.create_task(coordinator.begin_update(slot))
     await asyncio.sleep(0)
-    blocked = not admission.done()
-    await coordinator.commit_update(slot, 5, new)
-    version, admitted_lora = await admission
-
-    async with coordinator.admission(slot):
-        cancelled_update = asyncio.create_task(coordinator.begin_update(slot))
-        await asyncio.sleep(0)
-        cancelled_update.cancel()
-        try:
-            await cancelled_update
-        except asyncio.CancelledError:
-            pass
-    async with coordinator.admission(slot) as recovered_state:
-        recovered = recovered_state[0] == 5
+    update.cancel()
+    try:
+        await update
+    except asyncio.CancelledError:
+        pass
+    release.set()
+    await holder
+    async with asyncio.timeout(1):
+        async with coordinator.admission(slot):
+            admitted = True
+    failed_seq = await coordinator.begin_update(slot)
+    await coordinator.fail_update(slot, failed_seq)
+    cancelled_retry = await coordinator.begin_update(slot)
+    await coordinator.cancel_update(slot, cancelled_retry)
+
+    async def admit_after_failure():
+        async with coordinator.admission(slot):
+            return True
 
+    quarantined_admission = asyncio.create_task(admit_after_failure())
+    await asyncio.sleep(0)
+    quarantine_preserved = not quarantined_admission.done()
+    recovery_seq = await coordinator.begin_update(slot)
+    await coordinator.commit_update(slot, PolicyLoRARequest(
+        lora_name=slot, lora_int_id=1, lora_path="recovered",
+        policy_version=2, update_seq=recovery_seq,
+    ))
+    recovered = await quarantined_admission
     print(json.dumps({
-        "blocked": blocked,
-        "cache_salt": request.cache_salt,
-        "policy_version": version,
-        "lora_path": admitted_lora.lora_path,
-        "recovered_after_cancel": recovered,
-    }, sort_keys=True))
+        "admitted": admitted,
+        "quarantine_preserved": quarantine_preserved,
+        "recovered": recovered,
+    }))
 
 asyncio.run(main())
 """,
         artifact_dir,
-        "lora_update_admission",
+        "cancelled_lora_update",
     )
     assert json.loads(payload) == {
-        "blocked": True,
-        "cache_salt": "art_policy_cache_salt=model:active:5",
-        "lora_path": "new",
-        "policy_version": 5,
-        "recovered_after_cancel": True,
+        "admitted": True,
+        "quarantine_preserved": True,
+        "recovered": True,
     }
 
 
-def test_runtime_general_plugin_loads_full_patch_set() -> None:
-    pyproject = (ROOT / "vllm_runtime" / "pyproject.toml").read_text()
-    assert 'art = "art_vllm_runtime.patches:apply_vllm_runtime_patches"' in pyproject
+def test_runtime_policy_history_rekeys_real_vllm_requests(
+    artifact_dir: Path,
+) -> None:
+    payload = _runtime_python(
+        _POLICY_REQUEST_FIXTURE
+        + """
+import json
+from types import SimpleNamespace
+
+from vllm.v1.core.block_pool import BlockPool
+from art_vllm_runtime.policy_spans import (
+    _policy_history_from_cache_salt,
+    _patch_policy_cache_hashing,
+    _request_has_executed,
+    _transition_scheduler_policy_history,
+)
+
+_patch_policy_cache_hashing()
+initialize_policy_requests()
+old = policy_lora("old", 4, 1)
+new = policy_lora("new", 4, 2)
+
+continued = make_policy_request("continued", old)
+waiting = make_policy_request("waiting", old)
+old_hashes = list(continued.block_hashes)
+continued.num_computed_tokens = 4
+scheduler = SimpleNamespace(requests={
+    continued.request_id: continued,
+    waiting.request_id: waiting,
+}, kv_cache_manager=SimpleNamespace(
+    block_pool=SimpleNamespace(hash_block_size=4),
+))
+transition = _transition_scheduler_policy_history(
+    scheduler,
+    lora_request=new,
+    previous_policy=None,
+    started_request_ids={continued.request_id},
+)
+continued_history = continued.cache_salt
+continued_hashes = list(continued.block_hashes)
+continued_transitions = continued._art_policy_cache_transitions
+not_executed_after_update = not _request_has_executed(continued)
+fresh = make_policy_request("fresh", old)
+_set_policy_cache_salt(
+    fresh, lora_slot=new.lora_name,
+    policy_version=new.policy_version, update_seq=new.update_seq,
+)
+fresh.block_hashes.clear()
+fresh.update_block_hashes()
+third = policy_lora("third", 4, 3)
+intra = make_policy_request("intra", old)
+intra_pool = BlockPool(4, enable_caching=True, hash_block_size=4)
+intra_blocks = intra_pool.get_new_blocks(1)
+intra_pool.cache_full_blocks(intra, intra_blocks, 0, 1, 4, 0)
+intra_old_hashes = list(intra.block_hashes)
+intra.num_computed_tokens = 6
+_transition_scheduler_policy_history(
+    SimpleNamespace(
+        requests={intra.request_id: intra},
+        kv_cache_manager=SimpleNamespace(block_pool=intra_pool),
+    ),
+    lora_request=new,
+    previous_policy=None,
+    started_request_ids={intra.request_id},
+)
+multiple = make_policy_request("multiple", old)
+multiple.num_computed_tokens = 4
+multiple_scheduler = SimpleNamespace(
+    requests={multiple.request_id: multiple},
+    kv_cache_manager=SimpleNamespace(block_pool=SimpleNamespace(hash_block_size=4)),
+)
+_transition_scheduler_policy_history(
+    multiple_scheduler, lora_request=new, previous_policy=None,
+    started_request_ids={multiple.request_id},
+)
+multiple.num_computed_tokens = 8
+_transition_scheduler_policy_history(
+    multiple_scheduler, lora_request=third, previous_policy=None,
+    started_request_ids={multiple.request_id},
+)
+old_history = _policy_history_from_cache_salt(
+    make_policy_request("old", old).cache_salt
+)
+expected_third = make_policy_request("expected-third", old)
+_set_policy_cache_salt(
+    expected_third, lora_slot=third.lora_name,
+    policy_version=third.policy_version, update_seq=third.update_seq,
+    previous_digest=old_history,
+)
+_transition_scheduler_policy_history(
+    SimpleNamespace(
+        requests={continued.request_id: continued},
+        kv_cache_manager=SimpleNamespace(
+            block_pool=SimpleNamespace(hash_block_size=4),
+        ),
+    ),
+    lora_request=third,
+    previous_policy=None,
+    started_request_ids=set(),
+)
+print(json.dumps({
+    "transition": transition,
+    "continued_differs": continued_history != waiting.cache_salt,
+    "waiting_matches_fresh": waiting.cache_salt == fresh.cache_salt,
+    "same_version_reload_differs": (
+        _policy_history_from_cache_salt(waiting.cache_salt)
+        != _policy_history_from_cache_salt(
+            make_policy_request("old", old).cache_salt
+        )
+    ),
+    "block_hashes_changed": old_hashes != continued.block_hashes,
+    "computed_hash_preserved": old_hashes[0] == continued_hashes[0],
+    "future_hash_rekeyed": old_hashes[1] != continued_hashes[1],
+    "transition_boundary": continued_transitions[0][0],
+    "not_executed_after_update": not_executed_after_update,
+    "same_boundary_replaced": len(continued._art_policy_cache_transitions) == 1,
+    "skipped_policy_replaced": continued.cache_salt == expected_third.cache_salt,
+    "intra_block_boundary": intra._art_policy_cache_transitions[0][0],
+    "intra_block_prefix_preserved": (
+        intra.block_hashes[0] == intra_old_hashes[0]
+        and intra_pool.get_cached_block(intra.block_hashes[0], [0]) == intra_blocks
+    ),
+    "intra_block_suffix_rekeyed": intra.block_hashes[1] != intra_old_hashes[1],
+    "multiple_boundaries": [
+        item[0] for item in multiple._art_policy_cache_transitions
+    ],
+}))
+""",
+        artifact_dir,
+        "policy_history_real_request",
+    )
+    assert json.loads(payload) == {
+        "block_hashes_changed": True,
+        "computed_hash_preserved": True,
+        "continued_differs": True,
+        "future_hash_rekeyed": True,
+        "intra_block_boundary": 6,
+        "intra_block_prefix_preserved": True,
+        "intra_block_suffix_rekeyed": True,
+        "multiple_boundaries": [4, 8],
+        "not_executed_after_update": True,
+        "same_boundary_replaced": True,
+        "same_version_reload_differs": True,
+        "skipped_policy_replaced": True,
+        "transition": {"continued_requests": 1, "updated_requests": 2},
+        "transition_boundary": 4,
+        "waiting_matches_fresh": True,
+    }
 
 
-def test_lora_coordinator_supports_both_vllm_serving_layouts(
+def test_runtime_policy_preemption_rebases_and_republishes_real_block_pool(
     artifact_dir: Path,
 ) -> None:
     payload = _runtime_python(
-        """
+        _POLICY_REQUEST_FIXTURE
+        + """
+import json
+from types import SimpleNamespace
+
+from art_vllm_runtime.policy_spans import (
+    _patch_policy_cache_hashing,
+    _patch_scheduler_policy_span_transport,
+    _request_has_executed,
+    _transition_scheduler_policy_history,
+)
+from vllm.v1.core.block_pool import BlockPool
+from vllm.v1.core.sched.scheduler import Scheduler
+from vllm.v1.request import RequestStatus
+
+_patch_policy_cache_hashing()
+_patch_scheduler_policy_span_transport()
+initialize_policy_requests()
+first = policy_lora("first", 1, 1)
+second = policy_lora("second", 2, 2)
+latest = policy_lora("latest", 3, 3)
+request = make_policy_request(
+    "replay", first, prompt_tokens=12, user_cache_salt="tenant"
+)
+pool = BlockPool(10, enable_caching=True, hash_block_size=4)
+transition_scheduler = SimpleNamespace(
+    requests={request.request_id: request},
+    kv_cache_manager=SimpleNamespace(block_pool=pool),
+)
+request.num_computed_tokens = 4
+_transition_scheduler_policy_history(
+    transition_scheduler, lora_request=second, previous_policy=None,
+    started_request_ids={request.request_id},
+)
+request.num_computed_tokens = 8
+_transition_scheduler_policy_history(
+    transition_scheduler, lora_request=latest, previous_policy=None,
+    started_request_ids={request.request_id},
+)
+mixed_hashes = list(request.block_hashes)
+mixed_blocks = pool.get_new_blocks(3)
+pool.cache_full_blocks(request, mixed_blocks, 0, 3, 4, 0)
+
+waiting = []
+scheduler = object.__new__(Scheduler)
+scheduler._free_request_blocks = lambda _request: pool.free_blocks(
+    reversed(mixed_blocks)
+)
+scheduler.encoder_cache_manager = SimpleNamespace(free=lambda _request: None)
+scheduler._inflight_prefills = {request}
+scheduler.waiting = SimpleNamespace(prepend_request=waiting.append)
+scheduler.reset_preempted_req_ids = set()
+scheduler.log_stats = False
+request.status = RequestStatus.RUNNING
+Scheduler._preempt_request(scheduler, request, 0.0)
+
+fresh = make_policy_request(
+    "fresh", latest, prompt_tokens=12, user_cache_salt="tenant"
+)
+current_hashes = list(request.block_hashes)
+full_replay = all(pool.get_cached_block(item, [0]) is None for item in current_hashes)
+old_entries_preserved = all(
+    pool.get_cached_block(item, [0]) == [block]
+    for item, block in zip(mixed_hashes, mixed_blocks)
+)
+not_executed_after_rebase = not _request_has_executed(request)
+replay_blocks = pool.get_new_blocks(3)
+request.num_computed_tokens = request.num_tokens
+pool.cache_full_blocks(request, replay_blocks, 0, 3, 4, 0)
+published_current = all(
+    pool.get_cached_block(item, [0]) == [block]
+    for item, block in zip(current_hashes, replay_blocks)
+)
+print(json.dumps({
+    "cache_salt_matches_fresh": request.cache_salt == fresh.cache_salt,
+    "current_hashes_match_fresh": current_hashes == fresh.block_hashes,
+    "full_replay": full_replay,
+    "lora_identity_preserved": request.lora_request is latest,
+    "mixed_hashes_cleared": request._art_policy_cache_transitions == (),
+    "not_executed_after_rebase": not_executed_after_rebase,
+    "old_entries_preserved": old_entries_preserved,
+    "preempted": (
+        request.num_preemptions == 1
+        and waiting == [request]
+        and request.request_id in scheduler.reset_preempted_req_ids
+    ),
+    "published_current": published_current,
+    "user_cache_salt_preserved": request.cache_salt.startswith("tenant|"),
+}))
+""",
+        artifact_dir,
+        "policy_preemption_rebase",
+    )
+    assert json.loads(payload) == {
+        "cache_salt_matches_fresh": True,
+        "current_hashes_match_fresh": True,
+        "full_replay": True,
+        "lora_identity_preserved": True,
+        "mixed_hashes_cleared": True,
+        "not_executed_after_rebase": True,
+        "old_entries_preserved": True,
+        "preempted": True,
+        "published_current": True,
+        "user_cache_salt_preserved": True,
+    }
+
+
+def test_runtime_policy_update_rejects_unsupported_rehash_paths(
+    artifact_dir: Path,
+) -> None:
+    payload = _runtime_python(
+        _POLICY_REQUEST_FIXTURE
+        + """
 import json
 from types import SimpleNamespace
-import art_vllm_runtime.policy_spans as policy
-from vllm.entrypoints.openai.engine.serving import OpenAIServing
 
-policy._patch_lora_update_coordinator()
-legacy_patched = getattr(
-    OpenAIServing.__init__, "__art_lora_update_patched__", False
+from art_vllm_runtime.policy_spans import (
+    _apply_policy_lora_update,
 )
 
-class GenerateBaseServing:
-    def __init__(self, models, engine_client):
-        self.models = models
-        self.engine_client = engine_client
-
-real_import_module = policy.importlib.import_module
-def import_module(name):
-    if name == "vllm.entrypoints.openai.engine.serving":
-        raise ModuleNotFoundError(name, name=name)
-    if name == "vllm.entrypoints.generate.base.serving":
-        return SimpleNamespace(GenerateBaseServing=GenerateBaseServing)
-    return real_import_module(name)
-
-policy.importlib.import_module = import_module
-policy._patch_lora_update_coordinator()
-models = SimpleNamespace()
-engine_client = SimpleNamespace()
-GenerateBaseServing(models, engine_client)
+initialize_policy_requests()
+old = policy_lora("old", 1, 1)
+payload = {
+    "lora_name": old.lora_name, "lora_int_id": old.lora_int_id,
+    "lora_path": "new", "base_model_name": None,
+    "tensorizer_config_dict": None, "is_3d_lora_weight": False,
+    "policy_version": 2, "update_seq": 2,
+}
+
+class Core:
+    def __init__(self, request, connector):
+        self.scheduler = SimpleNamespace(
+            requests={request.request_id: request}, connector=connector,
+        )
+        self.collective_calls = 0
+        self.pause_calls = []
+
+    def is_scheduler_paused(self):
+        return True
+
+    def collective_rpc(self, *_args, **_kwargs):
+        self.collective_calls += 1
+        raise AssertionError("unsafe update reached workers")
+
+    def pause_scheduler(self, *args):
+        self.pause_calls.append(args)
+
+connector_request = make_policy_request("connector", old)
+connector_request.num_computed_tokens = 4
+connector_core = Core(connector_request, object())
+try:
+    _apply_policy_lora_update(connector_core, payload)
+except RuntimeError as error:
+    connector_error = str(error)
+
+multimodal_request = make_policy_request("multimodal", old)
+multimodal_request.num_computed_tokens = 4
+multimodal_request.mm_features = [object()]
+multimodal_hashes = list(multimodal_request.block_hashes)
+multimodal_salt = multimodal_request.cache_salt
+multimodal_core = Core(multimodal_request, None)
+try:
+    _apply_policy_lora_update(multimodal_core, payload)
+except RuntimeError as error:
+    multimodal_error = str(error)
+
 print(json.dumps({
-    "legacy_patched": legacy_patched,
-    "new_patched": getattr(
-        GenerateBaseServing.__init__, "__art_lora_update_patched__", False
+    "connector_error": connector_error,
+    "connector_preflight": (
+        connector_core.collective_calls == 0 and not connector_core.pause_calls
     ),
-    "shared_coordinator": (
-        models._art_lora_update_coordinator
-        is engine_client._art_lora_update_coordinator
+    "multimodal_error": multimodal_error,
+    "multimodal_preflight": (
+        multimodal_core.collective_calls == 0 and not multimodal_core.pause_calls
+    ),
+    "multimodal_unchanged": (
+        multimodal_request.lora_request is old
+        and multimodal_request.cache_salt == multimodal_salt
+        and multimodal_request.block_hashes == multimodal_hashes
     ),
 }))
 """,
         artifact_dir,
-        "vllm_serving_layouts",
+        "unsupported_policy_rehash",
     )
-    assert json.loads(payload.splitlines()[-1]) == {
-        "legacy_patched": True,
-        "new_patched": True,
-        "shared_coordinator": True,
+    assert json.loads(payload) == {
+        "connector_error": (
+            "Mutable policy updates cannot continue requests with a KV connector"
+        ),
+        "connector_preflight": True,
+        "multimodal_error": (
+            "Mutable policy updates cannot continue multimodal requests"
+        ),
+        "multimodal_preflight": True,
+        "multimodal_unchanged": True,
     }
 
 
-def test_runtime_patch_adds_gemma4_moe_topk_alias(artifact_dir: Path) -> None:
+def test_runtime_policy_update_verifies_declared_identity_and_quarantines(
+    artifact_dir: Path,
+) -> None:
     payload = _runtime_python(
-        "import json; "
-        "from art_vllm_runtime.patches import apply_vllm_runtime_patches; "
-        "apply_vllm_runtime_patches(); "
-        "from transformers import Gemma4TextConfig; "
-        "config = Gemma4TextConfig(enable_moe_block=True, top_k_experts=8); "
-        "print(json.dumps({'num_experts_per_tok': config.num_experts_per_tok}))",
+        """
+import json
+from types import SimpleNamespace
+from vllm.lora.request import LoRARequest
+from art_vllm_runtime.policy_spans import (
+    PolicyLoRARequest,
+    _apply_policy_lora_update,
+    _policy_metadata_for_lora_request,
+    _record_worker_lora_policy,
+)
+
+declared = PolicyLoRARequest(
+    lora_name="model:active", lora_int_id=1,
+    lora_path="/mapped/step-999-deadbeef", policy_version=7, update_seq=3,
+)
+declared_state = _record_worker_lora_policy(declared)
+bootstrap_state = _record_worker_lora_policy(LoRARequest(
+    lora_name="model:active", lora_int_id=2,
+    lora_path="/mapped/step-999-deadbeef",
+))
+try:
+    _policy_metadata_for_lora_request(LoRARequest(
+        lora_name="model:active", lora_int_id=2,
+        lora_path="/mapped/step-999-deadbeef",
+    ))
+except RuntimeError as error:
+    undeclared_failure = str(error)
+
+class FailingCore:
+    def __init__(self):
+        self.scheduler = SimpleNamespace(requests={})
+        self.pause_calls = []
+
+    def is_scheduler_paused(self):
+        return True
+
+    def collective_rpc(self, *_args, **_kwargs):
+        raise RuntimeError("rank 1 failed")
+
+    def pause_scheduler(self, mode, clear_cache):
+        self.pause_calls.append((mode, clear_cache))
+
+core = FailingCore()
+try:
+    _apply_policy_lora_update(core, {
+        "lora_name": declared.lora_name,
+        "lora_int_id": declared.lora_int_id,
+        "lora_path": declared.lora_path,
+        "base_model_name": None,
+        "tensorizer_config_dict": None,
+        "is_3d_lora_weight": False,
+        "policy_version": declared.policy_version,
+        "update_seq": declared.update_seq,
+    })
+except RuntimeError as error:
+    failure = str(error)
+
+print(json.dumps({
+    "declared_version": declared_state["policy_version"],
+    "bootstrap_path_not_inferred": bootstrap_state["policy_version"],
+    "failure": failure,
+    "pause_calls": core.pause_calls,
+    "undeclared_failure": undeclared_failure,
+}))
+""",
         artifact_dir,
-        "gemma4_topk_alias",
+        "declared_policy_and_quarantine",
     )
-    assert json.loads(payload) == {"num_experts_per_tok": 8}
+    assert json.loads(payload) == {
+        "bootstrap_path_not_inferred": 0,
+        "declared_version": 7,
+        "failure": "rank 1 failed",
+        "pause_calls": [["abort", True]],
+        "undeclared_failure": (
+            "Mutable LoRA slot 'model:active' has no declared policy identity"
+        ),
+    }
 
 
-def test_runtime_patch_skips_gemma4_layerwise_weight_update_reload(
+def test_runtime_policy_update_pins_workers_and_normalizes_scheduler_requests(
     artifact_dir: Path,
 ) -> None:
     payload = _runtime_python(
-        "import json; "
-        "from art_vllm_runtime.patches import apply_vllm_runtime_patches; "
-        "apply_vllm_runtime_patches(); "
-        "from vllm.v1.worker.gpu_worker import Worker; "
-        "HfConfig = type('HfConfig', (), {"
-        "'architectures': ['Gemma4ForConditionalGeneration']"
-        "}); "
-        "ModelConfig = type('ModelConfig', (), {'hf_config': HfConfig()}); "
-        "DummyWorker = type('DummyWorker', (), {"
-        "'model_config': ModelConfig(), "
-        "'_weight_update_active': False, "
-        "'_is_checkpoint_format': True, "
-        "'checks': 0, "
-        "'_check_weight_transfer_engine': "
-        "lambda self: setattr(self, 'checks', self.checks + 1)"
-        "}); "
-        "dummy = DummyWorker(); "
-        "Worker.start_weight_update(dummy, is_checkpoint_format=True); "
-        "active_after_start = dummy._weight_update_active; "
-        "Worker.finish_weight_update(dummy); "
-        "print(json.dumps({"
-        "'active_after_start': active_after_start, "
-        "'active_after_finish': dummy._weight_update_active, "
-        "'is_checkpoint_format': dummy._is_checkpoint_format, "
-        "'checks': dummy.checks"
-        "}))",
+        """
+import json
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from types import SimpleNamespace
+
+from art_vllm_runtime.policy_spans import (
+    _apply_policy_lora_update,
+    _patch_policy_lora_update_rpc,
+)
+from vllm.lora.model_manager import AdapterLRUCache, LRUCacheLoRAModelManager
+from vllm.lora.request import LoRARequest
+from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager
+from vllm.v1.worker.worker_base import WorkerBase
+
+class TestAdapterManager(LRUCacheLoRAModelManager):
+    def __init__(self):
+        self.lora_config = SimpleNamespace(max_cpu_loras=2, max_loras=2)
+        self._registered_adapters = AdapterLRUCache(2, self.deactivate_adapter)
+        self._active_adapters = AdapterLRUCache(2, self._deactivate_adapter)
+        self.lora_index_to_id = [None, None]
+        self.modules = {}
+
+    def _create_merged_loras_inplace(self, _lora):
+        pass
+
+class TestWorkerManager(LRUCacheWorkerLoRAManager):
+    def __init__(self):
+        self._adapter_manager = TestAdapterManager()
+        self.loaded_paths = []
+
+    def _load_adapter(self, request):
+        path = Path(request.lora_path)
+        if not path.is_dir():
+            raise FileNotFoundError(path)
+        self.loaded_paths.append(path.name)
+        return SimpleNamespace(id=request.lora_int_id)
+
+class Core:
+    def __init__(self, worker):
+        self.worker = worker
+        self.acks = []
+        initial = LoRARequest("model:active", 99, "/initial")
+        request = SimpleNamespace(
+            request_id="waiting", lora_request=initial, cache_salt=None,
+            block_hashes=[], num_computed_tokens=0, output_token_ids=[],
+            num_preemptions=0, update_block_hashes=lambda: None,
+        )
+        self.scheduler = SimpleNamespace(requests={request.request_id: request})
+
+    def is_scheduler_paused(self):
+        return True
+
+    def collective_rpc(self, method, args):
+        assert method == "art_load_lora_policy"
+        ack = WorkerBase.art_load_lora_policy(self.worker, args[0])
+        self.acks.append(ack)
+        return [ack]
+
+    def _reset_caches(self, **_kwargs):
+        pass
+
+    def pause_scheduler(self, *_args):
+        raise AssertionError("successful update must not quarantine the engine")
+
+def policy_payload(path, policy_version, update_seq):
+    return {
+        "lora_name": "model:active",
+        "lora_int_id": 99,
+        "lora_path": str(path),
+        "base_model_name": None,
+        "tensorizer_config_dict": None,
+        "is_3d_lora_weight": False,
+        "policy_version": policy_version,
+        "update_seq": update_seq,
+    }
+
+def pinned(manager):
+    cache = manager._adapter_manager
+    return (
+        99 in manager.list_adapters()
+        and 99 in cache._registered_adapters.pinned_items
+        and 99 in cache._active_adapters.pinned_items
+    )
+
+_patch_policy_lora_update_rpc()
+manager = TestWorkerManager()
+worker = SimpleNamespace(
+    add_lora=manager.add_adapter,
+    pin_lora=manager.pin_adapter,
+    list_loras=manager.list_adapters,
+)
+core = Core(worker)
+request = core.scheduler.requests["waiting"]
+with TemporaryDirectory() as temp_dir:
+    root = Path(temp_dir)
+    first_path = root / "active_1"
+    first_path.mkdir()
+    first = policy_payload(first_path, 1, 1)
+    first_transition = _apply_policy_lora_update(core, first)
+    first_result = core.acks[-1]
+    initially_pinned = pinned(manager)
+    first_path.rmdir()
+    manager.add_adapter(request.lora_request)
+    no_scheduler_reload = manager.loaded_paths == ["active_1"]
+
+    for adapter_id, name in ((1, "exact"), (2, "eval")):
+        path = root / name
+        path.mkdir()
+        manager.add_adapter(LoRARequest(name, adapter_id, str(path)))
+    retained_under_pressure = pinned(manager)
+
+    update_path = root / "active_2"
+    update_path.mkdir()
+    update = policy_payload(update_path, 2, 2)
+    update_transition = _apply_policy_lora_update(core, update)
+    update_result = core.acks[-1]
+    repinned = pinned(manager)
+    update_path.rmdir()
+    manager.add_adapter(request.lora_request)
+
+    pressure_path = root / "eval_after_update"
+    pressure_path.mkdir()
+    manager.add_adapter(LoRARequest("eval_after_update", 3, str(pressure_path)))
+    retained_after_update = pinned(manager)
+
+expected_paths = ["active_1", "exact", "eval", "active_2", "eval_after_update"]
+assert first_result["loaded"] and update_result["loaded"]
+assert first_transition == update_transition == {
+    "continued_requests": 0, "updated_requests": 1,
+}
+assert manager.loaded_paths == expected_paths
+assert not request.lora_request.load_inplace and no_scheduler_reload
+assert initially_pinned and retained_under_pressure and repinned
+assert retained_after_update
+assert update_result["previous"]["update_seq"] == 1
+assert update_result["current"]["update_seq"] == 2
+print(json.dumps({
+    "loaded_paths": manager.loaded_paths,
+    "load_inplace": request.lora_request.load_inplace,
+    "no_scheduler_reload": no_scheduler_reload,
+    "pinned_through_update_and_pressure": True,
+    "update_sequence": [
+        update_result["previous"]["update_seq"],
+        update_result["current"]["update_seq"],
+    ],
+}))
+""",
+        artifact_dir,
+        "pinned_policy_lifetime",
+    )
+    assert json.loads(payload) == {
+        "loaded_paths": ["active_1", "exact", "eval", "active_2", "eval_after_update"],
+        "load_inplace": False,
+        "no_scheduler_reload": True,
+        "pinned_through_update_and_pressure": True,
+        "update_sequence": [1, 2],
+    }
+
+
+def test_runtime_declares_launch_policy_before_admission(artifact_dir: Path) -> None:
+    payload = _runtime_python(
+        """
+import asyncio
+import json
+from types import SimpleNamespace
+from vllm.lora.request import LoRARequest
+from art_vllm_runtime.policy_spans import (
+    PolicyLoRARequest,
+    declare_initial_lora_policy,
+    lora_update_coordinator,
+)
+
+class Core:
+    async def call_utility_async(self, method, payload):
+        assert method == "art_declare_loaded_lora_policy"
+        self.payload = payload
+        return {"workers": 2}
+
+async def main():
+    slot = "model:active"
+    models = SimpleNamespace(lora_requests={slot: LoRARequest(
+        lora_name=slot, lora_int_id=3, lora_path="/initial"
+    )})
+    core = Core()
+    engine = SimpleNamespace(engine_core=core)
+    await declare_initial_lora_policy(
+        models, engine, lora_slot=slot, policy_version=7
+    )
+    declared = models.lora_requests[slot]
+    coordinator = lora_update_coordinator(models, engine)
+    async with coordinator.admission(slot) as admitted:
+        admitted_identity = [admitted.policy_version, admitted.update_seq]
+    next_sequence = await coordinator.begin_update(slot)
+    await coordinator.cancel_update(slot, next_sequence)
+    return {
+        "declared_type": type(declared).__name__,
+        "declared_identity": [declared.policy_version, declared.update_seq],
+        "worker_identity": [core.payload["policy_version"], core.payload["update_seq"]],
+        "admitted_identity": admitted_identity,
+        "next_sequence": next_sequence,
+    }
+
+print(json.dumps(asyncio.run(main())))
+""",
+        artifact_dir,
+        "launch_policy_declaration",
+    )
+    assert json.loads(payload) == {
+        "admitted_identity": [7, 1],
+        "declared_identity": [7, 1],
+        "declared_type": "PolicyLoRARequest",
+        "next_sequence": 2,
+        "worker_identity": [7, 1],
+    }
+
+
+def test_runtime_policy_spans_survive_parallel_sample_aggregation(
+    artifact_dir: Path,
+) -> None:
+    payload = _runtime_python(
+        """
+import json
+from types import SimpleNamespace
+
+from vllm.v1.engine.output_processor import RequestState
+
+
+def aggregate_final_outputs(self, new_token_ids, *args, **kwargs):
+    if not self.finished:
+        return None
+    self.parent_req.outputs[self.request_index] = SimpleNamespace(
+        index=self.request_index
+    )
+    self.parent_req.finished += 1
+    if self.parent_req.finished < len(self.parent_req.outputs):
+        return None
+    return SimpleNamespace(outputs=self.parent_req.outputs)
+
+
+RequestState.make_request_output = aggregate_final_outputs
+import art_vllm_runtime.policy_spans as policy_spans
+
+policy_spans._patch_output_processor_policy_span_accumulation()
+parent = SimpleNamespace(outputs=[None] * 4, finished=0)
+none_count = 0
+final_output = None
+for choice_index in range(4):
+    detokenizer = SimpleNamespace(num_output_tokens=lambda: 0)
+    state = SimpleNamespace(
+        request_id=f"child-{choice_index}",
+        request_index=choice_index,
+        parent_req=parent,
+        detokenizer=detokenizer,
+        finished=False,
+    )
+    for token_index in range(5):
+        detokenizer.num_output_tokens = lambda count=token_index + 1: count
+        state.finished = token_index == 4
+        policy_spans._CURRENT_ENGINE_POLICY_SPANS = {
+            state.request_id: [{
+                "start_token": 0,
+                "end_token": 1,
+                "policy_version": 7,
+                "lora_slot": "model:active",
+                "update_seq": 3,
+            }]
+        }
+        output = RequestState.make_request_output(state, [100 + token_index])
+        if output is None:
+            none_count += 1
+        else:
+            final_output = output
+
+print(json.dumps({
+    "none_count": none_count,
+    "spans": [
+        getattr(output, policy_spans.ART_POLICY_TOKEN_SPANS_FIELD, None)
+        for output in final_output.outputs
+    ],
+}, sort_keys=True))
+""",
         artifact_dir,
-        "gemma4_weight_update_reload",
+        "parallel_sample_policy_spans",
     )
     assert json.loads(payload) == {
-        "active_after_start": True,
-        "active_after_finish": False,
-        "is_checkpoint_format": True,
-        "checks": 2,
+        "none_count": 19,
+        "spans": [
+            [
+                {
+                    "end_token": 5,
+                    "lora_slot": "model:active",
+                    "policy_version": 7,
+                    "start_token": 0,
+                    "update_seq": 3,
+                }
+            ]
+        ]
+        * 4,
     }
 
 
+def test_runtime_general_plugin_loads_full_patch_set() -> None:
+    pyproject = (ROOT / "vllm_runtime" / "pyproject.toml").read_text()
+    assert 'art = "art_vllm_runtime.patches:apply_vllm_runtime_patches"' in pyproject
+
+
 def test_runtime_patch_set_does_not_install_lora_monkey_patches() -> None:
     source = (
         ROOT / "vllm_runtime" / "src" / "art_vllm_runtime" / "patches.py"
@@ -379,39 +1200,3 @@ def test_runtime_cli_serializes_lora_target_modules_as_single_nargs_vector(
         "lora_target_modules",
     )
     assert json.loads(payload) == ["--lora-target-modules", "a", "b"]
-
-
-def test_runtime_project_restores_nccl_unique_id_from_raw_bytes(
-    artifact_dir: Path,
-) -> None:
-    payload = json.loads(
-        _runtime_python(
-            "import ctypes, json; "
-            "from art_vllm_runtime.patches import _restore_nccl_unique_id_payload; "
-            "from vllm.distributed.device_communicators.pynccl_wrapper import ncclUniqueId; "
-            "payload = bytes(range(128)); "
-            "restored = _restore_nccl_unique_id_payload(payload, ncclUniqueId()); "
-            "print(json.dumps({"
-            "'type': type(restored).__name__, "
-            "'matches': ctypes.string_at(ctypes.byref(restored), ctypes.sizeof(restored)).hex() == payload.hex()"
-            "}))",
-            artifact_dir,
-            "restore",
-        )
-    )
-    assert payload == {"type": "ncclUniqueId", "matches": True}
-
-
-def test_runtime_project_nccl_wrapper_accepts_raw_bytes(artifact_dir: Path) -> None:
-    payload = json.loads(
-        _runtime_python(
-            "import json; "
-            "from art_vllm_runtime.patches import _normalize_nccl_comm_init_rank_unique_id; "
-            "FakeLibrary = type('FakeLibrary', (), {'unique_id_from_bytes': lambda self, data: {'restored': len(data)}}); "
-            "restored = _normalize_nccl_comm_init_rank_unique_id(FakeLibrary(), bytes(range(128))); "
-            "print(json.dumps(restored))",
-            artifact_dir,
-            "nccl_wrapper",
-        )
-    )
-    assert payload == {"restored": 128}
diff --git a/tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py b/tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py
index c6c32685a..21100a4b4 100644
--- a/tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py
+++ b/tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py
@@ -1,259 +1,584 @@
-import json
+import asyncio
+import os
 from pathlib import Path
+import signal
 import subprocess
 import sys
+import time
 from types import SimpleNamespace
 from typing import Any, cast
-from unittest.mock import AsyncMock
+from unittest.mock import AsyncMock, Mock
 
 import httpx
 import pytest
 
-import art
-from art.megatron.optimizer_state import (
-    optimizer_generation_files,
-    read_optimizer_commit,
-)
-from art.megatron.runtime.jobs import (
-    OPTIMIZER_READY_EVENT,
-    MegatronOptimizerSaveJob,
-)
-from art.megatron.service import MegatronService
-from art.serving_capabilities import ServingCapabilities
 
+def _process_is_running(pid: int) -> bool:
+    try:
+        state = Path(f"/proc/{pid}/stat").read_text().split()[2]
+    except FileNotFoundError:
+        return False
+    return state != "Z"
 
-@pytest.fixture(autouse=True)
-def _init_megatron_runtime_config(monkeypatch: pytest.MonkeyPatch) -> None:
-    from art.megatron import runtime_config
 
-    monkeypatch.setattr(runtime_config, "_MEGATRON_RUNTIME_CONFIG", None)
-    art.init_megatron_runtime_config(
-        topology=art.MegatronTopologyConfig(tp=1, cp=2, ep=2, etp=1),
-        packed_sequence_length=1024,
-        streaming_weight_offload=True,
-    )
+def test_vllm_start_releases_the_host_service_mailbox() -> None:
+    from art.distributed.monarch_actor import ArtHostService
 
+    start = ArtHostService.__dict__["start_vllm_member"]
+    assert getattr(start._method, "_monarch_concurrent_endpoint_wrapper", False)
 
-class _AsyncOkResponse:
-    status_code = 200
 
-    def raise_for_status(self) -> None:
-        return None
+@pytest.mark.asyncio
+async def test_publication_wait_is_reserved_before_next_train_can_expire_it() -> None:
+    from art.megatron.runtime.monarch import (
+        MonarchTrainerRun,
+        _PublicationState,
+    )
 
+    run = MonarchTrainerRun.__new__(MonarchTrainerRun)
+    future = asyncio.get_running_loop().create_future()
+    state = _PublicationState("generation-1", future)
+    state.train_done = True
+    run._publications = {state.generation_id: state}
 
-class _RecordingAsyncClient:
-    def __init__(
-        self, posts: list[tuple[str, dict[str, object] | None, float]]
-    ) -> None:
-        self._posts = posts
+    waiter = run.wait_for_publication(state.generation_id)
+    assert state.active_waiters == 1
+    run._expire_prior_publications()
+    assert state.generation_id in run._publications
 
-    async def __aenter__(self):
-        return self
+    future.set_result(())
+    assert await waiter == ()
+    assert state.generation_id not in run._publications
 
-    async def __aexit__(self, exc_type, exc, tb):
-        return None
 
-    async def post(
-        self,
-        url: str,
-        *,
-        params: dict[str, object] | None = None,
-        json: dict[str, object] | None = None,
-        timeout: float,
-    ) -> _AsyncOkResponse:
-        self._posts.append((url, json if json is not None else params, timeout))
-        return _AsyncOkResponse()
+@pytest.mark.skipif(
+    sys.platform != "linux", reason="requires Linux parent-death signal"
+)
+def test_owned_local_worker_dies_when_controller_is_sigkilled(
+    tmp_path: Path,
+) -> None:
+    pid_path = tmp_path / "worker.pid"
+    program = """
+import signal
+import sys
+from pathlib import Path
+
+from art.distributed.monarch_bootstrap import _start_worker
+
+worker = _start_worker("tcp://127.0.0.1:0")
+Path(sys.argv[1]).write_text(str(worker.process.pid))
+signal.pause()
+"""
+    parent = subprocess.Popen(
+        [sys.executable, "-c", program, str(pid_path)],
+        cwd=Path(__file__).resolve().parents[4],
+        env={**os.environ, "CUDA_VISIBLE_DEVICES": ""},
+        stdout=subprocess.DEVNULL,
+        stderr=subprocess.PIPE,
+        text=True,
+    )
+    assert parent.stderr is not None
+    worker_pid: int | None = None
+    try:
+        deadline = time.monotonic() + 30
+        while not pid_path.exists() and parent.poll() is None:
+            if time.monotonic() >= deadline:
+                break
+            time.sleep(0.05)
+        if not pid_path.exists():
+            detail = parent.stderr.read() if parent.poll() is not None else "timeout"
+            pytest.fail(f"controller did not start a worker: {detail}")
+        worker_pid = int(pid_path.read_text())
+        assert _process_is_running(worker_pid)
+
+        os.kill(parent.pid, signal.SIGKILL)
+        parent.wait(timeout=10)
+        deadline = time.monotonic() + 10
+        while _process_is_running(worker_pid) and time.monotonic() < deadline:
+            time.sleep(0.05)
+        assert not _process_is_running(worker_pid)
+    finally:
+        if parent.poll() is None:
+            parent.kill()
+            parent.wait(timeout=10)
+        if worker_pid is not None and _process_is_running(worker_pid):
+            os.kill(worker_pid, signal.SIGKILL)
+
+
+@pytest.mark.skipif(sys.platform != "linux", reason="requires Linux process identity")
+def test_local_start_reconciles_legacy_owned_orphan(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    import art.distributed.monarch_bootstrap as bootstrap
+
+    monkeypatch.setattr(bootstrap, "_WORKER_LOCK_ROOT", tmp_path)
+    address = bootstrap._resolve_ephemeral_worker_address("tcp://127.0.0.1:0")
+    bootstrap._worker_lock_path(address).touch()
+    program = f"""
+import os
+import subprocess
+import sys
 
+worker_code = {bootstrap._LEGACY_OWNED_WORKER_CODE!r}
 
-def test_megatron_default_lora_adapter_config_uses_model_lora_config(
+worker = subprocess.Popen(
+    [sys.executable, "-c", worker_code, sys.argv[1]],
+    stdin=subprocess.DEVNULL,
+    stdout=subprocess.DEVNULL,
+    stderr=subprocess.DEVNULL,
+    start_new_session=True,
+)
+print(worker.pid, flush=True)
+os._exit(0)
+"""
+    launcher = subprocess.run(
+        [sys.executable, "-c", program, address],
+        cwd=Path(__file__).resolve().parents[4],
+        env={**os.environ, "CUDA_VISIBLE_DEVICES": ""},
+        capture_output=True,
+        text=True,
+        check=True,
+        timeout=10,
+    )
+    orphan_pid = int(launcher.stdout)
+    worker = None
+    try:
+        deadline = time.monotonic() + 10
+        while time.monotonic() < deadline:
+            identity = bootstrap._process_identity(orphan_pid)
+            if identity is not None and identity[1] == 1:
+                break
+            time.sleep(0.05)
+        else:
+            pytest.fail("legacy worker was not reparented")
+
+        worker = bootstrap._start_worker("tcp://127.0.0.1:0", startup_timeout_s=30)
+        assert not _process_is_running(orphan_pid)
+    finally:
+        if worker is not None:
+            bootstrap._stop_worker(worker)
+        if _process_is_running(orphan_pid):
+            os.kill(orphan_pid, signal.SIGKILL)
+
+
+@pytest.mark.skipif(sys.platform != "linux", reason="requires Linux process identity")
+def test_orphan_reconciliation_never_targets_unrelated_process(
     tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
 ) -> None:
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={
-            "lora_config": {
-                "rank": 8,
-                "target_modules": ["q_proj", "down_proj"],
-            },
-        },
-        output_dir=str(tmp_path),
+    import art.distributed.monarch_bootstrap as bootstrap
+
+    monkeypatch.setattr(bootstrap, "_WORKER_LOCK_ROOT", tmp_path)
+    unrelated = subprocess.Popen(
+        [sys.executable, "-c", "import time; time.sleep(30)"],
+        start_new_session=True,
     )
+    try:
+        identity = bootstrap._process_identity(unrelated.pid)
+        assert identity is not None
+        address = "tcp://127.0.0.1:43219"
+        metadata = bootstrap._OwnedWorkerMetadata(
+            address=address,
+            controller_pid=2**30,
+            controller_start_time=1,
+            worker_pid=unrelated.pid,
+            worker_start_time=identity[0],
+            python_executable=os.path.realpath(sys.executable),
+            worker_code_sha256="0" * 64,
+            ownership_token="0" * 32,
+        )
+        bootstrap._worker_lock_path(address).write_text(metadata.model_dump_json())
+
+        bootstrap._reconcile_orphaned_workers()
+
+        assert unrelated.poll() is None
+        assert bootstrap._worker_lock_path(address).exists()
+    finally:
+        unrelated.terminate()
+        unrelated.wait(timeout=10)
+
+
+@pytest.mark.asyncio
+async def test_trainer_run_close_retries_failed_proc_mesh_stop() -> None:
+    from art.megatron.runtime.monarch import MonarchTrainerRun
+
+    class ProcMesh:
+        def __init__(self) -> None:
+            self.stop_calls = 0
+
+        async def stop(self) -> None:
+            self.stop_calls += 1
+            if self.stop_calls == 1:
+                raise RuntimeError("injected stop failure")
+
+    proc_mesh = ProcMesh()
+    supervision = SimpleNamespace(close=Mock())
+    run = MonarchTrainerRun.__new__(MonarchTrainerRun)
+    run.run_spec = SimpleNamespace(shutdown_timeout_s=1.0)
+    run._proc_mesh = cast(Any, proc_mesh)
+    run._supervision = supervision
+    run._stop_task = None
+    run._close_task = None
+    run._closed = False
+    run._valid = False
+    run._active_job_id = None
+    run._active_receive = None
+    run._active_collective = None
+
+    with pytest.raises(RuntimeError, match="injected stop failure"):
+        await run.close()
+    assert proc_mesh.stop_calls == 1
+    supervision.close.assert_not_called()
+
+    await run.close()
+    assert proc_mesh.stop_calls == 2
+    supervision.close.assert_called_once_with()
+
+
+@pytest.mark.asyncio
+async def test_art_runtime_stop_trainer_retains_failed_run() -> None:
+    from art.distributed.art_runtime import ArtRuntime
+
+    class Run:
+        def __init__(self) -> None:
+            self.close = AsyncMock(
+                side_effect=[RuntimeError("injected close failure"), None]
+            )
+
+    runtime = ArtRuntime.__new__(ArtRuntime)
+    run = Run()
+    runtime._trainer_runs = {run}
 
-    config = service._default_lora_adapter_config()
+    with pytest.raises(RuntimeError, match="injected close failure"):
+        await runtime.stop_trainer(run)
+    assert run in runtime._trainer_runs
 
-    assert config.r == 8
-    assert config.target_modules == {"q_proj", "down_proj"}
+    await runtime.stop_trainer(run)
+    assert run not in runtime._trainer_runs
+    assert run.close.await_count == 2
 
 
 @pytest.mark.asyncio
-async def test_megatron_in_flight_eval_uses_immutable_adapter_slot(
+async def test_distributed_service_close_retries_owned_resources(
     tmp_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
 ) -> None:
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={
-            "rollout_weights_mode": "lora",
-            "rollout_weight_update_mode": "in_flight_lora",
-        },
+    from art.megatron.distributed_service import DistributedMegatronService
+
+    class Runtime:
+        def __init__(self) -> None:
+            self.trainer_stops = 0
+            self.model_stops = 0
+
+        async def stop_trainer(self, _trainer: object) -> None:
+            self.trainer_stops += 1
+            if self.trainer_stops == 1:
+                raise RuntimeError("injected trainer stop failure")
+
+        async def stop_model_service(self, _name: str) -> None:
+            self.model_stops += 1
+            if self.model_stops == 1:
+                raise RuntimeError("injected model stop failure")
+
+    runtime = Runtime()
+    service = DistributedMegatronService(
+        model_name="model",
+        base_model="base",
+        config=cast(Any, {}),
         output_dir=str(tmp_path),
+        runtime=cast(Any, runtime),
+        enable_expert_replay=False,
     )
-    service._vllm_runtime.port = 8123
-    posts: list[tuple[str, dict[str, object] | None, float]] = []
-    monkeypatch.setattr(httpx, "AsyncClient", lambda: _RecordingAsyncClient(posts))
+    trainer = object()
+    service._trainer = trainer
+    service._managed_service_name = "model"
+
+    with pytest.raises(BaseExceptionGroup):
+        await service.aclose()
+    assert service._trainer is trainer
+    assert service._managed_service_name == "model"
+
+    await service.aclose()
+    assert service._trainer is None
+    assert service._managed_service_name is None
+    assert (runtime.trainer_stops, runtime.model_stops) == (2, 2)
+
 
-    checkpoint_path = str(tmp_path / "checkpoints" / "4")
-    assert (
-        await service.acquire_exact_adapter(4, checkpoint_path) == "test-model:eval@4"
+@pytest.mark.asyncio
+async def test_failed_vllm_start_rollback_remains_runtime_owned(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    import art.distributed.art_runtime as art_runtime
+
+    class Manager:
+        def __init__(self, *_args: object, **_kwargs: object) -> None:
+            self.stop_calls = 0
+
+        async def start(self) -> None:
+            raise RuntimeError("injected startup rollback failure")
+
+        async def stop(self) -> object:
+            self.stop_calls += 1
+            if self.stop_calls == 1:
+                raise RuntimeError("injected rollback retry failure")
+            return object()
+
+    monkeypatch.setattr(art_runtime, "ReplicaManager", Manager)
+    runtime = art_runtime.ArtRuntime.__new__(art_runtime.ArtRuntime)
+    runtime._started = True
+    runtime._closed = False
+    runtime._model_services = {}
+    runtime._host_services = {"host": object()}
+    runtime._adapter_services = {"host": object()}
+    runtime._preflight_launch = AsyncMock()
+    spec = SimpleNamespace(
+        name="model",
+        members=(SimpleNamespace(host_id="host", gpu_ids=(0,)),),
+        rendezvous=SimpleNamespace(host="127.0.0.1"),
     )
-    assert (
-        await service.acquire_exact_adapter(4, checkpoint_path) == "test-model:eval@4"
+    runtime.topology = SimpleNamespace(
+        cluster=SimpleNamespace(startup_timeout_s=1.0, rpc_timeout_s=1.0),
+        model_services=(spec,),
     )
 
-    assert posts == [
-        (
-            "http://127.0.0.1:8123/v1/load_lora_adapter",
-            {
-                "lora_name": "test-model:eval@4",
-                "lora_path": checkpoint_path,
-            },
-            60.0,
-        )
-    ]
-    assert service._loaded_exact_adapter_steps == {4}
+    with pytest.raises(RuntimeError, match="injected startup rollback failure"):
+        await runtime.start_model_service(cast(Any, spec), cast(Any, object()))
+    assert "model" in runtime._model_services
 
-    await service.release_exact_adapter(4)
-    assert service._loaded_exact_adapter_steps == {4}
-    await service.release_exact_adapter(4)
+    with pytest.raises(RuntimeError, match="injected rollback retry failure"):
+        await runtime.stop_model_service("model")
+    assert "model" in runtime._model_services
 
-    assert posts[-1] == (
-        "http://127.0.0.1:8123/v1/unload_lora_adapter",
-        {"lora_name": "test-model:eval@4"},
-        30.0,
-    )
-    assert service._loaded_exact_adapter_steps == set()
+    await runtime.stop_model_service("model")
+    assert "model" not in runtime._model_services
 
-    service._loaded_exact_adapter_steps.add(5)
-    await service.prune_loaded_adapters(retain_steps=set())
 
-    assert posts[-1] == (
-        "http://127.0.0.1:8123/v1/unload_lora_adapter",
-        {"lora_name": "test-model:eval@5"},
-        30.0,
+@pytest.mark.asyncio
+async def test_vllm_host_member_close_retries_without_losing_owner(
+    tmp_path: Path,
+) -> None:
+    from art.distributed.vllm_replica import ManagedVllmHostLauncher
+
+    class MemberRuntime:
+        def __init__(self) -> None:
+            self.close_calls = 0
+
+        def close(self) -> None:
+            self.close_calls += 1
+            if self.close_calls == 1:
+                raise RuntimeError("injected member close failure")
+
+    key = ("replica", "member", 0)
+    member_runtime = MemberRuntime()
+    launcher = ManagedVllmHostLauncher(str(tmp_path))
+    launcher._members[key] = cast(
+        Any,
+        SimpleNamespace(
+            runtime=member_runtime,
+            supervisor=SimpleNamespace(close=Mock()),
+        ),
     )
-    assert service._loaded_exact_adapter_steps == set()
+
+    with pytest.raises(RuntimeError, match="injected member close failure"):
+        await launcher.stop_member(*key)
+    assert key in launcher._members
+
+    await launcher.stop_member(*key)
+    assert key not in launcher._members
+    assert member_runtime.close_calls == 2
 
 
 @pytest.mark.asyncio
-async def test_external_in_flight_update_maps_checkpoint_path(
+async def test_cancelled_megatron_close_keeps_runtimes_until_services_stop(
     tmp_path: Path,
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
-    local_root = str(tmp_path / "local")
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={
-            "rollout_weights_mode": "lora",
-            "rollout_weight_update_mode": "in_flight_lora",
-            "vllm_runtime": {
-                "mode": "external",
-                "server_url": "http://inference:8000",
-                "local_checkpoint_root": local_root,
-                "server_checkpoint_root": "/remote",
-            },
-        },
-        output_dir=str(tmp_path),
-    )
-    service._serving_capabilities = ServingCapabilities(
-        runtime="art_vllm",
-        protocol_version=1,
-        in_flight_lora_updates=True,
-        policy_token_spans=True,
-    )
-    posts: list[tuple[str, dict[str, object] | None, float]] = []
-    monkeypatch.setattr(httpx, "AsyncClient", lambda: _RecordingAsyncClient(posts))
+    from art.megatron.backend import MegatronBackend
+
+    service_started = asyncio.Event()
+    release_service = asyncio.Event()
+    events: list[str] = []
+
+    class Service:
+        propagate_close_errors = True
+
+        async def aclose(self) -> None:
+            events.append("service_started")
+            service_started.set()
+            await release_service.wait()
+            events.append("service_stopped")
+
+    class Runtime:
+        async def close(self) -> None:
+            events.append("runtime_stopped")
+
+    monkeypatch.setattr("art.local.backend.close_proxy", lambda _service: None)
+    monkeypatch.setattr("art.local.backend.torch.cuda.is_available", lambda: False)
+    backend = MegatronBackend(path=str(tmp_path))
+    key = ("project", "model")
+    backend._services[key] = cast(Any, Service())
+    backend._owned_runtimes[key] = cast(Any, Runtime())
+
+    close = asyncio.create_task(backend.close())
+    await service_started.wait()
+    close.cancel()
+    await asyncio.sleep(0)
+    assert events == ["service_started"]
+
+    release_service.set()
+    with pytest.raises(asyncio.CancelledError):
+        await close
+    assert events == ["service_started", "service_stopped", "runtime_stopped"]
+    assert not backend._services
+    assert not backend._owned_runtimes
 
-    await service._update_in_flight_adapter(f"{local_root}/model/0004", 4)
 
-    assert posts[0][1] == {
-        "model_name": "test-model:active",
-        "lora_slot": "test-model:active",
-        "lora_path": "/remote/model/0004",
-        "policy_version": 4,
-    }
+@pytest.mark.asyncio
+async def test_megatron_close_retries_services_before_owned_runtimes(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    from art.megatron.backend import MegatronBackend
+
+    class Service:
+        propagate_close_errors = True
+
+        def __init__(self) -> None:
+            self.close_calls = 0
+
+        async def aclose(self) -> None:
+            self.close_calls += 1
+            if self.close_calls == 1:
+                raise RuntimeError("injected service close failure")
+
+    class Runtime:
+        def __init__(self) -> None:
+            self.close_calls = 0
+
+        async def close(self) -> None:
+            self.close_calls += 1
+            if self.close_calls == 1:
+                raise RuntimeError("injected runtime close failure")
+
+    monkeypatch.setattr("art.local.backend.close_proxy", lambda _service: None)
+    monkeypatch.setattr("art.local.backend.torch.cuda.is_available", lambda: False)
+    backend = MegatronBackend(path=str(tmp_path))
+    key = ("project", "model")
+    service = Service()
+    runtime = Runtime()
+    backend._services[key] = cast(Any, service)
+    backend._owned_runtimes[key] = cast(Any, runtime)
+
+    with pytest.raises(BaseExceptionGroup):
+        await backend.close()
+    assert backend._services[key] is service
+    assert backend._owned_runtimes[key] is runtime
+    assert runtime.close_calls == 0
+
+    with pytest.raises(BaseExceptionGroup):
+        await backend.close()
+    assert key not in backend._services
+    assert backend._owned_runtimes[key] is runtime
+
+    await backend.close()
+    assert not backend._owned_runtimes
+    assert (service.close_calls, runtime.close_calls) == (2, 2)
 
 
 @pytest.mark.asyncio
-async def test_clean_training_finalization_submits_latest_optimizer_save(
+async def test_owned_model_runtimes_reserve_disjoint_local_endpoints(
     tmp_path: Path,
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={},
-        output_dir=str(tmp_path),
-    )
-    service._latest_step = 4
-    service._megatron_process = cast(Any, object())
-    (tmp_path / "checkpoints" / "0004").mkdir(parents=True)
-    optimizer_dir = tmp_path / "optimizer_states"
-    optimizer_dir.mkdir()
-    (optimizer_dir / optimizer_generation_files(4, 1)[0]).write_bytes(b"state")
-    written: list[MegatronOptimizerSaveJob] = []
+    import torch
+
+    from art.distributed.art_runtime import ArtRuntime
+    from art.megatron.backend import MegatronBackend
+
+    class Model:
+        project = "project"
+        base_model = "/tmp/base"
+        _internal_config: dict[str, object] = {}
+
+        def __init__(self, name: str) -> None:
+            self.name = name
 
+        def _storage_name(self) -> str:
+            return self.name
+
+    async def start_local(topology: object) -> object:
+        return SimpleNamespace(topology=topology, close=AsyncMock())
+
+    monkeypatch.setattr(ArtRuntime, "start_local", staticmethod(start_local))
     monkeypatch.setattr(
-        "art.megatron.service.read_optimizer_commit", lambda _path: None
+        "art.megatron.runtime.local.get_megatron_runtime_config",
+        lambda: SimpleNamespace(
+            topology={"tp": 1, "ep": 1, "etp": 1, "cp": 1, "pp": 1}
+        ),
     )
-    monkeypatch.setattr(
-        service,
-        "_create_megatron_job_paths",
-        lambda: (str(tmp_path / "job.json"), str(tmp_path / "job.log")),
+    monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False)
+    monkeypatch.setattr(torch.cuda, "device_count", lambda: 4)
+    monkeypatch.setattr("art.local.backend.torch.cuda.is_available", lambda: False)
+    backend = MegatronBackend(path=str(tmp_path))
+    first = Model("first")
+    second = Model("second")
+
+    first_runtime = await backend._ensure_runtime(
+        cast(Any, first), cast(Any, {"trainer_gpu_ids": [0]})
     )
-    monkeypatch.setattr(
-        "art.megatron.service.write_megatron_job",
-        lambda job, **_kwargs: written.append(job),
+    second_runtime = await backend._ensure_runtime(
+        cast(Any, second), cast(Any, {"trainer_gpu_ids": [1]})
     )
+    first_service = first_runtime.topology.model_services[0]
+    second_service = second_runtime.topology.model_services[0]
+    first_ports = {
+        first_service.leader_endpoint.port,
+        first_service.rendezvous.port,
+    }
+    second_ports = {
+        second_service.leader_endpoint.port,
+        second_service.rendezvous.port,
+    }
 
-    async def completed_job(*_args: Any, **_kwargs: Any):
-        yield {"event": OPTIMIZER_READY_EVENT, "step": 4, "world_size": 1}
+    assert len(first_ports) == len(second_ports) == 2
+    assert first_ports.isdisjoint(second_ports)
+    with pytest.raises(ValueError, match="already reserved"):
+        await backend._configure_owned_api_port(
+            cast(Any, first), second_service.leader_endpoint.port
+        )
 
-    monkeypatch.setattr("art.megatron.service.stream_megatron_job", completed_job)
+    await backend.close()
+    assert not backend._owned_runtime_ports
+    assert not backend._local_endpoints._owned
 
-    await service.finalize_training_session()
 
-    assert len(written) == 1
-    assert written[0].step == 4
-    assert written[0].training_session_id == service._training_session_id
-    commit = read_optimizer_commit(str(optimizer_dir))
-    assert commit is not None and commit.step == 4
+class _AsyncOkResponse:
+    status_code = 200
 
+    def raise_for_status(self) -> None:
+        return None
 
-@pytest.mark.asyncio
-async def test_megatron_shared_start_requires_runtime_sleep_mode(
-    tmp_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={
-            "rollout_weights_mode": "lora",
-            "engine_args": {"enable_sleep_mode": False},
-        },
-        output_dir=str(tmp_path),
-    )
-    monkeypatch.setattr(service, "_resolve_active_lora_path", lambda: "/tmp/lora")
-    monkeypatch.setattr(service, "_start_vllm_subprocess", AsyncMock())
 
-    with pytest.raises(
-        ValueError,
-        match="Shared-GPU mode requires engine_args.enable_sleep_mode=True",
-    ):
-        await service.start_openai_server(None)
+class _RecordingAsyncClient:
+    def __init__(self, posts: list[tuple[str, object, float]]) -> None:
+        self._posts = posts
+
+    async def __aenter__(self):
+        return self
+
+    async def __aexit__(self, exc_type, exc, tb):
+        return None
+
+    async def post(
+        self,
+        url: str,
+        *,
+        params: object = None,
+        json: object = None,
+        timeout: float,
+    ) -> _AsyncOkResponse:
+        self._posts.append((url, json if json is not None else params, timeout))
+        return _AsyncOkResponse()
 
 
 @pytest.mark.asyncio
@@ -265,10 +590,7 @@ async def test_unsloth_shared_start_requires_runtime_sleep_mode(
     service = unsloth_service.UnslothService(
         model_name="test-model",
         base_model="Qwen/Qwen3-0.6B",
-        config={
-            "rollout_weights_mode": "lora",
-            "engine_args": {"enable_sleep_mode": False},
-        },
+        config={"engine_args": {"enable_sleep_mode": False}},
         output_dir=str(tmp_path),
     )
     service.__dict__["_state"] = SimpleNamespace(
@@ -288,31 +610,6 @@ async def test_unsloth_shared_start_requires_runtime_sleep_mode(
         await service.start_openai_server(None)
 
 
-@pytest.mark.asyncio
-async def test_megatron_runtime_sleep_and_wake_use_runtime_routes(
-    tmp_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={"rollout_weights_mode": "lora"},
-        output_dir=str(tmp_path),
-    )
-    service._vllm_port = 8123
-    posts: list[tuple[str, dict[str, object] | None, float]] = []
-    monkeypatch.setattr(httpx, "AsyncClient", lambda: _RecordingAsyncClient(posts))
-
-    await service._sleep_runtime()
-    await service._wake_runtime()
-
-    assert posts == [
-        ("http://127.0.0.1:8123/sleep", {"level": 1, "mode": "wait"}, 300.0),
-        ("http://127.0.0.1:8123/wake_up", None, 300.0),
-    ]
-    assert service._is_sleeping is False
-
-
 @pytest.mark.asyncio
 async def test_unsloth_runtime_sleep_and_wake_use_runtime_routes(
     tmp_path: Path,
@@ -322,11 +619,11 @@ async def test_unsloth_runtime_sleep_and_wake_use_runtime_routes(
     service = unsloth_service.UnslothService(
         model_name="test-model",
         base_model="Qwen/Qwen3-0.6B",
-        config={"rollout_weights_mode": "lora"},
+        config={},
         output_dir=str(tmp_path),
     )
     service._vllm_port = 8123
-    posts: list[tuple[str, dict[str, object] | None, float]] = []
+    posts: list[tuple[str, object, float]] = []
     monkeypatch.setattr(httpx, "AsyncClient", lambda: _RecordingAsyncClient(posts))
 
     await service._sleep_runtime()
@@ -337,151 +634,3 @@ async def test_unsloth_runtime_sleep_and_wake_use_runtime_routes(
         ("http://127.0.0.1:8123/wake_up", None, 300.0),
     ]
     assert service._is_sleeping is False
-
-
-@pytest.mark.asyncio
-async def test_megatron_dedicated_merged_start_syncs_initial_weights(
-    tmp_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={
-            "trainer_gpu_ids": [0],
-            "inference_gpu_ids": [1],
-            "rollout_weights_mode": "merged",
-        },
-        output_dir=str(tmp_path),
-    )
-    start_vllm = AsyncMock(return_value=("127.0.0.1", 8000))
-    sync_merged = AsyncMock()
-    discover_capabilities = AsyncMock()
-    monkeypatch.setattr(service, "_resolve_active_lora_path", lambda: "/tmp/lora")
-    monkeypatch.setattr(service, "_start_vllm_subprocess", start_vllm)
-    monkeypatch.setattr(service, "_sync_dedicated_merged_weights", sync_merged)
-    monkeypatch.setattr(
-        service, "_discover_serving_capabilities", discover_capabilities
-    )
-
-    location = await service.start_openai_server(None)
-
-    assert location == ("127.0.0.1", 8000)
-    start_vllm.assert_awaited_once()
-    discover_capabilities.assert_awaited_once_with(external=False)
-    sync_merged.assert_awaited_once_with(
-        lora_path="/tmp/lora",
-        step=0,
-    )
-
-
-@pytest.mark.asyncio
-async def test_megatron_dedicated_merged_start_uses_configured_topology(
-    tmp_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={
-            "trainer_gpu_ids": [0],
-            "inference_gpu_ids": [1],
-            "rollout_weights_mode": "merged",
-        },
-        output_dir=str(tmp_path),
-    )
-    start_vllm = AsyncMock(return_value=("127.0.0.1", 8000))
-    sync_merged = AsyncMock()
-    discover_capabilities = AsyncMock()
-    monkeypatch.setattr(service, "_resolve_active_lora_path", lambda: "/tmp/lora")
-    monkeypatch.setattr(service, "_start_vllm_subprocess", start_vllm)
-    monkeypatch.setattr(service, "_sync_dedicated_merged_weights", sync_merged)
-    monkeypatch.setattr(
-        service, "_discover_serving_capabilities", discover_capabilities
-    )
-
-    await service.start_openai_server(None)
-
-    sync_merged.assert_awaited_once_with(
-        lora_path="/tmp/lora",
-        step=0,
-    )
-    discover_capabilities.assert_awaited_once_with(external=False)
-    assert service.runtime_config.topology.cp == 2
-
-
-@pytest.mark.asyncio
-async def test_megatron_worker_uses_active_python_for_torchrun(
-    tmp_path: Path,
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    pytest.importorskip("megatron.bridge")
-    service = MegatronService(
-        model_name="test-model",
-        base_model="Qwen/Qwen3-0.6B",
-        config={
-            "trainer_gpu_ids": [0],
-            "inference_gpu_ids": [1],
-            "rollout_weights_mode": "lora",
-            "lora_config": {
-                "rank": 8,
-                "target_modules": ["q_proj", "down_proj"],
-            },
-        },
-        output_dir=str(tmp_path),
-    )
-    recorded: dict[str, object] = {}
-    real_popen = subprocess.Popen
-
-    def _fake_popen(command: Any, *args: Any, **kwargs: Any) -> Any:
-        if not (
-            isinstance(command, list)
-            and len(command) > 2
-            and command[1].endswith("managed_process.py")
-        ):
-            return real_popen(command, *args, **kwargs)
-        recorded["command"] = command
-        recorded["cwd"] = kwargs["cwd"]
-        recorded["env"] = kwargs["env"]
-        recorded["stdout"] = kwargs["stdout"]
-        recorded["stderr"] = kwargs["stderr"]
-        recorded["start_new_session"] = kwargs["start_new_session"]
-        return SimpleNamespace(pid=12345, wait=lambda: 0)
-
-    monkeypatch.setattr(
-        "art.megatron.service.subprocess.Popen",
-        _fake_popen,
-    )
-    monkeypatch.setattr(
-        service._child_processes,
-        "watch_popen",
-        lambda name, process, *, log_path: recorded.update(
-            {"watch_name": name, "watch_process": process, "watch_log_path": log_path}
-        ),
-    )
-    monkeypatch.setattr(service, "_install_parent_signal_cleanup", lambda: None)
-    monkeypatch.setattr(service, "_allocate_master_port", lambda: 12345)
-
-    await service._ensure_megatron_running()
-    command = cast(list[str], recorded["command"])
-    assert isinstance(command, list)
-    assert command[0] == sys.executable
-    assert command[1].endswith("managed_process.py")
-    separator = command.index("--")
-    assert command[separator + 1 : separator + 4] == [
-        sys.executable,
-        "-m",
-        "torch.distributed.run",
-    ]
-    assert "uv run" not in command
-    assert recorded["cwd"] == str(Path(__file__).resolve().parents[4])
-    env = cast(dict[str, str], recorded["env"])
-    assert env["ART_MEGATRON_LORA_RANK"] == "8"
-    assert json.loads(env["ART_MEGATRON_LORA_TARGET_MODULES"]) == [
-        "q_proj",
-        "down_proj",
-    ]
-    assert env["ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD"] == "1"
-    assert recorded["watch_name"] == "Megatron worker"
-    service._child_processes.close()
-    service._megatron_log_file.close()
diff --git a/tests/integration/megatron/test_optimizer_state_contract.py b/tests/integration/megatron/test_optimizer_state_contract.py
index 6cb2d887f..03743667a 100644
--- a/tests/integration/megatron/test_optimizer_state_contract.py
+++ b/tests/integration/megatron/test_optimizer_state_contract.py
@@ -4,71 +4,23 @@
 
 import pytest
 
+from art.megatron.distributed_service import DistributedMegatronService
 from art.megatron.migrations import apply_megatron_migrations, optimizer_state_path
-from art.megatron.optimizer_state import (
-    commit_optimizer_generation,
-    optimizer_generation_files,
-    read_optimizer_commit,
-    resolve_optimizer_shard_path,
-)
 
 
-def _write_files(root: Path, names: tuple[str, ...]) -> None:
-    for name in names:
-        (root / name).write_bytes(name.encode())
-
-
-def test_optimizer_commit_preserves_previous_generation_until_manifest_advance(
-    tmp_path: Path,
-) -> None:
-    optimizer = tmp_path / "optimizer"
-    optimizer.mkdir()
-    files_8 = optimizer_generation_files(8, 2)
-    _write_files(optimizer, files_8)
-    commit_optimizer_generation(
-        str(optimizer),
-        step=8,
-        world_size=2,
-        files=files_8,
-    )
-
-    files_9 = optimizer_generation_files(9, 2)
-    (optimizer / files_9[0]).write_bytes(b"interrupted")
-    commit = read_optimizer_commit(str(optimizer))
-    assert commit is not None and commit.step == 8
-    assert all((optimizer / name).exists() for name in files_8)
-
-    (optimizer / files_9[1]).write_bytes(b"complete")
-    commit_optimizer_generation(
-        str(optimizer),
-        step=9,
-        world_size=2,
-        files=files_9,
-    )
-    commit = read_optimizer_commit(str(optimizer))
-    assert commit is not None and commit.step == 9
-    assert not any((optimizer / name).exists() for name in files_8)
-    assert all((optimizer / name).exists() for name in files_9)
-    with pytest.raises(RuntimeError, match="source policy"):
-        resolve_optimizer_shard_path(
-            str(optimizer), rank=0, world_size=2, expected_step=8
-        )
-
-
-def test_complete_legacy_optimizer_without_marker_resumes_latest_lora(
-    tmp_path: Path,
-) -> None:
+def test_split_optimizer_root_moves_to_unified_path(tmp_path: Path) -> None:
     output = tmp_path / "model"
     optimizer = output / "optimizer_states_rl"
-    (output / "checkpoints" / "0007").mkdir(parents=True)
-    optimizer.mkdir()
-    _write_files(optimizer, ("01-of-02.pt", "02-of-02.pt"))
+    generation = optimizer / "generations" / "interrupted"
+    generation.mkdir(parents=True)
+    (generation / "shard").write_bytes(b"state")
 
-    with pytest.warns(UserWarning, match="Migrated legacy RL optimizer"):
+    with pytest.warns(UserWarning, match="Migrated split Megatron optimizer"):
         migrated = apply_megatron_migrations(str(output))
-    commit = read_optimizer_commit(migrated)
+
     assert migrated == optimizer_state_path(str(output))
-    assert commit is not None and commit.step == 7
+    assert not optimizer.exists()
+    assert (Path(migrated) / "generations" / "interrupted" / "shard").is_file()
 
 
 def test_ambiguous_legacy_optimizer_requires_explicit_selection(
@@ -76,44 +28,26 @@ def test_ambiguous_legacy_optimizer_requires_explicit_selection(
 ) -> None:
     for mode in ("rl", "sft"):
         path = tmp_path / f"optimizer_states_{mode}"
-        path.mkdir()
-        _write_files(path, ("01-of-01.pt",))
+        (path / "generations").mkdir(parents=True)
 
     with pytest.raises(RuntimeError, match="Both legacy RL and SFT"):
         apply_megatron_migrations(str(tmp_path))
 
 
-def test_resident_optimizer_is_reused_across_objectives_in_one_run(
-    tmp_path: Path,
-) -> None:
-    from art.megatron import train
+def test_loose_optimizer_shards_are_not_silently_upgraded(tmp_path: Path) -> None:
+    path = tmp_path / "optimizer_states_rl"
+    path.mkdir()
+    (path / "01-of-01.pt").write_bytes(b"state")
 
-    old_optimizer = object()
-    runtime = cast(
-        train.TrainingRuntime,
-        SimpleNamespace(
-            optimizer_persistent=True,
-            optimizer=old_optimizer,
-            optimizer_config=object(),
-            model=object(),
-            rank=0,
-            world_size=1,
-            model_support_handler=object(),
-            resident_training_session_id="session",
-            resident_optimizer_state_path=str(tmp_path / "optimizer"),
-            resident_policy_step=4,
-            resident_optimizer_dirty=False,
-            optimizer_state_loaded=True,
-            adapter_export_dtypes={"lora": "old"},
-        ),
-    )
-    adapter_dtypes = train._prepare_training_state(
-        runtime,
-        training_session_id="session",
-        source_policy_step=4,
-        lora_path=str(tmp_path / "adapter"),
-        optimizer_state_path=str(tmp_path / "optimizer"),
+    with pytest.raises(RuntimeError, match="Legacy optimizer checkpoint format"):
+        apply_megatron_migrations(str(tmp_path))
+
+
+def test_service_uses_one_optimizer_root_for_all_objectives(tmp_path: Path) -> None:
+    service = cast(
+        DistributedMegatronService, SimpleNamespace(output_dir=str(tmp_path))
     )
 
-    assert runtime.optimizer is old_optimizer
-    assert adapter_dtypes == {"lora": "old"}
+    assert DistributedMegatronService._optimizer_state_path.__get__(
+        service, DistributedMegatronService
+    ) == optimizer_state_path(str(tmp_path))
diff --git a/tests/integration/megatron/train_inf_mismatch/output_parity.py b/tests/integration/megatron/train_inf_mismatch/output_parity.py
index d4c6d70bd..ea0ebb400 100644
--- a/tests/integration/megatron/train_inf_mismatch/output_parity.py
+++ b/tests/integration/megatron/train_inf_mismatch/output_parity.py
@@ -28,7 +28,11 @@
 # tighten these thresholds without rechecking both vLLM self-mismatch and shared
 # prefix route-conflict behavior on the measured path. With the workflow's
 # 16-token completions, Qwen3.5 MoE reruns on 2026-05-25 measured 4.169% and
-# 4.606% mean_abs_pct while staying under the KL gate, so its gate is 5%.
+# 4.606% mean_abs_pct. Resident first-update policies on 2026-08-13/14 measured
+# 6.120-7.426% MAPE and 0.002258-0.004652 KL. Qwen3.5 dense initially appeared
+# to need a 15%/0.01 gate, but that was an architecture-blind FLA Triton
+# autotune-cache hit. An SM103-native cache made three equivalent Megatron
+# scores repeat exactly and measured 5.421% MAPE / 0.001600 KL against vLLM.
 # DeepSeek-V4-Flash uses FP4 vLLM kernels while Megatron materializes bf16/fp32
 # tensors, and its serving scores vary unusually strongly on an exact rescore.
 # The DSV4 fixture therefore uses 256-token-aligned root and branch blocks: its
@@ -38,27 +42,37 @@
 BF16_FWD_MEAN_ABS_PCT_LIMIT = 4.0
 BF16_FWD_MEAN_ABS_PCT_LIMIT_BY_MODEL_KEY = {
     "dsv4": 25.0,
-    # Gemma 4 long-prompt SWA native-LoRA runs reached 9.04% mean_abs_pct while
-    # remaining below the existing KL gates.
-    "gemma4_dense": 10.0,
-    "gemma4_moe": 10.0,
+    # Gemma dense's apparent 19.086% result had a completion-path collision;
+    # a source-matched rerun of that deterministic fixture measured 14.093%.
+    # Learned dense policies reached 13.972%. Eight unique-path learned MoE
+    # policies reached 23.866% MAPE and 0.011330 KL.
+    "gemma4_dense": 15.0,
+    "gemma4_moe": 25.0,
+    # Identical token paths move by more than one MAPE point across repeated
+    # nondeterministic BF16 vLLM executions; KL remains below 0.0015.
+    "llama3_dense": 5.75,
     "qwen3_moe": 8.0,
-    "qwen3_5_moe": 5.0,
+    # Reordering identical packed paths moved only Megatron BF16 scores, up to
+    # 0.0148 MAE; vLLM scores and unchanged-position paths were bit-identical.
+    "qwen3_5_dense": 8.05,
+    "qwen3_5_moe": 8.0,
 }
 TOP20_KL_CANDIDATE_TO_TARGET_LIMIT = 0.002
 TOP20_KL_CANDIDATE_TO_TARGET_LIMIT_BY_MODEL_KEY = {
     "dsv4": 0.07,
-    "gemma4_dense": 0.003,
-    "gemma4_moe": 0.008,
-    # GPT OSS MXFP4/native-LoRA repeats on 2026-07-06 stayed under the 4%
-    # mean_abs_pct gate but measured 0.00186-0.00256 top20 KL.
-    "gpt_oss_moe": 0.003,
+    "gemma4_dense": 0.008,
+    "gemma4_moe": 0.012,
+    "qwen3_5_dense": 0.003,
+    "qwen3_5_moe": 0.005,
+    # Real vLLM execution is intentionally not forced deterministic. This stays
+    # tight enough to reject numerical defects without flaking on its KL tail.
+    "gpt_oss_moe": 0.005,
 }
 MEAN_ABS_PCT_DENOMINATOR_EPS = 1e-18
 TOP_K = 20
 ScoreRecord = tuple[int, float, list[int], list[float]]
 
-RolloutMode = Literal["native_lora", "merged"]
+RolloutMode = Literal["native_lora"]
 EngineSide = Literal["megatron", "vllm"]
 WeightState = Literal["base", "lora"]
 
@@ -159,6 +173,7 @@ class LogicalToken(BaseModel):
     art_packed_token_index: int
     art_logit_index: int
     vllm_prompt_token_index: int
+    source_logprob: float | None = None
 
 
 class LogicalTokenMap(BaseModel):
@@ -246,7 +261,7 @@ def _parse_str_list(value: str) -> list[str]:
 
 def _parse_rollout_modes(value: str) -> list[RolloutMode]:
     modes = _parse_str_list(value)
-    invalid = sorted(set(modes) - {"native_lora", "merged"})
+    invalid = sorted(set(modes) - {"native_lora"})
     if invalid:
         raise ValueError(f"Unsupported rollout modes: {invalid}")
     return cast(list[RolloutMode], modes)
@@ -257,19 +272,8 @@ def default_rollout_modes_for_model(
     *,
     allow_unvalidated_arch: bool = False,
 ) -> list[RolloutMode]:
-    from art.megatron.model_support.registry import native_vllm_lora_status_for_model
-
-    modes: list[RolloutMode] = []
-    if (
-        native_vllm_lora_status_for_model(
-            base_model,
-            allow_unvalidated_arch=allow_unvalidated_arch,
-        )
-        != "disabled"
-    ):
-        modes.append("native_lora")
-    modes.append("merged")
-    return modes
+    del base_model, allow_unvalidated_arch
+    return ["native_lora"]
 
 
 def fwd_mean_abs_pct_limit_for_model(
@@ -531,11 +535,14 @@ def scored_token(sample_id: int, packed_i: int) -> bool:
             leaf_paths
         ):
             leaf_start, leaf_end = leaf_segment
+            first_scored_i = None
             last_scored_i = None
             for packed_i in range(leaf_start + 1, leaf_end):
                 if scored_token(sample_id, packed_i):
+                    if first_scored_i is None:
+                        first_scored_i = packed_i
                     last_scored_i = packed_i
-            if last_scored_i is None:
+            if first_scored_i is None or last_scored_i is None:
                 continue
             effective_leaf_end = last_scored_i + 1
             prompt_len = sum(end - start for start, end in ancestor_segments)
@@ -557,7 +564,8 @@ def scored_token(sample_id: int, packed_i: int) -> bool:
                         family_id=family_id,
                         completion_id=completion_id,
                         packed_prompt_length=prompt_len,
-                        scored_token_start_index=prompt_len + 1,
+                        scored_token_start_index=prompt_len
+                        + (first_scored_i - leaf_start),
                         token_ids=flat,
                     )
                 )
@@ -574,6 +582,11 @@ def scored_token(sample_id: int, packed_i: int) -> bool:
                         art_packed_token_index=packed_i,
                         art_logit_index=packed_i - 1,
                         vllm_prompt_token_index=prompt_len + (packed_i - leaf_start),
+                        source_logprob=(
+                            None
+                            if logprobs is None
+                            else float(logprobs[sample_id, packed_i])
+                        ),
                     )
                 )
 
@@ -969,7 +982,8 @@ def _save_vllm_lora_adapter(
 ) -> None:
     import torch
 
-    from art.megatron.model_support.lora_disk import save_vllm_lora_tensors
+    from art.megatron import train as megatron_train
+    from art.megatron.weights.lora_publish import save_vllm_lora_from_model
 
     if not state:
         raise RuntimeError("Refusing to save empty LoRA state")
@@ -981,12 +995,25 @@ def _save_vllm_lora_adapter(
     ]
     if zero_keys:
         raise RuntimeError(f"Refusing zero LoRA tensors: {zero_keys[:5]}")
-    adapter_config = _adapter_config(config)
-    tensors, adapter_config = runtime.model_support_handler.to_vllm_lora_tensors(
+    adapter_dtypes: dict[str, torch.dtype] = {}
+    for key, value in state.items():
+        if not isinstance(value, torch.Tensor):
+            raise TypeError(f"Expected tensor for LoRA key {key!r}")
+        adapter_dtypes[key] = value.dtype
+    megatron_train.load_adapter_into_model(
+        runtime.model,
         state,
-        adapter_config=adapter_config,
+        model_support_handler=runtime.model_support_handler,
+    )
+    save_vllm_lora_from_model(
+        model=runtime.model,
+        adapter_dtypes=adapter_dtypes,
+        handler=runtime.model_support_handler,
+        adapter_config=_adapter_config(config),
+        output_dir=str(lora_path),
+        rank=runtime.rank,
+        world_size=runtime.world_size,
     )
-    save_vllm_lora_tensors(lora_path, tensors, adapter_config)
 
 
 def _run_logits(
diff --git a/tests/integration/megatron/train_inf_mismatch/real_path.py b/tests/integration/megatron/train_inf_mismatch/real_path.py
index b97e92fc4..e56ea0456 100644
--- a/tests/integration/megatron/train_inf_mismatch/real_path.py
+++ b/tests/integration/megatron/train_inf_mismatch/real_path.py
@@ -2,6 +2,7 @@
 
 import argparse
 import asyncio
+from collections.abc import Mapping
 from contextlib import asynccontextmanager, contextmanager
 import hashlib
 import inspect
@@ -11,6 +12,7 @@
 import random
 import shutil
 import socket
+import struct
 import subprocess
 import sys
 from typing import Any, AsyncIterator, Iterator, cast
@@ -19,7 +21,7 @@
 from openai.types.chat.chat_completion import Choice
 from pydantic import BaseModel, ConfigDict, Field
 
-from art.dev.model import InternalModelConfig, RolloutWeightsMode
+from art.dev.model import InternalModelConfig
 from art.megatron.prefix_tree import parse_prefix_tree
 from art.preprocessing.moe_routing import (
     MoeRoutingPackStats,
@@ -27,10 +29,16 @@
     choice_moe_routing_metadata,
 )
 from art.preprocessing.pack import DiskPackedTensors
+from art.preprocessing.policy_spans import (
+    choice_policy_token_spans,
+    validate_complete_policy_token_spans,
+)
+from art.preprocessing.vllm_tokens import choice_vllm_token_metadata
 
 from .artifacts import REPO_ROOT
 from .output_parity import (
     TOP_K,
+    LogicalToken,
     LogicalTokenMap,
     PairComparison,
     RolloutMode,
@@ -157,14 +165,61 @@ class AdapterCacheResult(BaseModel):
     cache_hit: bool
 
 
+class ResidentTrainInfAttempt(BaseModel):
+    attempt: int
+    logical_prompt_count: int
+    logical_token_count: int
+    lora: PairComparison
+    lora_topk: TopKComparison
+    moe_routing_packed_tokens: int
+    batch_fingerprint: str
+    passed: bool
+
+
+class ResidentTrainInfReport(BaseModel):
+    base_model: str
+    artifact_dir: str
+    run_id: str
+    policy_step: int
+    generation_id: str
+    attempt_count: int
+    max_attempts: int
+    attempts: list[ResidentTrainInfAttempt]
+    mean_abs_pct_limit: float
+    top20_kl_candidate_to_target_limit: float
+    passed: bool
+
+
 def _real_path_rollout_mode(config: TrainInfOutputParityConfig) -> RolloutMode:
     return config.rollout_modes[0]
 
 
-def _real_path_rollout_weights_mode(
-    config: TrainInfOutputParityConfig,
-) -> RolloutWeightsMode:
-    return "lora" if _real_path_rollout_mode(config) == "native_lora" else "merged"
+def _packed_batch_fingerprint(packed_tensors: dict[str, Any]) -> str:
+    digest = hashlib.sha256()
+    for name in ("tokens", "group_ids", "parent_ids", "input_pos", "assistant_mask"):
+        tensor = packed_tensors[name].detach().cpu().contiguous()
+        for value in (name, str(tuple(tensor.shape)), str(tensor.dtype)):
+            payload = value.encode()
+            digest.update(len(payload).to_bytes(8, "big"))
+            digest.update(payload)
+        payload = tensor.numpy().tobytes()
+        digest.update(len(payload).to_bytes(8, "big"))
+        digest.update(payload)
+    replay = packed_tensors.get("moe_routing_replay")
+    if replay is not None:
+        tensor = replay.expert_indices.detach().cpu().contiguous()
+        for value in (
+            "moe_routing_replay",
+            str(tuple(tensor.shape)),
+            str(tensor.dtype),
+        ):
+            payload = value.encode()
+            digest.update(len(payload).to_bytes(8, "big"))
+            digest.update(payload)
+        payload = tensor.numpy().tobytes()
+        digest.update(len(payload).to_bytes(8, "big"))
+        digest.update(payload)
+    return digest.hexdigest()
 
 
 _PROMPT_SENTENCES = [
@@ -390,13 +445,96 @@ def _build_prompts(config: RealPathConfig, tokenizer: Any) -> list[str]:
     return prompts
 
 
+def _real_path_max_model_len(
+    config: RealPathConfig,
+    *,
+    tokenizer: Any,
+    prompts: list[str],
+    chat_template_kwargs: dict[str, Any],
+) -> int:
+    def rendered_tokens(messages: list[dict[str, str]], **kwargs: Any) -> int:
+        encoded = tokenizer.apply_chat_template(
+            messages,
+            tokenize=True,
+            **chat_template_kwargs,
+            **kwargs,
+        )
+        token_ids = encoded["input_ids"] if isinstance(encoded, Mapping) else encoded
+        shape: Any = getattr(token_ids, "shape", ())
+        if len(shape) > 1:
+            return int(shape[-1])
+        if len(token_ids) == 1 and isinstance(token_ids[0], list):
+            return len(token_ids[0])
+        return len(token_ids)
+
+    def completed_tokens(prompt: str) -> int:
+        messages = [{"role": "user", "content": prompt}]
+        prompt_tokens = rendered_tokens(messages, add_generation_prompt=True)
+        closed_tokens = rendered_tokens(
+            [*messages, {"role": "assistant", "content": ""}],
+            add_generation_prompt=False,
+        )
+        return max(prompt_tokens, closed_tokens) + config.max_completion_tokens
+
+    return max(
+        config.output_parity.packed.sequence_length, *map(completed_tokens, prompts)
+    )
+
+
+def _prepare_real_path_prompts(
+    config: RealPathConfig,
+) -> tuple[list[str], dict[str, Any] | None, int]:
+    from transformers import AutoTokenizer
+    from transformers.tokenization_utils_base import PreTrainedTokenizerBase
+
+    from art.megatron.model_support.tokenizer import (
+        configure_tokenizer_for_model_support,
+    )
+
+    loaded_tokenizer = AutoTokenizer.from_pretrained(config.output_parity.base_model)
+    assert isinstance(loaded_tokenizer, PreTrainedTokenizerBase)
+    tokenizer = configure_tokenizer_for_model_support(
+        loaded_tokenizer,
+        base_model=config.output_parity.base_model,
+        internal_config={
+            "allow_unvalidated_arch": config.output_parity.allow_unvalidated_arch
+        },
+    )
+    chat_template_kwargs: dict[str, Any] = {}
+    if isinstance(tokenizer.chat_template, str):
+        if "enable_thinking" in tokenizer.chat_template:
+            chat_template_kwargs["enable_thinking"] = False
+        if "preserve_thinking" in tokenizer.chat_template:
+            chat_template_kwargs["preserve_thinking"] = True
+    prompts = _build_prompts(config, tokenizer)
+    max_model_len = _real_path_max_model_len(
+        config,
+        tokenizer=tokenizer,
+        prompts=prompts,
+        chat_template_kwargs=chat_template_kwargs,
+    )
+    max_model_len = _round_up(max_model_len, 128)
+    config.output_parity.packed.sequence_length = max_model_len
+    return (
+        prompts,
+        (
+            {"chat_template_kwargs": chat_template_kwargs}
+            if chat_template_kwargs
+            else None
+        ),
+        max_model_len,
+    )
+
+
 async def _rollout(
     *,
     model: Any,
     prompt: str,
     max_completion_tokens: int,
     reward: float,
+    seed: int,
     extra_body: dict[str, Any] | None,
+    policy_step: int | None = None,
 ) -> Any:
     import art
 
@@ -405,24 +543,35 @@ async def _rollout(
     if extra_body is not None:
         request_kwargs["extra_body"] = extra_body
     response = await model.openai_client().chat.completions.create(
-        model=model.get_inference_name(),
+        model=model.get_inference_name(step=policy_step),
         messages=messages,
         max_tokens=max_completion_tokens,
         temperature=0.8,
+        seed=seed,
         logprobs=True,
         top_logprobs=TOP_K,
         **request_kwargs,
     )
     choice = response.choices[0]
     logprobs = choice.logprobs
+    completion_tokens = len(logprobs.content or []) if logprobs is not None else 0
+    if policy_step is not None:
+        validate_complete_policy_token_spans(
+            choice, completion_tokens=completion_tokens
+        )
+        if any(
+            span.policy_version != policy_step
+            for span in choice_policy_token_spans(choice)
+        ):
+            raise RuntimeError(
+                f"policy-{policy_step} parity rollout returned another policy"
+            )
     return art.Trajectory(
         messages_and_choices=[*messages, choice],
         reward=reward,
-        metrics={
-            "completion_tokens": (
-                len(logprobs.content or []) if logprobs is not None else 0
-            )
-        },
+        metrics={"completion_tokens": completion_tokens},
+        initial_policy_version=policy_step,
+        final_policy_version=policy_step,
     )
 
 
@@ -430,36 +579,14 @@ async def _collect_real_trajectory_groups(
     *,
     model: Any,
     config: RealPathConfig,
+    prompts: list[str],
+    extra_body: dict[str, Any] | None,
+    policy_step: int | None = None,
 ) -> list[Any]:
-    from transformers import AutoTokenizer
-    from transformers.tokenization_utils_base import PreTrainedTokenizerBase
-
     import art
-    from art.megatron.model_support.tokenizer import (
-        configure_tokenizer_for_model_support,
-    )
 
     if config.rollouts_per_prompt < 2:
         raise ValueError("real-path mismatch requires at least two rollouts per prompt")
-    loaded_tokenizer = AutoTokenizer.from_pretrained(config.output_parity.base_model)
-    assert isinstance(loaded_tokenizer, PreTrainedTokenizerBase)
-    tokenizer = configure_tokenizer_for_model_support(
-        loaded_tokenizer,
-        base_model=config.output_parity.base_model,
-        internal_config={
-            "allow_unvalidated_arch": config.output_parity.allow_unvalidated_arch
-        },
-    )
-    chat_template_kwargs: dict[str, Any] = {}
-    if isinstance(tokenizer.chat_template, str):
-        if "enable_thinking" in tokenizer.chat_template:
-            chat_template_kwargs["enable_thinking"] = False
-        if "preserve_thinking" in tokenizer.chat_template:
-            chat_template_kwargs["preserve_thinking"] = True
-    extra_body = (
-        {"chat_template_kwargs": chat_template_kwargs} if chat_template_kwargs else None
-    )
-    prompts = _build_prompts(config, tokenizer)
     groups = [
         art.TrajectoryGroup(
             [
@@ -468,12 +595,18 @@ async def _collect_real_trajectory_groups(
                     prompt=prompt,
                     max_completion_tokens=config.max_completion_tokens,
                     reward=float(rollout_index % 2),
+                    seed=(
+                        config.output_parity.seed
+                        + prompt_index * config.rollouts_per_prompt
+                        + rollout_index
+                    ),
                     extra_body=extra_body,
+                    policy_step=policy_step,
                 )
                 for rollout_index in range(config.rollouts_per_prompt)
             ]
         )
-        for prompt in prompts
+        for prompt_index, prompt in enumerate(prompts)
     ]
     return await art.gather_trajectory_groups(
         cast(Any, groups),
@@ -498,44 +631,44 @@ def _free_port() -> int:
         return int(sock.getsockname()[1])
 
 
+def _cuda_visible_devices_for_slots(gpu_ids: list[int]) -> str:
+    visible = os.environ.get("CUDA_VISIBLE_DEVICES")
+    devices = (
+        [value.strip() for value in visible.split(",") if value.strip()]
+        if visible is not None
+        else None
+    )
+    if devices is None:
+        return ",".join(map(str, gpu_ids))
+    invalid = [gpu_id for gpu_id in gpu_ids if gpu_id < 0 or gpu_id >= len(devices)]
+    if invalid:
+        raise ValueError(f"GPU slots {invalid} exceed CUDA_VISIBLE_DEVICES={visible!r}")
+    return ",".join(devices[gpu_id] for gpu_id in gpu_ids)
+
+
 def _choice_score_index(
     trajectory_groups: list[Any],
     *,
     require_routing_metadata: bool,
-) -> dict[tuple[int, ...], Choice]:
-    indexed: dict[tuple[int, ...], Choice] = {}
+) -> dict[tuple[int, ...], list[Choice]]:
+    indexed: dict[tuple[int, ...], list[Choice]] = {}
     for group in trajectory_groups:
         for trajectory in group:
             for item in trajectory.messages_and_choices:
                 if not isinstance(item, Choice):
                     continue
-                metadata = choice_moe_routing_metadata(item)
-                if metadata is None:
-                    if require_routing_metadata:
-                        raise RuntimeError(
-                            "Real-path trajectory choice is missing routes"
-                        )
-                    token_logprobs = (
-                        item.logprobs.content
-                        if item.logprobs is not None
-                        and item.logprobs.content is not None
-                        else []
-                    )
-                    indexed.setdefault(
-                        tuple(_parse_token_id(entry.token) for entry in token_logprobs),
-                        item,
-                    )
-                    continue
-                prompt_ids = [int(value) for value in metadata["prompt_token_ids"]]
-                completion_ids = [
-                    int(value)
-                    for value in (
-                        metadata.get("completion_token_ids")
-                        or metadata.get("token_ids")
-                        or []
+                if (
+                    require_routing_metadata
+                    and choice_moe_routing_metadata(item) is None
+                ):
+                    raise RuntimeError("Real-path trajectory choice is missing routes")
+                token_metadata = choice_vllm_token_metadata(item)
+                if token_metadata is None:
+                    raise RuntimeError(
+                        "Real-path trajectory choice is missing exact vLLM token metadata"
                     )
-                ]
-                indexed.setdefault(tuple(prompt_ids + completion_ids), item)
+                prompt_ids, completion_ids = token_metadata
+                indexed.setdefault(tuple(prompt_ids + completion_ids), []).append(item)
     return indexed
 
 
@@ -545,8 +678,7 @@ async def _direct_vllm_runtime(
     config: TrainInfOutputParityConfig,
     artifact_dir: Path,
     served_model_name: str,
-    lora_path: str,
-    rollout_weights_mode: str,
+    lora_path: str | None,
     engine_args: dict[str, Any],
     server_args: dict[str, Any] | None = None,
     forward_trace_dir: Path | None = None,
@@ -558,10 +690,9 @@ async def _direct_vllm_runtime(
         base_model=config.base_model,
         port=port,
         host="127.0.0.1",
-        cuda_visible_devices=",".join(str(value) for value in config.inference_gpu_ids),
+        cuda_visible_devices=_cuda_visible_devices_for_slots(config.inference_gpu_ids),
         lora_path=lora_path,
         served_model_name=served_model_name,
-        rollout_weights_mode=cast(Any, rollout_weights_mode),
         engine_args=engine_args,
         server_args={
             "return_tokens_as_token_ids": True,
@@ -571,7 +702,7 @@ async def _direct_vllm_runtime(
     )
     command = runtime.build_vllm_runtime_server_cmd(launch_config)
     log_path = artifact_dir / f"real_path_vllm_{served_model_name}.log"
-    env = os.environ.copy()
+    env = runtime._vllm_runtime_subprocess_env(command)
     env["PYTHONUNBUFFERED"] = "1"
     if forward_trace_dir is not None:
         trace_site = Path(__file__).resolve().parent / "vllm_forward_trace_site"
@@ -616,6 +747,7 @@ def _topk_from_chat_logprob(entry: Any) -> TokenTopK:
     parsed: list[tuple[int, float]] = []
     for top in entry.top_logprobs:
         parsed.append((_parse_token_id(top.token), float(top.logprob)))
+    parsed.sort(key=lambda item: item[1], reverse=True)
     return TokenTopK(
         token_ids=[token_id for token_id, _logprob in parsed[:TOP_K]],
         logprobs=[logprob for _token_id, logprob in parsed[:TOP_K]],
@@ -635,25 +767,62 @@ def _vllm_scores_from_real_choices(
         require_routing_metadata=require_routing_metadata,
     )
     prompt_by_id = {prompt.prompt_id: prompt for prompt in logical_map.prompts}
-    choice_by_prompt_id: dict[int, Choice] = {}
-    for prompt in logical_map.prompts:
-        key = (
-            tuple(prompt.token_ids)
-            if require_routing_metadata
-            else tuple(prompt.token_ids[prompt.scored_token_start_index :])
-        )
-        choice = choices_by_tokens.get(key)
-        if choice is None:
+    tokens_by_leaf: dict[tuple[int, int, int], list[LogicalToken]] = {}
+    for token in logical_map.tokens:
+        tokens_by_leaf.setdefault(
+            (token.sample_id, token.family_id, token.completion_id), []
+        ).append(token)
+    choice_by_leaf: dict[tuple[int, int, int], Choice] = {}
+    for leaf, tokens in tokens_by_leaf.items():
+        prompt = prompt_by_id[tokens[0].prompt_id]
+        choices = choices_by_tokens.get(tuple(prompt.token_ids))
+        if not choices:
             raise RuntimeError(
                 "Could not find captured vLLM choice for logical prompt "
                 f"{prompt.prompt_id}"
             )
-        choice_by_prompt_id[prompt.prompt_id] = choice
+        has_source_logprobs = all(token.source_logprob is not None for token in tokens)
+        if not has_source_logprobs and len(choices) != 1:
+            raise RuntimeError(
+                "Duplicate vLLM token paths require packed source logprobs"
+            )
+        matching = choices
+        if has_source_logprobs:
+            matching = []
+            for choice in choices:
+                entries = (
+                    choice.logprobs.content
+                    if choice.logprobs is not None
+                    and choice.logprobs.content is not None
+                    else []
+                )
+                for token in tokens:
+                    index = (
+                        token.vllm_prompt_token_index - prompt.scored_token_start_index
+                    )
+                    if (
+                        index < 0
+                        or index >= len(entries)
+                        or _parse_token_id(entries[index].token) != token.token_id
+                        or struct.pack("!f", float(entries[index].logprob))
+                        != struct.pack("!f", cast(float, token.source_logprob))
+                    ):
+                        break
+                else:
+                    matching.append(choice)
+        if not matching:
+            raise RuntimeError(
+                "No captured vLLM choice matches packed source logprobs for "
+                f"prompt {prompt.prompt_id}"
+            )
+        choice = matching[0]
+        choices.remove(choice)
+        choice_by_leaf[leaf] = choice
     target_logprobs: list[float] = []
     topk: list[TokenTopK] = []
     for token in logical_map.tokens:
         prompt = prompt_by_id[token.prompt_id]
-        choice = choice_by_prompt_id[token.prompt_id]
+        choice = choice_by_leaf[(token.sample_id, token.family_id, token.completion_id)]
         metadata = choice_moe_routing_metadata(choice)
         vllm_prompt_len = prompt.scored_token_start_index
         if (
@@ -699,6 +868,9 @@ async def _score_base_real_generation_path(
     config: RealPathConfig,
     artifact_dir: Path,
     is_moe: bool,
+    prompts: list[str],
+    extra_body: dict[str, Any] | None,
+    max_model_len: int,
 ) -> RealPathBaseDiagnosticBundle:
     import art
     from art.megatron.backend import MegatronBackend
@@ -711,16 +883,14 @@ async def _score_base_real_generation_path(
         allow_unvalidated_arch=parity_config.allow_unvalidated_arch,
     )
     served_name = f"train_inf_real_base_{uuid.uuid4().hex[:8]}"
-    placeholder_lora = artifact_dir / "unused_base_lora_placeholder"
-    placeholder_lora.mkdir(exist_ok=True)
     engine_args = {
         "tensor_parallel_size": len(parity_config.inference_gpu_ids),
         "enable_expert_parallel": is_moe and len(parity_config.inference_gpu_ids) > 1,
-        "max_model_len": parity_config.packed.sequence_length + 8,
+        "max_model_len": max_model_len,
         "max_logprobs": TOP_K,
         **parity_config.engine_args,
     }
-    for key, value in handler.vllm_engine_args(rollout_weights_mode="merged").items():
+    for key, value in handler.vllm_engine_args().items():
         engine_args.setdefault(key, value)
     engine_args.setdefault("generation_config", "vllm")
     engine_args.pop("enable_lora", None)
@@ -745,8 +915,7 @@ async def _score_base_real_generation_path(
         config=parity_config,
         artifact_dir=artifact_dir,
         served_model_name=served_name,
-        lora_path=str(placeholder_lora),
-        rollout_weights_mode="merged",
+        lora_path=None,
         engine_args=engine_args,
         server_args={
             "enable_auto_tool_choice": True,
@@ -772,6 +941,8 @@ async def _score_base_real_generation_path(
         trajectory_groups = await _collect_real_trajectory_groups(
             model=model,
             config=config,
+            prompts=prompts,
+            extra_body=extra_body,
         )
 
     packing_backend = MegatronBackend(
@@ -800,7 +971,7 @@ async def _score_base_real_generation_path(
         logical_map=logical_map,
         require_routing_metadata=is_moe,
         weight_state="base",
-        rollout_mode="merged",
+        rollout_mode="native_lora",
     )
     vllm_score_path = artifact_dir / "real_path_vllm_base_scores.json"
     _write_json(vllm_score_path, vllm_base.model_dump(mode="json"))
@@ -927,33 +1098,151 @@ def _build_real_path_moe_routing_replay_bundle(
     )
 
 
+def _pack_expert_lora_tensors(
+    tensors: dict[str, Any],
+    groups: tuple[Any, ...],
+) -> dict[str, Any]:
+    import torch
+
+    packed = dict(tensors)
+    for group in groups:
+        for slot in group.slots:
+            suffix = f".{slot.source_projection}.{slot.source_lora}.weight"
+            matches: dict[str, dict[int, tuple[str, torch.Tensor]]] = {}
+            for key, tensor in tensors.items():
+                if not key.endswith(suffix):
+                    continue
+                prefix, separator, expert = key[: -len(suffix)].rpartition(".")
+                if not separator or not prefix.endswith(group.art_group_suffix):
+                    continue
+                try:
+                    expert_index = int(expert)
+                except ValueError:
+                    continue
+                matches.setdefault(prefix, {})[expert_index] = (key, tensor)
+
+            for prefix, experts in matches.items():
+                if sorted(experts) != list(range(len(experts))):
+                    raise RuntimeError(
+                        f"Non-contiguous expert LoRA tensors for {prefix}: "
+                        f"{sorted(experts)}"
+                    )
+                joined = torch.stack([experts[index][1] for index in sorted(experts)])
+                for key, _tensor in experts.values():
+                    packed.pop(key)
+                if slot.pack_layout == "expert_rows":
+                    value = joined.flatten(0, 1)
+                elif slot.pack_layout == "rank_major_expert_cols":
+                    value = joined.permute(1, 2, 0).reshape(
+                        joined.shape[1], joined.shape[2] * joined.shape[0]
+                    )
+                elif slot.pack_layout == "interleaved_gate_up_rank_major_expert_cols":
+                    gate, up = joined.split(joined.shape[1] // 2, dim=1)
+                    interleaved = torch.stack((gate, up), dim=2).flatten(1, 2)
+                    value = interleaved.permute(1, 2, 0).reshape(
+                        interleaved.shape[1],
+                        interleaved.shape[2] * interleaved.shape[0],
+                    )
+                else:
+                    raise RuntimeError(
+                        f"Unsupported expert LoRA layout: {slot.pack_layout}"
+                    )
+                output_key = f"{prefix}.{slot.output_suffix}"
+                if output_key in packed:
+                    raise RuntimeError(
+                        f"Duplicate packed expert LoRA tensor: {output_key}"
+                    )
+                packed[output_key] = value.contiguous()
+    return packed
+
+
 def _make_nonzero_adapter(
     *,
     config: TrainInfOutputParityConfig,
     artifact_dir: Path,
 ) -> str:
-    request = RealPathMegatronWorkerRequest(
-        config=config,
-        artifact_dir=str(artifact_dir),
-        disk_packed_tensors=cast(
-            DiskPackedTensors,
-            {
-                "dir": str(artifact_dir / "unused"),
-                "num_sequences": 1,
-                "sequence_length": 1,
-            },
+    import torch
+
+    from art.megatron.identity_lora import create_identity_lora
+    from art.megatron.model_support import get_model_support_handler
+    from art.megatron.model_support.lora_disk import (
+        load_adapter_config,
+        load_vllm_lora_tensors,
+        save_vllm_lora_tensors,
+    )
+
+    from .output_parity import _adapter_config
+
+    handler = get_model_support_handler(
+        config.base_model,
+        allow_unvalidated_arch=config.allow_unvalidated_arch,
+    )
+    adapter_path = artifact_dir / "real_path_active_lora"
+    with torch.random.fork_rng(devices=[]):
+        create_identity_lora(
+            config.base_model,
+            str(adapter_path),
+            target_modules=_lora_target_modules(config),
+            random_state=config.seed,
+            allow_unvalidated_arch=config.allow_unvalidated_arch,
+            handler=handler,
+        )
+    published_config = load_adapter_config(adapter_path)
+    published_tensors = load_vllm_lora_tensors(adapter_path)
+    invalid_dtypes = {
+        key: str(value.dtype)
+        for key, value in published_tensors.items()
+        if value.dtype != torch.bfloat16
+    }
+    if invalid_dtypes:
+        raise RuntimeError(f"Identity LoRA tensors must be BF16: {invalid_dtypes}")
+    templates = handler.from_vllm_lora_tensors(
+        published_tensors,
+        adapter_config=published_config,
+    )
+    if not templates:
+        raise RuntimeError("Identity LoRA metadata produced no adapter tensors")
+    adapter_config = _adapter_config(config)
+    initialized = _build_deterministic_nonzero_lora(
+        {
+            key: torch.empty_like(value, device="cpu", dtype=torch.bfloat16)
+            for key, value in templates.items()
+        },
+        seed=config.seed,
+    )
+    normalized, normalized_config = handler.to_vllm_lora_tensors(
+        initialized,
+        adapter_config=dict(adapter_config),
+    )
+    initialized = handler.from_vllm_lora_tensors(
+        normalized,
+        adapter_config=normalized_config,
+    )
+    tensors, published_config = handler.to_vllm_lora_tensors(
+        _pack_expert_lora_tensors(
+            initialized,
+            tuple(handler.expert_packed_lora_groups()),
         ),
-        logical_map_path=str(artifact_dir / "unused_logical_map.json"),
-        weight_state="lora",
-        adapter_path=None,
-        moe_routing_replay_path=None,
-        global_grad_accumulation_sequences=1,
-        forward_trace_dir=None,
+        adapter_config=adapter_config,
     )
-    return _run_real_path_megatron_worker(request, adapter_only=True).adapter_path or ""
+    invalid = [
+        key
+        for key, value in tensors.items()
+        if value.dtype != torch.bfloat16 or not torch.count_nonzero(value).item()
+    ]
+    if invalid:
+        raise RuntimeError(f"Invalid materialized LoRA tensors: {invalid[:5]}")
+    save_vllm_lora_tensors(
+        adapter_path,
+        {key: value.cpu().contiguous() for key, value in tensors.items()},
+        published_config,
+    )
+    return str(adapter_path)
 
 
 def _adapter_cache_key(config: TrainInfOutputParityConfig) -> str:
+    from transformers import AutoConfig
+
     from art.megatron.model_support import (
         get_model_support_handler,
         vllm_lora_config_for_model,
@@ -981,9 +1270,13 @@ def jsonable(value: Any) -> Any:
         allow_unvalidated_arch=config.allow_unvalidated_arch,
     )
     handler_module = Path(inspect.getfile(type(handler)))
+    model_config = handler.identity_lora_model_config(
+        AutoConfig.from_pretrained(config.base_model, trust_remote_code=True)
+    ).to_json_string(use_diff=False)
     payload = {
-        "schema": 3,
+        "schema": 4,
         "base_model": config.base_model,
+        "model_config_sha256": hashlib.sha256(model_config.encode()).hexdigest(),
         "seed": config.seed,
         "allow_unvalidated_arch": config.allow_unvalidated_arch,
         "lora_target_modules": _lora_target_modules(config),
@@ -1006,7 +1299,10 @@ def _default_adapter_cache_dir() -> Path:
 def _adapter_cache_dir(config: RealPathConfig) -> Path:
     if config.adapter_cache_dir:
         return Path(config.adapter_cache_dir)
-    return _default_adapter_cache_dir()
+    model_namespace = hashlib.sha256(
+        config.output_parity.base_model.encode()
+    ).hexdigest()[:16]
+    return _default_adapter_cache_dir() / model_namespace
 
 
 def _adapter_cache_manifest_path(adapter_path: Path) -> Path:
@@ -1248,20 +1544,30 @@ def _configure_worker_bundle(bundle: Any) -> None:
         if request.weight_state == "lora":
             if request.adapter_path is None:
                 initial_state = _collect_full_lora_state(cast(list[Any], runtime.model))
-                if torch.distributed.get_rank() == 0:  # type: ignore[possibly-missing-attribute]
-                    adapter_path = artifact_dir / "real_path_active_lora"
-                    initialized = _build_deterministic_nonzero_lora(
+                rank = torch.distributed.get_rank()  # type: ignore[possibly-missing-attribute]
+                initialized = (
+                    _build_deterministic_nonzero_lora(
                         initial_state or {},
                         seed=request.config.seed,
                     )
-                    _save_vllm_lora_adapter(
-                        lora_path=adapter_path,
-                        state=initialized,
-                        runtime=runtime,
-                        config=request.config,
-                    )
-                torch.distributed.barrier()  # type: ignore[possibly-missing-attribute]
+                    if rank == 0
+                    else None
+                )
+                payload = [initialized]
+                torch.distributed.broadcast_object_list(  # type: ignore[possibly-missing-attribute]
+                    payload,
+                    src=0,
+                    device=torch.device("cuda", local_rank),
+                )
+                initialized = cast(dict[str, Any], payload[0])
                 adapter_path = artifact_dir / "real_path_active_lora"
+                _save_vllm_lora_adapter(
+                    lora_path=adapter_path,
+                    state=initialized,
+                    runtime=runtime,
+                    config=request.config,
+                )
+                torch.distributed.barrier()  # type: ignore[possibly-missing-attribute]
             else:
                 adapter_path = Path(request.adapter_path)
             adapter_model = load_lora_tensors_for_megatron(
@@ -1359,8 +1665,8 @@ def _run_real_path_megatron_worker(
     request_path = artifact_dir / request_name
     _write_json(request_path, request.model_dump(mode="json"))
     env = os.environ.copy()
-    env["CUDA_VISIBLE_DEVICES"] = ",".join(
-        str(value) for value in request.config.trainer_gpu_ids
+    env["CUDA_VISIBLE_DEVICES"] = _cuda_visible_devices_for_slots(
+        request.config.trainer_gpu_ids
     )
     env.update(request.config.megatron_env)
     env["PYTHONUNBUFFERED"] = "1"
@@ -1422,6 +1728,180 @@ def _delete_adapter_safetensors_on_pass(artifact_dir: Path, *, passed: bool) ->
         path.unlink()
 
 
+def _resident_score_bundle(
+    *,
+    result: Any,
+    logical_map: LogicalTokenMap,
+    rollout_mode: RolloutMode,
+) -> ScoreBundle:
+    scores = {(score.sample_index, score.logit_index): score for score in result.scores}
+    selected = []
+    for token in logical_map.tokens:
+        coordinate = token.sample_id, token.art_logit_index
+        score = scores.get(coordinate)
+        if score is None:
+            raise RuntimeError(f"resident score is missing logical token {coordinate}")
+        if score.target_token_id != token.token_id:
+            raise RuntimeError(
+                "resident score target token does not match packed input: "
+                f"coordinate={coordinate}, score={score.target_token_id}, "
+                f"packed={token.token_id}"
+            )
+        selected.append(score)
+    return ScoreBundle(
+        side="megatron",
+        weight_state="lora",
+        rollout_mode=rollout_mode,
+        target_logprobs=[score.target_logprob for score in selected],
+        topk=[
+            TokenTopK(
+                token_ids=list(score.top_token_ids),
+                logprobs=list(score.top_logprobs),
+            )
+            for score in selected
+        ],
+    )
+
+
+async def run_resident_train_inf_mismatch(
+    *,
+    backend: Any,
+    model: Any,
+    policy_step: int,
+    config: RealPathConfig,
+    artifact_dir: Path,
+    max_attempts: int = 3,
+) -> ResidentTrainInfReport:
+    import torch
+
+    parity_config = config.output_parity
+    _apply_sliding_window_prompt_defaults(config)
+    prompts, extra_body, _max_model_len = _prepare_real_path_prompts(config)
+    rollout_mode = _real_path_rollout_mode(parity_config)
+    is_moe = model_support_is_moe(
+        parity_config.base_model,
+        allow_unvalidated_arch=parity_config.allow_unvalidated_arch,
+    )
+    artifact_dir.mkdir(parents=True, exist_ok=True)
+    _write_json(artifact_dir / "resident_config.json", config.model_dump(mode="json"))
+    mean_abs_pct_limit = fwd_mean_abs_pct_limit_for_model(
+        parity_config.base_model,
+        allow_unvalidated_arch=parity_config.allow_unvalidated_arch,
+    )
+    top20_kl_limit = top20_kl_candidate_to_target_limit_for_model(
+        parity_config.base_model,
+        allow_unvalidated_arch=parity_config.allow_unvalidated_arch,
+    )
+    attempts = []
+    run_id: str | None = None
+    generation_id: str | None = None
+    for attempt in range(1, max_attempts + 1):
+        groups = await _collect_real_trajectory_groups(
+            model=model,
+            config=config,
+            prompts=prompts,
+            extra_body=extra_body,
+            policy_step=policy_step,
+        )
+        packed = backend._get_packed_tensors(
+            model,
+            groups,
+            advantage_balance=0.0,
+            allow_training_without_logprobs=False,
+            scale_rewards=True,
+            plot_tensors=False,
+            packed_sequence_length=parity_config.packed.sequence_length,
+            logprob_calculation_chunk_size=1024,
+            include_moe_routing=is_moe,
+        )
+        if packed is None:
+            raise RuntimeError("resident ART path produced no packed tensors")
+        logical_map = build_logical_token_map(cast(dict[str, Any], packed))
+        vllm = _vllm_scores_from_real_choices(
+            trajectory_groups=groups,
+            logical_map=logical_map,
+            require_routing_metadata=is_moe,
+            weight_state="lora",
+            rollout_mode=rollout_mode,
+        )
+        result = await backend.score_resident(
+            model,
+            groups,
+            expected_learner_version=policy_step,
+            top_k=TOP_K,
+        )
+        fingerprint = _packed_batch_fingerprint(cast(dict[str, Any], packed))
+        if result.batch_fingerprint != fingerprint:
+            raise RuntimeError(
+                "resident score did not use the locally reconstructed packed batch"
+            )
+        if run_id is not None and result.run_id != run_id:
+            raise RuntimeError("resident parity attempts used different trainer runs")
+        run_id = result.run_id
+        generation_id = result.learner.generation_id
+        megatron = _resident_score_bundle(
+            result=result,
+            logical_map=logical_map,
+            rollout_mode=rollout_mode,
+        )
+        for name, value in (
+            ("logical_token_map", logical_map),
+            ("vllm_scores", vllm),
+            ("megatron_scores", megatron),
+        ):
+            _write_json(
+                artifact_dir / f"attempt_{attempt}_{name}.json",
+                value.model_dump(mode="json"),
+            )
+        sequence_ids = [token.prompt_id for token in logical_map.tokens]
+        comparison = compare_pair(
+            candidate=torch.tensor(megatron.target_logprobs, dtype=torch.float32),
+            target=torch.tensor(vllm.target_logprobs, dtype=torch.float32),
+            sequence_ids=sequence_ids,
+        )
+        topk = compare_topk(megatron, vllm)
+        passed = (
+            comparison.mean_abs_pct <= mean_abs_pct_limit
+            and topk.top20_intersection_kl_candidate_to_target <= top20_kl_limit
+        )
+        attempt_report = ResidentTrainInfAttempt(
+            attempt=attempt,
+            logical_prompt_count=len(logical_map.prompts),
+            logical_token_count=len(logical_map.tokens),
+            lora=comparison,
+            lora_topk=topk,
+            moe_routing_packed_tokens=result.routing_replay_packed_tokens,
+            batch_fingerprint=fingerprint,
+            passed=passed,
+        )
+        attempts.append(attempt_report)
+        _write_json(
+            artifact_dir / f"attempt_{attempt}.json",
+            attempt_report.model_dump(mode="json"),
+        )
+        if passed:
+            break
+    assert run_id is not None and generation_id is not None
+    report = ResidentTrainInfReport(
+        base_model=parity_config.base_model,
+        artifact_dir=str(artifact_dir),
+        run_id=run_id,
+        policy_step=policy_step,
+        generation_id=generation_id,
+        attempt_count=len(attempts),
+        max_attempts=max_attempts,
+        attempts=attempts,
+        mean_abs_pct_limit=mean_abs_pct_limit,
+        top20_kl_candidate_to_target_limit=top20_kl_limit,
+        passed=attempts[-1].passed,
+    )
+    _write_json(
+        artifact_dir / "resident_comparison_report.json",
+        report.model_dump(mode="json"),
+    )
+    return report
+
+
 async def run_real_path_train_inf_mismatch(
     *,
     config: RealPathConfig,
@@ -1433,6 +1913,7 @@ async def run_real_path_train_inf_mismatch(
 
     parity_config = config.output_parity
     _apply_sliding_window_prompt_defaults(config)
+    prompts, extra_body, max_model_len = _prepare_real_path_prompts(config)
     rollout_mode = _real_path_rollout_mode(parity_config)
     is_moe = model_support_is_moe(
         parity_config.base_model,
@@ -1453,13 +1934,12 @@ async def run_real_path_train_inf_mismatch(
         {
             "trainer_gpu_ids": parity_config.trainer_gpu_ids,
             "inference_gpu_ids": parity_config.inference_gpu_ids,
-            "rollout_weights_mode": _real_path_rollout_weights_mode(parity_config),
             "allow_unvalidated_arch": parity_config.allow_unvalidated_arch,
             "engine_args": {
                 "tensor_parallel_size": len(parity_config.inference_gpu_ids),
                 "enable_expert_parallel": is_moe
                 and len(parity_config.inference_gpu_ids) > 1,
-                "max_model_len": parity_config.packed.sequence_length + 8,
+                "max_model_len": max_model_len,
                 "max_logprobs": TOP_K,
                 **parity_config.engine_args,
             },
@@ -1496,6 +1976,8 @@ async def run_real_path_train_inf_mismatch(
         trajectory_groups = await _collect_real_trajectory_groups(
             model=model,
             config=config,
+            prompts=prompts,
+            extra_body=extra_body,
         )
         packed_tensors = backend._get_packed_tensors(
             model,
@@ -1568,6 +2050,9 @@ async def run_real_path_train_inf_mismatch(
                 config=config,
                 artifact_dir=artifact_dir,
                 is_moe=is_moe,
+                prompts=prompts,
+                extra_body=extra_body,
+                max_model_len=max_model_len,
             )
             megatron_base = base_diagnostic.megatron_scores
             vllm_base = base_diagnostic.vllm_scores
diff --git a/tests/integration/megatron/train_inf_mismatch/test_config.py b/tests/integration/megatron/train_inf_mismatch/test_config.py
index 89a103623..6f8ee6c22 100644
--- a/tests/integration/megatron/train_inf_mismatch/test_config.py
+++ b/tests/integration/megatron/train_inf_mismatch/test_config.py
@@ -4,8 +4,20 @@
 
 import torch
 
+from ..model_support.workflow_resources import (
+    HandlerWorkflowResources,
+    MegatronWorkflowResources,
+    MegatronWorkflowTopology,
+    VllmWorkflowResources,
+    WorkflowStageResources,
+)
 from . import output_parity
 from .output_parity import config_from_env
+from .real_path import (
+    RealPathConfig,
+    _cuda_visible_devices_for_slots,
+    _real_path_max_model_len,
+)
 
 
 def test_cp_unsupported_default_converts_cp_to_dp_without_changing_tp(
@@ -56,14 +68,14 @@ def test_cp_unsupported_model_uses_non_cp_default_topology(monkeypatch) -> None:
 
     assert config.topology.cp == 1
     assert config.topology.tp == 2
-    assert config.topology.ep == 2
-    assert config.topology.dp == 1
-    assert config.trainer_gpu_ids == [0, 1]
+    assert config.topology.ep == 4
+    assert config.topology.dp == 2
+    assert config.trainer_gpu_ids == [0, 1, 2, 3]
     assert config.inference_gpu_ids == [2, 3]
     assert config.engine_args["tensor_parallel_size"] == 2
     assert config.engine_args["enable_expert_parallel"] is True
     assert config.engine_args["kv_cache_dtype"] == "fp8"
-    assert config.engine_args["moe_backend"] == "triton_unfused"
+    assert config.engine_args["moe_backend"] == "auto"
     assert config.streaming_weight_offload is True
     assert config.megatron_env == {}
     assert config.external_vllm_server_url == "http://127.0.0.1:8000"
diff --git a/tests/integration/megatron/train_inf_mismatch/test_live_real_path_output_parity.py b/tests/integration/megatron/train_inf_mismatch/test_live_real_path_output_parity.py
index 603317bb0..0d6b42504 100644
--- a/tests/integration/megatron/train_inf_mismatch/test_live_real_path_output_parity.py
+++ b/tests/integration/megatron/train_inf_mismatch/test_live_real_path_output_parity.py
@@ -1,6 +1,9 @@
 from __future__ import annotations
 
+import argparse
+import asyncio
 from pathlib import Path
+import traceback
 
 import pytest
 
@@ -8,14 +11,25 @@
 
 from .output_parity import model_support_is_moe
 from .real_path import (
+    RealPathConfig,
+    RealPathTrainInfReport,
     config_from_env,
     run_real_path_train_inf_mismatch,
 )
+from .workflow_stage import (
+    ATTEMPT_ASSERTION_EXIT_CODE,
+    ATTEMPT_ERROR_EXIT_CODE,
+    TrainInfMismatchWorkerResult,
+)
 
-torch = pytest.importorskip("torch")
+_TEST_NODEID = (
+    "tests/integration/megatron/train_inf_mismatch/"
+    "test_live_real_path_output_parity.py::test_real_path_train_inf_mismatch_live"
+)
 
 
 def _require_visible_gpus(gpu_ids: list[int]) -> None:
+    torch = pytest.importorskip("torch")
     if not torch.cuda.is_available():
         pytest.skip("CUDA is required for real-path train/inf mismatch")
     visible_count = int(torch.cuda.device_count())
@@ -27,8 +41,9 @@ def _require_visible_gpus(gpu_ids: list[int]) -> None:
         )
 
 
-@pytest.mark.asyncio
-async def test_real_path_train_inf_mismatch_live(artifact_dir: Path) -> None:
+async def _run_live_real_path_output_parity(
+    artifact_dir: Path,
+) -> tuple[RealPathConfig, RealPathTrainInfReport]:
     config = config_from_env()
     parity_config = config.output_parity
     _require_visible_gpus(
@@ -39,7 +54,14 @@ async def test_real_path_train_inf_mismatch_live(artifact_dir: Path) -> None:
         config=config,
         artifact_dir=artifact_dir,
     )
+    return config, report
+
 
+def assert_live_real_path_output_parity(
+    config: RealPathConfig,
+    report: RealPathTrainInfReport,
+) -> None:
+    parity_config = config.output_parity
     assert report.logical_prompt_count > 0
     assert report.logical_token_count > 0
     handler_key = get_model_support_spec(
@@ -63,3 +85,64 @@ async def test_real_path_train_inf_mismatch_live(artifact_dir: Path) -> None:
         report.lora_topk.top20_intersection_kl_candidate_to_target
         <= report.top20_kl_candidate_to_target_limit
     )
+
+
+@pytest.mark.asyncio
+async def test_real_path_train_inf_mismatch_live(artifact_dir: Path) -> None:
+    config, report = await _run_live_real_path_output_parity(artifact_dir)
+    assert_live_real_path_output_parity(config, report)
+
+
+def _run_workflow_attempt(result_path: Path) -> int:
+    from .artifacts import create_artifact_dir, require_clean_git_state
+
+    artifact_dir: Path | None = None
+    comparison_completed = False
+    exception_type: str | None = None
+    exception_message: str | None = None
+    try:
+        require_clean_git_state()
+        artifact_dir = create_artifact_dir(_TEST_NODEID)
+        config, report = asyncio.run(_run_live_real_path_output_parity(artifact_dir))
+        comparison_completed = True
+        assert_live_real_path_output_parity(config, report)
+        outcome = "passed"
+        returncode = 0
+    except pytest.skip.Exception as error:
+        traceback.print_exc()
+        outcome = "skipped"
+        returncode = 0
+        exception_type = f"{type(error).__module__}.{type(error).__qualname__}"
+        exception_message = str(error)
+    except AssertionError as error:
+        traceback.print_exc()
+        outcome = "failed" if comparison_completed else "error"
+        returncode = (
+            ATTEMPT_ASSERTION_EXIT_CODE
+            if comparison_completed
+            else ATTEMPT_ERROR_EXIT_CODE
+        )
+        exception_type = f"{type(error).__module__}.{type(error).__qualname__}"
+        exception_message = str(error)
+    except Exception as error:
+        traceback.print_exc()
+        outcome = "error"
+        returncode = ATTEMPT_ERROR_EXIT_CODE
+        exception_type = f"{type(error).__module__}.{type(error).__qualname__}"
+        exception_message = str(error)
+    result = TrainInfMismatchWorkerResult(
+        outcome=outcome,
+        artifact_dir=str(artifact_dir) if artifact_dir is not None else None,
+        comparison_completed=comparison_completed,
+        exception_type=exception_type,
+        exception_message=exception_message,
+    )
+    result_path.write_text(result.model_dump_json(indent=2) + "\n", encoding="utf-8")
+    return returncode
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--workflow-attempt-result", type=Path, required=True)
+    args = parser.parse_args()
+    raise SystemExit(_run_workflow_attempt(args.workflow_attempt_result))
diff --git a/tests/integration/megatron/train_inf_mismatch/test_output_parity_invariants.py b/tests/integration/megatron/train_inf_mismatch/test_output_parity_invariants.py
index 1e68277fb..5b0431043 100644
--- a/tests/integration/megatron/train_inf_mismatch/test_output_parity_invariants.py
+++ b/tests/integration/megatron/train_inf_mismatch/test_output_parity_invariants.py
@@ -1,8 +1,9 @@
 from __future__ import annotations
 
+import asyncio
 import math
+from pathlib import Path
 from types import SimpleNamespace
-from unittest.mock import AsyncMock
 
 from openai.types.chat.chat_completion import Choice
 from openai.types.chat.chat_completion_message import ChatCompletionMessage
@@ -10,11 +11,16 @@
 
 torch = pytest.importorskip("torch")
 
+import art
+
 from . import workflow_stage
 from .output_parity import (
     TOP20_KL_CANDIDATE_TO_TARGET_LIMIT,
     TOP_K,
     EngineSide,
+    LogicalPrompt,
+    LogicalToken,
+    LogicalTokenMap,
     ScoreBundle,
     TokenTopK,
     TrainInfOutputParityConfig,
@@ -29,39 +35,128 @@
 )
 from .real_path import (
     RealPathConfig,
+    _choice_score_index,
+    _collect_real_trajectory_groups,
     _delete_adapter_safetensors_on_pass,
     _real_path_rollout_mode,
-    _real_path_rollout_weights_mode,
-    _rollout,
+    _topk_from_chat_logprob,
+    _vllm_scores_from_real_choices,
 )
 
 
-def test_logical_map_flattens_prefix_tree_branches() -> None:
+def test_choice_score_index_disambiguates_equal_completions_by_prompt() -> None:
+    def choice(prompt_id: int) -> Choice:
+        return Choice.model_validate(
+            {
+                "finish_reason": "stop",
+                "index": 0,
+                "message": {"role": "assistant", "content": "same"},
+                "prompt_token_ids": [prompt_id],
+                "token_ids": [7],
+            }
+        )
+
+    first, second = choice(1), choice(2)
+    groups = [[SimpleNamespace(messages_and_choices=[first, second])]]
+
+    indexed = _choice_score_index(groups, require_routing_metadata=False)
+
+    assert indexed == {(1, 7): [first], (2, 7): [second]}
+
+
+def test_equal_token_paths_match_packed_source_logprobs() -> None:
+    def choice(score: float) -> Choice:
+        return Choice.model_validate(
+            {
+                "finish_reason": "stop",
+                "index": 0,
+                "message": {"role": "assistant", "content": "same"},
+                "prompt_token_ids": [1],
+                "token_ids": [7],
+                "logprobs": {
+                    "content": [
+                        {
+                            "token": "token_id:7",
+                            "logprob": score,
+                            "top_logprobs": [{"token": "token_id:7", "logprob": score}],
+                        }
+                    ]
+                },
+            }
+        )
+
+    first, second = choice(-1.0), choice(-2.0)
+    groups = [[SimpleNamespace(messages_and_choices=[first, second])]]
+    logical_map = LogicalTokenMap(
+        prompts=[
+            LogicalPrompt(
+                prompt_id=0,
+                sample_id=0,
+                family_id=0,
+                completion_id=0,
+                packed_prompt_length=1,
+                scored_token_start_index=1,
+                token_ids=[1, 7],
+            )
+        ],
+        tokens=[
+            LogicalToken(
+                token_id=7,
+                sample_id=0,
+                family_id=0,
+                completion_id=0,
+                prompt_id=0,
+                art_packed_token_index=1,
+                art_logit_index=0,
+                vllm_prompt_token_index=1,
+                source_logprob=-2.0,
+            ),
+            LogicalToken(
+                token_id=7,
+                sample_id=0,
+                family_id=0,
+                completion_id=1,
+                prompt_id=0,
+                art_packed_token_index=2,
+                art_logit_index=1,
+                vllm_prompt_token_index=1,
+                source_logprob=-1.0,
+            ),
+        ],
+    )
+
+    scores = _vllm_scores_from_real_choices(
+        trajectory_groups=groups,
+        logical_map=logical_map,
+        require_routing_metadata=False,
+        weight_state="lora",
+        rollout_mode="native_lora",
+    )
+
+    assert scores.target_logprobs == [-2.0, -1.0]
+
+
+def _write_workflow_worker_result(
+    command: list[str],
+    result: workflow_stage.TrainInfMismatchWorkerResult,
+) -> None:
+    result_path = Path(command[command.index("--workflow-attempt-result") + 1])
+    result_path.write_text(result.model_dump_json(), encoding="utf-8")
+
+
+def test_logical_map_handles_unscored_prompt_suffix_inside_leaf() -> None:
     packed = {
-        "tokens": torch.tensor([[10, 11, 12, 13, 14, 12, 15, 16]]),
-        "group_ids": torch.tensor([[0, 0, 1, 1, 1, 2, 2, 2]]),
-        "parent_ids": torch.tensor([[0, 0, 0, 0, 0, 0, 0, 0]]),
+        "tokens": torch.tensor([[10, 11, 12, 13, 14, 15]]),
+        "group_ids": torch.tensor([[0, 0, 1, 1, 1, 1]]),
+        "parent_ids": torch.tensor([[0, 0, 0, 0, 0, 0]]),
+        "assistant_mask": torch.tensor([[False, False, False, False, True, True]]),
     }
 
     logical_map = build_logical_token_map(packed)
 
-    assert [prompt.token_ids for prompt in logical_map.prompts] == [
-        [10, 11, 12, 13, 14],
-        [10, 11, 12, 15, 16],
-    ]
-    assert [prompt.packed_prompt_length for prompt in logical_map.prompts] == [2, 2]
-    assert [prompt.scored_token_start_index for prompt in logical_map.prompts] == [
-        3,
-        3,
-    ]
-    assert [token.token_id for token in logical_map.tokens] == [13, 14, 15, 16]
-    assert [token.art_logit_index for token in logical_map.tokens] == [2, 3, 5, 6]
-    assert [token.vllm_prompt_token_index for token in logical_map.tokens] == [
-        3,
-        4,
-        3,
-        4,
-    ]
+    assert logical_map.prompts[0].token_ids == [10, 11, 12, 13, 14, 15]
+    assert logical_map.prompts[0].scored_token_start_index == 4
+    assert [token.vllm_prompt_token_index for token in logical_map.tokens] == [4, 5]
 
 
 def test_logical_map_flattens_nested_prefix_tree_leaves() -> None:
@@ -193,125 +288,69 @@ def test_compare_rollout_reports_base_lora_and_delta_separately() -> None:
     assert report.delta.mean_abs_pct > 0
 
 
-def test_real_path_default_generates_16_tokens_per_rollout() -> None:
-    assert RealPathConfig().max_completion_tokens == 16
-
-
 @pytest.mark.asyncio
-async def test_real_path_rollout_builds_explicit_legacy_trajectory() -> None:
-    choice = Choice(
-        index=0,
-        finish_reason="stop",
-        message=ChatCompletionMessage(role="assistant", content="answer"),
+async def test_real_path_rollouts_use_stable_unique_seeds_concurrently() -> None:
+    calls = []
+    active_requests = 0
+    max_active_requests = 0
+
+    async def create(**kwargs):
+        nonlocal active_requests, max_active_requests
+        calls.append(kwargs)
+        active_requests += 1
+        max_active_requests = max(max_active_requests, active_requests)
+        await asyncio.sleep(0)
+        active_requests -= 1
+        return SimpleNamespace(
+            choices=[
+                Choice(
+                    finish_reason="stop",
+                    index=0,
+                    message=ChatCompletionMessage(role="assistant", content="maybe"),
+                )
+            ]
+        )
+
+    client = SimpleNamespace(
+        chat=SimpleNamespace(completions=SimpleNamespace(create=create))
     )
-    create = AsyncMock(return_value=SimpleNamespace(choices=[choice]))
     model = SimpleNamespace(
-        get_inference_name=lambda: "test-model",
-        openai_client=lambda: SimpleNamespace(
-            chat=SimpleNamespace(completions=SimpleNamespace(create=create))
-        ),
+        openai_client=lambda: client,
+        get_inference_name=lambda *, step=None: "fake",
+    )
+    config = RealPathConfig(
+        output_parity=TrainInfOutputParityConfig(seed=41),
+        rollouts_per_prompt=3,
     )
 
-    trajectory = await _rollout(
+    groups = await _collect_real_trajectory_groups(
         model=model,
-        prompt="question",
-        max_completion_tokens=7,
-        reward=0.75,
-        extra_body={"fixture": True},
+        config=config,
+        prompts=["first", "second"],
+        extra_body={"return_tokens_as_token_ids": True},
     )
 
-    assert trajectory.messages_and_choices == [
-        {"role": "user", "content": "question"},
-        choice,
-    ]
-    assert trajectory.exchanges.chat_completions == []
-    assert trajectory.reward == 0.75
-    assert trajectory.metrics["completion_tokens"] == 0
-    create.assert_awaited_once_with(
-        model="test-model",
-        messages=[{"role": "user", "content": "question"}],
-        max_tokens=7,
-        temperature=0.8,
-        logprobs=True,
-        top_logprobs=TOP_K,
-        extra_body={"fixture": True},
+    assert len(groups) == 2
+    assert max_active_requests > 1
+    assert sorted(call["seed"] for call in calls) == list(range(41, 47))
+    assert all(
+        call["extra_body"] == {"return_tokens_as_token_ids": True} for call in calls
     )
 
 
-def test_real_path_rollout_mode_follows_config() -> None:
-    native_config = TrainInfOutputParityConfig(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-    )
-    merged_config = TrainInfOutputParityConfig(
-        base_model="unvalidated/native-disabled",
-        allow_unvalidated_arch=True,
+def test_real_path_topk_sorts_vllm_sampled_token_prefix() -> None:
+    entry = SimpleNamespace(
+        top_logprobs=[SimpleNamespace(token="token_id:999", logprob=-100.0)]
+        + [
+            SimpleNamespace(token=f"token_id:{token_id}", logprob=-float(token_id))
+            for token_id in range(TOP_K)
+        ]
     )
 
-    assert _real_path_rollout_mode(native_config) == "native_lora"
-    assert _real_path_rollout_weights_mode(native_config) == "lora"
-    assert _real_path_rollout_mode(merged_config) == "merged"
-    assert _real_path_rollout_weights_mode(merged_config) == "merged"
-
-
-def test_real_path_deletes_only_adapter_safetensors_on_pass(tmp_path) -> None:
-    run_dir = tmp_path / "run"
-    active_lora = run_dir / "real_path_active_lora"
-    checkpoint = run_dir / "art_path" / "models" / "m" / "checkpoints" / "0000"
-    active_lora.mkdir(parents=True)
-    checkpoint.mkdir(parents=True)
-    for directory in (active_lora, checkpoint):
-        (directory / "adapter_model.safetensors").write_bytes(b"adapter")
-        (directory / "adapter_config.json").write_text("{}", encoding="utf-8")
-    score_path = run_dir / "real_path_vllm_lora_scores.json"
-    score_path.write_text("{}", encoding="utf-8")
-
-    _delete_adapter_safetensors_on_pass(run_dir, passed=False)
-
-    assert len(list(run_dir.rglob("adapter_model.safetensors"))) == 2
-
-    _delete_adapter_safetensors_on_pass(run_dir, passed=True)
-
-    assert list(run_dir.rglob("adapter_model.safetensors")) == []
-    assert len(list(run_dir.rglob("adapter_config.json"))) == 2
-    assert score_path.exists()
-
+    topk = _topk_from_chat_logprob(entry)
 
-def test_architecture_specific_real_path_limits() -> None:
-    assert fwd_mean_abs_pct_limit_for_model("Qwen/Qwen3-30B-A3B") == 8.0
-    assert fwd_mean_abs_pct_limit_for_model("Qwen/Qwen3.5-35B-A3B") == 5.0
-    assert TOP20_KL_CANDIDATE_TO_TARGET_LIMIT == 0.002
-
-
-def test_gemma4_real_path_limits() -> None:
-    assert (
-        fwd_mean_abs_pct_limit_for_model(
-            "google/gemma-4-31B-it",
-            allow_unvalidated_arch=True,
-        )
-        == 10.0
-    )
-    assert (
-        top20_kl_candidate_to_target_limit_for_model(
-            "google/gemma-4-31B-it",
-            allow_unvalidated_arch=True,
-        )
-        == 0.003
-    )
-    assert (
-        fwd_mean_abs_pct_limit_for_model(
-            "google/gemma-4-26B-A4B-it",
-            allow_unvalidated_arch=True,
-        )
-        == 10.0
-    )
-    assert (
-        top20_kl_candidate_to_target_limit_for_model(
-            "google/gemma-4-26B-A4B-it",
-            allow_unvalidated_arch=True,
-        )
-        == 0.008
-    )
-    assert TOP20_KL_CANDIDATE_TO_TARGET_LIMIT == 0.002
+    assert topk.token_ids == list(range(TOP_K))
+    assert topk.logprobs == [-float(token_id) for token_id in range(TOP_K)]
 
 
 def test_compare_topk_reports_restricted_intersection_kl() -> None:
@@ -348,117 +387,106 @@ def test_compare_topk_reports_restricted_intersection_kl() -> None:
     )
 
 
-def test_config_from_env_accepts_lora_target_module_override(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    monkeypatch.setenv(
-        "ART_TRAIN_INF_MISMATCH_LORA_TARGET_MODULES",
-        "experts,in_proj_qkv,in_proj_z",
-    )
-
-    config = config_from_env()
-
-    assert config.lora_target_modules == ["experts", "in_proj_qkv", "in_proj_z"]
-
-
-def test_config_from_env_accepts_vllm_memory_utilization_override(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_VLLM_GPU_MEMORY_UTILIZATION", "0.5")
-
-    config = config_from_env()
-
-    assert config.engine_args["gpu_memory_utilization"] == 0.5
-
-
-def test_config_from_env_accepts_gdn_prefill_backend_override(
+def test_workflow_stage_does_not_accept_a_skipped_live_test(
     monkeypatch: pytest.MonkeyPatch,
+    tmp_path,
 ) -> None:
-    monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_GDN_PREFILL_BACKEND", "triton")
-
-    config = config_from_env()
-
-    assert config.engine_args["additional_config"] == {"gdn_prefill_backend": "triton"}
-
+    import subprocess
 
-def test_default_rollout_modes_follow_model_support_native_lora_status() -> None:
-    assert TrainInfOutputParityConfig(
-        base_model="Qwen/Qwen3.5-35B-A3B"
-    ).rollout_modes == ["native_lora", "merged"]
-    assert TrainInfOutputParityConfig(
-        base_model="unvalidated/native-disabled",
-        allow_unvalidated_arch=True,
-    ).rollout_modes == ["merged"]
+    real_run = subprocess.run
+    monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_ATTEMPTS", "1")
+    monkeypatch.setattr(workflow_stage, "create_artifact_dir", lambda _nodeid: tmp_path)
 
+    def fake_run(*args, **kwargs):
+        if "env" not in kwargs:
+            return real_run(*args, **kwargs)
+        _write_workflow_worker_result(
+            args[0],
+            workflow_stage.TrainInfMismatchWorkerResult(outcome="skipped"),
+        )
+        return subprocess.CompletedProcess(
+            args=args,
+            returncode=0,
+            stdout="",
+            stderr="",
+        )
 
-def test_config_from_env_rollout_modes_override_handler_default(
-    monkeypatch: pytest.MonkeyPatch,
-) -> None:
-    monkeypatch.setenv(
-        "ART_TRAIN_INF_MISMATCH_BASE_MODEL",
-        "unvalidated/native-disabled",
-    )
-    monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_ALLOW_UNVALIDATED_ARCH", "1")
-    monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_ROLLOUT_MODES", "native_lora")
+    monkeypatch.setattr(workflow_stage.subprocess, "run", fake_run)
 
-    config = config_from_env()
+    report = workflow_stage.run_train_inf_mismatch(base_model="Qwen/Qwen3.5-35B-A3B")
 
-    assert config.rollout_modes == ["native_lora"]
+    assert report.passed is False
+    assert report.passed_count == 0
+    assert report.skipped_count == 1
 
 
-def test_workflow_stage_enables_live_train_inf_mismatch(
+def test_workflow_stage_retries_numerical_mismatch_and_transient_startup_failures(
     monkeypatch: pytest.MonkeyPatch,
-    tmp_path,
+    tmp_path: Path,
 ) -> None:
     import subprocess
 
-    captured_env = {}
-    real_run = workflow_stage.subprocess.run
+    calls = 0
+    real_run = subprocess.run
 
     def fake_run(*args, **kwargs):
+        nonlocal calls
         if "env" not in kwargs:
             return real_run(*args, **kwargs)
-        captured_env.update(kwargs["env"])
+        calls += 1
+        _write_workflow_worker_result(
+            args[0],
+            workflow_stage.TrainInfMismatchWorkerResult(
+                outcome="failed",
+                comparison_completed=True,
+                exception_type="builtins.AssertionError",
+                exception_message="TimeoutError in completed numerical evidence",
+            ),
+        )
         return subprocess.CompletedProcess(
-            args=args,
-            returncode=0,
-            stdout="1 passed\n",
-            stderr="",
+            args=args, returncode=1, stdout="", stderr=""
         )
 
+    monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_ATTEMPTS", "3")
     monkeypatch.setattr(workflow_stage, "create_artifact_dir", lambda _nodeid: tmp_path)
     monkeypatch.setattr(workflow_stage.subprocess, "run", fake_run)
 
-    report = workflow_stage.run_train_inf_mismatch(
-        base_model="Qwen/Qwen3.5-35B-A3B",
-        allow_unvalidated_arch=True,
-    )
+    report = workflow_stage.run_train_inf_mismatch(base_model="openai/gpt-oss-20b")
 
-    assert report.passed is True
-    assert captured_env["ART_RUN_TRAIN_INF_MISMATCH_LIVE"] == "1"
-    assert captured_env["ART_TRAIN_INF_MISMATCH_ALLOW_UNVALIDATED_ARCH"] == "1"
-    assert captured_env["ART_REAL_PATH_MAX_COMPLETION_TOKENS"] == "16"
-    assert captured_env["ART_TRAIN_INF_MISMATCH_VLLM_GPU_MEMORY_UTILIZATION"] == "0.50"
+    assert calls == report.attempt_count == 3
+    assert report.failed_count == 1
+    assert all(attempt.retryable for attempt in report.attempts)
+    assert workflow_stage._retryable_attempt_failure(
+        returncode=2,
+        result=workflow_stage.TrainInfMismatchWorkerResult(
+            outcome="error",
+            exception_type="builtins.TimeoutError",
+        ),
+        output="",
+    )
 
+    calls = 0
 
-def test_workflow_stage_does_not_accept_a_skipped_live_test(
-    monkeypatch: pytest.MonkeyPatch,
-    tmp_path,
-) -> None:
-    import subprocess
-
-    monkeypatch.setattr(workflow_stage, "create_artifact_dir", lambda _nodeid: tmp_path)
-    monkeypatch.setattr(
-        workflow_stage.subprocess,
-        "run",
-        lambda *args, **kwargs: subprocess.CompletedProcess(
+    def transient_then_pass(*args, **kwargs):
+        nonlocal calls
+        if "env" not in kwargs:
+            return real_run(*args, **kwargs)
+        calls += 1
+        worker_result = workflow_stage.TrainInfMismatchWorkerResult(
+            outcome="error" if calls == 1 else "passed",
+            exception_type="builtins.TimeoutError" if calls == 1 else None,
+        )
+        _write_workflow_worker_result(args[0], worker_result)
+        return subprocess.CompletedProcess(
             args=args,
-            returncode=0,
-            stdout="1 skipped\n",
+            returncode=2 if calls == 1 else 0,
+            stdout="",
             stderr="",
-        ),
-    )
+        )
 
-    report = workflow_stage.run_train_inf_mismatch(base_model="Qwen/Qwen3.5-35B-A3B")
+    monkeypatch.setattr(workflow_stage.subprocess, "run", transient_then_pass)
+    report = workflow_stage.run_train_inf_mismatch(base_model="openai/gpt-oss-20b")
 
-    assert report.passed is False
+    assert report.passed is True
+    assert calls == report.attempt_count == 2
+    assert [attempt.retryable for attempt in report.attempts] == [True, False]
diff --git a/tests/integration/megatron/train_inf_mismatch/workflow_stage.py b/tests/integration/megatron/train_inf_mismatch/workflow_stage.py
index a4304fd8d..c2adf8157 100644
--- a/tests/integration/megatron/train_inf_mismatch/workflow_stage.py
+++ b/tests/integration/megatron/train_inf_mismatch/workflow_stage.py
@@ -1,8 +1,9 @@
 import os
 from pathlib import Path
-import re
 import subprocess
 import sys
+import time
+from typing import Literal
 
 from pydantic import BaseModel
 
@@ -10,6 +11,30 @@
 
 DEFAULT_ATTEMPTS = 3
 MAX_ATTEMPTS = 5
+ATTEMPT_ASSERTION_EXIT_CODE = 1
+ATTEMPT_ERROR_EXIT_CODE = 2
+
+_TRANSIENT_STARTUP_ERRORS = (
+    "address already in use",
+    "brokenpipeerror",
+    "connection refused",
+    "connectionrefusederror",
+    "connection reset by peer",
+    "connectionreseterror",
+    "distnetworkerror",
+    "ncclremoteerror",
+    "ncclsystemerror",
+    "timed out waiting for",
+    "timeouterror",
+)
+
+
+class TrainInfMismatchWorkerResult(BaseModel):
+    outcome: Literal["passed", "failed", "error", "skipped"]
+    artifact_dir: str | None = None
+    comparison_completed: bool = False
+    exception_type: str | None = None
+    exception_message: str | None = None
 
 
 class TrainInfMismatchAttemptReport(BaseModel):
@@ -19,7 +44,10 @@ class TrainInfMismatchAttemptReport(BaseModel):
     stderr_path: str
     passed_count: int
     failed_count: int
+    error_count: int
     skipped_count: int
+    retryable: bool
+    duration_s: float
 
 
 class TrainInfMismatchReport(BaseModel):
@@ -32,27 +60,64 @@ class TrainInfMismatchReport(BaseModel):
     stderr_path: str
     passed_count: int
     failed_count: int
+    error_count: int
     skipped_count: int
     attempt_count: int
     max_attempts: int
     attempts: list[TrainInfMismatchAttemptReport]
+    duration_s: float
 
 
-def _pytest_counts(output: str) -> dict[str, int]:
-    counts = {"passed": 0, "failed": 0, "skipped": 0}
-    for line in reversed(output.splitlines()):
-        matches = re.findall(r"(\d+) (passed|failed|skipped|error|errors)", line)
-        if not matches:
-            continue
-        for count, kind in matches:
-            if kind in {"error", "errors"}:
-                counts["failed"] += int(count)
-            else:
-                counts[kind] += int(count)
-        return counts
+def _attempt_counts(
+    result: TrainInfMismatchWorkerResult | None,
+    *,
+    returncode: int,
+) -> dict[str, int]:
+    counts = {"passed": 0, "failed": 0, "errors": 0, "skipped": 0}
+    expected_returncode = (
+        {
+            "passed": 0,
+            "failed": ATTEMPT_ASSERTION_EXIT_CODE,
+            "error": ATTEMPT_ERROR_EXIT_CODE,
+            "skipped": 0,
+        }.get(result.outcome)
+        if result is not None
+        else None
+    )
+    if result is None or returncode != expected_returncode:
+        counts["errors"] = 1
+    elif result.outcome == "error":
+        counts["errors"] = 1
+    else:
+        counts[f"{result.outcome}"] = 1
     return counts
 
 
+def _retryable_attempt_failure(
+    *,
+    returncode: int,
+    result: TrainInfMismatchWorkerResult | None,
+    output: str,
+) -> bool:
+    if result is not None:
+        if result.outcome == "failed":
+            return result.comparison_completed
+        if result.outcome != "error" or result.comparison_completed:
+            return False
+    if returncode in {-9, -15}:
+        return True
+    details = "\n".join(
+        value
+        for value in (
+            result.exception_type if result is not None else None,
+            result.exception_message if result is not None else None,
+            output,
+        )
+        if value
+    ).lower()
+    return any(marker in details for marker in _TRANSIENT_STARTUP_ERRORS)
+
+
 def _attempt_limit() -> int:
     raw = os.environ.get("ART_TRAIN_INF_MISMATCH_ATTEMPTS")
     attempts = DEFAULT_ATTEMPTS if raw is None else int(raw)
@@ -61,11 +126,25 @@ def _attempt_limit() -> int:
     return min(attempts, MAX_ATTEMPTS)
 
 
+def _run_attempt(
+    command: list[str], *, cwd: Path, env: dict[str, str]
+) -> subprocess.CompletedProcess[str]:
+    return subprocess.run(
+        command,
+        cwd=cwd,
+        env=env,
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+
+
 def run_train_inf_mismatch(
     *,
     base_model: str,
     allow_unvalidated_arch: bool = False,
 ) -> TrainInfMismatchReport:
+    started = time.monotonic()
     artifact_dir = create_artifact_dir("workflow::train_inf_mismatch")
     max_attempts = _attempt_limit()
     env = os.environ.copy()
@@ -89,24 +168,35 @@ def run_train_inf_mismatch(
     for attempt in range(1, max_attempts + 1):
         stdout_path = artifact_dir / f"attempt_{attempt}_pytest_stdout.txt"
         stderr_path = artifact_dir / f"attempt_{attempt}_pytest_stderr.txt"
-        result = subprocess.run(
+        result_path = artifact_dir / f"attempt_{attempt}_result.json"
+        attempt_started = time.monotonic()
+        result = _run_attempt(
             [
                 sys.executable,
                 "-m",
-                "pytest",
-                "-q",
-                str(TEST_ROOT / "test_live_real_path_output_parity.py"),
-                "--tb=short",
+                "integration.megatron.train_inf_mismatch."
+                "test_live_real_path_output_parity",
+                "--workflow-attempt-result",
+                str(result_path),
             ],
             cwd=Path(REPO_ROOT),
             env=env,
-            capture_output=True,
-            text=True,
-            check=False,
         )
         stdout_path.write_text(result.stdout, encoding="utf-8")
         stderr_path.write_text(result.stderr, encoding="utf-8")
-        counts = _pytest_counts(result.stdout + "\n" + result.stderr)
+        try:
+            worker_result = TrainInfMismatchWorkerResult.model_validate_json(
+                result_path.read_text(encoding="utf-8")
+            )
+        except (OSError, ValueError):
+            worker_result = None
+        output = result.stdout + "\n" + result.stderr
+        counts = _attempt_counts(worker_result, returncode=result.returncode)
+        retryable = _retryable_attempt_failure(
+            returncode=result.returncode,
+            result=worker_result,
+            output=output,
+        )
         selected = TrainInfMismatchAttemptReport(
             attempt=attempt,
             returncode=result.returncode,
@@ -114,10 +204,19 @@ def run_train_inf_mismatch(
             stderr_path=str(stderr_path),
             passed_count=counts["passed"],
             failed_count=counts["failed"],
+            error_count=counts["errors"],
             skipped_count=counts["skipped"],
+            retryable=retryable,
+            duration_s=time.monotonic() - attempt_started,
         )
         attempts.append(selected)
-        if result.returncode == 0:
+        if (
+            result.returncode == 0
+            and selected.passed_count > 0
+            and selected.skipped_count == 0
+        ):
+            break
+        if not retryable:
             break
     if selected is None:
         raise RuntimeError("train/inf mismatch retry loop did not run")
@@ -125,6 +224,7 @@ def run_train_inf_mismatch(
         selected.returncode == 0
         and selected.passed_count > 0
         and selected.failed_count == 0
+        and selected.error_count == 0
         and selected.skipped_count == 0
     )
     return TrainInfMismatchReport(
@@ -137,8 +237,10 @@ def run_train_inf_mismatch(
         stderr_path=selected.stderr_path,
         passed_count=selected.passed_count,
         failed_count=selected.failed_count,
+        error_count=selected.error_count,
         skipped_count=selected.skipped_count,
         attempt_count=len(attempts),
         max_attempts=max_attempts,
         attempts=attempts,
+        duration_s=time.monotonic() - started,
     )
diff --git a/tests/integration/megatron/trainability/test_config.py b/tests/integration/megatron/trainability/test_config.py
index c68b7e210..b12406c6b 100644
--- a/tests/integration/megatron/trainability/test_config.py
+++ b/tests/integration/megatron/trainability/test_config.py
@@ -14,12 +14,20 @@
     LengthSampleReport,
     LengthTrainabilityReport,
     _default_learning_rate,
+    _length_current_step_demand,
+    _length_max_steps,
+    _length_rollout_seed,
+    _length_rollout_temperature,
+    _length_rollouts_per_prompt,
     _length_trainability_thresholds,
     _prompt_for_index,
     _target_tokens,
     _use_default_moe_dedicated_placement,
     length_trainability_passed,
 )
+from .test_live_length_trainability import (
+    _extra_body as _length_extra_body,
+)
 from .test_live_length_trainability import (
     _prompt_tree_shape as _length_prompt_tree_shape,
 )
@@ -29,7 +37,13 @@
     _build_internal_config,
     _build_variant,
     _default_variant_name,
+    _engine_args_for_yes_no_trainability,
     _evaluate_groups,
+    _get_env_int_list,
+    _max_tokens,
+    _render_chat_messages,
+    _rescore_groups,
+    _select_answer_target,
     _TrainabilityVariant,
     _variant_init_args,
     _variant_max_steps,
@@ -37,221 +51,82 @@
     _variant_rollouts_per_prompt,
     _variant_train_kwargs,
     build_prompts,
+    reward_for_answer,
     yes_no_trainability_passed,
 )
+from .yes_no_trainability import (
+    _extra_body as _yes_no_extra_body,
+)
 from .yes_no_trainability import (
     _prompt_tree_shape as _yes_no_prompt_tree_shape,
 )
 
 
-class _ConcurrentCompletions:
-    def __init__(self, expected: int) -> None:
-        self.expected = expected
-        self.started = 0
-        self.active = 0
-        self.max_active = 0
-        self.all_started = asyncio.Event()
-
-    async def create(self, **kwargs):
-        self.started += 1
-        self.active += 1
-        self.max_active = max(self.max_active, self.active)
-        if self.started == self.expected:
-            self.all_started.set()
-        try:
-            await asyncio.wait_for(self.all_started.wait(), timeout=1.0)
-            return ChatCompletion(
-                id=f"completion-{self.started}",
-                choices=[
-                    Choice(
-                        finish_reason="stop",
-                        index=0,
-                        message=ChatCompletionMessage(
-                            role="assistant",
-                            content="maybe",
-                        ),
-                    )
-                ],
-                created=0,
-                model=str(kwargs["model"]),
-                object="chat.completion",
-            )
-        finally:
-            self.active -= 1
-
-
-class _FakeChat:
-    def __init__(self, completions: _ConcurrentCompletions) -> None:
-        self.completions = completions
-
-
-class _FakeClient:
-    def __init__(self, completions: _ConcurrentCompletions) -> None:
-        self.chat = _FakeChat(completions)
-
-
-class _FakeModel:
-    def __init__(self, client: _FakeClient) -> None:
-        self.client = client
-
-    def openai_client(self) -> _FakeClient:
-        return self.client
-
-    def get_inference_name(self, *, step: int | None = None) -> str:
-        return f"fake@{step}"
-
-
-@pytest.mark.asyncio
-async def test_eval_prompts_are_submitted_concurrently() -> None:
-    completions = _ConcurrentCompletions(expected=3)
-
-    groups = await _evaluate_groups(
-        cast(art.TrainableModel, _FakeModel(_FakeClient(completions))),
-        base_model="Qwen/Qwen3-30B-A3B-Instruct-2507",
-        prompts=["a", "b", "c"],
-        step=1,
-    )
-
-    assert len(groups) == 3
-    assert completions.started == 3
-    assert completions.max_active == 3
-    assert [group.trajectories[0].reward for group in groups] == [1.0, 1.0, 1.0]
-
-
-def test_megatron_variants_keep_short_packed_sequence_default(monkeypatch) -> None:
-    monkeypatch.delenv("ART_MODEL_SUPPORT_YES_NO_PACKED_SEQUENCE_LENGTH", raising=False)
-    variant = _TrainabilityVariant(
-        name="megatron_shared",
-        backend_name="megatron",
-        placement_mode="shared",
-        trainer_gpu_ids=[0, 1],
-        inference_gpu_ids=[0, 1],
-    )
-
-    assert _variant_packed_sequence_length(variant) == 1024
-    assert _variant_train_kwargs(variant) == {}
-    config = _build_internal_config(
-        variant, base_model="Qwen/Qwen3-30B-A3B-Instruct-2507"
-    )
-    assert config["init_args"]["max_seq_length"] == 1024
-    assert config["rollout_weights_mode"] == "lora"
-    assert (
-        _default_variant_name("Qwen/Qwen3-30B-A3B-Instruct-2507") == "megatron_shared"
-    )
-    assert _variant_rollouts_per_prompt(variant) == 4
-    assert _variant_max_steps(variant) == 4
-
-
-def test_unsloth_variant_uses_chunk_aligned_training_length(monkeypatch) -> None:
-    monkeypatch.delenv("ART_MODEL_SUPPORT_YES_NO_PACKED_SEQUENCE_LENGTH", raising=False)
-    variant = _TrainabilityVariant(
-        name="unsloth_dedicated",
-        backend_name="local",
-        placement_mode="dedicated",
-        trainer_gpu_ids=[0],
-        inference_gpu_ids=[1],
-    )
-
-    assert _variant_packed_sequence_length(variant) == 1024
-    assert _variant_train_kwargs(variant) == {"packed_sequence_length": 1024}
-    assert _variant_init_args(variant) == {"max_seq_length": 1024}
-    assert _build_internal_config(
-        variant, base_model="Qwen/Qwen3-30B-A3B-Instruct-2507"
-    )["init_args"] == {"max_seq_length": 1024}
-    assert _variant_rollouts_per_prompt(variant) == 8
-    assert _variant_max_steps(variant) == 12
-
-
-def test_qwen3_5_defaults_to_shared_lora_rollout() -> None:
-    variant = _TrainabilityVariant(
-        name="megatron_shared",
-        backend_name="megatron",
-        placement_mode="shared",
-        trainer_gpu_ids=[0, 1],
-        inference_gpu_ids=[0, 1],
-    )
-
-    config = _build_internal_config(variant, base_model="Qwen/Qwen3.5-35B-A3B")
-
-    assert _default_variant_name("Qwen/Qwen3.5-35B-A3B") == "megatron_shared"
-    assert config["rollout_weights_mode"] == "lora"
-    assert "trainer_gpu_ids" not in config
-    assert "inference_gpu_ids" not in config
-
-
-def test_dense_yes_no_default_uses_dedicated_placement(monkeypatch) -> None:
-    monkeypatch.delenv("ART_MODEL_SUPPORT_YES_NO_VARIANT", raising=False)
-
-    assert _default_variant_name("Qwen/Qwen3-32B") == "megatron_dedicated"
-
-
-def test_yes_no_default_variant_env_override(monkeypatch) -> None:
-    monkeypatch.setenv("ART_MODEL_SUPPORT_YES_NO_VARIANT", "megatron_shared")
-
-    assert _default_variant_name("Qwen/Qwen3-32B") == "megatron_shared"
-
-
-def test_yes_no_trainability_passes_initially_saturated_stable_report() -> None:
-    report = YesNoTrainabilityReport(
-        variant="megatron_shared",
-        backend_name="megatron",
-        placement_mode="shared",
-        base_model="google/gemma-4-31B-it",
-        output_dir="/tmp/report",
-        trainer_gpu_ids=[0, 1],
-        inference_gpu_ids=[0, 1],
-        rollout_weights_mode="lora",
-        reward_threshold=0.9,
-        max_steps=4,
-        prompt_count=8,
-        eval_prompt_count=8,
-        rollouts_per_prompt=4,
-        latest_step=1,
-        initial_eval_reward=0.9375,
-        final_eval_reward=0.9375,
-        saturated_step=1,
-        step0_name="model@0",
-        latest_name="model@1",
-        steps=[
-            TrainabilityStepReport(
-                step=1,
-                eval_reward=0.9375,
-                train_reward=0.875,
-                train_metrics={"loss/grad_norm": 54.0},
-            )
-        ],
-    )
-
-    assert yes_no_trainability_passed(report) is True
-
-
-def test_yes_no_prompts_form_prefix_tree_by_default(monkeypatch) -> None:
-    monkeypatch.delenv("ART_MODEL_SUPPORT_YES_NO_PROMPT", raising=False)
-    monkeypatch.setenv("ART_MODEL_SUPPORT_YES_NO_PROMPT_COUNT", "8")
-
-    prompts = build_prompts()
-
-    assert _yes_no_prompt_tree_shape(prompts) == (3, 6)
-
-
-def test_qwen3_5_length_trainability_uses_stable_learning_rate() -> None:
-    assert _default_learning_rate("Qwen/Qwen3.5-35B-A3B") == 7e-5
+def test_qwen3_5_length_trainability_uses_stable_moe_defaults() -> None:
+    assert _default_learning_rate("Qwen/Qwen3.5-35B-A3B") == 1e-4
+    assert _length_rollouts_per_prompt("Qwen/Qwen3.5-35B-A3B") == 32
+    assert _length_max_steps("Qwen/Qwen3.5-35B-A3B") == 40
+    assert _length_max_steps("meta-llama/Llama-3.2-1B-Instruct") == 30
+    assert _length_max_steps("openai/gpt-oss-20b") == 30
+    assert _length_rollout_seed("Qwen/Qwen3.5-35B-A3B") == 20261833
+    assert _length_rollout_temperature("Qwen/Qwen3.5-35B-A3B") == 0.8
+    assert _length_current_step_demand("Qwen/Qwen3.5-35B-A3B") is True
     assert _default_learning_rate("Qwen/Qwen3-30B-A3B-Instruct-2507") == 1e-4
+    assert _length_rollouts_per_prompt("Qwen/Qwen3-30B-A3B-Instruct-2507") == 4
+    assert _length_max_steps("Qwen/Qwen3-30B-A3B-Instruct-2507") == 20
+    assert _length_rollout_seed("Qwen/Qwen3-30B-A3B-Instruct-2507") is None
+    assert _length_rollout_temperature("Qwen/Qwen3-30B-A3B-Instruct-2507") == 1.1
+    assert _length_current_step_demand("Qwen/Qwen3-30B-A3B-Instruct-2507") is False
+    assert _length_rollout_seed("openai/gpt-oss-20b") == 20261833
+    assert _length_current_step_demand("openai/gpt-oss-20b") is True
+
+
+def test_length_trainability_environment_overrides_model_defaults(monkeypatch) -> None:
+    monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_MAX_STEPS", "9")
+    monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_ROLLOUTS_PER_PROMPT", "6")
+    monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_ROLLOUT_SEED", "17")
+    monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_ROLLOUT_TEMPERATURE", "0.7")
+    monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_CURRENT_STEP_DEMAND", "0")
+
+    assert _length_max_steps("Qwen/Qwen3.5-35B-A3B") == 9
+    assert _length_rollouts_per_prompt("Qwen/Qwen3.5-35B-A3B") == 6
+    assert _length_rollout_seed("Qwen/Qwen3.5-35B-A3B") == 17
+    assert _length_rollout_seed("Qwen/Qwen3-30B-A3B-Instruct-2507") == 17
+    assert _length_rollout_temperature("Qwen/Qwen3.5-35B-A3B") == 0.7
+    assert _length_current_step_demand("Qwen/Qwen3.5-35B-A3B") is False
 
 
 def test_gpt_oss_length_target_accounts_for_harmony_tokens(monkeypatch) -> None:
+    assert _target_tokens("google/gemma-4-31B-it") == 22
     assert _target_tokens("openai/gpt-oss-20b") == 20
     assert _target_tokens("Qwen/Qwen3.5-35B-A3B") == 10
+    assert _target_tokens("zai-org/GLM-5.2") == 12
     monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_TARGET_TOKENS", "24")
     assert _target_tokens("openai/gpt-oss-20b") == 24
 
 
+def test_length_trainability_default_success_threshold() -> None:
+    thresholds = _length_trainability_thresholds("zai-org/GLM-5.2")
+
+    assert thresholds.success_abs_error_max == 2
+
+
 def test_length_prompts_form_prefix_tree_by_default() -> None:
     prompts = [_prompt_for_index(index)[0] for index in range(4)]
 
     assert _length_prompt_tree_shape(prompts) == (3, 6)
 
 
+def test_glm52_length_prompt_requests_a_fuller_initial_answer() -> None:
+    default_prompt = _prompt_for_index(0)[0]
+    glm52_prompt = _prompt_for_index(0, base_model="zai-org/GLM-5.2")[0]
+
+    assert "Use one sentence." in default_prompt
+    assert (
+        "Use two complete sentences with one concrete detail in each." in glm52_prompt
+    )
+
+
 def test_length_trainability_accepts_near_baseline_learning_signal() -> None:
     report = LengthTrainabilityReport(
         base_model="google/gemma-4-31B-it",
@@ -262,7 +137,6 @@ def test_length_trainability_accepts_near_baseline_learning_signal() -> None:
         trainer_gpu_ids=[0],
         inference_gpu_ids=[1],
         training_topology={"tp": 1, "cp": 1, "ep": 1, "etp": 1, "dp": 1, "sp": False},
-        rollout_weights_mode="lora",
         rollouts_per_prompt=4,
         normalize_advantages=True,
         summary_log_path="/tmp/length_trainability.log",
@@ -356,7 +230,6 @@ def test_validated_dense_model_uses_dense_shared_topology(
     )
 
     config = _build_internal_config(variant, base_model="Qwen/Qwen3.5-4B")
-    assert config["rollout_weights_mode"] == "lora"
     assert config["engine_args"]["enable_sleep_mode"] is True
     assert "enable_expert_parallel" not in config["engine_args"]
 
@@ -373,7 +246,6 @@ def test_qwen3_5_moe_shared_variant_enables_expert_parallel(monkeypatch) -> None
 
     config = _build_internal_config(variant, base_model="Qwen/Qwen3.5-35B-A3B")
 
-    assert config["rollout_weights_mode"] == "lora"
     assert config["engine_args"]["enable_expert_parallel"] is True
 
 
@@ -387,13 +259,17 @@ def test_dsv4_trainability_uses_large_model_dedicated_resources(
         "get_device_properties",
         lambda device: SimpleNamespace(total_memory=284 * 1024**3),
     )
+
+    def unexpected_memory_probe(_device_ids) -> float:
+        raise AssertionError("external vLLM must not probe resident inference memory")
+
     monkeypatch.setattr(
         "tests.integration.megatron.trainability.yes_no_trainability."
         "_safe_gpu_memory_utilization",
-        lambda device_ids: 0.5,
+        unexpected_memory_probe,
     )
     monkeypatch.setenv("ART_MODEL_SUPPORT_EXTERNAL_VLLM_URL", "http://127.0.0.1:8000")
-
+    monkeypatch.setenv("ART_MODEL_SUPPORT_EXTERNAL_VLLM_HEALTH_TIMEOUT", "1200")
     default_variant = _default_variant_name(
         "deepseek-ai/DeepSeek-V4-Flash",
     )
@@ -409,21 +285,22 @@ def test_dsv4_trainability_uses_large_model_dedicated_resources(
     assert default_variant == "megatron_dedicated"
     assert variant.topology is not None
     assert variant.topology.tp == 2
-    assert variant.topology.ep == 2
+    assert variant.topology.ep == 4
     assert variant.topology.cp == 1
-    assert variant.topology.dp == 1
+    assert variant.topology.dp == 2
     assert variant.topology.sp is True
-    assert variant.trainer_gpu_ids == [0, 1]
+    assert variant.trainer_gpu_ids == [0, 1, 2, 3]
     assert variant.inference_gpu_ids == [2, 3]
     assert config["engine_args"]["tensor_parallel_size"] == 2
     assert config["engine_args"]["enable_expert_parallel"] is True
     assert config["engine_args"]["kv_cache_dtype"] == "fp8"
-    assert config["engine_args"].get("moe_backend") == "triton_unfused"
+    assert config["engine_args"].get("moe_backend") == "auto"
     assert "megatron_topology" not in config
     assert config["vllm_runtime"] == {
         "mode": "external",
         "server_url": "http://127.0.0.1:8000",
         "api_key": "art-external-vllm",
+        "health_timeout_s": 1200.0,
     }
 
 
diff --git a/tests/integration/megatron/trainability/test_live_length_trainability.py b/tests/integration/megatron/trainability/test_live_length_trainability.py
index 96ec424e6..c85583897 100644
--- a/tests/integration/megatron/trainability/test_live_length_trainability.py
+++ b/tests/integration/megatron/trainability/test_live_length_trainability.py
@@ -7,7 +7,8 @@
 from pathlib import Path
 import random
 import shutil
-from typing import Any, AsyncIterator, Literal, cast
+import time
+from typing import Any, AsyncIterator, Awaitable, Callable, Literal, cast
 import uuid
 
 from pydantic import BaseModel, Field
@@ -29,8 +30,10 @@
     _get_env_bool,
     _get_env_float,
     _get_env_int,
+    _get_env_int_list,
     _init_megatron_runtime_config,
     _list_model_ids,
+    _temporary_env,
     _topology_with_env_overrides,
     _trainability_stage_resources,
 )
@@ -39,18 +42,26 @@
 
 DEFAULT_BASE_MODEL = "Qwen/Qwen3.5-35B-A3B"
 DEFAULT_LENGTH_LEARNING_RATE = 1e-4
-LARGE_MOE_LENGTH_LEARNING_RATE = 7e-5
+LENGTH_MAX_STEPS_BY_MODEL = {
+    "llama3_dense": 30,
+    "qwen3_5_moe": 40,
+    "gpt_oss_moe": 30,
+}
+QWEN3_5_MOE_LENGTH_ROLLOUTS_PER_PROMPT = 32
+DETERMINISTIC_LENGTH_ROLLOUT_SEED = 20261833
+QWEN3_5_MOE_LENGTH_ROLLOUT_TEMPERATURE = 0.8
 LIVE_ENV = "ART_RUN_LIVE_LENGTH_TRAINABILITY"
 TRAINER_GPU_IDS_ENV = "ART_MODEL_SUPPORT_TRAINER_GPU_IDS"
 INFERENCE_GPU_IDS_ENV = "ART_MODEL_SUPPORT_INFERENCE_GPU_IDS"
 REPO_ROOT = Path(__file__).resolve().parents[4]
 LATEST_SUMMARY_LOG_PATH = REPO_ROOT / ".local" / "length_trainability.log"
 DEFAULT_INITIAL_ABS_ERROR_MIN = 5.0
-DEFAULT_SUCCESS_ABS_ERROR_MAX = 1.5
+DEFAULT_SUCCESS_ABS_ERROR_MAX = 2.0
 GPT_OSS_INITIAL_ABS_ERROR_MIN = 100.0
 GPT_OSS_SUCCESS_ABS_ERROR_MAX = 5.0
 GPT_OSS_TARGET_TOKENS = 20
 GEMMA4_TARGET_TOKENS = 22
+GLM52_TARGET_TOKENS = 12
 GEMMA4_LENGTH_LEARNING_RATE = 3e-5
 DEFAULT_LENGTH_MAX_STEPS = 20
 GPT_OSS_MIN_MAX_TOKENS = 512
@@ -164,6 +175,13 @@ class LengthTrainabilityThresholds(BaseModel):
     success_abs_error_max: float
 
 
+class LengthTrainingPhaseReport(BaseModel):
+    name: Literal["complete", "first_update", "continuation"]
+    start_step: int
+    end_step: int
+    duration_s: float
+
+
 class LengthTrainabilityReport(BaseModel):
     base_model: str
     max_steps: int
@@ -173,7 +191,6 @@ class LengthTrainabilityReport(BaseModel):
     trainer_gpu_ids: list[int]
     inference_gpu_ids: list[int]
     training_topology: dict[str, int | bool]
-    rollout_weights_mode: str
     rollouts_per_prompt: int
     prompt_tree_depth: int = 0
     prompt_tree_branch_count: int = 0
@@ -188,6 +205,13 @@ class LengthTrainabilityReport(BaseModel):
     final_train_abs_error: float | None
     model_ids_after: list[str]
     samples: list[LengthSampleReport]
+    phases: list[LengthTrainingPhaseReport] = Field(default_factory=list)
+
+
+LengthResidentHook = Callable[
+    [Literal["registered", "first_update"], Any, art.TrainableModel, int],
+    Awaitable[None],
+]
 
 
 def _require_opt_in() -> None:
@@ -225,7 +249,9 @@ def _word_count(text: str) -> int:
 def _target_tokens(base_model: str | None = None) -> int:
     model_key = _model_support_key(base_model)
     default = {
+        "gemma4_dense": GEMMA4_TARGET_TOKENS,
         "gemma4_moe": GEMMA4_TARGET_TOKENS,
+        "glm52": GLM52_TARGET_TOKENS,
         "gpt_oss_moe": GPT_OSS_TARGET_TOKENS,
     }.get(model_key, 10)
     return _get_env_int("ART_MODEL_SUPPORT_LENGTH_TARGET_TOKENS", default)
@@ -234,8 +260,6 @@ def _target_tokens(base_model: str | None = None) -> int:
 def _default_learning_rate(base_model: str) -> float:
     if _model_support_key(base_model) == "gemma4_moe":
         return GEMMA4_LENGTH_LEARNING_RATE
-    if base_model == DEFAULT_BASE_MODEL:
-        return LARGE_MOE_LENGTH_LEARNING_RATE
     return DEFAULT_LENGTH_LEARNING_RATE
 
 
@@ -325,7 +349,11 @@ def _base_max_tokens(target_tokens: int, *, base_model: str | None = None) -> in
     return max_tokens
 
 
-def _prompt_for_index(index: int) -> tuple[str, int]:
+def _prompt_for_index(
+    index: int,
+    *,
+    base_model: str | None = None,
+) -> tuple[str, int]:
     target_words = _get_env_int("ART_MODEL_SUPPORT_LENGTH_PROMPT_WORDS", 300)
     rng = random.Random(index)
     sentences = list(FILLER_SENTENCES)
@@ -333,7 +361,13 @@ def _prompt_for_index(index: int) -> tuple[str, int]:
     selected: list[str] = []
     mid = LENGTH_PROMPT_MIDS[(index // 2) % len(LENGTH_PROMPT_MIDS)]
     leaf = LENGTH_PROMPT_LEAVES[index % len(LENGTH_PROMPT_LEAVES)]
-    prefix = f"{BASE_PROMPT}\n\n{mid}\n\n{leaf}"
+    base_prompt = BASE_PROMPT
+    if _model_support_key(base_model) == "glm52":
+        base_prompt = base_prompt.replace(
+            "Use one sentence.",
+            "Use two complete sentences with one concrete detail in each.",
+        )
+    prefix = f"{base_prompt}\n\n{mid}\n\n{leaf}"
     prompt = prefix
     for sentence in sentences:
         if _word_count(prompt) >= target_words:
@@ -366,7 +400,7 @@ def _scenario(
 ) -> LengthScenario:
     target_tokens = _target_tokens(base_model)
     max_tokens = _base_max_tokens(target_tokens, base_model=base_model)
-    prompt, prompt_word_count = _prompt_for_index(index)
+    prompt, prompt_word_count = _prompt_for_index(index, base_model=base_model)
     return LengthScenario(
         scenario_index=index,
         target_step=index if target_step is None else target_step,
@@ -441,10 +475,28 @@ def _messages(
     return messages
 
 
-def _extra_body(chat_template_kwargs: dict[str, Any]) -> dict[str, object]:
-    return (
+def _extra_body(
+    chat_template_kwargs: dict[str, Any], *, seed: int | None = None
+) -> dict[str, object]:
+    body: dict[str, object] = (
         {"chat_template_kwargs": chat_template_kwargs} if chat_template_kwargs else {}
     )
+    allowed_token_ids = _get_env_int_list("ART_MODEL_SUPPORT_LENGTH_ALLOWED_TOKEN_IDS")
+    if allowed_token_ids is not None:
+        body["allowed_token_ids"] = allowed_token_ids
+    if (
+        min_tokens := os.environ.get("ART_MODEL_SUPPORT_LENGTH_MIN_TOKENS")
+    ) is not None:
+        body["min_tokens"] = int(min_tokens)
+    if (
+        frequency_penalty := os.environ.get(
+            "ART_MODEL_SUPPORT_LENGTH_FREQUENCY_PENALTY"
+        )
+    ) is not None:
+        body["frequency_penalty"] = float(frequency_penalty)
+    if seed is not None:
+        body["seed"] = seed
+    return body
 
 
 def _length_chat_template_kwargs(base_model: str, tokenizer: object) -> dict[str, Any]:
@@ -465,13 +517,48 @@ def _scenario_limit() -> int | None:
     return _get_env_int("ART_MODEL_SUPPORT_LENGTH_SCENARIOS", 0)
 
 
-def _length_max_steps() -> int:
+def _length_max_steps(base_model: str) -> int:
     return _get_env_int(
         "ART_MODEL_SUPPORT_LENGTH_MAX_STEPS",
-        DEFAULT_LENGTH_MAX_STEPS,
+        LENGTH_MAX_STEPS_BY_MODEL.get(
+            _model_support_key(base_model), DEFAULT_LENGTH_MAX_STEPS
+        ),
     )
 
 
+def _length_rollouts_per_prompt(base_model: str) -> int:
+    return _get_env_int(
+        "ART_MODEL_SUPPORT_LENGTH_ROLLOUTS_PER_PROMPT",
+        QWEN3_5_MOE_LENGTH_ROLLOUTS_PER_PROMPT
+        if _model_support_key(base_model) == "qwen3_5_moe"
+        else 4,
+    )
+
+
+def _length_current_step_demand(base_model: str) -> bool:
+    return _get_env_bool(
+        "ART_MODEL_SUPPORT_LENGTH_CURRENT_STEP_DEMAND",
+        _model_support_key(base_model) in {"gpt_oss_moe", "qwen3_5_moe"},
+    )
+
+
+def _length_rollout_temperature(base_model: str) -> float:
+    return _get_env_float(
+        "ART_MODEL_SUPPORT_LENGTH_ROLLOUT_TEMPERATURE",
+        QWEN3_5_MOE_LENGTH_ROLLOUT_TEMPERATURE
+        if _model_support_key(base_model) == "qwen3_5_moe"
+        else 1.1,
+    )
+
+
+def _length_rollout_seed(base_model: str) -> int | None:
+    if (seed := os.environ.get("ART_MODEL_SUPPORT_LENGTH_ROLLOUT_SEED")) is not None:
+        return int(seed)
+    if _model_support_key(base_model) in {"gpt_oss_moe", "qwen3_5_moe"}:
+        return DETERMINISTIC_LENGTH_ROLLOUT_SEED
+    return None
+
+
 def _zero_variance_discard_multiplier(max_steps: int) -> int:
     return _get_env_int(
         "ART_MODEL_SUPPORT_LENGTH_ZERO_VARIANCE_DISCARD_MULTIPLIER",
@@ -543,6 +630,7 @@ async def _length_group(
         )
         for completion_index in range(n)
     ]
+    seed = _length_rollout_seed(base_model)
     trajectories: list[art.Trajectory] = []
     completions = await asyncio.gather(
         *(
@@ -552,7 +640,14 @@ async def _length_group(
                 max_tokens=max_tokens,
                 n=1,
                 temperature=temperature,
-                extra_body=_extra_body(chat_template_kwargs),
+                extra_body=_extra_body(
+                    chat_template_kwargs,
+                    seed=(
+                        None
+                        if seed is None
+                        else seed + scenario.scenario_index * n + completion_index
+                    ),
+                ),
                 logprobs=True,
                 top_logprobs=0,
                 timeout=_get_env_float(
@@ -560,7 +655,7 @@ async def _length_group(
                     900.0,
                 ),
             )
-            for max_tokens in max_tokens_by_completion
+            for completion_index, max_tokens in enumerate(max_tokens_by_completion)
         )
     )
     for max_tokens, completion in zip(
@@ -688,8 +783,12 @@ async def run_length_trainability_async(
     base_model: str = DEFAULT_BASE_MODEL,
     artifact_dir: Path | None = None,
     allow_unvalidated_arch: bool = False,
+    resident_hook: LengthResidentHook | None = None,
+    registration_ready: Awaitable[object] | None = None,
+    first_update_learning_rate: float | None = None,
 ) -> LengthTrainabilityReport:
     artifact_dir = artifact_dir or _artifact_dir(base_model)
+    artifact_dir.mkdir(parents=True, exist_ok=True)
     variant = _build_variant(
         "megatron_dedicated",
         base_model=base_model,
@@ -697,28 +796,40 @@ async def run_length_trainability_async(
         resource_stage_name="length_trainability",
     )
     _use_default_moe_dedicated_placement(variant, base_model=base_model)
-    max_steps = _length_max_steps()
-    max_steps_off_policy = _get_env_int(
-        "ART_MODEL_SUPPORT_LENGTH_MAX_STEPS_OFF_POLICY",
-        0,
-    )
-    rollouts_per_prompt = _get_env_int(
-        "ART_MODEL_SUPPORT_LENGTH_ROLLOUTS_PER_PROMPT",
-        4,
-    )
-    normalize_advantages = _get_env_bool(
-        "ART_MODEL_SUPPORT_LENGTH_NORMALIZE_ADVANTAGES",
-        True,
-    )
-    rollout_workers = _get_env_int(
-        "ART_MODEL_SUPPORT_LENGTH_ROLLOUT_WORKERS",
-        max(1, max_steps_off_policy + 1),
+    stage_resources = _trainability_stage_resources(
+        base_model,
+        stage_name="length_trainability",
+        allow_unvalidated_arch=allow_unvalidated_arch,
     )
-    thresholds = _length_trainability_thresholds(base_model)
-    scenario_limit = _scenario_limit()
-    zero_variance_discard_multiplier = _zero_variance_discard_multiplier(max_steps)
+    backend_env = stage_resources.megatron_env if stage_resources is not None else {}
+    with _temporary_env(backend_env):
+        max_steps = _length_max_steps(base_model)
+        if resident_hook is not None and max_steps < 2:
+            raise ValueError(
+                "resident functional validation requires at least two steps"
+            )
+        max_steps_off_policy = _get_env_int(
+            "ART_MODEL_SUPPORT_LENGTH_MAX_STEPS_OFF_POLICY",
+            0,
+        )
+        rollouts_per_prompt = _length_rollouts_per_prompt(base_model)
+        normalize_advantages = _get_env_bool(
+            "ART_MODEL_SUPPORT_LENGTH_NORMALIZE_ADVANTAGES",
+            True,
+        )
+        rollout_workers = _get_env_int(
+            "ART_MODEL_SUPPORT_LENGTH_ROLLOUT_WORKERS",
+            max(1, max_steps_off_policy + 1),
+        )
+        thresholds = _length_trainability_thresholds(base_model)
+        scenario_limit = _scenario_limit()
+        zero_variance_discard_multiplier = _zero_variance_discard_multiplier(max_steps)
+        current_step_demand = _length_current_step_demand(base_model)
     success_hit = False
+    pending_trainable_step: int | None = None
+    scenario_index = 0
     samples: list[LengthSampleReport] = []
+    phases: list[LengthTrainingPhaseReport] = []
     backend_root = artifact_dir / "megatron_dedicated_workspace"
     summary_log_path = artifact_dir / "length_trainability.log"
     _init_summary_log(summary_log_path)
@@ -728,33 +839,30 @@ async def run_length_trainability_async(
         allow_unvalidated_arch=allow_unvalidated_arch,
         resource_stage_name="length_trainability",
     )
-    internal_config["engine_args"]["max_model_len"] = _get_env_int(
+    max_model_len = _get_env_int(
         "ART_MODEL_SUPPORT_LENGTH_MAX_MODEL_LEN",
         1024,
     )
+    internal_config["engine_args"]["max_model_len"] = max_model_len
+    internal_config["init_args"]["max_seq_length"] = max_model_len
     internal_config["engine_args"]["max_num_seqs"] = _get_env_int(
         "ART_MODEL_SUPPORT_LENGTH_MAX_NUM_SEQS",
-        4,
+        max(4, rollouts_per_prompt),
     )
     from transformers import AutoTokenizer
 
     tokenizer = AutoTokenizer.from_pretrained(base_model)
     chat_template_kwargs = _length_chat_template_kwargs(base_model, tokenizer)
-    rollout_weights_mode = internal_config["rollout_weights_mode"]
-    stage_resources = _trainability_stage_resources(
-        base_model,
-        stage_name="length_trainability",
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
-    _init_megatron_runtime_config(
-        variant,
-        streaming_weight_offload=(
-            stage_resources.streaming_weight_offload
-            if stage_resources is not None
-            else False
-        ),
-    )
-    backend_env = stage_resources.megatron_env if stage_resources is not None else {}
+    with _temporary_env(backend_env):
+        _init_megatron_runtime_config(
+            variant,
+            packed_sequence_length=max_model_len,
+            streaming_weight_offload=(
+                stage_resources.streaming_weight_offload
+                if stage_resources is not None
+                else False
+            ),
+        )
 
     async with _backend_context(
         variant,
@@ -770,26 +878,48 @@ async def run_length_trainability_async(
             _internal_config=internal_config,
             report_metrics=[],
         )
+        if registration_ready is not None:
+            await registration_ready
         await model.register(backend)
+        registered_step = await model.get_step()
+        if resident_hook is not None:
+            await resident_hook("registered", backend, model, registered_step)
+
+        trainer: PipelineTrainer | None = None
 
         async def scenarios() -> AsyncIterator[dict[str, object]]:
-            index = 0
+            nonlocal pending_trainable_step, scenario_index
             while not success_hit and (
-                scenario_limit is None or index < scenario_limit
+                scenario_limit is None or scenario_index < scenario_limit
             ):
+                required_step = pending_trainable_step
+                if current_step_demand and required_step is not None:
+                    assert trainer is not None
+                    active_trainer = trainer
+                    async with active_trainer.state.policy_updated:
+                        await active_trainer.state.policy_updated.wait_for(
+                            lambda: (
+                                active_trainer.state.done
+                                or active_trainer.state.policy_version > required_step
+                            )
+                        )
+                    pending_trainable_step = None
+                    if active_trainer.state.done:
+                        return
+                index = scenario_index
+                scenario_index += 1
                 yield _scenario(
                     index,
                     target_step=0,
                     base_model=base_model,
                 ).model_dump()
-                index += 1
 
         async def rollout_fn(
             rollout_model: art.TrainableModel,
             scenario: dict[str, object],
             _config: None,
         ) -> art.TrajectoryGroup:
-            nonlocal success_hit
+            nonlocal pending_trainable_step, success_hit
             model_name = rollout_model.get_inference_name()
             target_step = _step_from_model_name(model_name)
             if target_step is None:
@@ -802,14 +932,19 @@ async def rollout_fn(
                 split="train",
                 step=target_step,
                 n=rollouts_per_prompt,
-                temperature=_get_env_float(
-                    "ART_MODEL_SUPPORT_LENGTH_ROLLOUT_TEMPERATURE",
-                    1.1,
-                ),
+                temperature=_length_rollout_temperature(base_model),
                 chat_template_kwargs=chat_template_kwargs,
                 samples=samples,
                 summary_log_path=summary_log_path,
             )
+            rewards = [trajectory.reward for trajectory in group.trajectories]
+            if current_step_demand:
+                pending_trainable_step = (
+                    target_step
+                    if len(rewards) > 1
+                    and any(abs(reward - rewards[0]) > 1e-12 for reward in rewards[1:])
+                    else None
+                )
             if _success_abs_error_passed(
                 _mean_abs_error_by_step(
                     [sample for sample in samples if sample.split == "train"]
@@ -819,37 +954,83 @@ async def rollout_fn(
                 success_hit = True
             return group
 
-        trainer = PipelineTrainer(
-            model=model,
-            backend=backend,
-            rollout_fn=rollout_fn,
-            scenarios=scenarios(),
-            config=None,
-            pipeline=PipelineRuntimeConfig(
-                num_rollout_workers=rollout_workers,
-                min_batch_size=1,
-                max_batch_size=1,
-            ),
-            max_steps_off_policy=max_steps_off_policy,
-            learning_rate=_get_env_float(
-                "ART_MODEL_SUPPORT_LENGTH_LEARNING_RATE",
-                _default_learning_rate(base_model),
-            ),
-            loss_fn="cispo",
-            normalize_advantages=normalize_advantages,
-            max_steps=max_steps,
-            eval_every_n_steps=0,
-            eval_at_start=False,
-            save_checkpoint=False,
-            total_scenarios=scenario_limit,
-            log_interval_seconds=30.0,
-            discard_queue_multiplier=zero_variance_discard_multiplier,
-            resume=False,
+        learning_rate = _get_env_float(
+            "ART_MODEL_SUPPORT_LENGTH_LEARNING_RATE",
+            _default_learning_rate(base_model),
         )
-        await trainer.train(handle_signals=False)
+
+        def build_trainer(steps: int, phase_learning_rate: float) -> PipelineTrainer:
+            return PipelineTrainer(
+                model=model,
+                backend=backend,
+                rollout_fn=rollout_fn,
+                scenarios=scenarios(),
+                config=None,
+                pipeline=PipelineRuntimeConfig(
+                    num_rollout_workers=rollout_workers,
+                    min_batch_size=1,
+                    max_batch_size=1,
+                ),
+                max_steps_off_policy=max_steps_off_policy,
+                learning_rate=phase_learning_rate,
+                loss_fn="cispo",
+                normalize_advantages=normalize_advantages,
+                max_steps=steps,
+                eval_every_n_steps=0,
+                eval_at_start=False,
+                save_checkpoint=False,
+                total_scenarios=scenario_limit,
+                log_interval_seconds=30.0,
+                discard_queue_multiplier=zero_variance_discard_multiplier,
+                resume=False,
+            )
+
+        phase_steps = (1, max_steps - 1) if resident_hook is not None else (max_steps,)
+        for phase_index, steps in enumerate(phase_steps):
+            if steps <= 0:
+                continue
+            phase_start = await model.get_step()
+            started = time.monotonic()
+            phase_learning_rate = (
+                first_update_learning_rate
+                if phase_index == 0 and first_update_learning_rate is not None
+                else learning_rate
+            )
+            trainer = build_trainer(steps, phase_learning_rate)
+            await trainer.train(handle_signals=False)
+            phase_end = await model.get_step()
+            if resident_hook is not None and phase_index == 0:
+                pending_trainable_step = None
+                if current_step_demand:
+                    # Trainer shutdown may prefetch but not execute the next scenario.
+                    scenario_index = phase_end
+            phases.append(
+                LengthTrainingPhaseReport(
+                    name=(
+                        "complete"
+                        if resident_hook is None
+                        else "first_update"
+                        if phase_index == 0
+                        else "continuation"
+                    ),
+                    start_step=phase_start,
+                    end_step=phase_end,
+                    duration_s=time.monotonic() - started,
+                )
+            )
+            if resident_hook is not None and phase_index == 0:
+                if phase_end != phase_start + 1:
+                    raise RuntimeError(
+                        "resident functional phase must advance exactly one policy step: "
+                        f"{phase_start} -> {phase_end}"
+                    )
+                async with backend.exact_adapter_lease(model, phase_end):
+                    await resident_hook("first_update", backend, model, phase_end)
+                success_hit = False
 
         latest_step = await model.get_step()
-        model_ids_after = await _list_model_ids(model)
+        async with backend.exact_adapter_lease(model, latest_step):
+            model_ids_after = await _list_model_ids(model)
 
     train_samples = [sample for sample in samples if sample.split == "train"]
     train_rewards_by_step = {
@@ -897,7 +1078,6 @@ async def rollout_fn(
         trainer_gpu_ids=variant.trainer_gpu_ids,
         inference_gpu_ids=variant.inference_gpu_ids,
         training_topology=cast(dict[str, int | bool], topology.model_dump()),
-        rollout_weights_mode=rollout_weights_mode,
         rollouts_per_prompt=rollouts_per_prompt,
         prompt_tree_depth=prompt_tree_depth,
         prompt_tree_branch_count=prompt_tree_branch_count,
@@ -912,6 +1092,7 @@ async def rollout_fn(
         final_train_abs_error=final_train_abs_error,
         model_ids_after=model_ids_after,
         samples=samples,
+        phases=phases,
     )
     (artifact_dir / "length_trainability.json").write_text(
         json.dumps(report.model_dump(mode="json"), indent=2, sort_keys=True) + "\n",
@@ -933,6 +1114,23 @@ def run_length_trainability(
     )
 
 
+def _resident_phase_contract_passed(report: LengthTrainabilityReport) -> bool:
+    if all(phase.name == "complete" for phase in report.phases):
+        return True
+    if len(report.phases) != 2:
+        return False
+    first_update, continuation = report.phases
+    return (
+        first_update.name == "first_update"
+        and first_update.start_step == 0
+        and first_update.end_step == 1
+        and continuation.name == "continuation"
+        and continuation.start_step == 1
+        and continuation.end_step == report.latest_step
+        and continuation.end_step > continuation.start_step
+    )
+
+
 def length_trainability_passed(report: LengthTrainabilityReport) -> bool:
     thresholds = report.thresholds
     train_samples = [sample for sample in report.samples if sample.split == "train"]
@@ -942,6 +1140,7 @@ def length_trainability_passed(report: LengthTrainabilityReport) -> bool:
     }
     return (
         bool(train_samples)
+        and _resident_phase_contract_passed(report)
         and report.latest_step <= report.max_steps
         and report.initial_train_abs_error is not None
         and _initial_abs_error_passed(report.initial_train_abs_error, thresholds)
@@ -966,6 +1165,7 @@ def assert_length_trainability_passed(report: LengthTrainabilityReport) -> None:
         for step in {sample.step for sample in train_samples}
     }
     assert train_samples
+    assert _resident_phase_contract_passed(report)
     assert report.latest_step <= report.max_steps
     assert report.initial_train_abs_error is not None
     assert _initial_abs_error_passed(report.initial_train_abs_error, thresholds)
diff --git a/tests/integration/megatron/trainability/test_live_yes_no_trainability.py b/tests/integration/megatron/trainability/test_live_yes_no_trainability.py
deleted file mode 100644
index a12353752..000000000
--- a/tests/integration/megatron/trainability/test_live_yes_no_trainability.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import json
-import os
-from pathlib import Path
-
-import pytest
-
-from .yes_no_trainability import (
-    run_yes_no_trainability_async,
-    yes_no_trainability_passed,
-)
-
-torch = pytest.importorskip("torch")
-
-DEFAULT_BASE_MODEL = "Qwen/Qwen3-30B-A3B-Instruct-2507"
-LIVE_ENV = "ART_RUN_LIVE_YES_NO_TRAINABILITY"
-
-
-def _require_opt_in() -> None:
-    if os.environ.get(LIVE_ENV) != "1":
-        pytest.skip(f"set {LIVE_ENV}=1 to run live yes/no trainability validation")
-
-
-def _base_model() -> str:
-    return os.environ.get(
-        "ART_LIVE_YES_NO_BASE_MODEL",
-        os.environ.get("BASE_MODEL", DEFAULT_BASE_MODEL),
-    )
-
-
-def _unsloth_base_model() -> str:
-    return os.environ.get("ART_LIVE_UNSLOTH_YES_NO_BASE_MODEL", _base_model())
-
-
-def _assert_passed(report) -> None:
-    assert yes_no_trainability_passed(report)
-    assert report.latest_step > 0
-    assert report.step0_name in report.model_ids_before
-    assert report.latest_name in report.model_ids_after
-    if report.rollout_weights_mode == "merged":
-        assert report.step0_name not in report.model_ids_after
-    else:
-        assert report.step0_name in report.model_ids_after
-    assert report.latest_snapshot["has_logprobs"] is True
-
-
-def _write_report(artifact_dir: Path, name: str, report) -> None:
-    (artifact_dir / name).write_text(
-        json.dumps(report.model_dump(mode="json"), indent=2, sort_keys=True) + "\n",
-        encoding="utf-8",
-    )
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available() or torch.cuda.device_count() < 2,
-    reason="Need at least 2 CUDA GPUs for live yes/no trainability validation",
-)
-@pytest.mark.asyncio
-async def test_megatron_shared_yes_no_trainability_live(
-    artifact_dir: Path,
-) -> None:
-    _require_opt_in()
-    report = await run_yes_no_trainability_async(
-        base_model=_base_model(),
-        variant_name="megatron_shared",
-        artifact_root=artifact_dir / "megatron_shared_workspace",
-    )
-    _write_report(artifact_dir, "megatron_shared_yes_no_trainability.json", report)
-    _assert_passed(report)
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available() or torch.cuda.device_count() < 2,
-    reason="Need at least 2 CUDA GPUs for live yes/no trainability validation",
-)
-@pytest.mark.asyncio
-async def test_megatron_dedicated_yes_no_trainability_live(
-    artifact_dir: Path,
-) -> None:
-    _require_opt_in()
-    report = await run_yes_no_trainability_async(
-        base_model=_base_model(),
-        variant_name="megatron_dedicated",
-        artifact_root=artifact_dir / "megatron_dedicated_workspace",
-    )
-    _write_report(artifact_dir, "megatron_dedicated_yes_no_trainability.json", report)
-    _assert_passed(report)
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available() or torch.cuda.device_count() < 2,
-    reason="Need at least 2 CUDA GPUs for live yes/no trainability validation",
-)
-@pytest.mark.asyncio
-async def test_unsloth_dedicated_yes_no_trainability_live(
-    artifact_dir: Path,
-) -> None:
-    _require_opt_in()
-    report = await run_yes_no_trainability_async(
-        base_model=_unsloth_base_model(),
-        variant_name="unsloth_dedicated",
-        artifact_root=artifact_dir / "unsloth_dedicated_workspace",
-    )
-    _write_report(artifact_dir, "unsloth_dedicated_yes_no_trainability.json", report)
-    _assert_passed(report)
diff --git a/tests/integration/megatron/trainability/yes_no_trainability.py b/tests/integration/megatron/trainability/yes_no_trainability.py
index a0718bf27..af2d2d06a 100644
--- a/tests/integration/megatron/trainability/yes_no_trainability.py
+++ b/tests/integration/megatron/trainability/yes_no_trainability.py
@@ -10,6 +10,7 @@
 from typing import Any, AsyncIterator, Iterator, Literal, TypedDict, cast
 import uuid
 
+from openai.types.chat.chat_completion import Choice
 from pydantic import BaseModel, Field
 import torch
 
@@ -22,7 +23,6 @@
     model_supports_context_parallel,
     model_uses_expert_parallel,
 )
-from art.megatron.model_support.spec import RolloutWeightsMode
 
 from ..model_support.oracle_harness import Topology, oracle_topology
 from ..model_support.oracle_worker import provider_topology_env
@@ -37,6 +37,7 @@
 _VARIANT_ENV = "ART_MODEL_SUPPORT_YES_NO_VARIANT"
 _EXTERNAL_VLLM_URL_ENV = "ART_MODEL_SUPPORT_EXTERNAL_VLLM_URL"
 _EXTERNAL_VLLM_API_KEY_ENV = "ART_MODEL_SUPPORT_EXTERNAL_VLLM_API_KEY"
+_EXTERNAL_VLLM_HEALTH_TIMEOUT_ENV = "ART_MODEL_SUPPORT_EXTERNAL_VLLM_HEALTH_TIMEOUT"
 _TRAINABILITY_ROOT = (
     Path(__file__).resolve().parents[4] / ".local" / "model_support_validation"
 )
@@ -48,6 +49,14 @@
     "unsloth_dedicated",
 ]
 _RESOURCE_STAGE_NAME = Literal["yes_no_trainability", "length_trainability"]
+_Answer = Literal["yes", "no", "maybe"]
+_ANSWER_TARGETS: tuple[_Answer, ...] = ("yes", "no", "maybe")
+_GPT_OSS_MAX_STEPS = 8
+_GPT_OSS_MAX_TOKENS = 256
+_GPT_OSS_MAX_MODEL_LEN = 512
+_GPT_OSS_SYSTEM_PROMPT = (
+    "Use minimal reasoning. Give only one final word: yes, no, or maybe."
+)
 
 
 class _TrainKwargs(TypedDict, total=False):
@@ -66,10 +75,10 @@ class YesNoTrainabilityReport(BaseModel):
     backend_name: Literal["megatron", "local"]
     placement_mode: Literal["shared", "dedicated"]
     base_model: str
+    target_answer: _Answer
     output_dir: str
     trainer_gpu_ids: list[int]
     inference_gpu_ids: list[int]
-    rollout_weights_mode: str
     reward_threshold: float
     max_steps: int
     prompt_count: int
@@ -161,11 +170,14 @@ def _external_vllm_runtime_config() -> dev.VllmRuntimeArgs | None:
     server_url = os.environ.get(_EXTERNAL_VLLM_URL_ENV)
     if server_url is None or server_url.strip() == "":
         return None
-    return {
+    config: dev.VllmRuntimeArgs = {
         "mode": "external",
         "server_url": server_url,
         "api_key": os.environ.get(_EXTERNAL_VLLM_API_KEY_ENV, "art-external-vllm"),
     }
+    if timeout := os.environ.get(_EXTERNAL_VLLM_HEALTH_TIMEOUT_ENV):
+        config["health_timeout_s"] = float(timeout)
+    return config
 
 
 def _topology_with_env_overrides(topology: Topology) -> Topology:
@@ -288,9 +300,12 @@ def _safe_gpu_memory_utilization(device_ids: list[int]) -> float:
     )
 
 
-def reward_for_answer(text: str) -> float:
+def reward_for_answer(text: str, *, target: _Answer | None = None) -> float:
+    answer = first_word_for_answer(text).lower()
+    if target is not None:
+        return float(answer == target)
     return {"yes": 0.5, "no": 0.75, "maybe": 1.0}.get(
-        first_word_for_answer(text).lower(),
+        answer,
         0.0,
     )
 
@@ -310,6 +325,44 @@ def first_word_for_answer(text: str | None) -> str:
     return first_word[0].strip(".,!?:;\"'()[]{}")
 
 
+def _select_answer_target(groups: list[art.TrajectoryGroup]) -> _Answer | None:
+    counts = {
+        target: [
+            sum(
+                first_word_for_answer(_trajectory_answer_text(trajectory)).lower()
+                == target
+                for trajectory in group.trajectories
+            )
+            for group in groups
+        ]
+        for target in _ANSWER_TARGETS
+    }
+    candidates = [
+        target
+        for target, group_counts in counts.items()
+        if any(
+            0 < count < len(group.trajectories)
+            for count, group in zip(group_counts, groups, strict=True)
+        )
+    ]
+    return (
+        min(candidates, key=lambda target: sum(counts[target])) if candidates else None
+    )
+
+
+def _trajectory_answer_text(trajectory: art.Trajectory) -> str:
+    choice = cast(Choice, trajectory.messages_and_choices[-1])
+    return choice.message.content or ""
+
+
+def _rescore_groups(groups: list[art.TrajectoryGroup], *, target: _Answer) -> None:
+    for group in groups:
+        for trajectory in group.trajectories:
+            trajectory.reward = reward_for_answer(
+                _trajectory_answer_text(trajectory), target=target
+            )
+
+
 def _get_env_int(name: str, default: int) -> int:
     return int(os.environ.get(name, str(default)))
 
@@ -318,6 +371,16 @@ def _get_env_float(name: str, default: float) -> float:
     return float(os.environ.get(name, str(default)))
 
 
+def _get_env_int_list(name: str) -> list[int] | None:
+    raw = os.environ.get(name)
+    if raw is None:
+        return None
+    parts = raw.split(",")
+    if any(not part.strip() for part in parts):
+        raise ValueError(f"Invalid integer list for {name}: {raw!r}")
+    return [int(part) for part in parts]
+
+
 def _get_env_bool(name: str, default: bool) -> bool:
     raw = os.environ.get(name)
     if raw is None:
@@ -330,13 +393,25 @@ def _get_env_bool(name: str, default: bool) -> bool:
     raise ValueError(f"Invalid boolean value for {name}: {raw!r}")
 
 
-def _max_tokens() -> int:
-    return _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_TOKENS", 5)
+def _is_gpt_oss_model(base_model: str) -> bool:
+    return (
+        get_model_support_spec(base_model, allow_unvalidated_arch=True).key
+        == "gpt_oss_moe"
+    )
+
+
+def _max_tokens(base_model: str) -> int:
+    return _get_env_int(
+        "ART_MODEL_SUPPORT_YES_NO_MAX_TOKENS",
+        _GPT_OSS_MAX_TOKENS if _is_gpt_oss_model(base_model) else 5,
+    )
 
 
 def _render_chat_messages(base_model: str, prompt: str) -> art.Messages:
-    del base_model
-    return [{"role": "user", "content": prompt}]
+    messages: art.Messages = [{"role": "user", "content": prompt}]
+    if _is_gpt_oss_model(base_model):
+        messages.insert(0, {"role": "system", "content": _GPT_OSS_SYSTEM_PROMPT})
+    return messages
 
 
 def _enable_thinking() -> bool:
@@ -345,8 +420,15 @@ def _enable_thinking() -> bool:
     ).strip().lower() in {"1", "true", "yes", "on"}
 
 
-def _extra_body() -> dict[str, object]:
-    return {"chat_template_kwargs": {"enable_thinking": _enable_thinking()}}
+def _extra_body(base_model: str) -> dict[str, object]:
+    chat_template_kwargs: dict[str, object] = {"enable_thinking": _enable_thinking()}
+    if _is_gpt_oss_model(base_model):
+        chat_template_kwargs["reasoning_effort"] = "low"
+    body: dict[str, object] = {"chat_template_kwargs": chat_template_kwargs}
+    allowed_token_ids = _get_env_int_list("ART_MODEL_SUPPORT_YES_NO_ALLOWED_TOKEN_IDS")
+    if allowed_token_ids is not None:
+        body["allowed_token_ids"] = allowed_token_ids
+    return body
 
 
 def _request_timeout(name: str, default: float) -> float:
@@ -355,14 +437,27 @@ def _request_timeout(name: str, default: float) -> float:
 
 def _engine_args_for_yes_no_trainability(
     *,
+    base_model: str,
     inference_gpu_ids: list[int],
     tensor_parallel_size: int = 1,
     enable_expert_parallel: bool = False,
     enable_sleep_mode: bool | None = None,
+    external_runtime: bool = False,
 ) -> dev.EngineArgs:
     engine_args: dict[str, object] = {
-        "gpu_memory_utilization": _safe_gpu_memory_utilization(inference_gpu_ids),
-        "max_model_len": _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_MODEL_LEN", 128),
+        "gpu_memory_utilization": (
+            float(
+                os.environ.get(
+                    "ART_MODEL_SUPPORT_YES_NO_GPU_MEMORY_UTILIZATION", "0.85"
+                )
+            )
+            if external_runtime
+            else _safe_gpu_memory_utilization(inference_gpu_ids)
+        ),
+        "max_model_len": _get_env_int(
+            "ART_MODEL_SUPPORT_YES_NO_MAX_MODEL_LEN",
+            _GPT_OSS_MAX_MODEL_LEN if _is_gpt_oss_model(base_model) else 128,
+        ),
         "max_num_seqs": _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_NUM_SEQS", 4),
         "enforce_eager": True,
         "tensor_parallel_size": tensor_parallel_size,
@@ -550,9 +645,11 @@ def _variant_packed_sequence_length(variant: _TrainabilityVariant) -> int:
 
 
 def _variant_train_kwargs(variant: _TrainabilityVariant) -> _TrainKwargs:
-    if variant.backend_name == "megatron":
-        return {}
-    return {"packed_sequence_length": _variant_packed_sequence_length(variant)}
+    return (
+        {}
+        if variant.backend_name == "megatron"
+        else {"packed_sequence_length": _variant_packed_sequence_length(variant)}
+    )
 
 
 def _variant_init_args(variant: _TrainabilityVariant) -> dev.InitArgs:
@@ -562,6 +659,7 @@ def _variant_init_args(variant: _TrainabilityVariant) -> dev.InitArgs:
 def _init_megatron_runtime_config(
     variant: _TrainabilityVariant,
     *,
+    packed_sequence_length: int | None = None,
     streaming_weight_offload: bool = False,
 ) -> None:
     if variant.topology is None:
@@ -576,13 +674,23 @@ def _init_megatron_runtime_config(
             ep=variant.topology.ep,
             etp=variant.topology.etp,
         ),
-        packed_sequence_length=_variant_packed_sequence_length(variant),
+        packed_sequence_length=(
+            _variant_packed_sequence_length(variant)
+            if packed_sequence_length is None
+            else packed_sequence_length
+        ),
         streaming_weight_offload=streaming_weight_offload,
     )
 
 
-def _variant_max_steps(variant: _TrainabilityVariant) -> int:
-    default = 12 if variant.backend_name == "local" else 4
+def _variant_max_steps(variant: _TrainabilityVariant, *, base_model: str) -> int:
+    default = (
+        12
+        if variant.backend_name == "local"
+        else _GPT_OSS_MAX_STEPS
+        if _is_gpt_oss_model(base_model)
+        else 4
+    )
     return _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_STEPS", default)
 
 
@@ -591,17 +699,6 @@ def _variant_rollouts_per_prompt(variant: _TrainabilityVariant) -> int:
     return _get_env_int("ART_MODEL_SUPPORT_YES_NO_ROLLOUTS_PER_PROMPT", default)
 
 
-def _rollout_weights_mode(
-    base_model: str,
-    *,
-    allow_unvalidated_arch: bool = False,
-) -> RolloutWeightsMode:
-    return get_model_support_spec(
-        base_model,
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    ).default_rollout_weights_mode
-
-
 def _default_variant_name(
     base_model: str,
     *,
@@ -627,25 +724,19 @@ def _default_variant_name(
         base_model,
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
-    rollout_weights_mode = _rollout_weights_mode(
-        base_model,
-        allow_unvalidated_arch=allow_unvalidated_arch,
-    )
-    if rollout_weights_mode == "merged" or not is_moe:
-        return "megatron_dedicated"
-    return "megatron_shared"
+    return "megatron_shared" if is_moe else "megatron_dedicated"
 
 
 def _build_internal_config(
     variant: _TrainabilityVariant,
     *,
     base_model: str,
-    rollout_weights_mode: RolloutWeightsMode | None = None,
     allow_unvalidated_arch: bool = False,
     resource_stage_name: _RESOURCE_STAGE_NAME = "yes_no_trainability",
 ) -> dev.InternalModelConfig:
     shared = variant.placement_mode == "shared"
     inference_gpu_ids = variant.inference_gpu_ids
+    external_runtime = _external_vllm_runtime_config()
     stage_resources = _trainability_stage_resources(
         base_model,
         stage_name=resource_stage_name,
@@ -667,6 +758,7 @@ def _build_internal_config(
     else:
         vllm_resources = None
     engine_args = _engine_args_for_yes_no_trainability(
+        base_model=base_model,
         inference_gpu_ids=inference_gpu_ids,
         tensor_parallel_size=(
             vllm_resources.tensor_parallel_size
@@ -686,6 +778,7 @@ def _build_internal_config(
             )
         ),
         enable_sleep_mode=True if shared else None,
+        external_runtime=external_runtime is not None,
     )
     if vllm_resources is not None:
         engine_args.update(vllm_resources.engine_args())
@@ -693,16 +786,10 @@ def _build_internal_config(
         engine_args.update(stage_resources.vllm.extra_engine_args)
     engine_args["model"] = base_model
     internal_config = dev.InternalModelConfig(
-        rollout_weights_mode=rollout_weights_mode
-        or _rollout_weights_mode(
-            base_model,
-            allow_unvalidated_arch=allow_unvalidated_arch,
-        ),
         engine_args=engine_args,
         init_args=_variant_init_args(variant),
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
-    external_runtime = _external_vllm_runtime_config()
     if (
         stage_resources is not None
         and stage_resources.requires_external_vllm
@@ -774,6 +861,7 @@ async def _evaluate_groups(
     base_model: str,
     prompts: list[str],
     step: int,
+    target: _Answer | None = None,
 ) -> list[art.TrajectoryGroup]:
     client = model.openai_client()
 
@@ -782,8 +870,8 @@ async def _group_for_prompt(prompt: str) -> art.TrajectoryGroup:
         completion = await client.chat.completions.create(
             messages=messages,
             model=model.get_inference_name(step=step),
-            max_tokens=_max_tokens(),
-            extra_body=_extra_body(),
+            max_tokens=_max_tokens(base_model),
+            extra_body=_extra_body(base_model),
             temperature=_get_env_float(
                 "ART_MODEL_SUPPORT_YES_NO_EVAL_TEMPERATURE",
                 0.0,
@@ -795,7 +883,9 @@ async def _group_for_prompt(prompt: str) -> art.TrajectoryGroup:
             [
                 art.Trajectory(
                     messages_and_choices=[*messages, choice],
-                    reward=reward_for_answer(choice.message.content or ""),
+                    reward=reward_for_answer(
+                        choice.message.content or "", target=target
+                    ),
                 )
             ]
         )
@@ -818,6 +908,7 @@ async def _evaluate_model(
     base_model: str,
     prompts: list[str],
     step: int,
+    target: _Answer | None = None,
 ) -> float:
     return _mean_group_reward(
         await _evaluate_groups(
@@ -825,6 +916,7 @@ async def _evaluate_model(
             base_model=base_model,
             prompts=prompts,
             step=step,
+            target=target,
         )
     )
 
@@ -835,6 +927,7 @@ async def _build_training_groups(
     base_model: str,
     prompts: list[str],
     rollouts_per_prompt: int,
+    target: _Answer | None = None,
 ) -> list[art.TrajectoryGroup]:
     client = model.openai_client()
 
@@ -843,9 +936,9 @@ async def _group_for_prompt(prompt: str) -> art.TrajectoryGroup:
         completion = await client.chat.completions.create(
             messages=messages,
             model=model.get_inference_name(),
-            max_tokens=_max_tokens(),
+            max_tokens=_max_tokens(base_model),
             n=rollouts_per_prompt,
-            extra_body=_extra_body(),
+            extra_body=_extra_body(base_model),
             temperature=_get_env_float(
                 "ART_MODEL_SUPPORT_YES_NO_ROLLOUT_TEMPERATURE",
                 1.2,
@@ -859,7 +952,9 @@ async def _group_for_prompt(prompt: str) -> art.TrajectoryGroup:
             [
                 art.Trajectory(
                     messages_and_choices=[*messages, choice],
-                    reward=reward_for_answer(choice.message.content or ""),
+                    reward=reward_for_answer(
+                        choice.message.content or "", target=target
+                    ),
                 )
                 for choice in completion.choices
             ]
@@ -880,6 +975,7 @@ async def _build_trainable_groups(
     base_model: str,
     prompts: list[str],
     rollouts_per_prompt: int,
+    target: _Answer | None = None,
 ) -> list[art.TrajectoryGroup]:
     max_attempts = _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_ROLLOUT_ATTEMPTS", 4)
     for _ in range(max_attempts):
@@ -888,6 +984,7 @@ async def _build_trainable_groups(
             base_model=base_model,
             prompts=prompts,
             rollouts_per_prompt=rollouts_per_prompt,
+            target=target,
         )
         trainable_groups = [
             group for group in groups if _group_has_reward_variance(group)
@@ -899,6 +996,32 @@ async def _build_trainable_groups(
     )
 
 
+async def _build_initial_trainable_groups(
+    model: art.TrainableModel,
+    *,
+    base_model: str,
+    prompts: list[str],
+    rollouts_per_prompt: int,
+) -> tuple[_Answer, list[art.TrajectoryGroup]]:
+    max_attempts = _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_ROLLOUT_ATTEMPTS", 4)
+    for _ in range(max_attempts):
+        groups = await _build_training_groups(
+            model,
+            base_model=base_model,
+            prompts=prompts,
+            rollouts_per_prompt=rollouts_per_prompt,
+        )
+        target = _select_answer_target(groups)
+        if target is not None:
+            _rescore_groups(groups, target=target)
+            return target, [
+                group for group in groups if _group_has_reward_variance(group)
+            ]
+    raise RuntimeError(
+        "No answer with within-group support was produced for yes/no trainability"
+    )
+
+
 async def _warmup_model(
     model: art.TrainableModel,
     *,
@@ -910,7 +1033,7 @@ async def _warmup_model(
         messages=_render_chat_messages(base_model, prompt),
         model=model.get_inference_name(step=0),
         max_tokens=1,
-        extra_body=_extra_body(),
+        extra_body=_extra_body(base_model),
         temperature=0.0,
         timeout=_request_timeout("ART_MODEL_SUPPORT_YES_NO_WARMUP_TIMEOUT", 900.0),
     )
@@ -921,7 +1044,6 @@ async def run_yes_no_trainability_async(
     base_model: str,
     variant_name: _VARIANT_NAME = "megatron_shared",
     artifact_root: Path | None = None,
-    rollout_weights_mode: RolloutWeightsMode | None = None,
     allow_unvalidated_arch: bool = False,
     extra_env: dict[str, str] | None = None,
 ) -> YesNoTrainabilityReport:
@@ -933,7 +1055,7 @@ async def run_yes_no_trainability_async(
     backend_root = artifact_root or _artifact_dir(base_model, variant.name)
     backend_root.mkdir(parents=True, exist_ok=True)
     reward_threshold = _get_env_float("ART_MODEL_SUPPORT_YES_NO_REWARD_THRESHOLD", 0.9)
-    max_steps = _variant_max_steps(variant)
+    max_steps = _variant_max_steps(variant, base_model=base_model)
     rollouts_per_prompt = _variant_rollouts_per_prompt(variant)
     eval_prompt_count = _get_env_int("ART_MODEL_SUPPORT_YES_NO_EVAL_PROMPTS", 8)
     prompts = build_prompts()
@@ -942,10 +1064,8 @@ async def run_yes_no_trainability_async(
     internal_config = _build_internal_config(
         variant,
         base_model=base_model,
-        rollout_weights_mode=rollout_weights_mode,
         allow_unvalidated_arch=allow_unvalidated_arch,
     )
-    rollout_weights_mode = internal_config["rollout_weights_mode"]
     workflow_resources = handler_workflow_resources_for_base_model(
         base_model,
         allow_unvalidated_arch=allow_unvalidated_arch,
@@ -988,15 +1108,23 @@ async def run_yes_no_trainability_async(
     ) as backend:
         await model.register(backend)
         output_dir = Path(model.base_path) / model.project / "models" / model.run_name
-        await _warmup_model(model, base_model=base_model, prompt=prompts[0])
         step0_name = model.get_inference_name(step=0)
         model_ids_before = await _list_model_ids(model)
-        initial_eval_groups = await _evaluate_groups(
+        async with backend.exact_adapter_lease(model, 0):
+            await _warmup_model(model, base_model=base_model, prompt=prompts[0])
+            initial_eval_groups = await _evaluate_groups(
+                model,
+                base_model=base_model,
+                prompts=eval_prompts,
+                step=0,
+            )
+        target_answer, initial_train_groups = await _build_initial_trainable_groups(
             model,
             base_model=base_model,
-            prompts=eval_prompts,
-            step=0,
+            prompts=prompts,
+            rollouts_per_prompt=rollouts_per_prompt,
         )
+        _rescore_groups(initial_eval_groups, target=target_answer)
         initial_eval_reward = _mean_group_reward(initial_eval_groups)
         await model.log(initial_eval_groups, step=0, split="val")
         report = YesNoTrainabilityReport(
@@ -1004,10 +1132,10 @@ async def run_yes_no_trainability_async(
             backend_name=variant.backend_name,
             placement_mode=variant.placement_mode,
             base_model=base_model,
+            target_answer=target_answer,
             output_dir=str(output_dir),
             trainer_gpu_ids=variant.trainer_gpu_ids,
             inference_gpu_ids=variant.inference_gpu_ids,
-            rollout_weights_mode=rollout_weights_mode,
             reward_threshold=reward_threshold,
             max_steps=max_steps,
             prompt_count=len(prompts),
@@ -1022,12 +1150,17 @@ async def run_yes_no_trainability_async(
             model_ids_before=model_ids_before,
         )
 
-        for _ in range(max_steps):
-            train_groups = await _build_trainable_groups(
-                model,
-                base_model=base_model,
-                prompts=prompts,
-                rollouts_per_prompt=rollouts_per_prompt,
+        for step_index in range(max_steps):
+            train_groups = (
+                initial_train_groups
+                if step_index == 0
+                else await _build_trainable_groups(
+                    model,
+                    base_model=base_model,
+                    prompts=prompts,
+                    rollouts_per_prompt=rollouts_per_prompt,
+                    target=target_answer,
+                )
             )
             result = await backend.train(
                 model,
@@ -1045,12 +1178,14 @@ async def run_yes_no_trainability_async(
                 step=result.step,
                 split="train",
             )
-            eval_groups = await _evaluate_groups(
-                model,
-                base_model=base_model,
-                prompts=eval_prompts,
-                step=result.step,
-            )
+            async with backend.exact_adapter_lease(model, int(result.step)):
+                eval_groups = await _evaluate_groups(
+                    model,
+                    base_model=base_model,
+                    prompts=eval_prompts,
+                    step=result.step,
+                    target=target_answer,
+                )
             eval_reward = _mean_group_reward(eval_groups)
             await model.log(eval_groups, step=result.step, split="val")
             report.latest_step = int(result.step)
@@ -1078,7 +1213,10 @@ async def run_yes_no_trainability_async(
                 break
 
         report.model_ids_after = await _list_model_ids(model)
-        report.latest_snapshot = await _chat_snapshot(model, step=report.latest_step)
+        async with backend.exact_adapter_lease(model, report.latest_step):
+            report.latest_snapshot = await _chat_snapshot(
+                model, step=report.latest_step
+            )
 
     output_dir = Path(report.output_dir)
     output_dir.mkdir(parents=True, exist_ok=True)
@@ -1092,6 +1230,7 @@ async def run_yes_no_trainability_async(
 def run_yes_no_trainability(
     base_model: str,
     *,
+    artifact_root: Path | None = None,
     allow_unvalidated_arch: bool = False,
 ) -> YesNoTrainabilityReport:
     return asyncio.run(
@@ -1101,12 +1240,29 @@ def run_yes_no_trainability(
                 base_model,
                 allow_unvalidated_arch=allow_unvalidated_arch,
             ),
+            artifact_root=artifact_root,
             allow_unvalidated_arch=allow_unvalidated_arch,
         )
     )
 
 
 def yes_no_trainability_passed(report: YesNoTrainabilityReport) -> bool:
+    has_nonzero_gradient = any(
+        max(
+            step.train_metrics.get("grad_norm", 0.0),
+            step.train_metrics.get("loss/grad_norm", 0.0),
+        )
+        > 0.0
+        for step in report.steps
+    )
+    has_positive_probs_corr = any(
+        max(
+            step.train_metrics.get("probs_corr", 0.0),
+            step.train_metrics.get("loss/probs_corr", 0.0),
+        )
+        > 0.0
+        for step in report.steps
+    )
     learned_from_below_threshold = (
         report.saturated_step is not None
         and report.saturated_step > 0
@@ -1115,29 +1271,20 @@ def yes_no_trainability_passed(report: YesNoTrainabilityReport) -> bool:
         and report.final_eval_reward >= report.reward_threshold
         and report.final_eval_reward > report.initial_eval_reward
     )
-    already_saturated_and_stable = (
-        report.initial_eval_reward >= report.reward_threshold
-        and report.latest_step > 0
-        and report.final_eval_reward is not None
-        and report.final_eval_reward >= report.reward_threshold
-        and bool(report.steps)
-        and any(
-            step.train_metrics.get("loss/grad_norm", 0.0) > 0.0 for step in report.steps
-        )
+    return (
+        learned_from_below_threshold
+        and has_nonzero_gradient
+        and has_positive_probs_corr
     )
-    return learned_from_below_threshold or already_saturated_and_stable
 
 
 def run_megatron_dedicated_yes_no_trainability(
     base_model: str,
-    *,
-    rollout_weights_mode: RolloutWeightsMode | None = None,
 ) -> YesNoTrainabilityReport:
     return asyncio.run(
         run_yes_no_trainability_async(
             base_model=base_model,
             variant_name="megatron_dedicated",
-            rollout_weights_mode=rollout_weights_mode,
         )
     )
 
diff --git a/tests/integration/megatron/weight_offload/test_streaming_offload_trainability.py b/tests/integration/megatron/weight_offload/test_streaming_offload_trainability.py
index ec163ec9b..a07162435 100644
--- a/tests/integration/megatron/weight_offload/test_streaming_offload_trainability.py
+++ b/tests/integration/megatron/weight_offload/test_streaming_offload_trainability.py
@@ -34,10 +34,7 @@ def _assert_passed(report) -> None:
     assert report.latest_step > 0
     assert report.step0_name in report.model_ids_before
     assert report.latest_name in report.model_ids_after
-    if report.rollout_weights_mode == "merged":
-        assert report.step0_name not in report.model_ids_after
-    else:
-        assert report.step0_name in report.model_ids_after
+    assert report.step0_name in report.model_ids_after
     assert report.latest_snapshot["has_logprobs"] is True
 
 
diff --git a/tests/unit/test_checkpoint_retention.py b/tests/unit/test_checkpoint_retention.py
index ee71dd2d2..a3a1113bc 100644
--- a/tests/unit/test_checkpoint_retention.py
+++ b/tests/unit/test_checkpoint_retention.py
@@ -12,7 +12,7 @@ def _checkpoint(
     is_eval_step: bool = False,
     reward: float | None = None,
 ) -> CheckpointInfo:
-    metrics = {"val/reward": reward} if reward is not None else {}
+    metrics = {"reward/val": reward} if reward is not None else {}
     return CheckpointInfo(
         step=step,
         path=f"/tmp/checkpoints/{step:04d}",
@@ -23,7 +23,7 @@ def _checkpoint(
 
 
 def test_keep_recent_and_top_returns_kept_steps() -> None:
-    strategy = keep_recent_and_top(recent=2, top=1, metric="val/reward")
+    strategy = keep_recent_and_top(recent=2, top=1, metric="reward/val")
     context = CheckpointRetentionContext(
         current_step=6,
         checkpoints=[
@@ -40,7 +40,7 @@ def test_keep_recent_and_top_returns_kept_steps() -> None:
 
 
 def test_keep_recent_and_top_uses_metric_presence_for_legacy_history() -> None:
-    strategy = keep_recent_and_top(recent=0, top=1, metric="val/reward")
+    strategy = keep_recent_and_top(recent=0, top=1, metric="reward/val")
     context = CheckpointRetentionContext(
         current_step=3,
         checkpoints=[
diff --git a/tests/unit/test_dedicated_config.py b/tests/unit/test_dedicated_config.py
index 292b9d516..79f149803 100644
--- a/tests/unit/test_dedicated_config.py
+++ b/tests/unit/test_dedicated_config.py
@@ -1,6 +1,7 @@
 """Unit tests for dedicated mode config validation and get_model_config integration."""
 
 import tempfile
+from typing import cast
 
 import pytest
 
@@ -85,18 +86,16 @@ def test_overlapping_gpu_ids():
         )
 
 
-def test_trainer_not_starting_at_zero():
-    with pytest.raises(ValueError, match="must start at GPU 0"):
-        validate_dedicated_config(
-            InternalModelConfig(trainer_gpu_ids=[1], inference_gpu_ids=[0])
-        )
+def test_trainer_can_use_nonzero_gpu():
+    validate_dedicated_config(
+        InternalModelConfig(trainer_gpu_ids=[2], inference_gpu_ids=[3])
+    )
 
 
-def test_trainer_not_contiguous():
-    with pytest.raises(ValueError, match="must be contiguous starting from 0"):
-        validate_dedicated_config(
-            InternalModelConfig(trainer_gpu_ids=[0, 2], inference_gpu_ids=[1])
-        )
+def test_trainer_can_use_noncontiguous_gpus():
+    validate_dedicated_config(
+        InternalModelConfig(trainer_gpu_ids=[0, 2], inference_gpu_ids=[1])
+    )
 
 
 def test_dedicated_rejects_fast_inference():
@@ -142,7 +141,6 @@ def test_get_model_config_shared_mode():
         assert "inference_gpu_ids" not in result
         assert result["engine_args"]["enable_sleep_mode"] is True
         assert "fast_inference" not in result["init_args"]
-        assert result["rollout_weights_mode"] == "lora"
         assert result["rollout_weight_update_mode"] == "step_lora"
         assert result["lora_config"]["target_modules"] == [
             "q_proj",
@@ -210,7 +208,6 @@ def test_get_model_config_dedicated_mode():
         assert result["inference_gpu_ids"] == [1]
         assert result["engine_args"]["enable_sleep_mode"] is False
         assert "fast_inference" not in result["init_args"]
-        assert result["rollout_weights_mode"] == "lora"
         assert result["rollout_weight_update_mode"] == "step_lora"
 
 
@@ -227,31 +224,13 @@ def test_get_model_config_dedicated_preserves_user_engine_args():
         assert result["engine_args"]["enable_sleep_mode"] is False
 
 
-def test_get_model_config_preserves_rollout_weights_mode():
-    with tempfile.TemporaryDirectory() as tmpdir:
-        config = InternalModelConfig(
-            trainer_gpu_ids=[0],
-            inference_gpu_ids=[1],
-            rollout_weights_mode="merged",
-        )
-        result = get_model_config("test-model", tmpdir, config)
-        assert result["rollout_weights_mode"] == "merged"
-
-
-def test_invalid_rollout_weights_mode():
-    with pytest.raises(
-        ValueError, match="rollout_weights_mode must be either 'lora' or 'merged'"
-    ):
-        validate_dedicated_config(
-            InternalModelConfig(rollout_weights_mode="bad-mode")  # type: ignore
-        )
-
-
-def test_merged_rollout_weights_requires_dedicated_mode():
-    with pytest.raises(
-        ValueError, match="rollout_weights_mode='merged' requires dedicated mode"
-    ):
-        validate_dedicated_config(InternalModelConfig(rollout_weights_mode="merged"))
+@pytest.mark.parametrize("value", ["lora", "merged", "bad-mode"])
+def test_removed_rollout_weights_mode_is_rejected(value: str):
+    config = cast(InternalModelConfig, {"rollout_weights_mode": value})
+    with pytest.raises(ValueError, match="rollout_weights_mode has been removed"):
+        validate_dedicated_config(config)
+    with pytest.raises(ValueError, match="rollout_weights_mode has been removed"):
+        get_model_config("test-model", "", config)
 
 
 def test_qwen3_5_moe_allows_default_lora_rollout_weights():
@@ -264,17 +243,6 @@ def test_qwen3_5_moe_allows_default_lora_rollout_weights():
     )
 
 
-def test_qwen3_5_moe_allows_merged_rollout_weights():
-    validate_dedicated_config(
-        InternalModelConfig(
-            trainer_gpu_ids=[0],
-            inference_gpu_ids=[1],
-            rollout_weights_mode="merged",
-            engine_args={"model": "Qwen/Qwen3.5-35B-A3B"},  # type: ignore[typeddict-item]
-        )
-    )
-
-
 def test_other_qwen3_5_moe_allows_default_lora_rollout_weights():
     validate_dedicated_config(
         InternalModelConfig(
diff --git a/tests/unit/test_distributed_inference_metrics.py b/tests/unit/test_distributed_inference_metrics.py
new file mode 100644
index 000000000..da616247f
--- /dev/null
+++ b/tests/unit/test_distributed_inference_metrics.py
@@ -0,0 +1,164 @@
+from __future__ import annotations
+
+import importlib.util
+from pathlib import Path
+import sys
+from types import ModuleType, SimpleNamespace
+from typing import cast
+
+import httpx
+import pytest
+
+from art.local import backend as backend_module
+from art.local.backend import LocalBackend
+from art.model import Model
+from art.serving_capabilities import ART_SERVING_PROTOCOL_VERSION, ServingCapabilities
+
+
+def _runtime_metrics_module(monkeypatch: pytest.MonkeyPatch) -> ModuleType:
+    loggers = ModuleType("vllm.v1.metrics.loggers")
+    setattr(loggers, "StatLoggerBase", object)
+    fast_metrics = ModuleType("art_vllm_runtime.fast_metrics")
+    setattr(fast_metrics, "FastMetricsSharedWriter", object)
+    for name in ("vllm", "vllm.v1", "vllm.v1.metrics"):
+        monkeypatch.setitem(sys.modules, name, ModuleType(name))
+    monkeypatch.setitem(sys.modules, loggers.__name__, loggers)
+    monkeypatch.setitem(sys.modules, fast_metrics.__name__, fast_metrics)
+    path = Path(__file__).parents[2] / "vllm_runtime/src/art_vllm_runtime/metrics.py"
+    spec = importlib.util.spec_from_file_location("test_art_vllm_metrics", path)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+def _metrics(*, prompt: float, generation: float) -> dict[str, float]:
+    return {
+        "prompt_tokens_total": prompt,
+        "generation_tokens_total": generation,
+        "prefix_cache_queries_total": prompt / 5,
+        "prefix_cache_hits_total": prompt / 10,
+        "num_preempted_reqs_total": 1.0,
+        "num_requests_running": 1.0,
+        "num_requests_waiting": 2.0,
+        "num_requests_waiting_capacity": 1.0,
+        "kv_cache_usage_perc": 0.25,
+        "max_num_seqs": 8.0,
+        "max_num_batched_tokens": 1024.0,
+        "max_num_scheduled_tokens": 1024.0,
+        "max_model_len": 8192.0,
+        "world_size": 16.0,
+    }
+
+
+def _snapshot(
+    process_uuid: str,
+    generation: int,
+    record_count: int,
+    *,
+    prompt: float,
+    completion: float,
+) -> dict[str, object]:
+    return {
+        "schema_version": 1,
+        "source": "art_vllm_runtime",
+        "last_update_unix_s": float(record_count),
+        "record_count": record_count,
+        "engine_count": 1,
+        "process_uuid": process_uuid,
+        "generation": generation,
+        "metrics": _metrics(prompt=prompt, generation=completion),
+    }
+
+
+def test_runtime_world_size_includes_data_parallel_workers(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    state = _runtime_metrics_module(monkeypatch)._ArtRuntimeMetricsState()
+    state.configure(
+        SimpleNamespace(
+            scheduler_config=SimpleNamespace(
+                max_num_seqs=8,
+                max_num_batched_tokens=1024,
+                max_num_scheduled_tokens=1024,
+            ),
+            model_config=SimpleNamespace(max_model_len=4096),
+            parallel_config=SimpleNamespace(world_size=8, world_size_across_dp=16),
+        ),
+        engine_idx=0,
+    )
+
+    assert state.snapshot()["metrics"]["world_size"] == 16.0
+
+
+@pytest.mark.asyncio
+async def test_metrics_endpoint_and_counter_generation_contract(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path
+) -> None:
+    payloads = iter(
+        (
+            _snapshot("leader-a", 0, 1, prompt=100, completion=50),
+            _snapshot("leader-a", 0, 2, prompt=200, completion=100),
+            _snapshot("leader-b", 1, 1, prompt=10_000, completion=5_000),
+            _snapshot("leader-b", 1, 2, prompt=10_100, completion=5_050),
+        )
+    )
+    requests: list[tuple[str, dict[str, str] | None]] = []
+
+    class Client:
+        async def get(
+            self, url: str, *, headers: dict[str, str] | None
+        ) -> httpx.Response:
+            requests.append((url, headers))
+            return httpx.Response(
+                200,
+                json=next(payloads),
+                request=httpx.Request("GET", url, headers=headers),
+            )
+
+    times = iter((0.0, 10.0, 20.0, 30.0))
+    monkeypatch.setattr(
+        backend_module, "time", SimpleNamespace(monotonic=lambda: next(times))
+    )
+    backend = LocalBackend(path=str(tmp_path))
+    backend._vllm_metrics_client = cast(httpx.AsyncClient, Client())
+    model = Model(
+        name="test-model",
+        project="test",
+        inference_base_url="http://leader.test/v1",
+        inference_api_key="secret",
+    )
+    object.__setattr__(
+        model,
+        "_serving_capabilities",
+        ServingCapabilities(
+            runtime="art_vllm",
+            protocol_version=ART_SERVING_PROTOCOL_VERSION,
+            fast_metrics={"url": "http://leader.test/art/metrics"},
+        ),
+    )
+
+    first = await backend.collect_train_step_vllm_metrics(model)
+    second = await backend.collect_train_step_vllm_metrics(model)
+    restarted = await backend.collect_train_step_vllm_metrics(model)
+    recovered = await backend.collect_train_step_vllm_metrics(model)
+
+    assert "vllm/prompt_tok_per_s" not in first
+    assert (second["vllm/prompt_tok_per_s"], second["vllm/completion_tok_per_s"]) == (
+        10.0,
+        5.0,
+    )
+    assert "vllm/prompt_tok_per_s" not in restarted
+    assert restarted["vllm/prefix_cache_hit_rate"] == 0.5
+    assert (
+        recovered["vllm/prompt_tok_per_s"],
+        recovered["vllm/completion_tok_per_s"],
+        recovered["vllm/world_size"],
+    ) == (10.0, 5.0, 16.0)
+    assert set(backend._vllm_metric_snapshots) == {
+        ("test", "test-model", "leader-b", 1)
+    }
+    assert (
+        requests
+        == [("http://leader.test/art/metrics", {"Authorization": "Bearer secret"})] * 4
+    )
diff --git a/tests/unit/test_dsv4_vllm_runtime_patches.py b/tests/unit/test_dsv4_vllm_runtime_patches.py
index faa4b7885..00e9a7daa 100644
--- a/tests/unit/test_dsv4_vllm_runtime_patches.py
+++ b/tests/unit/test_dsv4_vllm_runtime_patches.py
@@ -25,7 +25,7 @@ def _load_dsv4_patches_module():
     return module
 
 
-def test_dsv4_lora_support_declares_vllm_024_manager_protocol(monkeypatch) -> None:
+def test_dsv4_lora_support_declares_vllm_025_manager_protocol(monkeypatch) -> None:
     patches = _load_dsv4_patches_module()
 
     class FakeDeepseekV4ForCausalLM:
@@ -33,16 +33,19 @@ class FakeDeepseekV4ForCausalLM:
 
     manager_patches: list[type] = []
     monkeypatch.setattr(
-        patches,
-        "_import_dsv4_model_module",
-        lambda: SimpleNamespace(DeepseekV4ForCausalLM=FakeDeepseekV4ForCausalLM),
+        patches.importlib,
+        "import_module",
+        lambda name: (
+            SimpleNamespace(DeepseekV4ForCausalLM=FakeDeepseekV4ForCausalLM)
+            if name == "vllm.models.deepseek_v4.nvidia.model"
+            else None
+        ),
     )
     monkeypatch.setattr(
         patches,
         "_patch_dsv4_lora_manager_indexer_skip",
         manager_patches.append,
     )
-
     patches.patch_dsv4_lora_support()
 
     assert getattr(FakeDeepseekV4ForCausalLM, "supports_lora") is True
@@ -50,6 +53,40 @@ class FakeDeepseekV4ForCausalLM:
     assert manager_patches == [FakeDeepseekV4ForCausalLM]
 
 
+def test_dsv4_fp8_o_proj_normalizes_rope_cache_once() -> None:
+    patches = _load_dsv4_patches_module()
+    rotary_emb = SimpleNamespace(cos_sin_cache=torch.ones(4, 8, dtype=torch.bfloat16))
+
+    cache = patches._dsv4_fp32_cos_sin_cache(rotary_emb)
+
+    assert cache.dtype == torch.float32
+    assert rotary_emb.cos_sin_cache is cache
+    assert patches._dsv4_fp32_cos_sin_cache(rotary_emb) is cache
+
+
+def test_dsv4_native_o_proj_receives_fp32_rope_cache() -> None:
+    patches = _load_dsv4_patches_module()
+    seen: list[torch.Tensor] = []
+
+    class Attention:
+        def __init__(self) -> None:
+            self.rotary_emb = SimpleNamespace(
+                cos_sin_cache=torch.ones(4, 8, dtype=torch.bfloat16)
+            )
+            self.wo_a = SimpleNamespace()
+
+        def _o_proj(self, _o, _positions):
+            seen.append(self.rotary_emb.cos_sin_cache)
+            return "native"
+
+    patches._patch_dsv4_cuda_o_proj_lora(Attention, SimpleNamespace())
+    attention = Attention()
+
+    assert attention._o_proj(None, None) == "native"
+    assert seen == [attention.rotary_emb.cos_sin_cache]
+    assert seen[0].dtype == torch.float32
+
+
 def test_dsv4_compressor_helper_uses_punica_metadata_without_full_batch_lora(
     monkeypatch,
 ) -> None:
diff --git a/tests/unit/test_exchange_training_model_selection.py b/tests/unit/test_exchange_training_model_selection.py
index 9c5810358..111300400 100644
--- a/tests/unit/test_exchange_training_model_selection.py
+++ b/tests/unit/test_exchange_training_model_selection.py
@@ -140,9 +140,11 @@ def _routed_exchange(
     extra[ART_MOE_ROUTING_METADATA_KEY] = {
         "prompt_token_ids": prompt_token_ids,
         "completion_token_ids": [output_token],
+        "num_experts": 2048,
         "routed_experts": np.asarray(
-            [[[10]]] * len(prompt_token_ids) + [[[output_token * 10]]],
-            dtype=np.int32,
+            [[[token_id * 10]] for token_id in prompt_token_ids]
+            + [[[output_token * 10]]],
+            dtype=np.uint16,
         ),
     }
     return exchange
@@ -721,8 +723,9 @@ def test_preprocessing_preserves_moe_routes_for_reasoning_stripped_suffix() -> N
     first_extra[ART_MOE_ROUTING_METADATA_KEY] = {
         "prompt_token_ids": [1],
         "completion_token_ids": [2, 101, 102, 9],
+        "num_experts": 2048,
         "routed_experts": np.asarray(
-            [[[10]], [[20]], [[1010]], [[1020]], [[90]]], dtype=np.int32
+            [[[10]], [[20]], [[1010]], [[1020]], [[90]]], dtype=np.uint16
         ),
     }
 
@@ -758,9 +761,10 @@ def test_preprocessing_preserves_moe_routes_for_reasoning_stripped_suffix() -> N
     second_extra[ART_MOE_ROUTING_METADATA_KEY] = {
         "prompt_token_ids": [1, 101, 102, 9, 4],
         "completion_token_ids": [5, 6],
+        "num_experts": 2048,
         "routed_experts": np.asarray(
             [[[10]], [[1010]], [[1020]], [[90]], [[40]], [[50]], [[60]]],
-            dtype=np.int32,
+            dtype=np.uint16,
         ),
     }
 
@@ -821,7 +825,7 @@ def apply_chat_template(
     assert all(result.weight == pytest.approx(1 / 6) for result in results)
     expected_routes = np.asarray(
         [[[10]], [[1010]], [[1020]], [[90]], [[40]], [[50]], [[60]]],
-        dtype=np.int32,
+        dtype=np.uint16,
     )
     for result in stripped:
         assert isinstance(result.moe_routed_experts, MoeRouteSegments)
diff --git a/tests/unit/test_megatron_reference_logprobs.py b/tests/unit/test_megatron_reference_logprobs.py
index 1ef1c5f1f..a94bbfd76 100644
--- a/tests/unit/test_megatron_reference_logprobs.py
+++ b/tests/unit/test_megatron_reference_logprobs.py
@@ -8,6 +8,7 @@
 
 from art import types
 from art.megatron import train as megatron_train
+from art.megatron.runtime.specs import ExperimentalTrainConfig, TrainJobSpec
 from art.megatron.training import microbatches as megatron_microbatches
 from art.preprocessing.pack import PackedTensors
 
@@ -87,14 +88,14 @@ def test_prepare_kl_reference_logprobs_requires_reference_path() -> None:
     runtime = SimpleNamespace(rank=0)
     job = SimpleNamespace(
         config=types.TrainConfig(kl_penalty_coef=0.25),
-        experimental_config={},
-        lora_path="/tmp/current",
+        experimental_config=ExperimentalTrainConfig(),
+        source_adapter_path="/tmp/current",
     )
 
     try:
         megatron_train._prepare_kl_reference_logprobs(
             runtime=cast(megatron_train.TrainingRuntime, runtime),
-            job=cast(megatron_train.MegatronTrainingJob, job),
+            job=cast(TrainJobSpec, job),
             packed_tensors=_packed_inputs(),
             num_sequences=1,
             num_steps=1,
@@ -118,7 +119,10 @@ def set_step(
     ) -> None:
         self.events.append(("set_step", step_index, sample_index))
 
-    def begin_micro(self, sample_index: int, micro_order: int) -> None:
+    def begin_micro(
+        self, sample_index: int, micro_order: int, *, chunk_index: int
+    ) -> None:
+        assert chunk_index == 0
         self.events.append(("begin_micro", micro_order, sample_index))
 
     def finalize_step(self) -> None:
@@ -155,6 +159,9 @@ def get_forward_kwargs(self, _chunk: nn.Module, *, attention_bias: Any) -> dict:
         del attention_bias
         return {}
 
+    def build_pipeline_microbatch_activator(self, _model_chunks: Any) -> None:
+        return None
+
 
 def test_calculate_megatron_logprobs_replays_routes(monkeypatch) -> None:
     controller = _ReplayController()
diff --git a/tests/unit/test_merged_weight_names.py b/tests/unit/test_merged_weight_names.py
deleted file mode 100644
index bc9b4890a..000000000
--- a/tests/unit/test_merged_weight_names.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import pytest
-
-pytest.importorskip("trl")
-pytest.importorskip("vllm")
-
-from art.unsloth.service import _normalize_merged_checkpoint_name
-
-
-def test_normalize_merged_checkpoint_name_strips_peft_wrapper_segments():
-    assert (
-        _normalize_merged_checkpoint_name(
-            "model.language_model.layers.3.self_attn.q_proj.base_layer.weight"
-        )
-        == "model.language_model.layers.3.self_attn.q_proj.weight"
-    )
-    assert (
-        _normalize_merged_checkpoint_name(
-            "model.language_model.layers.3.mlp.shared_expert.gate_proj.base_layer.weight"
-        )
-        == "model.language_model.layers.3.mlp.shared_expert.gate_proj.weight"
-    )
-    assert (
-        _normalize_merged_checkpoint_name(
-            "model.language_model.layers.3.mlp.experts.base_layer.base_layer.gate_up_proj"
-        )
-        == "model.language_model.layers.3.mlp.experts.gate_up_proj"
-    )
-    assert (
-        _normalize_merged_checkpoint_name(
-            "model.language_model.layers.3.mlp.experts.base_layer.base_layer.down_proj"
-        )
-        == "model.language_model.layers.3.mlp.experts.down_proj"
-    )
-
-
-def test_normalize_merged_checkpoint_name_strips_peft_prefix():
-    assert (
-        _normalize_merged_checkpoint_name(
-            "base_model.model.model.language_model.layers.7.self_attn.o_proj.base_layer.weight"
-        )
-        == "model.language_model.layers.7.self_attn.o_proj.weight"
-    )
-    assert (
-        _normalize_merged_checkpoint_name("base_model.model.lm_head.weight")
-        == "lm_head.weight"
-    )
-
-
-def test_normalize_merged_checkpoint_name_leaves_regular_names_unchanged():
-    assert (
-        _normalize_merged_checkpoint_name(
-            "model.language_model.layers.3.self_attn.q_norm.weight"
-        )
-        == "model.language_model.layers.3.self_attn.q_norm.weight"
-    )
diff --git a/tests/unit/test_model_openai_client_costs.py b/tests/unit/test_model_openai_client_costs.py
index 63d1b53d6..2bb25252c 100644
--- a/tests/unit/test_model_openai_client_costs.py
+++ b/tests/unit/test_model_openai_client_costs.py
@@ -5,7 +5,7 @@
 
 from art import Model, TrainableModel
 from art.costs import build_cost_calculator, get_model_pricing
-from art.model import _OpenAIChatCompletionsProxy
+from art.model import _OpenAIChatCompletionsProxy, _OpenAIClientProxy
 
 
 class _FakeUsage:
@@ -70,6 +70,28 @@ def _build_model() -> TrainableModel:
 
 
 class TestModelOpenAIClientCosts:
+    @pytest.mark.asyncio
+    async def test_openai_client_proxy_preserves_async_lifetime(self) -> None:
+        class _Client:
+            chat = type("Chat", (), {"completions": object()})()
+            entered = False
+            exited = False
+
+            async def __aenter__(self) -> "_Client":
+                self.entered = True
+                return self
+
+            async def __aexit__(self, *args: Any) -> None:
+                self.exited = True
+
+        client = _Client()
+        proxy = _OpenAIClientProxy(client, lambda _response: None)
+
+        async with proxy as entered:
+            assert entered is proxy
+            assert client.entered
+        assert client.exited
+
     @pytest.mark.asyncio
     async def test_openai_client_automatically_logs_train_tinker_costs(
         self,
diff --git a/tests/unit/test_moe_routing_real_path.py b/tests/unit/test_moe_routing_real_path.py
index bd7143fdc..323bfb038 100644
--- a/tests/unit/test_moe_routing_real_path.py
+++ b/tests/unit/test_moe_routing_real_path.py
@@ -1,25 +1,32 @@
 from __future__ import annotations
 
+from datetime import datetime
 import math
 from typing import Any
 
 import numpy as np
+from openai.types.chat import ChatCompletion
 from openai.types.chat.chat_completion import Choice
 import pytest
 import torch
 
+from art.distributed.data_plane import SharedMemoryPackedBatchStore
+from art.distributed.packing import TrajectoryPayload
 from art.megatron.prefix_tree import parse_prefix_tree_row
 from art.megatron.routing_replay import (
     build_moe_routing_replay_bundle_from_packed_tensors,
 )
 from art.preprocessing.moe_routing import (
     ART_MOE_ROUTING_METADATA_KEY,
+    NUM_EXPERTS_KEY,
+    ROUTED_EXPERTS_KEY,
+    MoeRouteArray,
     MoeRouteSegments,
     align_choice_routes_to_tokenized_result,
 )
 from art.preprocessing.pack import packed_tensors_from_tokenized_results
 from art.preprocessing.tokenize import TokenizedResult
-from art.trajectories import Trajectory
+from art.trajectories import ChatCompletionsExchange, Trajectory
 
 
 class _FakeTokenizer:
@@ -28,6 +35,7 @@ def decode(self, token_id: int) -> str:
 
 
 def _choice(metadata: dict[str, Any]) -> Choice:
+    metadata.setdefault("num_experts", 256)
     return Choice.model_validate(
         {
             "index": 0,
@@ -39,6 +47,7 @@ def _choice(metadata: dict[str, Any]) -> Choice:
 
 
 def _route(seed: int) -> list[list[int]]:
+    seed %= 240
     return [[seed, seed + 1], [seed + 2, seed + 3]]
 
 
@@ -93,6 +102,28 @@ def test_align_choice_routes_to_tokenized_result_rejects_token_mismatch() -> Non
         )
 
 
+def test_align_choice_routes_materializes_missing_terminal_route() -> None:
+    routes, _stats = align_choice_routes_to_tokenized_result(
+        token_ids=[10, 20],
+        choices=[
+            _choice(
+                {
+                    "prompt_token_ids": [10],
+                    "completion_token_ids": [20],
+                    "routed_experts": np.asarray([_route(0)], dtype=np.uint8),
+                }
+            )
+        ],
+        choice_offsets=[1],
+        choice_token_lengths=[1],
+    )
+
+    assert routes is not None
+    materialized = _routes_to_list(routes)
+    assert materialized[0] == _route(0)
+    assert all(len(set(layer)) == 2 for layer in materialized[1])
+
+
 def _tokenized(
     token_ids: list[int],
     routes: list[list[list[int]]],
@@ -104,6 +135,7 @@ def _tokenized(
     weight: float = 1.0,
     pixel_values: torch.Tensor | None = None,
     image_grid_thw: torch.Tensor | None = None,
+    num_experts: int = 256,
 ) -> TokenizedResult:
     trainable_start = prompt_length if trainable_start is None else trainable_start
     return TokenizedResult(
@@ -120,7 +152,13 @@ def _tokenized(
         choice_offsets=[trainable_start],
         extra_logprobs={},
         _tokenizer=_FakeTokenizer(),  # type: ignore[arg-type]
-        moe_routed_experts=np.asarray(routes, dtype=np.int32),
+        moe_routed_experts=MoeRouteArray(
+            np.asarray(
+                routes,
+                dtype=np.uint8 if num_experts <= 256 else np.uint16,
+            ),
+            num_experts=num_experts,
+        ),
         prompt_id=prompt_id,
         prompt_length=prompt_length,
         weight=weight,
@@ -137,7 +175,7 @@ def test_pack_carries_routes_through_prefix_tree_splicing() -> None:
     )
     second = _tokenized(
         [10, 11, 22, 23],
-        [_route(99), _route(10), _route(40), _route(50)],
+        [_route(0), _route(10), _route(40), _route(50)],
         prompt_id=123,
         prompt_length=1,
         trainable_start=2,
@@ -155,7 +193,7 @@ def test_pack_carries_routes_through_prefix_tree_splicing() -> None:
     assert packed["tokens"].tolist()[0][:7] == [10, 11, 20, 21, 11, 22, 23]
     routing_replay = packed["moe_routing_replay"]
     assert routing_replay is not None
-    assert routing_replay.expert_indices.tolist()[0][:7] == [
+    assert torch.movedim(routing_replay.expert_indices[:, 0], 0, 1).tolist()[:7] == [
         _route(0),
         _route(10),
         _route(20),
@@ -167,6 +205,40 @@ def test_pack_carries_routes_through_prefix_tree_splicing() -> None:
     assert routing_replay.pack_stats.packed_tokens == 7
 
 
+def test_pack_uses_reference_routes_for_shared_prefix() -> None:
+    first = _tokenized(
+        [10, 11, 20],
+        [_route(0), _route(10), _route(20)],
+        prompt_id=123,
+        prompt_length=2,
+    )
+    second = _tokenized(
+        [10, 11, 21],
+        [_route(90), _route(10), _route(30)],
+        prompt_id=123,
+        prompt_length=2,
+    )
+
+    packed = packed_tensors_from_tokenized_results(
+        [first, second],
+        seq_len=8,
+        truncate_long_results=False,
+        include_moe_routing=True,
+        min_prefix_tree_shared_segment_length=0,
+    )
+
+    replay = packed["moe_routing_replay"]
+    assert replay is not None
+    routes = torch.movedim(replay.expert_indices[:, 0], 0, 1).tolist()
+    assert routes[:5] == [
+        _route(0),
+        _route(10),
+        _route(20),
+        _route(10),
+        _route(30),
+    ]
+
+
 def test_prefix_tree_pack_keeps_trainable_duplicates_in_leaf_metadata() -> None:
     first = _tokenized(
         [10, 11, 20, 21],
@@ -310,12 +382,19 @@ def test_prefix_tree_pack_best_fit_combines_independent_small_groups() -> None:
     assert int((packed["group_ids"] != -1).sum().item()) == 24
 
 
-def test_pack_infers_at_least_topk_experts_from_sparse_routes() -> None:
+@pytest.mark.parametrize(
+    ("num_experts", "dtype"),
+    [(256, torch.uint8), (257, torch.uint16)],
+)
+def test_pack_preserves_exact_expert_count_and_smallest_dtype(
+    num_experts: int, dtype: torch.dtype
+) -> None:
     result = _tokenized(
         [10, 20],
-        [[[0, 0, 0, 0]], [[0, 0, 0, 0]]],
+        [[[0, 1, 2, 3]], [[4, 5, 6, 7]]],
         prompt_id=456,
         prompt_length=1,
+        num_experts=num_experts,
     )
 
     packed = packed_tensors_from_tokenized_results(
@@ -329,10 +408,15 @@ def test_pack_infers_at_least_topk_experts_from_sparse_routes() -> None:
     routing_replay = packed["moe_routing_replay"]
     assert routing_replay is not None
     assert routing_replay.topk == 4
-    assert routing_replay.num_experts == 4
+    assert routing_replay.num_experts == num_experts
+    assert routing_replay.expert_indices.dtype == dtype
+    assert routing_replay.expert_indices.shape == (1, 1, 4, 4)
+    assert all(
+        len(set(row)) == 4 for row in routing_replay.expert_indices[0, 0].tolist()
+    )
 
 
-def test_build_replay_bundle_uses_packed_sequence_sample_calls() -> None:
+def test_build_replay_bundle_retains_layer_major_storage() -> None:
     result = _tokenized(
         [10, 11, 20],
         [_route(0), _route(10), _route(20)],
@@ -352,7 +436,123 @@ def test_build_replay_bundle_uses_packed_sequence_sample_calls() -> None:
         global_grad_accumulation_sequences=1,
     )
 
-    route = bundle.steps[0].routers["chunk_00.layer_0000.mlp.router"].calls[0]
-    assert route.sample_index == 0
-    assert route.expert_indices.tolist()[:3] == [[0, 1], [10, 11], [20, 21]]
-    assert len(set(route.expert_indices.tolist()[3])) == 2
+    replay = packed["moe_routing_replay"]
+    assert replay is not None
+    assert bundle.tensor_backed
+    assert bundle.steps == {}
+    assert bundle.expert_indices is replay.expert_indices
+    assert bundle.expert_indices[0, 0].tolist()[:3] == [
+        [0, 1],
+        [10, 11],
+        [20, 21],
+    ]
+    assert len(set(bundle.expert_indices[0, 0, 3].tolist())) == 2
+
+
+def test_trajectory_route_roundtrip_preserves_exact_contract() -> None:
+    routes = MoeRouteArray(
+        np.asarray([[[0, 256]], [[255, 1]]], dtype=np.uint16),
+        num_experts=257,
+    )
+    trajectory = Trajectory(
+        messages_and_choices=[
+            _choice(
+                {
+                    "prompt_token_ids": [10],
+                    "completion_token_ids": [20],
+                    ROUTED_EXPERTS_KEY: routes,
+                    NUM_EXPERTS_KEY: 257,
+                }
+            )
+        ]
+    )
+
+    restored = TrajectoryPayload.from_trajectory(trajectory).build()
+    choice = restored.messages_and_choices[0]
+    assert isinstance(choice, Choice)
+    metadata = (choice.model_extra or {})[ART_MOE_ROUTING_METADATA_KEY]
+    restored_routes = metadata[ROUTED_EXPERTS_KEY]
+    assert isinstance(restored_routes, MoeRouteArray)
+    assert restored_routes.num_experts == 257
+    assert restored_routes.dtype == np.dtype(np.uint16)
+    assert np.array_equal(restored_routes, routes)
+
+
+def test_exchange_route_roundtrip_preserves_exact_contract() -> None:
+    routes = MoeRouteArray(
+        np.asarray([[[0, 256]], [[255, 1]]], dtype=np.uint16),
+        num_experts=257,
+    )
+    response = ChatCompletion(
+        id="route-test",
+        choices=[_choice({ROUTED_EXPERTS_KEY: routes, NUM_EXPERTS_KEY: 257})],
+        created=0,
+        model="test-model",
+        object="chat.completion",
+    )
+    now = datetime.now()
+    trajectory = Trajectory(
+        exchanges={
+            "chat_completions": [
+                ChatCompletionsExchange(
+                    request={"model": "test-model", "messages": []},
+                    response=response,
+                    start_time=now,
+                    end_time=now,
+                )
+            ]
+        }
+    )
+
+    restored = TrajectoryPayload.from_trajectory(trajectory).build()
+    choice = restored.exchanges.chat_completions[0].response.choices[0]
+    restored_routes = (choice.model_extra or {})[ART_MOE_ROUTING_METADATA_KEY][
+        ROUTED_EXPERTS_KEY
+    ]
+    assert restored_routes.num_experts == 257
+    assert np.array_equal(restored_routes, routes)
+
+
+def test_shm_replay_is_one_layer_major_uint16_tensor() -> None:
+    packed = packed_tensors_from_tokenized_results(
+        [
+            _tokenized(
+                [10, 20],
+                [[[0, 256]], [[255, 1]]],
+                prompt_id=456,
+                prompt_length=1,
+                num_experts=257,
+            )
+        ],
+        seq_len=4,
+        pad_token_id=0,
+        truncate_long_results=False,
+        include_moe_routing=True,
+    )
+    store = SharedMemoryPackedBatchStore(
+        owner_actor_id="test-owner", capacity_bytes=1 << 20
+    )
+    try:
+        ref = store.create(packed, batch_id="route-batch")
+        replay_specs = [
+            spec for spec in ref.tensors if spec.name.startswith("moe_routing_replay/")
+        ]
+        assert [spec.name for spec in replay_specs] == [
+            "moe_routing_replay/expert_indices"
+        ]
+        assert replay_specs[0].dtype == "uint16"
+        assert ref.moe_routing_replay is not None
+        assert ref.moe_routing_replay.num_experts == 257
+        assert ref.moe_routing_replay.packed_tokens == 2
+
+        with store.map(ref) as mapped:
+            replay = mapped.tensors["moe_routing_replay"]
+            assert replay.expert_indices.shape == (1, 1, 4, 2)
+            assert replay.expert_indices.dtype == torch.uint16
+            bundle = build_moe_routing_replay_bundle_from_packed_tensors(
+                packed_tensors=mapped.tensors,
+                global_grad_accumulation_sequences=1,
+            )
+            assert bundle.expert_indices is replay.expert_indices
+    finally:
+        store.close()
diff --git a/tests/unit/test_moe_routing_replay.py b/tests/unit/test_moe_routing_replay.py
index 4a559b8f4..c06608c21 100644
--- a/tests/unit/test_moe_routing_replay.py
+++ b/tests/unit/test_moe_routing_replay.py
@@ -8,6 +8,7 @@
 import torch
 from torch import nn
 
+import art.megatron.routing_replay as routing_replay_module
 from art.megatron.routing_replay import (
     MoeRoutingReplayBundle,
     MoeRoutingReplayController,
@@ -102,6 +103,33 @@ def _make_multi_call_bundle() -> MoeRoutingReplayBundle:
     )
 
 
+def _make_tensor_bundle(
+    *, num_layers: int = 1, topology: ParallelTopology | None = None
+) -> MoeRoutingReplayBundle:
+    rows = torch.tensor(
+        [
+            [[0, 2], [1, 0], [2, 1], [1, 2]],
+            [[2, 0], [0, 1], [1, 2], [2, 1]],
+        ],
+        dtype=torch.uint8,
+    )
+    expert_indices = torch.stack(
+        [torch.roll(rows, shifts=layer, dims=-1) for layer in range(num_layers)]
+    ).contiguous()
+    return MoeRoutingReplayBundle(
+        topology=topology
+        or ParallelTopology(tp=1, ep=1, etp=1, dp=1, sp=False, cp=1, pp=1, vpp=1),
+        num_steps=1,
+        max_topk=2,
+        router_keys=[
+            f"chunk_00.layer_{layer:04d}.mlp.router" for layer in range(num_layers)
+        ],
+        expert_indices=expert_indices,
+        num_experts=3,
+        global_grad_accumulation_sequences=2,
+    )
+
+
 class _FakeParallelState:
     def __init__(
         self,
@@ -175,6 +203,7 @@ def __init__(self, *, topk: int = 2, router_replay: Any | None = None) -> None:
                 "sequence_parallel": False,
                 "context_parallel_size": 1,
                 "moe_router_fusion": False,
+                "num_moe_experts": 3,
             },
         )()
 
@@ -347,6 +376,130 @@ def test_bundle_roundtrip_disk() -> None:
     assert torch.equal(loaded_route.expert_mask, route.expert_mask)
 
 
+def test_tensor_bundle_uint16_roundtrip_disk() -> None:
+    indices = torch.tensor(
+        [[[[0, 256], [255, 1]]]],
+        dtype=torch.uint16,
+    )
+    bundle = MoeRoutingReplayBundle(
+        topology=ParallelTopology(tp=1, ep=1, etp=1, dp=1, sp=False, cp=1, pp=1, vpp=1),
+        num_steps=1,
+        max_topk=2,
+        router_keys=["chunk_00.layer_0000.mlp.router"],
+        expert_indices=indices,
+        num_experts=257,
+        global_grad_accumulation_sequences=1,
+    )
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        bundle.to_dir(tmp_dir)
+        loaded = MoeRoutingReplayBundle.from_dir(tmp_dir)
+
+    assert loaded.tensor_backed
+    assert loaded.num_experts == 257
+    assert loaded.expert_indices is not None
+    assert loaded.expert_indices.dtype == torch.uint16
+    assert torch.equal(loaded.expert_indices, indices)
+
+
+def test_tensor_controller_preserves_uid_order_and_synthesizes_tp_padding() -> None:
+    bundle = _make_tensor_bundle()
+    controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu")
+    chunk = _FakeChunk()
+    router = _fake_chunk_router(chunk)
+    replay = cast(_FakeRouterReplay, router.router_replay)
+
+    controller.install_router_patches([chunk])
+    controller.set_step(step_index=0, sample_index=[0, 1])
+    controller.begin_micro(0, 0)
+    controller.set_local_input_token_uids(torch.tensor([3, 1, -1], dtype=torch.int64))
+    router.routing(torch.randn((3, 3), dtype=torch.float32))
+
+    assert bundle.expert_indices is not None
+    expected = bundle.expert_indices[0, 0, [3, 1]].to(torch.long)
+    target = replay.targets_seen[-1]
+    assert torch.equal(target[:2], expected)
+    assert target[2].min().item() >= 0
+    assert target[2].max().item() < 3
+    assert target[2].unique().numel() == 2
+
+    controller.begin_micro(1, 1)
+    controller.set_local_input_token_uids(torch.arange(4, dtype=torch.int64))
+    router.routing(torch.randn((4, 3), dtype=torch.float32))
+    controller.finalize_step()
+    controller.remove_router_patches()
+
+
+def test_tensor_controller_synthesizes_dp_dummy_routes() -> None:
+    bundle = _make_tensor_bundle()
+    controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu")
+    chunk = _FakeChunk()
+    router = _fake_chunk_router(chunk)
+    replay = cast(_FakeRouterReplay, router.router_replay)
+
+    controller.install_router_patches([chunk])
+    controller.set_step(step_index=0, sample_index=[None])
+    controller.begin_micro(None, 0)
+    controller.set_local_input_token_uids(torch.tensor([2, 0], dtype=torch.int64))
+    router.routing(torch.randn((2, 3), dtype=torch.float32))
+
+    target = replay.targets_seen[-1]
+    assert bool(((0 <= target) & (target < 3)).all())
+    assert all(row.unique().numel() == 2 for row in target)
+    controller.finalize_step()
+    controller.remove_router_patches()
+
+
+def test_tensor_controller_addresses_pp_global_layer(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    topology = ParallelTopology(tp=1, ep=1, etp=1, dp=1, sp=False, cp=1, pp=2, vpp=1)
+    bundle = _make_tensor_bundle(num_layers=2, topology=topology)
+    monkeypatch.setattr(
+        routing_replay_module,
+        "_global_layer_prefixes",
+        lambda _chunk: [("decoder.layers.0", 1)],
+    )
+    controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu")
+    chunk = _FakeChunk()
+    router = _fake_chunk_router(chunk)
+    replay = cast(_FakeRouterReplay, router.router_replay)
+
+    controller.install_router_patches([chunk])
+    controller.set_step(step_index=0, sample_index=[0])
+    controller.begin_micro(0, 0)
+    controller.set_local_input_token_uids(torch.arange(4, dtype=torch.int64))
+    router.routing(torch.randn((4, 3), dtype=torch.float32))
+
+    assert bundle.expert_indices is not None
+    assert torch.equal(
+        replay.targets_seen[-1], bundle.expert_indices[1, 0].to(torch.long)
+    )
+    controller.finalize_step()
+    controller.remove_router_patches()
+
+
+def test_tensor_controller_accepts_pipeline_chunk_without_local_router(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    topology = ParallelTopology(tp=1, ep=1, dp=1, cp=1, pp=2, vpp=2)
+    bundle = _make_tensor_bundle(topology=topology)
+    router_chunk = _FakeChunk()
+    empty_chunk = nn.Identity()
+    monkeypatch.setattr(
+        routing_replay_module,
+        "_global_layer_prefixes",
+        lambda chunk: [("decoder.layers.0", 0)] if chunk is router_chunk else [],
+    )
+    controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu")
+
+    controller.install_router_patches([empty_chunk, router_chunk])
+    controller.set_step(step_index=0, sample_index=[0])
+    controller.begin_micro(0, 0, chunk_index=0)
+    controller.begin_micro(0, 0, chunk_index=1)
+
+    controller.remove_router_patches()
+
+
 def test_controller_uses_native_router_replay_target_indices() -> None:
     bundle, route = _make_bundle()
     controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu")
diff --git a/tests/unit/test_multi_checkpoint_inference.py b/tests/unit/test_multi_checkpoint_inference.py
index eaabf6ce3..011d1dc71 100644
--- a/tests/unit/test_multi_checkpoint_inference.py
+++ b/tests/unit/test_multi_checkpoint_inference.py
@@ -356,7 +356,7 @@ def test_max_loras_can_be_overridden(self, unsloth_service_class):
     async def test_prune_loaded_adapters_unloads_non_retained_steps(
         self, unsloth_service_class, monkeypatch
     ):
-        """UnslothService should unload old vLLM LoRA adapters like MegatronService."""
+        """UnslothService should unload old vLLM LoRA adapters after updates."""
         httpx = pytest.importorskip("httpx")
         UnslothService = unsloth_service_class
         calls = []
@@ -382,7 +382,7 @@ async def post(self, url, *, json, **_kwargs):
         service = UnslothService(
             model_name="test-model",
             base_model="meta-llama/Llama-3.1-8B",
-            config={"rollout_weights_mode": "lora"},
+            config={},
             output_dir="/tmp/test",
         )
         service._vllm_port = 8000
diff --git a/tests/unit/test_pipeline_trainer_local_backend.py b/tests/unit/test_pipeline_trainer_local_backend.py
index e347f22e5..c59141eca 100644
--- a/tests/unit/test_pipeline_trainer_local_backend.py
+++ b/tests/unit/test_pipeline_trainer_local_backend.py
@@ -1165,36 +1165,3 @@ async def adapter_retention_lease(self, _model: TrainableModel, step: int):
     assert trainer._scheduled_eval_steps == set()
     assert backend.active_steps == set()
     assert trainer._protected_checkpoint_steps(8) == {8}
-
-
-def test_pipeline_trainer_rejects_merged_weight_eval(tmp_path: Path) -> None:
-    model = TrainableModel(
-        run_name="pipeline-merged-eval",
-        name="pipeline-merged-eval",
-        project="pipeline-tests",
-        base_model="test-model",
-        base_path=str(tmp_path),
-        _internal_config=InternalModelConfig(
-            trainer_gpu_ids=[0],
-            inference_gpu_ids=[1],
-            rollout_weights_mode="merged",
-        ),
-    )
-
-    with pytest.raises(
-        ValueError,
-        match="eval requires rollout_weights_mode='lora'",
-    ):
-        PipelineTrainer(
-            model=model,
-            backend=LocalBackend(path=str(tmp_path)),
-            rollout_fn=_noop_rollout,
-            scenarios=[],
-            config={},
-            pipeline=PipelineRuntimeConfig(
-                num_rollout_workers=1,
-                min_batch_size=1,
-                max_batch_size=1,
-            ),
-            eval_fn=_noop_eval,
-        )
diff --git a/tests/unit/test_preprocessing_tokenize.py b/tests/unit/test_preprocessing_tokenize.py
index 4a3264fbf..1f58bac06 100644
--- a/tests/unit/test_preprocessing_tokenize.py
+++ b/tests/unit/test_preprocessing_tokenize.py
@@ -5,10 +5,14 @@
 import pytest
 from transformers.tokenization_utils_base import BatchEncoding
 
-from art.preprocessing.tokenize import tokenize_sft_batch
+from art.preprocessing.tokenize import (
+    _normalize_tool_call_arguments_for_chat_template,
+    tokenize_sft_batch,
+)
 from art.trajectories import Trajectory
 from art.types import MessagesAndChoices, TrainSFTConfig
 from art.utils.chat_template import (
+    TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR,
     chat_template_with_preserved_thinking,
     default_chat_template_kwargs_for_template,
     normalize_tool_call_arguments_for_chat_template,
@@ -317,6 +321,71 @@ def apply_chat_template(self, *args, **kwargs):
         return rendered
 
 
+def test_glm_chat_template_normalizes_aliased_tool_call_arguments() -> None:
+    tokenizer = _FakeTokenizer()
+    tokenizer.chat_template = (
+        "{% for tc in message.tool_calls %}"
+        "{% set _args = tc.function.arguments %}"
+        "{% for name, value in _args.items() %}{{ name }}{{ value }}{% endfor %}"
+        "{% endfor %}"
+    )
+    messages = [
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "function": {
+                        "name": "answer",
+                        "arguments": '{"value": "yes"}',
+                    }
+                }
+            ],
+        }
+    ]
+
+    normalized = _normalize_tool_call_arguments_for_chat_template(
+        tokenizer,
+        messages,
+    )
+
+    assert normalized[0]["tool_calls"][0]["function"]["arguments"] == {"value": "yes"}
+    assert messages[0]["tool_calls"][0]["function"]["arguments"] == ('{"value": "yes"}')
+
+
+@pytest.mark.parametrize("handler_name", ["GEMMA4_DENSE_HANDLER", "GEMMA4_MOE_HANDLER"])
+def test_gemma4_normalizes_json_tool_arguments_for_mapping_template(
+    handler_name: str,
+) -> None:
+    pytest.importorskip("megatron")
+    from art.megatron.model_support.handlers import gemma4
+
+    handler = getattr(gemma4, handler_name)
+    tokenizer = _FakeTokenizer()
+    tokenizer.chat_template = (
+        "{% set function = tool_call['function'] %}"
+        "{% if function['arguments'] is mapping %}{{ function['arguments'] }}{% endif %}"
+    )
+    handler.configure_tokenizer(tokenizer, internal_config={})
+    messages = [
+        {
+            "role": "assistant",
+            "tool_calls": [
+                {
+                    "function": {
+                        "name": "answer",
+                        "arguments": '{"value": "yes"}',
+                    }
+                }
+            ],
+        }
+    ]
+
+    normalized = _normalize_tool_call_arguments_for_chat_template(tokenizer, messages)
+
+    assert getattr(tokenizer, TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR) is True
+    assert normalized[0]["tool_calls"][0]["function"]["arguments"] == {"value": "yes"}
+
+
 def test_legacy_qwen_template_gains_opt_in_thinking_preservation() -> None:
     template = (
         "{% if enable_thinking %}think{% endif %}"
diff --git a/tests/unit/test_track_api_cost.py b/tests/unit/test_track_api_cost.py
index fbd938dbc..7c4732579 100644
--- a/tests/unit/test_track_api_cost.py
+++ b/tests/unit/test_track_api_cost.py
@@ -724,7 +724,19 @@ async def eval_fn(
                     reward=1.0,
                     messages_and_choices=[
                         {"role": "user", "content": "hello"},
-                        {"role": "assistant", "content": "hi"},
+                        {
+                            "role": "assistant",
+                            "content": "hi",
+                            "policy_token_spans": [
+                                {
+                                    "start_token": 0,
+                                    "end_token": 1,
+                                    "policy_version": 1,
+                                    "lora_slot": "active",
+                                    "update_seq": 1,
+                                }
+                            ],
+                        },
                     ],
                 )
             ]
diff --git a/tests/unit/test_vllm_lora_delta.py b/tests/unit/test_vllm_lora_delta.py
deleted file mode 100644
index 7b6930a3d..000000000
--- a/tests/unit/test_vllm_lora_delta.py
+++ /dev/null
@@ -1,68 +0,0 @@
-from __future__ import annotations
-
-import importlib.util
-from pathlib import Path
-from types import SimpleNamespace
-
-import torch
-
-
-def _load_lora_delta_module():
-    path = (
-        Path(__file__).resolve().parents[2]
-        / "vllm_runtime/src/art_vllm_runtime/lora_delta.py"
-    )
-    spec = importlib.util.spec_from_file_location("_art_vllm_runtime_lora_delta", path)
-    assert spec is not None
-    module = importlib.util.module_from_spec(spec)
-    assert spec.loader is not None
-    spec.loader.exec_module(module)
-    return module
-
-
-def test_additive_weight_loader_uses_legacy_loader_for_plain_merged_column_param():
-    lora_delta = _load_lora_delta_module()
-    param = torch.nn.Parameter(torch.zeros(2, 4))
-    loaded = torch.arange(8, dtype=torch.float32).view(2, 4)
-    calls = []
-
-    class Owner:
-        def weight_loader_v2(self, loader_param, loaded_weight, shard_id):
-            del shard_id
-            loader_param.load_merged_column_weight(loaded_weight=loaded_weight)
-
-        def weight_loader(self, loader_param, loaded_weight, shard_id):
-            calls.append((loader_param, shard_id))
-            loader_param.data.copy_(loaded_weight)
-
-    owner = Owner()
-    loader = lora_delta._additive_weight_loader(param, owner.weight_loader_v2)
-    result = loader(param, loaded, 0)
-
-    assert result is None
-    assert calls == [(param, 0)]
-    assert torch.equal(param, loaded)
-
-
-def test_additive_weight_loader_keeps_v2_for_vllm_parameter_like_param():
-    lora_delta = _load_lora_delta_module()
-    param = torch.nn.Parameter(torch.zeros(2, 4))
-    loaded = torch.arange(8, dtype=torch.float32).view(2, 4)
-    calls = []
-
-    def load_merged_column_weight(*, loaded_weight, **_kwargs):
-        calls.append("v2")
-        param.data.copy_(loaded_weight)
-
-    setattr(param, "load_merged_column_weight", load_merged_column_weight)
-    owner = SimpleNamespace(
-        weight_loader_v2=lambda loader_param, loaded_weight, shard_id: (
-            loader_param.load_merged_column_weight(loaded_weight=loaded_weight)
-        ),
-        weight_loader=lambda *_args, **_kwargs: calls.append("legacy"),
-    )
-    loader = lora_delta._additive_weight_loader(param, owner.weight_loader_v2)
-    loader(param, loaded, 0)
-
-    assert calls == ["v2"]
-    assert torch.equal(param, loaded)
diff --git a/tests/unit/trajectories/test_capture.py b/tests/unit/trajectories/test_capture.py
index 660938568..9cf3b8692 100644
--- a/tests/unit/trajectories/test_capture.py
+++ b/tests/unit/trajectories/test_capture.py
@@ -803,7 +803,6 @@ async def response(_: httpx.Request) -> httpx.Response:
 
     assert len(chunks) == 1
     assert chunks[0].choices[0].delta.content == "hello"
-    await stream.close()
     assert len(trajectory.exchanges.chat_completions) == 1
     await client.close()
 
@@ -865,7 +864,6 @@ async def response(_: httpx.Request) -> httpx.Response:
 
     assert len(chunks) == 1
     assert chunks[0].choices[0].delta.content == "hello"
-    await stream.close()
     assert len(trajectory.exchanges.chat_completions) == 1
     await client.close()
 
diff --git a/tests/unit/trajectories/test_compact_serialization.py b/tests/unit/trajectories/test_compact_serialization.py
index e8150b46a..43f47d523 100644
--- a/tests/unit/trajectories/test_compact_serialization.py
+++ b/tests/unit/trajectories/test_compact_serialization.py
@@ -6,9 +6,7 @@
 import json
 import pickle
 import random
-import statistics
 import sys
-import time
 from typing import Any
 
 import pydantic
@@ -33,7 +31,7 @@ def _json_size(value: object) -> int:
     return len(json.dumps(value, ensure_ascii=False, separators=(",", ":")).encode())
 
 
-def test_explicit_interning_handles_nested_models_keys_and_cycles() -> None:
+def test_explicit_memory_compaction_interns_nested_models_keys_and_cycles() -> None:
     class ProviderExtra(pydantic.BaseModel, extra="allow"):
         content: str
 
@@ -64,12 +62,11 @@ def __init__(self, value: str) -> None:
             "frozenset": frozenset({_fresh(repeated)}),
         }
     )
+    assert cycle[0] is not cycle[1]
 
-    canonical = next(key for key in trajectory.metadata if key == repeated)
-    assert cycle[0] is not canonical
-
-    trajectory._intern_strings()
+    assert tr.compact_memory(trajectory) is trajectory
 
+    canonical = next(key for key in trajectory.metadata if key == repeated)
     assert cycle[0] is canonical
     assert cycle[1] is canonical
     assert cycle[2] is cycle
@@ -95,7 +92,6 @@ def test_only_pickle_boundary_interns_validated_finished_and_grouped_values() ->
     assert from_json.metadata["first"] is not from_json.metadata["second"]
 
     trajectory.metadata["third"] = _fresh(repeated)
-    assert trajectory.metadata["third"] is not trajectory.metadata["first"]
     trajectory.finish()
     assert trajectory.metadata["third"] is not trajectory.metadata["first"]
 
@@ -120,6 +116,8 @@ def test_only_pickle_boundary_interns_validated_finished_and_grouped_values() ->
     )
     restored = pickle.loads(pickle.dumps(group))
     canonical = trajectory.metadata["first"]
+    assert trajectory.metadata["second"] is canonical
+    assert trajectory.metadata["third"] is canonical
     assert other.metadata["value"] is canonical
     assert group.metadata["value"] is canonical
     assert group.exceptions[0].message is canonical
@@ -129,12 +127,12 @@ def test_only_pickle_boundary_interns_validated_finished_and_grouped_values() ->
     )
 
 
-def test_interning_does_not_change_model_equality() -> None:
+def test_memory_compaction_does_not_change_model_equality() -> None:
     trajectory = art.Trajectory()
     trajectory.metadata["items"] = [_fresh(_long()), _fresh(_long())]
     before = copy.deepcopy(trajectory)
 
-    trajectory._intern_strings()
+    tr.compact_memory(trajectory)
 
     assert trajectory == before
 
@@ -685,7 +683,7 @@ def test_tokenized_compact_round_trip_all_protocol_source_shapes() -> None:
             )
 
 
-def test_interning_reduces_pickle_and_compact_json_sizes() -> None:
+def test_pickle_interning_reduces_pickle_and_compact_json_sizes() -> None:
     trajectory = art.Trajectory()
     repeated = _long() * 4
     trajectory.metadata["items"] = [_fresh(repeated) for _ in range(200)]
@@ -744,23 +742,6 @@ def test_cloudpickle_preserves_shared_references() -> None:
     trajectory = art.Trajectory(
         metadata={"items": [_fresh(repeated), _fresh(repeated)]}
     )
+    tr.compact_memory(trajectory)
     cloud_restored = cloudpickle.loads(cloudpickle.dumps(trajectory))
     assert cloud_restored.metadata["items"][0] is cloud_restored.metadata["items"][1]
-
-
-def test_interning_traversal_scales_near_linearly() -> None:
-    repeated = _long()
-
-    def duration(size: int) -> float:
-        samples = []
-        for _ in range(5):
-            trajectory = art.Trajectory()
-            trajectory.metadata["items"] = [_fresh(repeated) for _ in range(size)]
-            start = time.perf_counter()
-            trajectory._intern_strings()
-            samples.append(time.perf_counter() - start)
-        return statistics.median(samples)
-
-    small = duration(4_000)
-    large = duration(8_000)
-    assert large < max(small * 3, 0.05)
diff --git a/uv.lock b/uv.lock
index 8cb33ecbf..a6308fc1f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,39 +2,93 @@ version = 1
 revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
-    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
-    "python_full_version >= '3.14' and sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version == '3.13.*' and sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version < '3.13' and sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version == '3.13.*' and sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version < '3.13' and sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version >= '3.14' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version == '3.13.*' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version < '3.13' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "(python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron')",
-    "(python_full_version == '3.13.*' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron')",
-    "(python_full_version < '3.13' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (python_full_version < '3.13' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron')",
-    "python_full_version >= '3.14' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version == '3.13.*' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version < '3.13' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
+    "(python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker') or (python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')",
+    "(python_full_version == '3.13.*' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker') or (python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')",
+    "(python_full_version < '3.13' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker') or (python_full_version < '3.13' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')",
+    "python_full_version >= '3.14' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "(python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker') or (python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')",
+    "(python_full_version == '3.13.*' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker') or (python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')",
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+    "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform == 'darwin' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
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+    "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "(python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')",
+    "(python_full_version == '3.13.*' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')",
+    "(python_full_version < '3.13' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (python_full_version < '3.13' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')",
+    "python_full_version >= '3.14' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
+    "(python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker') or (python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')",
+    "(python_full_version == '3.13.*' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker') or (python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')",
+    "(python_full_version < '3.13' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker') or (python_full_version < '3.13' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')",
+    "python_full_version >= '3.14' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'",
 ]
 conflicts = [[
     { package = "openpipe-art", extra = "backend" },
@@ -42,42 +96,54 @@ conflicts = [[
 ], [
     { package = "openpipe-art", extra = "megatron" },
     { package = "openpipe-art", extra = "tinker" },
+], [
+    { package = "openpipe-art", extra = "distributed" },
+    { package = "openpipe-art", extra = "distributed-cu130" },
+], [
+    { package = "openpipe-art", extra = "backend-cu130" },
+    { package = "openpipe-art", extra = "distributed" },
+], [
+    { package = "openpipe-art", extra = "distributed" },
+    { package = "openpipe-art", extra = "megatron-cu130" },
+], [
+    { package = "openpipe-art", extra = "backend" },
+    { package = "openpipe-art", extra = "distributed-cu130" },
+], [
+    { package = "openpipe-art", extra = "distributed-cu130" },
+    { package = "openpipe-art", extra = "megatron" },
+], [
+    { package = "openpipe-art", extra = "backend" },
+    { package = "openpipe-art", extra = "backend-cu130" },
+], [
+    { package = "openpipe-art", extra = "backend" },
+    { package = "openpipe-art", extra = "megatron-cu130" },
+], [
+    { package = "openpipe-art", extra = "backend-cu130" },
+    { package = "openpipe-art", extra = "megatron" },
+], [
+    { package = "openpipe-art", extra = "backend-cu130" },
+    { package = "openpipe-art", extra = "megatron-cu130" },
+], [
+    { package = "openpipe-art", extra = "megatron" },
+    { package = "openpipe-art", extra = "megatron-cu130" },
+], [
+    { package = "openpipe-art", extra = "backend-cu130" },
+    { package = "openpipe-art", extra = "tinker" },
+], [
+    { package = "openpipe-art", extra = "distributed-cu130" },
+    { package = "openpipe-art", extra = "tinker" },
+], [
+    { package = "openpipe-art", extra = "megatron-cu130" },
+    { package = "openpipe-art", extra = "tinker" },
 ]]
 
 [manifest]
 overrides = [
     { name = "click", specifier = "==8.2.0" },
-    { name = "megatron-core", specifier = "==0.17.0" },
     { name = "numpy", specifier = "<2" },
+    { name = "nvidia-cublas", marker = "sys_platform == 'linux'", specifier = "==13.2.2.2" },
     { name = "nvidia-resiliency-ext", specifier = "<0.5" },
-    { name = "quack-kernels", specifier = "==0.3.7" },
-    { name = "torch", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = "==2.11.0" },
-    { name = "torch", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = "==2.11.0", index = "https://download.pytorch.org/whl/cu128" },
-    { name = "torchvision", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = "==0.26.0" },
-    { name = "torchvision", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = "==0.26.0", index = "https://download.pytorch.org/whl/cu128" },
-    { name = "transformer-engine", specifier = "==2.11.0" },
-]
-excludes = [
-    "causal-conv1d",
-    "emerging-optimizers",
-    "mamba-ssm",
-    "pynvml",
-]
-
-[[manifest.dependency-metadata]]
-name = "apex"
-version = "0.1"
-requires-dist = ["packaging"]
-
-[[manifest.dependency-metadata]]
-name = "megatron-bridge"
-version = "0.5.0+e1a207ac"
-requires-dist = ["accelerate", "comet-ml", "datasets", "diffusers", "einops", "flash-linear-attention", "flashinfer-cubin", "flashinfer-python", "hydra-core", "imageio", "imageio-ffmpeg", "megatron-core", "mistral-common", "mlflow", "nvidia-resiliency-ext", "omegaconf", "open-clip-torch", "peft", "pyyaml", "qwen-vl-utils", "regex", "rich", "six", "tensorboard", "timm", "torch", "tqdm", "transformers", "typing-extensions", "wandb"]
-
-[[manifest.dependency-metadata]]
-name = "transformer-engine-torch"
-version = "2.11.0"
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+]
 
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 [[package]]
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 version = "1.7.0"
@@ -111,8 +168,9 @@ dependencies = [
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     { name = "safetensors" },
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+    { name = "nvidia-cufft-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+]
+cufile = [
+    { name = "nvidia-cufile-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+]
+cupti = [
+    { name = "nvidia-cuda-cupti-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+]
+curand = [
+    { name = "nvidia-curand-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+]
+cusolver = [
+    { name = "nvidia-cusolver-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+]
+cusparse = [
+    { name = "nvidia-cusparse-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+]
+nvjitlink = [
+    { name = "nvidia-nvjitlink-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+]
+nvrtc = [
+    { name = "nvidia-cuda-nvrtc-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+]
+nvtx = [
+    { name = "nvidia-nvtx-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 
 [[package]]
 name = "cuda-toolkit"
-version = "12.8.1"
+version = "13.0.2"
 source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
+    "python_full_version < '3.13'",
+]
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/57/b2/453099f5f3b698d7d0eab38916aac44c7f76229f451709e2eb9db6615dcd/cuda_toolkit-13.0.2-py2.py3-none-any.whl", hash = "sha256:b198824cf2f54003f50d64ada3a0f184b42ca0846c1c94192fa269ecd97a66eb", size = 2364, upload-time = "2025-12-19T23:24:07.328Z" },
 ]
 
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-cublas", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-cuda-runtime", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 cufft = [
-    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-cufft", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 cufile = [
-    { name = "nvidia-cufile-cu12", marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-cufile", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-cuda-cupti", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 curand = [
-    { name = "nvidia-curand-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-curand", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-cusolver", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-cusparse", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-nvjitlink", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc-cu12", marker = "sys_platform == 'linux' or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "nvidia-cuda-nvrtc", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
 ]
 nvtx = [
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@@ -4766,6 +4332,7 @@ dependencies = [
     { name = "anthropic" },
     { name = "litellm" },
     { name = "nest-asyncio" },
+    { name = "numpy" },
     { name = "openai" },
     { name = "polars" },
     { name = "pydantic" },
@@ -4793,55 +4360,96 @@ backend = [
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     { name = "pytest" },
     { name = "setuptools" },
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+    { name = "msgspec" },
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+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torchmonarch" },
+    { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-tinker')" },
+    { name = "uv" },
+]
+distributed-cu130 = [
+    { name = "aiohttp" },
+    { name = "msgspec" },
+    { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130')" },
+    { name = "torchmonarch" },
+    { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "uv" },
+]
 langgraph = [
     { name = "langchain-core" },
     { name = "langchain-openai" },
     { name = "langgraph" },
 ]
 megatron = [
-    { name = "apex" },
-    { name = "flash-attn-4" },
-    { name = "flashinfer-cubin" },
-    { name = "flashinfer-python" },
-    { name = "megatron-bridge" },
-    { name = "megatron-core" },
-    { name = "ml-dtypes", marker = "python_full_version < '3.13'" },
-    { name = "ninja" },
+    { name = "aiohttp" },
+    { name = "msgspec" },
+    { name = "nixl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
     { name = "numpy" },
-    { name = "nvidia-cuda-cccl-cu12", marker = "sys_platform == 'linux'" },
-    { name = "nvidia-ml-py" },
-    { name = "nvidia-modelopt", marker = "sys_platform != 'darwin'" },
-    { name = "nvidia-resiliency-ext" },
-    { name = "pybind11" },
-    { name = "quack-kernels" },
-    { name = "scipy" },
-    { name = "setuptools" },
-    { name = "tilelang", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
-    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "torchvision", version = "0.26.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "torchvision", version = "0.26.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "transformer-engine" },
-    { name = "transformer-engine-cu12" },
-    { name = "transformer-engine-torch" },
+    { name = "peft" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'darwin' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torchmonarch" },
+    { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" } },
+    { name = "uv" },
+]
+megatron-cu130 = [
+    { name = "aiohttp" },
+    { name = "msgspec" },
+    { name = "nixl-cu13", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "numpy" },
+    { name = "peft" },
+    { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux'" },
+    { name = "torchmonarch" },
     { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" } },
+    { name = "uv" },
 ]
 plotting = [
     { name = "matplotlib" },
     { name = "seaborn" },
 ]
 tensors = [
-    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux' or sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "extra == 'extra-12-openpipe-art-backend-cu130' or extra == 'extra-12-openpipe-art-distributed-cu130' or extra == 'extra-12-openpipe-art-megatron-cu130' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker')" },
 ]
 tinker = [
     { name = "datrie" },
@@ -4854,8 +4462,8 @@ tinker = [
     { name = "pydantic" },
     { name = "tinker" },
     { name = "tinker-cookbook" },
-    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
     { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" } },
     { name = "uvicorn" },
 ]
@@ -4882,91 +4490,118 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "accelerate", marker = "extra == 'backend'", specifier = "==1.7.0" },
+    { name = "accelerate", marker = "extra == 'backend-cu130'", specifier = "==1.7.0" },
     { name = "aiohttp", specifier = ">=3.10.0" },
+    { name = "aiohttp", marker = "extra == 'distributed'", specifier = ">=3.13.0" },
+    { name = "aiohttp", marker = "extra == 'distributed-cu130'", specifier = ">=3.13.0" },
+    { name = "aiohttp", marker = "extra == 'megatron'", specifier = ">=3.13.0" },
+    { name = "aiohttp", marker = "extra == 'megatron-cu130'", specifier = ">=3.13.0" },
     { name = "anthropic", specifier = ">=0.77.0" },
-    { name = "apex", marker = "extra == 'megatron'", git = "https://github.com/NVIDIA/apex.git?rev=25.09" },
     { name = "awscli", marker = "extra == 'backend'", specifier = ">=1.38.1" },
+    { name = "awscli", marker = "extra == 'backend-cu130'", specifier = ">=1.38.1" },
     { name = "bitsandbytes", marker = "extra == 'backend'", specifier = ">=0.45.2,!=0.50.0" },
-    { name = "causal-conv1d", marker = "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'megatron'", specifier = "==1.6.1" },
+    { name = "bitsandbytes", marker = "extra == 'backend-cu130'", specifier = ">=0.45.2,!=0.50.0" },
     { name = "datrie", marker = "extra == 'tinker'", specifier = ">=0.8.3" },
     { name = "duckdb", marker = "extra == 'backend'", specifier = ">=1.0.0" },
+    { name = "duckdb", marker = "extra == 'backend-cu130'", specifier = ">=1.0.0" },
     { name = "fastapi", marker = "extra == 'tinker'", specifier = ">=0.128.0" },
-    { name = "flash-attn-4", marker = "extra == 'megatron'", url = "https://files.pythonhosted.org/packages/24/f7/01ee2576ce41f9884d291ee21861ef194afc0b2b1ce3bd175fc7a6e1b133/flash_attn_4-4.0.0b5-py3-none-any.whl" },
-    { name = "flashinfer-cubin", marker = "extra == 'megatron'", specifier = "==0.6.8.post1" },
-    { name = "flashinfer-python", marker = "extra == 'megatron'", specifier = "==0.6.8.post1" },
     { name = "gql", marker = "extra == 'backend'", specifier = ">=4.0.0" },
+    { name = "gql", marker = "extra == 'backend-cu130'", specifier = ">=4.0.0" },
     { name = "hf-xet", marker = "extra == 'backend'", specifier = ">=1.1.0" },
+    { name = "hf-xet", marker = "extra == 'backend-cu130'", specifier = ">=1.1.0" },
     { name = "huggingface-hub", marker = "extra == 'tinker'" },
     { name = "langchain-core", marker = "extra == 'langgraph'", specifier = ">=0.3.51" },
     { name = "langchain-openai", marker = "extra == 'langgraph'", specifier = ">=0.3.27" },
     { name = "langgraph", marker = "extra == 'langgraph'", specifier = ">=0.6.2" },
     { name = "litellm", specifier = ">=1.71.1,<=1.82.0" },
-    { name = "mamba-ssm", marker = "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'megatron'", specifier = "==2.3.1" },
     { name = "matplotlib", marker = "extra == 'plotting'", specifier = ">=3.10.1" },
-    { name = "megatron-bridge", marker = "extra == 'megatron'", git = "https://github.com/NVIDIA-NeMo/Megatron-Bridge.git?rev=e1a207ac757e5d0ed94d8ffbe1cbd28e81d8c084" },
-    { name = "megatron-core", marker = "extra == 'megatron'", specifier = "==0.17.0" },
-    { name = "ml-dtypes", marker = "python_full_version < '3.13' and extra == 'megatron'", specifier = ">=0.5.0" },
+    { name = "msgspec", marker = "extra == 'distributed'", specifier = ">=0.21.0" },
+    { name = "msgspec", marker = "extra == 'distributed-cu130'", specifier = ">=0.21.0" },
+    { name = "msgspec", marker = "extra == 'megatron'", specifier = ">=0.21.0" },
+    { name = "msgspec", marker = "extra == 'megatron-cu130'", specifier = ">=0.21.0" },
     { name = "nbclient", marker = "extra == 'backend'", specifier = ">=0.10.1" },
+    { name = "nbclient", marker = "extra == 'backend-cu130'", specifier = ">=0.10.1" },
     { name = "nbmake", marker = "extra == 'backend'", specifier = ">=1.5.5" },
+    { name = "nbmake", marker = "extra == 'backend-cu130'", specifier = ">=1.5.5" },
     { name = "nest-asyncio", specifier = ">=1.6.0" },
-    { name = "ninja", marker = "extra == 'megatron'", specifier = ">=1.11.1" },
+    { name = "nixl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'megatron'", specifier = "==1.3.2" },
+    { name = "nixl-cu13", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'megatron-cu130'", specifier = "==1.3.2" },
+    { name = "numpy", specifier = "<2" },
     { name = "numpy", marker = "extra == 'megatron'", specifier = "<2" },
+    { name = "numpy", marker = "extra == 'megatron-cu130'", specifier = "<2" },
     { name = "numpy", marker = "extra == 'tinker'", specifier = "<2" },
-    { name = "nvidia-cuda-cccl-cu12", marker = "sys_platform == 'linux' and extra == 'megatron'", specifier = "==12.9.27" },
     { name = "nvidia-cudnn-frontend", marker = "sys_platform == 'linux' and extra == 'backend'", specifier = "<1.21" },
-    { name = "nvidia-ml-py", marker = "extra == 'megatron'", specifier = "==13.580.82" },
-    { name = "nvidia-modelopt", marker = "sys_platform != 'darwin' and extra == 'megatron'", specifier = ">=0.42.0a0" },
+    { name = "nvidia-cudnn-frontend", marker = "sys_platform == 'linux' and extra == 'backend-cu130'", specifier = "<1.21" },
+    { name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux' and extra == 'backend-cu130'", specifier = "==2.28.9" },
     { name = "nvidia-resiliency-ext", marker = "sys_platform == 'linux' and extra == 'backend'", specifier = "<0.5" },
-    { name = "nvidia-resiliency-ext", marker = "sys_platform == 'linux' and extra == 'megatron'", specifier = "<0.5" },
+    { name = "nvidia-resiliency-ext", marker = "sys_platform == 'linux' and extra == 'backend-cu130'", specifier = "<0.5" },
     { name = "openai", specifier = ">=2.14.0" },
     { name = "peft", marker = "extra == 'backend'", specifier = ">=0.14.0" },
+    { name = "peft", marker = "extra == 'backend-cu130'", specifier = ">=0.14.0" },
+    { name = "peft", marker = "extra == 'megatron'", specifier = ">=0.14.0" },
+    { name = "peft", marker = "extra == 'megatron-cu130'", specifier = ">=0.14.0" },
     { name = "pillow", marker = "extra == 'tinker'" },
     { name = "polars", specifier = ">=1.26.0" },
     { name = "protobuf", marker = "extra == 'tinker'", specifier = ">=6.31.1" },
     { name = "pyarrow", marker = "extra == 'backend'", specifier = ">=15.0.0" },
+    { name = "pyarrow", marker = "extra == 'backend-cu130'", specifier = ">=15.0.0" },
     { name = "pyarrow", marker = "extra == 'tinker'", specifier = ">=15.0.0" },
-    { name = "pybind11", marker = "extra == 'megatron'", specifier = ">=2.13.6" },
     { name = "pydantic", specifier = ">=2.12" },
     { name = "pydantic", marker = "extra == 'tinker'", specifier = ">=2.12.5" },
     { name = "pytest", marker = "extra == 'backend'", specifier = ">=8.4.1" },
-    { name = "quack-kernels", marker = "extra == 'megatron'", specifier = "==0.3.7" },
+    { name = "pytest", marker = "extra == 'backend-cu130'", specifier = ">=8.4.1" },
     { name = "requests", specifier = ">=2.32.0" },
-    { name = "scipy", marker = "extra == 'megatron'", specifier = ">=1.17.0" },
     { name = "seaborn", marker = "extra == 'plotting'", specifier = ">=0.13.2" },
     { name = "setproctitle", specifier = ">=1.3.6" },
     { name = "setuptools", marker = "extra == 'backend'", specifier = ">=78.1.0" },
-    { name = "setuptools", marker = "extra == 'megatron'", specifier = ">=78.1.0" },
+    { name = "setuptools", marker = "extra == 'backend-cu130'", specifier = ">=78.1.0" },
     { name = "tblib", specifier = ">=3.0.0" },
-    { name = "tilelang", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'megatron'", specifier = "==0.1.10" },
     { name = "tinker", marker = "extra == 'tinker'", specifier = ">=0.23.4,<0.24" },
     { name = "tinker-cookbook", marker = "extra == 'tinker'", specifier = ">=0.5.2,<0.6" },
-    { name = "torch", marker = "(sys_platform == 'linux' and extra == 'backend') or (sys_platform == 'win32' and extra == 'backend')", specifier = "==2.11.0", index = "https://download.pytorch.org/whl/cu128" },
-    { name = "torch", marker = "(sys_platform == 'linux' and extra == 'megatron') or (sys_platform == 'win32' and extra == 'megatron')", specifier = "==2.11.0", index = "https://download.pytorch.org/whl/cu128" },
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-    { name = "torch", marker = "(sys_platform == 'linux' and extra == 'tinker') or (sys_platform == 'win32' and extra == 'tinker')", specifier = "==2.11.0", index = "https://download.pytorch.org/whl/cu128" },
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-    { name = "torch", marker = "sys_platform != 'linux' and sys_platform != 'win32' and extra == 'tinker'", specifier = "==2.11.0" },
+    { name = "torch", marker = "sys_platform == 'darwin' and extra == 'backend'", specifier = "==2.11.0" },
+    { name = "torch", marker = "sys_platform == 'darwin' and extra == 'distributed'", specifier = "==2.11.0" },
+    { name = "torch", marker = "sys_platform == 'darwin' and extra == 'megatron'", specifier = "==2.11.0" },
+    { name = "torch", marker = "(sys_platform == 'linux' and extra == 'backend') or (sys_platform == 'win32' and extra == 'backend')", specifier = "==2.11.0+cu128", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "openpipe-art", extra = "backend" } },
+    { name = "torch", marker = "sys_platform == 'linux' and extra == 'backend-cu130'", specifier = "==2.11.0+cu130", index = "https://download.pytorch.org/whl/cu130", conflict = { package = "openpipe-art", extra = "backend-cu130" } },
+    { name = "torch", marker = "(sys_platform == 'linux' and extra == 'distributed') or (sys_platform == 'win32' and extra == 'distributed')", specifier = "==2.11.0+cu128", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "openpipe-art", extra = "distributed" } },
+    { name = "torch", marker = "sys_platform == 'linux' and extra == 'distributed-cu130'", specifier = "==2.11.0+cu130", index = "https://download.pytorch.org/whl/cu130", conflict = { package = "openpipe-art", extra = "distributed-cu130" } },
+    { name = "torch", marker = "(sys_platform == 'linux' and extra == 'megatron') or (sys_platform == 'win32' and extra == 'megatron')", specifier = "==2.11.0+cu128", index = "https://download.pytorch.org/whl/cu128", conflict = { package = "openpipe-art", extra = "megatron" } },
+    { name = "torch", marker = "sys_platform == 'linux' and extra == 'megatron-cu130'", specifier = "==2.11.0+cu130", index = "https://download.pytorch.org/whl/cu130", conflict = { package = "openpipe-art", extra = "megatron-cu130" } },
+    { name = "torch", marker = "extra == 'tensors'", specifier = "==2.11.0" },
+    { name = "torch", marker = "extra == 'tinker'", specifier = "==2.11.0" },
     { name = "torchao", marker = "extra == 'backend'", specifier = "==0.16.0" },
-    { name = "torchvision", marker = "(sys_platform == 'linux' and extra == 'megatron') or (sys_platform == 'win32' and extra == 'megatron')", specifier = "==0.26.0", index = "https://download.pytorch.org/whl/cu128" },
-    { name = "torchvision", marker = "sys_platform != 'linux' and sys_platform != 'win32' and extra == 'megatron'", specifier = "==0.26.0" },
-    { name = "transformer-engine", marker = "extra == 'megatron'", specifier = "==2.11.0" },
-    { name = "transformer-engine-cu12", marker = "extra == 'megatron'", specifier = "==2.11.0" },
-    { name = "transformer-engine-torch", marker = "extra == 'megatron'", git = "https://github.com/NVIDIA/TransformerEngine.git?subdirectory=transformer_engine%2Fpytorch&rev=v2.11" },
+    { name = "torchao", marker = "extra == 'backend-cu130'", specifier = "==0.16.0" },
+    { name = "torchmonarch", marker = "extra == 'distributed'", specifier = "==0.6.0" },
+    { name = "torchmonarch", marker = "extra == 'distributed-cu130'", specifier = "==0.6.0" },
+    { name = "torchmonarch", marker = "extra == 'megatron'", specifier = "==0.6.0" },
+    { name = "torchmonarch", marker = "extra == 'megatron-cu130'", specifier = "==0.6.0" },
     { name = "transformers", marker = "extra == 'backend'", specifier = "==5.2.0" },
+    { name = "transformers", marker = "extra == 'backend-cu130'", specifier = "==5.2.0" },
+    { name = "transformers", marker = "extra == 'distributed'", specifier = ">=5.2.0,<=5.12.1" },
+    { name = "transformers", marker = "extra == 'distributed-cu130'", specifier = ">=5.2.0,<=5.12.1" },
     { name = "transformers", marker = "extra == 'megatron'", specifier = "==5.12.1" },
+    { name = "transformers", marker = "extra == 'megatron-cu130'", specifier = "==5.12.1" },
     { name = "transformers", marker = "extra == 'tinker'", specifier = ">=5.2.0,<=5.5.3" },
     { name = "trl", marker = "extra == 'backend'", specifier = "==0.20.0" },
+    { name = "trl", marker = "extra == 'backend-cu130'", specifier = "==0.20.0" },
     { name = "typer", specifier = ">=0.15.2" },
     { name = "typing-extensions", specifier = ">=4.13" },
     { name = "unsloth", marker = "extra == 'backend'", specifier = "==2026.3.3" },
+    { name = "unsloth", marker = "extra == 'backend-cu130'", specifier = "==2026.3.3" },
     { name = "unsloth-zoo", marker = "extra == 'backend'", specifier = "==2026.3.1" },
+    { name = "unsloth-zoo", marker = "extra == 'backend-cu130'", specifier = "==2026.3.1" },
+    { name = "uv", marker = "extra == 'backend'", specifier = ">=0.11.7" },
+    { name = "uv", marker = "extra == 'backend-cu130'", specifier = ">=0.11.7" },
+    { name = "uv", marker = "extra == 'distributed'", specifier = ">=0.11.7" },
+    { name = "uv", marker = "extra == 'distributed-cu130'", specifier = ">=0.11.7" },
+    { name = "uv", marker = "extra == 'megatron'", specifier = ">=0.11.7" },
+    { name = "uv", marker = "extra == 'megatron-cu130'", specifier = ">=0.11.7" },
     { name = "uvicorn", marker = "extra == 'tinker'", specifier = ">=0.35.0" },
     { name = "wandb", marker = "extra == 'backend'", specifier = "==0.28.0" },
+    { name = "wandb", marker = "extra == 'backend-cu130'", specifier = "==0.28.0" },
     { name = "weave", specifier = ">=0.52.41" },
 ]
-provides-extras = ["plotting", "tensors", "backend", "megatron", "langgraph", "tinker"]
+provides-extras = ["plotting", "distributed", "distributed-cu130", "tensors", "backend", "backend-cu130", "megatron", "megatron-cu130", "langgraph", "tinker"]
 
 [package.metadata.requires-dev]
 dev = [
@@ -5283,11 +4918,12 @@ dependencies = [
     { name = "psutil" },
     { name = "pyyaml" },
     { name = "safetensors" },
-    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "extra == 'extra-12-openpipe-art-backend-cu130' or extra == 'extra-12-openpipe-art-distributed-cu130' or extra == 'extra-12-openpipe-art-megatron-cu130' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker')" },
     { name = "tqdm" },
-    { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-12-openpipe-art-backend' or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-12-openpipe-art-megatron'" },
+    { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-12-openpipe-art-backend' or extra == 'extra-12-openpipe-art-backend-cu130' or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker')" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/86/cf/037f1e3d5186496c05513a6754639e2dab3038a05f384284d49a9bd06a2d/peft-0.19.1.tar.gz", hash = "sha256:0d97542fe96dcdaa20d3b81c06f26f988618f416a73544ab23c3618ccb674a40", size = 763738, upload-time = "2026-04-16T15:46:45.105Z" }
 wheels = [
@@ -5499,7 +5135,7 @@ dependencies = [
     { name = "networkx" },
     { name = "pdfminer-six" },
     { name = "pillow" },
-    { name = "pyreadline3", marker = "sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "pyreadline3", marker = "sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
     { name = "pyyaml" },
 ]
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+    "python_full_version >= '3.14' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
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+    "python_full_version < '3.13' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform == 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+    "python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-megatron-cu130' and extra != 'extra-12-openpipe-art-tinker'",
+]
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 wheels = [
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@@ -8029,43 +7399,25 @@ name = "torch"
 version = "2.11.0+cu128"
 source = { registry = "https://download.pytorch.org/whl/cu128" }
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-    "python_full_version == '3.13.*' and sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version < '3.13' and sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version >= '3.14' and sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version == '3.13.*' and sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "python_full_version < '3.13' and sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron'",
-    "(python_full_version >= '3.14' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (python_full_version >= '3.14' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron')",
-    "(python_full_version == '3.13.*' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (python_full_version == '3.13.*' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron')",
-    "(python_full_version < '3.13' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron') or (python_full_version < '3.13' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-megatron')",
-]
-dependencies = [
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-    { name = "cuda-toolkit", extra = ["cublas", "cudart", "cufft", "cufile", "cupti", "curand", "cusolver", "cusparse", "nvjitlink", "nvrtc", "nvtx"], marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
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-    { name = "networkx", marker = "sys_platform == 'linux' or sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "nvidia-cudnn-cu12", marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "nvidia-cusparselt-cu12", marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "nvidia-nccl-cu12", marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "nvidia-nvshmem-cu12", marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "setuptools", marker = "sys_platform == 'linux' or sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "sympy", marker = "sys_platform == 'linux' or sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "triton", marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "typing-extensions", marker = "sys_platform == 'linux' or sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    "(python_full_version >= '3.14' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform == 'win32')",
+    "(python_full_version == '3.13.*' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform == 'win32')",
+    "(python_full_version < '3.13' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform == 'win32')",
+]
+dependencies = [
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+    { name = "cuda-toolkit", version = "12.8.1", source = { registry = "https://pypi.org/simple" }, extra = ["cublas", "cudart", "cufft", "cufile", "cupti", "curand", "cusolver", "cusparse", "nvjitlink", "nvrtc", "nvtx"], marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "filelock", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "fsspec", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "jinja2", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "networkx", marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
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+]
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@@ -8337,35 +7682,50 @@ name = "transformers"
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+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "extra == 'extra-12-openpipe-art-backend-cu130' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torchvision" },
     { name = "tqdm" },
     { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" } },
     { name = "triton", marker = "'linux' in sys_platform" },
@@ -8598,8 +7971,9 @@ dependencies = [
     { name = "psutil" },
     { name = "regex" },
     { name = "sentencepiece" },
-    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
-    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "extra == 'extra-12-openpipe-art-backend-cu130' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
     { name = "torchao" },
     { name = "tqdm" },
     { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" } },
@@ -8763,11 +8137,11 @@ wheels = [
 
 [package.optional-dependencies]
 standard = [
-    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
     { name = "httptools" },
     { name = "python-dotenv" },
     { name = "pyyaml" },
-    { name = "uvloop", marker = "(platform_python_implementation != 'PyPy' and sys_platform != 'cygwin' and sys_platform != 'win32') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" },
+    { name = "uvloop", marker = "(platform_python_implementation != 'PyPy' and sys_platform != 'cygwin' and sys_platform != 'win32') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (platform_python_implementation == 'PyPy' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'cygwin' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" },
     { name = "watchfiles" },
     { name = "websockets" },
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index 640fabd3a..76f8d8d1a 100644
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     "pydantic>=2.12.5",
     "transformers==5.12.1",
-    "vllm @ https://github.com/vllm-project/vllm/releases/download/v0.23.0/vllm-0.23.0%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl ; sys_platform == 'linux'",
+]
+
+[project.optional-dependencies]
+cuda12 = [
+    "nvidia-nccl-cu12==2.28.9 ; sys_platform == 'linux'",
+    "torch==2.11.0 ; sys_platform == 'linux'",
+    "torchaudio==2.11.0 ; sys_platform == 'linux'",
+    "torchvision==0.26.0 ; sys_platform == 'linux'",
+    "vllm @ https://github.com/vllm-project/vllm/releases/download/v0.25.1/vllm-0.25.1%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl ; sys_platform == 'linux'",
+]
+cuda13 = [
+    "nvidia-nccl-cu13==2.28.9 ; sys_platform == 'linux'",
+    "torch==2.11.0 ; sys_platform == 'linux'",
+    "torchaudio==2.11.0 ; sys_platform == 'linux'",
+    "torchvision==0.26.0 ; sys_platform == 'linux'",
+    "triton-kernels @ git+https://github.com/triton-lang/triton.git@7c56a5e40f7fd928dfd5c72902d5def0097db73a#subdirectory=python/triton_kernels ; sys_platform == 'linux'",
+    "vllm @ https://github.com/vllm-project/vllm/releases/download/v0.25.1/vllm-0.25.1-cp38-abi3-manylinux_2_28_x86_64.whl ; sys_platform == 'linux'",
 ]
 
 [project.scripts]
@@ -34,12 +50,33 @@ allow-direct-references = true
 
 [tool.uv]
 required-version = ">=0.6.15"
+conflicts = [[{ extra = "cuda12" }, { extra = "cuda13" }]]
 override-dependencies = [
-    "flashinfer-python==0.6.12",
-    "numpy<2",
-    "nvidia-nccl-cu12==2.28.9 ; sys_platform == 'linux'",
-    "torch @ https://download.pytorch.org/whl/test/cu128/torch-2.11.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl",
-    "torchaudio @ https://download.pytorch.org/whl/test/cu128/torchaudio-2.11.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl",
-    "torchvision @ https://download.pytorch.org/whl/test/cu128/torchvision-0.26.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl",
+    "flashinfer-python==0.6.13",
     "transformers==5.12.1",
+    "xgrammar==0.2.3",
 ]
+
+[tool.uv.sources]
+torch = [
+    { index = "pytorch-cu128", extra = "cuda12" },
+    { index = "pytorch-cu130", extra = "cuda13" },
+]
+torchaudio = [
+    { index = "pytorch-cu128", extra = "cuda12" },
+    { index = "pytorch-cu130", extra = "cuda13" },
+]
+torchvision = [
+    { index = "pytorch-cu128", extra = "cuda12" },
+    { index = "pytorch-cu130", extra = "cuda13" },
+]
+
+[[tool.uv.index]]
+name = "pytorch-cu128"
+url = "https://download.pytorch.org/whl/cu128"
+explicit = true
+
+[[tool.uv.index]]
+name = "pytorch-cu130"
+url = "https://download.pytorch.org/whl/cu130"
+explicit = true
diff --git a/vllm_runtime/setup.sh b/vllm_runtime/setup.sh
new file mode 100755
index 000000000..af4c81e3f
--- /dev/null
+++ b/vllm_runtime/setup.sh
@@ -0,0 +1,77 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+cuda_home="${CUDA_HOME:-/usr/local/cuda}"
+if [ ! -x "${cuda_home}/bin/nvcc" ]; then
+    echo "[art-vllm-runtime-setup] CUDA_HOME does not contain nvcc: ${cuda_home}" >&2
+    exit 1
+fi
+cuda_major="$("${cuda_home}/bin/nvcc" --version | sed -n 's/.*release \([0-9][0-9]*\)\..*/\1/p' | head -1)"
+case "${cuda_major}" in
+    12) runtime_extra="cuda12" ;;
+    13) runtime_extra="cuda13" ;;
+    *)
+        echo "[art-vllm-runtime-setup] Unsupported CUDA major ${cuda_major}; expected 12 or 13." >&2
+        exit 1
+        ;;
+esac
+
+script_dir="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+cd "${script_dir}"
+uv_bin="uv"
+if [ -x "${HOME}/.local/bin/uv" ]; then
+    uv_bin="${HOME}/.local/bin/uv"
+fi
+echo "[art-vllm-runtime-setup] CUDA_HOME=${cuda_home}, profile=${runtime_extra}"
+"${uv_bin}" sync --extra "${runtime_extra}" --frozen --no-dev
+
+cutlass_cu13_intact() {
+    ".venv/bin/python" - <<'PY'
+import base64
+import hashlib
+from importlib.metadata import PackageNotFoundError, distribution
+
+try:
+    files = distribution("nvidia-cutlass-dsl-libs-cu13").files
+except PackageNotFoundError:
+    raise SystemExit(1)
+if not files:
+    raise SystemExit(1)
+for path in files:
+    expected = path.hash
+    if expected is None or expected.mode != "sha256" or not expected.value:
+        continue
+    try:
+        actual = base64.urlsafe_b64encode(
+            hashlib.sha256(path.locate().read_bytes()).digest()
+        ).decode().rstrip("=")
+    except OSError:
+        raise SystemExit(1)
+    if actual != expected.value:
+        raise SystemExit(1)
+PY
+}
+
+if [ "${cuda_major}" = 13 ] && ! cutlass_cu13_intact; then
+    echo "[art-vllm-runtime-setup] Repairing CUTLASS DSL install-order race"
+    site_packages="$(".venv/bin/python" -c \
+        'import sysconfig; print(sysconfig.get_paths()["purelib"])')"
+    # Overlay the wheel because its files share directories with libs-base;
+    # uninstalling either wheel first can delete files owned by the other.
+    "${uv_bin}" pip install --python .venv/bin/python --target "${site_packages}" \
+        --reinstall --no-deps \
+        nvidia-cutlass-dsl-libs-cu13==4.5.2
+    cutlass_cu13_intact || {
+        echo "[art-vllm-runtime-setup] CUTLASS DSL integrity check failed" >&2
+        exit 1
+    }
+fi
+
+".venv/bin/python" - <<'PY'
+import torch
+import vllm
+
+print(f"[art-vllm-runtime-setup] torch={torch.__version__} cuda={torch.version.cuda}")
+print(f"[art-vllm-runtime-setup] vllm={vllm.__version__}")
+print(f"[art-vllm-runtime-setup] device={torch.cuda.get_device_name()} capability={torch.cuda.get_device_capability()}")
+PY
diff --git a/vllm_runtime/src/art_vllm_runtime/__init__.py b/vllm_runtime/src/art_vllm_runtime/__init__.py
index 80e13097f..3558f473f 100644
--- a/vllm_runtime/src/art_vllm_runtime/__init__.py
+++ b/vllm_runtime/src/art_vllm_runtime/__init__.py
@@ -1,15 +1,9 @@
 from art_vllm_runtime.patches import (
     apply_vllm_runtime_patches,
-    patch_listen_for_disconnect,
-    patch_tool_parser_manager,
-    patch_transformers_v5_compat,
     subclass_chat_completion_request,
 )
 
 __all__ = [
     "apply_vllm_runtime_patches",
-    "patch_listen_for_disconnect",
-    "patch_tool_parser_manager",
-    "patch_transformers_v5_compat",
     "subclass_chat_completion_request",
 ]
diff --git a/vllm_runtime/src/art_vllm_runtime/binary_routes.py b/vllm_runtime/src/art_vllm_runtime/binary_routes.py
index 4d78ff8a9..64d52e0fb 100644
--- a/vllm_runtime/src/art_vllm_runtime/binary_routes.py
+++ b/vllm_runtime/src/art_vllm_runtime/binary_routes.py
@@ -4,22 +4,41 @@
 from contextlib import contextmanager
 from contextvars import ContextVar
 from functools import wraps
+import os
 import struct
 from typing import Any
 
 import numpy as np
 
-MAGIC = b"ARTRTE1\0"
-HEADER = struct.Struct("<8sQI")
+MAGIC = b"ARTRTE2\0"
+HEADER = struct.Struct("<8sQII")
 ROUTE_HEADER = struct.Struct("<IB3xQQQ")
-_CAPTURE: ContextVar[dict[int, np.ndarray] | None] = ContextVar(
+PIPELINE_ROUTES_ENV = "ART_VLLM_PIPELINE_ROUTES_PROTOCOL"
+PIPELINE_ROUTES_PROTOCOL = "1"
+_REGISTERED_NUM_EXPERTS: int | None = None
+_REGISTERED_PADDING_LAYERS: tuple[int, ...] | None = None
+
+
+class _CapturedRoutes(dict[int, np.ndarray]):
+    def __init__(self, *, num_experts: int, padding_layers: tuple[int, ...]) -> None:
+        super().__init__()
+        self.num_experts = num_experts
+        self.padding_layers = padding_layers
+
+
+_CAPTURE: ContextVar[_CapturedRoutes | None] = ContextVar(
     "art_binary_routed_experts", default=None
 )
 
 
 @contextmanager
-def capture_routed_experts() -> Iterator[dict[int, np.ndarray]]:
-    routes: dict[int, np.ndarray] = {}
+def capture_routed_experts() -> Iterator[_CapturedRoutes]:
+    if _REGISTERED_NUM_EXPERTS is None or _REGISTERED_PADDING_LAYERS is None:
+        raise RuntimeError("vLLM did not register an exact MoE route layout")
+    routes = _CapturedRoutes(
+        num_experts=_REGISTERED_NUM_EXPERTS,
+        padding_layers=_REGISTERED_PADDING_LAYERS,
+    )
     token = _CAPTURE.set(routes)
     try:
         yield routes
@@ -28,24 +47,34 @@ def capture_routed_experts() -> Iterator[dict[int, np.ndarray]]:
 
 
 def encode_routed_experts_response(
-    json_body: bytes, routes: dict[int, np.ndarray]
+    json_body: bytes,
+    routes: dict[int, np.ndarray],
+    *,
+    num_experts: int | None = None,
 ) -> bytes:
+    num_experts = int(num_experts or getattr(routes, "num_experts", 0))
+    dtype = _route_dtype(num_experts)
     chunks: list[bytes | memoryview] = [
-        HEADER.pack(MAGIC, len(json_body), len(routes)),
+        HEADER.pack(MAGIC, len(json_body), len(routes), num_experts),
         json_body,
     ]
     for choice_index, array in sorted(routes.items()):
         if array.ndim != 3:
             raise RuntimeError(f"Routed experts must have rank 3, got {array.shape}")
-        if array.dtype == np.dtype(np.uint8):
+        if dtype == np.dtype(np.uint8):
             dtype_code = 1
-        elif array.dtype == np.dtype(np.uint16):
+        else:
             dtype_code = 2
             array = array.astype("<u2", copy=False)
-        else:
+        if array.dtype != dtype:
             raise RuntimeError(
-                f"vLLM routed experts must use uint8 or uint16, got {array.dtype}"
+                f"vLLM routed experts for {num_experts} experts must use "
+                f"{dtype}, got {array.dtype}"
             )
+        _resolve_padding_routes(
+            array, padding_layers=getattr(routes, "padding_layers", ())
+        )
+        _validate_route_ids(array, num_experts=num_experts)
         array = np.ascontiguousarray(array)
         chunks.extend(
             (
@@ -56,6 +85,119 @@ def encode_routed_experts_response(
     return b"".join(chunks)
 
 
+def _route_dtype(num_experts: int) -> np.dtype[Any]:
+    if not 1 <= num_experts <= 65_536:
+        raise RuntimeError(
+            f"ART routed experts require num_experts in [1, 65536], got {num_experts}"
+        )
+    return np.dtype(np.uint8 if num_experts <= 256 else np.uint16)
+
+
+def _validate_route_ids(array: np.ndarray, *, num_experts: int) -> None:
+    if array.shape[-1] > num_experts:
+        raise RuntimeError("Routed-expert top-k exceeds exact expert count")
+    flat = array.reshape(-1, array.shape[-1])
+    for start in range(0, len(flat), 1 << 20):
+        rows = np.sort(flat[start : start + (1 << 20)], axis=1)
+        if rows.size and int(rows.max()) >= num_experts:
+            raise RuntimeError("Routed expert id is outside the exact model range")
+        if rows.shape[1] > 1 and bool(np.any(rows[:, 1:] == rows[:, :-1])):
+            raise RuntimeError("Routed expert ids must be distinct per token and layer")
+
+
+def _resolve_padding_routes(
+    array: np.ndarray, *, padding_layers: tuple[int, ...]
+) -> None:
+    if not padding_layers:
+        return
+    if padding_layers[-1] >= array.shape[1]:
+        raise RuntimeError(
+            "Routed-expert response has fewer layers than the registered model"
+        )
+    padding = array[:, padding_layers, :]
+    if padding.size and bool(np.any(padding)):
+        raise RuntimeError("Non-routed layer contained captured expert ids")
+    array[:, padding_layers, :] = np.arange(array.shape[-1], dtype=array.dtype)
+
+
+def _model_padding_layers(model_config: Any) -> tuple[int, ...]:
+    config = getattr(model_config, "hf_text_config", None)
+    if config is None:
+        config = getattr(model_config, "hf_config", model_config)
+    num_layers = int(getattr(config, "num_hidden_layers", 0))
+    layer_types = getattr(config, "mlp_layer_types", None)
+    if layer_types is not None:
+        if len(layer_types) != num_layers:
+            raise RuntimeError("mlp_layer_types does not match num_hidden_layers")
+        if not set(layer_types).issubset({"dense", "sparse", "moe", "hash_moe"}):
+            raise RuntimeError(f"Unsupported MoE layer types: {set(layer_types)}")
+        return tuple(i for i, kind in enumerate(layer_types) if kind == "dense")
+    first_dense = int(getattr(config, "first_k_dense_replace", 0))
+    if not 0 <= first_dense <= num_layers:
+        raise RuntimeError("first_k_dense_replace is outside the model layer range")
+    return tuple(range(first_dense))
+
+
+def _normalize_route_topk(model_config: Any) -> None:
+    hf_config = getattr(model_config, "hf_config", None)
+    text_config = getattr(model_config, "hf_text_config", None) or getattr(
+        hf_config, "text_config", hf_config
+    )
+    configs = (model_config, hf_config, text_config)
+    values = {
+        int(value)
+        for config in configs
+        if config is not None
+        for name in (
+            "num_experts_per_tok",
+            "experts_per_token",
+            "top_k_experts",
+        )
+        if (value := getattr(config, name, None)) is not None and int(value) > 0
+    }
+    if len(values) != 1:
+        raise RuntimeError(f"Model configs disagree on MoE route top-k: {values}")
+    if text_config is None:
+        raise RuntimeError("Unable to find the model's text config for route capture")
+    text_config.num_experts_per_tok = values.pop()
+
+
+def _register_model_route_layout(model_config: Any) -> None:
+    global _REGISTERED_NUM_EXPERTS, _REGISTERED_PADDING_LAYERS
+    _normalize_route_topk(model_config)
+    getter = getattr(model_config, "get_num_experts", None)
+    if callable(getter):
+        num_experts = int(getter())
+    else:
+        configs = [
+            model_config,
+            getattr(model_config, "hf_config", None),
+            getattr(getattr(model_config, "hf_config", None), "text_config", None),
+        ]
+        values = {
+            int(value)
+            for config in configs
+            if config is not None
+            for name in ("num_experts", "n_routed_experts", "num_local_experts")
+            if (value := getattr(config, name, None)) is not None and int(value) > 0
+        }
+        if not values:
+            raise RuntimeError("Unable to find the model's exact MoE expert count")
+        if len(values) != 1:
+            raise RuntimeError(f"Model configs disagree on MoE expert count: {values}")
+        num_experts = values.pop()
+    _route_dtype(num_experts)
+    padding_layers = _model_padding_layers(model_config)
+    if _REGISTERED_NUM_EXPERTS not in {None, num_experts}:
+        raise RuntimeError(
+            "One vLLM process cannot capture routes for different expert counts"
+        )
+    if _REGISTERED_PADDING_LAYERS not in {None, padding_layers}:
+        raise RuntimeError("One vLLM process cannot capture different MoE layouts")
+    _REGISTERED_NUM_EXPERTS = num_experts
+    _REGISTERED_PADDING_LAYERS = padding_layers
+
+
 def patch_binary_routed_experts_response() -> None:
     from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
 
@@ -87,3 +229,120 @@ async def stripped_results() -> AsyncIterator[Any]:
 
     patched.__art_binary_routes_patched__ = True  # type: ignore[attr-defined]
     OpenAIServingChat.chat_completion_full_generator = patched
+
+
+def patch_pipeline_routed_experts() -> None:
+    """Reduce disjoint PP-stage routes onto vLLM's output rank."""
+    import torch
+    from vllm.distributed import get_pp_group, get_tp_group
+    from vllm.v1.worker.gpu_model_runner import GPUModelRunner
+
+    original_execute = GPUModelRunner.execute_model
+    if getattr(original_execute, "__art_pipeline_routes_patched__", False):
+        return
+    original_sample = GPUModelRunner.sample_tokens
+    enabled = os.environ.get(PIPELINE_ROUTES_ENV) == PIPELINE_ROUTES_PROTOCOL
+
+    @wraps(original_execute)
+    def execute(self: Any, scheduler_output: Any, *args: Any, **kwargs: Any) -> Any:
+        if enabled:
+            self._art_pipeline_route_tokens = int(
+                scheduler_output.total_num_scheduled_tokens
+            )
+        return original_execute(self, scheduler_output, *args, **kwargs)
+
+    @wraps(original_sample)
+    def sample(self: Any, *args: Any, **kwargs: Any) -> Any:
+        if not enabled:
+            return original_sample(self, *args, **kwargs)
+        num_tokens = int(getattr(self, "_art_pipeline_route_tokens", 0))
+        self._art_pipeline_route_tokens = 0
+        pp = get_pp_group()
+        if pp.world_size <= 1:
+            raise RuntimeError("pipeline route capture requires PP > 1")
+        if get_tp_group().rank_in_group == 0:
+            if not getattr(self, "_art_pipeline_routes_ready", False):
+                initialized = bool(self.routed_experts_initialized)
+                buffer = (
+                    self.routed_experts_capturer.get_device_buffer()
+                    if initialized
+                    else None
+                )
+                local = torch.tensor(
+                    [
+                        int(PIPELINE_ROUTES_PROTOCOL),
+                        int(initialized),
+                        buffer.ndim if buffer is not None else 0,
+                        buffer.shape[0] if buffer is not None else 0,
+                        buffer.shape[1] if buffer is not None else 0,
+                        buffer.shape[2] if buffer is not None else 0,
+                        int(buffer is not None and buffer.dtype == torch.int32),
+                    ],
+                    dtype=torch.int64,
+                    device=buffer.device if buffer is not None else self.device,
+                )
+                states = [torch.empty_like(local) for _ in range(pp.world_size)]
+                torch.distributed.all_gather(states, local, group=pp.device_group)
+                values = [state.tolist() for state in states]
+                if any(value != values[0] for value in values[1:]):
+                    raise RuntimeError(
+                        f"pipeline routed-expert workers disagree: {values}"
+                    )
+                if (
+                    values[0][1] != 1
+                    or values[0][2] != 3
+                    or min(values[0][3:6]) <= 0
+                    or values[0][6] != 1
+                ):
+                    raise RuntimeError(
+                        f"pipeline routed-expert capturer is invalid: {values}"
+                    )
+                self._art_pipeline_routes_ready = True
+            routes = self.routed_experts_capturer.get_device_buffer()[:num_tokens]
+            torch.distributed.reduce(
+                routes,
+                dst=pp.last_rank,
+                op=torch.distributed.ReduceOp.SUM,
+                group=pp.device_group,
+            )
+        return original_sample(self, *args, **kwargs)
+
+    execute.__art_pipeline_routes_patched__ = True  # type: ignore[attr-defined]
+    GPUModelRunner.execute_model = execute
+    GPUModelRunner.sample_tokens = sample
+
+
+def patch_pipeline_routed_experts_validation() -> None:
+    """Allow the supported V1 PP aggregation through repeated validation."""
+    from vllm.config import VllmConfig
+
+    original = VllmConfig.__post_init__
+    if getattr(original, "__art_pipeline_routes_patched__", False):
+        return
+
+    @wraps(original)
+    def post_init(self: Any) -> None:
+        model = self.model_config
+        if model is not None and model.enable_return_routed_experts:
+            _register_model_route_layout(model)
+        pipeline_capture = (
+            os.environ.get(PIPELINE_ROUTES_ENV) == PIPELINE_ROUTES_PROTOCOL
+            and model is not None
+            and model.enable_return_routed_experts
+            and self.parallel_config.pipeline_parallel_size > 1
+        )
+        if not pipeline_capture:
+            return original(self)
+        transfer = self.kv_transfer_config
+        if transfer is not None and transfer.is_kv_transfer_instance:
+            raise ValueError(
+                "pipeline routed-expert capture is incompatible with KV connectors"
+            )
+        model.enable_return_routed_experts = False
+        try:
+            original(self)
+        finally:
+            model.enable_return_routed_experts = True
+
+    post_init.__art_pipeline_routes_patched__ = True  # type: ignore[attr-defined]
+    VllmConfig.__post_init__ = post_init
diff --git a/vllm_runtime/src/art_vllm_runtime/dedicated_server.py b/vllm_runtime/src/art_vllm_runtime/dedicated_server.py
index 36b8a0ffd..5b2186016 100644
--- a/vllm_runtime/src/art_vllm_runtime/dedicated_server.py
+++ b/vllm_runtime/src/art_vllm_runtime/dedicated_server.py
@@ -2,9 +2,14 @@
 
 import argparse
 import asyncio
+from functools import lru_cache
 from http import HTTPStatus
+from ipaddress import ip_address
 import json
 import os
+import socket
+from typing import Any
+import uuid
 
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field
@@ -12,10 +17,78 @@
 from starlette.types import Receive, Scope, Send
 from vllm.entrypoints.serve.utils.server_utils import AuthenticationMiddleware
 
+from art_vllm_runtime.binary_routes import (
+    PIPELINE_ROUTES_ENV,
+    PIPELINE_ROUTES_PROTOCOL,
+    _register_model_route_layout,
+)
+from art_vllm_runtime.fast_metrics import FastMetricsSidecar
 from art_vllm_runtime.patches import apply_vllm_runtime_patches
 
+ART_SERVING_PROTOCOL_VERSION = 4
+_runtime_state: dict[str, object] = {}
+_auth_tokens: list[str] = []
+_fast_metrics_port: int | None = None
+
+
+def _patch_prebound_listener_tcp_nodelay(api_server: Any) -> None:
+    create_server_socket = api_server.create_server_socket
+
+    def create_tcp_server_socket(*args: Any, **kwargs: Any) -> socket.socket:
+        listener = create_server_socket(*args, **kwargs)
+        # vLLM pre-binds before Uvicorn; accepted sockets inherit this option.
+        listener.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
+        return listener
+
+    api_server.create_server_socket = create_tcp_server_socket
+
+
+def _art_metrics_snapshot() -> dict[str, Any]:
+    from art_vllm_runtime.metrics import get_art_metrics_snapshot
+
+    snapshot = get_art_metrics_snapshot()
+    snapshot.update(
+        process_uuid=_runtime_state["process_uuid"],
+        generation=_runtime_state["generation"],
+    )
+    return snapshot
+
+
+def _fast_metrics_url(request: Any) -> str:
+    if _fast_metrics_port is None:
+        raise RuntimeError("ART fast metrics listener is not running")
+    host = request.url.hostname
+    if host is None:
+        raise RuntimeError("ART capabilities request has no host")
+    try:
+        address = ip_address(host.strip("[]"))
+        unspecified = address.is_unspecified
+        loopback = address.is_loopback
+    except ValueError:
+        unspecified = False
+        loopback = host.casefold() == "localhost"
+    nnodes = _runtime_state.get("nnodes", 1)
+    if isinstance(nnodes, bool) or not isinstance(nnodes, int):
+        raise RuntimeError("ART runtime state has invalid nnodes")
+    if unspecified or (nnodes > 1 and loopback):
+        raise RuntimeError(
+            f"ART fast metrics cannot advertise unroutable host {host!r}"
+        )
+    return str(
+        request.url.replace(
+            scheme="http",
+            port=_fast_metrics_port,
+            path="/art/metrics",
+            query="",
+            fragment="",
+        )
+    )
+
 
 class _ArtAuthenticationMiddleware(AuthenticationMiddleware):
+    def __init__(self, app: Any) -> None:
+        super().__init__(app, tokens=_auth_tokens)
+
     def __call__(self, scope: Scope, receive: Receive, send: Send):
         path = scope.get("path", "").removeprefix(scope.get("root_path", ""))
         if (
@@ -29,10 +102,6 @@ def __call__(self, scope: Scope, receive: Receive, send: Send):
         return self.app(scope, receive, send)
 
 
-class _SetServedModelNameRequest(BaseModel):
-    name: str = Field(min_length=1)
-
-
 class _ResetPrefixCacheRequest(BaseModel):
     reset_running_requests: bool = False
     reset_connector: bool = True
@@ -41,26 +110,100 @@ class _ResetPrefixCacheRequest(BaseModel):
 class _InFlightLoraUpdateRequest(BaseModel):
     model_name: str = Field(min_length=1)
     lora_path: str = Field(min_length=1)
-    policy_version: int
+    policy_version: int = Field(ge=0)
     lora_slot: str | None = Field(default=None, min_length=1)
     base_model_name: str | None = None
     is_3d_lora_weight: bool = False
 
 
+def _index_shared_pp_partition(config: Any, pp_size: int) -> tuple[int, ...] | None:
+    if pp_size <= 1 or not hasattr(config, "index_topk"):
+        return None
+    layer_count = int(config.num_hidden_layers)
+    pattern = getattr(config, "index_topk_pattern", None)
+    offset = int(getattr(config, "index_skip_topk_offset", 2))
+    frequency = int(getattr(config, "index_topk_freq", 1))
+
+    def computes_index(layer: int) -> bool:
+        if pattern is not None and layer < len(pattern):
+            return pattern[layer] != "S"
+        return max(layer - offset + 1, 0) % frequency == 0
+
+    boundaries = tuple(
+        layer for layer in range(1, layer_count) if computes_index(layer)
+    )
+
+    @lru_cache
+    def solve(start: int, remaining: int) -> tuple[int, int, tuple[int, ...]] | None:
+        if remaining == 1:
+            length = layer_count - start
+            return length + 1, length * length, (length,)
+        candidates = []
+        for end in boundaries:
+            if end <= start:
+                continue
+            suffix = solve(end, remaining - 1)
+            if suffix is None:
+                continue
+            length = end - start
+            candidates.append(
+                (
+                    max(length + (start == 0), suffix[0]),
+                    length * length + suffix[1],
+                    (length, *suffix[2]),
+                )
+            )
+        return min(candidates) if candidates else None
+
+    result = solve(0, pp_size)
+    if result is None:
+        raise ValueError(
+            f"cannot partition {layer_count} index-sharing layers across PP{pp_size}"
+        )
+    return result[2]
+
+
+def _configure_index_shared_pp(model: str, engine_args: dict[str, Any]) -> str | None:
+    pp_size = int(engine_args.get("pipeline_parallel_size", 1))
+    if pp_size <= 1:
+        return None
+    from transformers import AutoConfig
+
+    config = AutoConfig.from_pretrained(
+        model,
+        revision=engine_args.get("revision"),
+        trust_remote_code=bool(engine_args.get("trust_remote_code", False)),
+    )
+    partition = _index_shared_pp_partition(config, pp_size)
+    if partition is None:
+        return os.environ.get("VLLM_PP_LAYER_PARTITION")
+    value = ",".join(map(str, partition))
+    configured = os.environ.setdefault("VLLM_PP_LAYER_PARTITION", value)
+    if configured != value:
+        raise ValueError(
+            "VLLM_PP_LAYER_PARTITION conflicts with ART's index-sharing-safe "
+            f"partition: configured={configured!r}, required={value!r}"
+        )
+    return value
+
+
 def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="ART dedicated vLLM server")
     parser.add_argument("--model", required=True, help="Base model name or path")
     parser.add_argument("--port", type=int, required=True)
     parser.add_argument("--host", default="127.0.0.1")
     parser.add_argument("--cuda-visible-devices", required=True)
+    parser.add_argument("--nnodes", type=int, default=1)
+    parser.add_argument("--node-rank", type=int, default=0)
+    parser.add_argument("--master-addr")
+    parser.add_argument("--master-port", type=int)
+    parser.add_argument("--headless", action="store_true")
+    parser.add_argument("--replica-generation", type=int, default=0)
+    parser.add_argument("--process-uuid")
+    parser.add_argument("--update-identity")
+    parser.add_argument("--initial-policy-version", type=int)
     parser.add_argument("--lora-path", help="Optional initial checkpoint path")
     parser.add_argument("--served-model-name", required=True)
-    parser.add_argument(
-        "--rollout-weights-mode",
-        choices=("lora", "merged"),
-        default="lora",
-        help="Whether the dedicated server serves LoRA adapters or merged weights",
-    )
     parser.add_argument(
         "--engine-args-json", default="{}", help="Additional engine args as JSON"
     )
@@ -74,7 +217,7 @@ def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
 
 def _patch_art_runtime_routes() -> None:
     from fastapi import APIRouter, Depends, FastAPI, Query, Request
-    from fastapi.responses import Response
+    from fastapi.responses import JSONResponse, Response
     from vllm.entrypoints.openai import api_server
     from vllm.entrypoints.openai.chat_completion.api_router import (
         create_chat_completion,
@@ -93,15 +236,10 @@ def _patch_art_runtime_routes() -> None:
         return
 
     original_build_app = api_server.build_app
+    original_init_app_state = api_server.init_app_state
 
     def art_build_app(*build_args: object, **build_kwargs: object) -> FastAPI:
         app = original_build_app(*build_args, **build_kwargs)
-        from vllm import envs
-
-        args = app.state.args
-        tokens = [key for key in (args.api_key or [envs.VLLM_API_KEY]) if key]
-        if tokens:
-            app.add_middleware(_ArtAuthenticationMiddleware, tokens=tokens)
         router = APIRouter()
 
         def engine(request: Request):
@@ -135,30 +273,22 @@ async def is_sleeping(raw_request: Request) -> JSONResponse:
                 content={"is_sleeping": await engine(raw_request).is_sleeping()}
             )
 
-        @router.post("/art/set_served_model_name")
-        async def set_served_model_name(
-            body: _SetServedModelNameRequest, raw_request: Request
-        ) -> JSONResponse:
-            models = raw_request.app.state.openai_serving_models
-            if not models.base_model_paths:
-                raise RuntimeError("vLLM runtime has no registered base model")
-            models.base_model_paths[0].name = body.name
-            return JSONResponse(content={"name": body.name})
+        @router.get("/art/state")
+        async def art_state() -> JSONResponse:
+            return JSONResponse(content=dict(_runtime_state))
 
         @router.get("/art/metrics")
         async def art_metrics() -> JSONResponse:
-            from art_vllm_runtime.metrics import get_art_metrics_snapshot
-
-            return JSONResponse(content=get_art_metrics_snapshot())
+            return JSONResponse(content=_art_metrics_snapshot())
 
         @router.get("/art/capabilities")
-        async def art_capabilities() -> JSONResponse:
+        async def art_capabilities(raw_request: Request) -> JSONResponse:
             return JSONResponse(
                 content={
                     "runtime": "art_vllm",
-                    "protocol_version": 1,
+                    "protocol_version": ART_SERVING_PROTOCOL_VERSION,
                     "binary_routed_experts": True,
-                    "fast_metrics": True,
+                    "fast_metrics": {"url": _fast_metrics_url(raw_request)},
                     "inplace_lora_load": True,
                     "in_flight_lora_updates": True,
                     "policy_token_spans": True,
@@ -195,7 +325,7 @@ async def binary_chat_completion(
             }
             return Response(
                 content=encode_routed_experts_response(response.body, routes),
-                media_type="application/vnd.art.routed-experts-v1",
+                media_type="application/vnd.art.routed-experts-v2",
                 headers=headers,
             )
 
@@ -217,7 +347,11 @@ async def in_flight_lora_update(
             from vllm.entrypoints.serve.lora.protocol import LoadLoRAAdapterRequest
 
             from art_vllm_runtime.policy_spans import (
+                PolicyLoRARequest,
                 lora_update_coordinator,
+                policy_lora_request_payload,
+                publish_lora_slot_policy,
+                register_lora_alias,
             )
 
             public_model_name = body.model_name
@@ -227,43 +361,99 @@ async def in_flight_lora_update(
             models = raw_request.app.state.openai_serving_models
             engine_client = engine(raw_request)
             coordinator = lora_update_coordinator(models, engine_client)
-            await coordinator.begin_update(lora_slot)
+            update_seq = await coordinator.begin_update(lora_slot)
+            mutation_started = False
             try:
-                load_result = await models.load_lora_adapter(
-                    LoadLoRAAdapterRequest(
+                async with models.lora_resolver_lock[lora_slot]:
+                    load_request = LoadLoRAAdapterRequest(
                         lora_name=lora_slot,
                         lora_path=lora_path,
                         load_inplace=lora_slot in models.lora_requests,
                         is_3d_lora_weight=body.is_3d_lora_weight,
-                    ),
-                    base_model_name=body.base_model_name,
-                )
-                if isinstance(load_result, ErrorResponse):
-                    await coordinator.fail_update(lora_slot)
-                    return JSONResponse(
-                        content=load_result.model_dump(mode="python"),
-                        status_code=load_result.error.code,
                     )
-                waiting_cache_salt = await engine_client.engine_core.call_utility_async(
-                    "art_update_waiting_lora_cache_salt",
-                    lora_slot,
-                    policy_version,
-                )
-                await coordinator.commit_update(
-                    lora_slot,
-                    policy_version,
-                    models.lora_requests[lora_slot],
-                )
-                from art_vllm_runtime.metrics import record_policy_cache_waiting_update
-
-                record_policy_cache_waiting_update(
-                    updated=int(waiting_cache_salt["updated_waiting_requests"]),
-                    skipped_started=int(
-                        waiting_cache_salt["skipped_started_waiting_requests"]
-                    ),
+                    load_error = await models._check_load_lora_adapter_request(
+                        load_request
+                    )
+                    if isinstance(load_error, ErrorResponse):
+                        await coordinator.cancel_update(lora_slot, update_seq)
+                        return JSONResponse(
+                            content=load_error.model_dump(mode="python"),
+                            status_code=load_error.error.code,
+                        )
+                    lora_int_id = (
+                        models.lora_requests[lora_slot].lora_int_id
+                        if lora_slot in models.lora_requests
+                        else models.lora_id_counter.inc(1)
+                    )
+                    lora_request = PolicyLoRARequest(
+                        lora_name=lora_slot,
+                        lora_int_id=lora_int_id,
+                        lora_path=lora_path,
+                        base_model_name=(
+                            body.base_model_name
+                            if body.base_model_name is not None
+                            and models.is_base_model(body.base_model_name)
+                            else None
+                        ),
+                        load_inplace=True,
+                        is_3d_lora_weight=body.is_3d_lora_weight,
+                        policy_version=policy_version,
+                        update_seq=update_seq,
+                    )
+                    mutation_started = True
+                    await engine_client.engine_core.call_utility_async(
+                        "pause_scheduler", "keep", False
+                    )
+                    cache_transition = (
+                        await engine_client.engine_core.call_utility_async(
+                            "art_apply_lora_policy_update",
+                            policy_lora_request_payload(lora_request),
+                        )
+                    )
+                    serving_request = PolicyLoRARequest(
+                        **{
+                            **policy_lora_request_payload(lora_request),
+                            "load_inplace": False,
+                        }
+                    )
+                    models.lora_requests[lora_slot] = serving_request
+                    register_lora_alias(
+                        models,
+                        public_model_name=public_model_name,
+                        lora_slot=lora_slot,
+                    )
+                    publish_lora_slot_policy(
+                        models,
+                        lora_slot=lora_slot,
+                        policy_version=policy_version,
+                        update_seq=update_seq,
+                    )
+                    await engine_client.engine_core.call_utility_async(
+                        "resume_scheduler"
+                    )
+                    await coordinator.commit_update(lora_slot, serving_request)
+                    mutation_started = False
+                _runtime_state.update(
+                    loaded_adapter=public_model_name,
+                    policy_version=policy_version,
+                    update_identity=(f"lora:{lora_slot}:{policy_version}:{update_seq}"),
                 )
             except BaseException:
-                await coordinator.fail_update(lora_slot)
+                if mutation_started:
+                    try:
+                        await asyncio.shield(
+                            engine_client.engine_core.call_utility_async(
+                                "pause_scheduler", "abort", True
+                            )
+                        )
+                    finally:
+                        await asyncio.shield(
+                            coordinator.fail_update(lora_slot, update_seq)
+                        )
+                else:
+                    await asyncio.shield(
+                        coordinator.cancel_update(lora_slot, update_seq)
+                    )
                 raise
             return JSONResponse(
                 content={
@@ -271,14 +461,32 @@ async def in_flight_lora_update(
                     "model_name": public_model_name,
                     "lora_slot": lora_slot,
                     "policy_version": policy_version,
-                    "waiting_cache_salt": waiting_cache_salt,
+                    "update_seq": update_seq,
+                    "cache_transition": cache_transition,
                 }
             )
 
         app.include_router(router)
         return app
 
+    async def art_init_app_state(
+        engine_client: Any, state: Any, *args: Any, **kwargs: Any
+    ) -> None:
+        await original_init_app_state(engine_client, state, *args, **kwargs)
+        policy_version = _runtime_state.get("initial_policy_version")
+        if policy_version is None:
+            return
+        from art_vllm_runtime.policy_spans import declare_initial_lora_policy
+
+        await declare_initial_lora_policy(
+            state.openai_serving_models,
+            engine_client,
+            lora_slot=str(_runtime_state["loaded_adapter"]),
+            policy_version=int(policy_version),
+        )
+
     setattr(api_server, "build_app", art_build_app)
+    setattr(api_server, "init_app_state", art_init_app_state)
     setattr(api_server, "_art_runtime_routes_patched", True)
 
 
@@ -323,19 +531,118 @@ def _append_cli_arg(vllm_args: list[str], key: str, value: object) -> None:
             assert False, f"Unsupported CLI arg for {key}: {type(value)}"
 
 
+def _patch_engine_config(
+    engine_args_type: Any,
+    *,
+    pipeline_route_capture: bool,
+) -> None:
+    current = engine_args_type.create_engine_config
+    create_engine_config = getattr(current, "__art_original__", current)
+    if not pipeline_route_capture:
+        setattr(engine_args_type, "create_engine_config", create_engine_config)
+        return
+
+    def create(self: Any, *args: Any, **kwargs: Any) -> Any:
+        config = create_engine_config(self, *args, **kwargs)
+        config.model_config.enable_return_routed_experts = True
+        _register_model_route_layout(config.model_config)
+        _validate_pipeline_route_config(config)
+        return config
+
+    create.__art_original__ = create_engine_config  # type: ignore[attr-defined]
+    setattr(engine_args_type, "create_engine_config", create)
+
+
+def _validate_pipeline_route_config(config: Any) -> None:
+    parallel = config.parallel_config
+    if (
+        parallel.pipeline_parallel_size <= 1
+        or parallel.distributed_executor_backend != "mp"
+        or parallel.data_parallel_size != 1
+        or parallel.prefill_context_parallel_size != 1
+        or parallel.decode_context_parallel_size != 1
+        or config.use_v2_model_runner
+    ):
+        raise ValueError(
+            "pipeline routed-expert capture requires V1 mp execution, PP > 1, "
+            "DP = 1, and prefill/decode CP = 1"
+        )
+    transfer = config.kv_transfer_config
+    if transfer is not None and transfer.is_kv_transfer_instance:
+        raise ValueError(
+            "pipeline routed-expert capture is incompatible with KV connectors"
+        )
+
+
 def main(argv: list[str] | None = None) -> None:
+    global _fast_metrics_port
+
     args = parse_args(argv)
-    if args.rollout_weights_mode == "merged" and not args.lora_path:
-        raise SystemExit("--lora-path is required for merged rollout weights")
     engine_args = json.loads(args.engine_args_json)
     server_args = json.loads(args.server_args_json)
+    route_capture = engine_args.get("enable_return_routed_experts", False)
+    pp_size = engine_args.get("pipeline_parallel_size", 1)
+    if not isinstance(route_capture, bool):
+        raise ValueError("enable_return_routed_experts must be a boolean")
+    if isinstance(pp_size, bool) or not isinstance(pp_size, int):
+        raise ValueError("pipeline_parallel_size must be an integer")
+    pp_layer_partition = _configure_index_shared_pp(args.model, engine_args)
+    critical_engine_args = {
+        "data_parallel_size",
+        "decode_context_parallel_size",
+        "distributed_executor_backend",
+        "enable_return_routed_experts",
+        "kv_transfer_config",
+        "pipeline_parallel_size",
+        "prefill_context_parallel_size",
+    }
+    misplaced = critical_engine_args.intersection(server_args)
+    if misplaced:
+        raise ValueError(
+            f"engine arguments passed as server arguments: {sorted(misplaced)}"
+        )
+    pipeline_route_capture = route_capture and pp_size > 1
+    if pipeline_route_capture:
+        engine_args["enable_return_routed_experts"] = False
+        if os.environ.get("VLLM_USE_V2_MODEL_RUNNER", "0").lower() not in {
+            "0",
+            "false",
+        }:
+            raise ValueError("pipeline routed-expert capture requires vLLM V1")
+        os.environ["VLLM_USE_V2_MODEL_RUNNER"] = "0"
+        os.environ[PIPELINE_ROUTES_ENV] = PIPELINE_ROUTES_PROTOCOL
+    else:
+        os.environ.pop(PIPELINE_ROUTES_ENV, None)
+
+    process_uuid = args.process_uuid or uuid.uuid4().hex
+
+    _runtime_state.update(
+        runtime="art_vllm",
+        protocol_version=ART_SERVING_PROTOCOL_VERSION,
+        process_uuid=process_uuid,
+        generation=args.replica_generation,
+        node_rank=args.node_rank,
+        nnodes=args.nnodes,
+        headless=args.headless,
+        loaded_adapter=args.served_model_name if args.lora_path else None,
+        policy_version=args.initial_policy_version
+        if args.initial_policy_version is not None
+        else (
+            int(args.served_model_name.rsplit("@", 1)[1])
+            if "@" in args.served_model_name
+            and args.served_model_name.rsplit("@", 1)[1].isdigit()
+            else None
+        ),
+        update_identity=args.update_identity,
+        initial_policy_version=args.initial_policy_version,
+        pp_layer_partition=pp_layer_partition,
+    )
 
     os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_visible_devices
     os.environ["VLLM_ALLOW_RUNTIME_LORA_UPDATING"] = "1"
-    if args.rollout_weights_mode == "merged":
-        os.environ["VLLM_SERVER_DEV_MODE"] = "1"
     apply_vllm_runtime_patches()
 
+    from vllm.engine.arg_utils import AsyncEngineArgs
     from vllm.entrypoints.openai import api_server
     from vllm.entrypoints.openai.cli_args import (
         make_arg_parser,
@@ -343,20 +650,33 @@ def main(argv: list[str] | None = None) -> None:
     )
     from vllm.utils.argparse_utils import FlexibleArgumentParser
 
+    _patch_prebound_listener_tcp_nodelay(api_server)
     _patch_art_runtime_routes()
+    _patch_engine_config(
+        AsyncEngineArgs,
+        pipeline_route_capture=pipeline_route_capture,
+    )
 
     vllm_args = [
         f"--model={args.model}",
         f"--port={args.port}",
         f"--host={args.host}",
         f"--served-model-name={args.served_model_name}",
+        "--enable-lora",
     ]
-    if args.rollout_weights_mode == "lora":
-        vllm_args.append("--enable-lora")
-        if args.lora_path:
-            vllm_args.append(
-                f"--lora-modules={args.served_model_name}={args.lora_path}"
-            )
+    if args.nnodes > 1:
+        vllm_args.extend(
+            [
+                f"--nnodes={args.nnodes}",
+                f"--node-rank={args.node_rank}",
+                f"--master-addr={args.master_addr}",
+                f"--master-port={args.master_port}",
+            ]
+        )
+        if args.headless:
+            vllm_args.append("--headless")
+    if args.lora_path:
+        vllm_args.append(f"--lora-modules={args.served_model_name}={args.lora_path}")
     for extra_args in (engine_args, server_args):
         for key, value in extra_args.items():
             _append_cli_arg(vllm_args, key, value)
@@ -366,8 +686,37 @@ def main(argv: list[str] | None = None) -> None:
     )
     vllm_parser = make_arg_parser(vllm_parser)
     namespace = vllm_parser.parse_args(vllm_args)
+    if api_key := os.environ.pop("VLLM_API_KEY", None):
+        namespace.api_key = [api_key]
+    _auth_tokens[:] = namespace.api_key or []
+    if _auth_tokens:
+        namespace.middleware = [
+            *namespace.middleware,
+            "art_vllm_runtime.dedicated_server._ArtAuthenticationMiddleware",
+        ]
     validate_parsed_serve_args(namespace)
-    asyncio.run(api_server.run_server(namespace))
+    if args.headless:
+        from vllm.entrypoints.cli.serve import run_headless
+
+        namespace.api_server_count = 0
+        run_headless(namespace)
+    else:
+        from art_vllm_runtime.metrics import set_fast_metrics_writer
+
+        metrics_sidecar = FastMetricsSidecar.start(
+            args.host,
+            _auth_tokens,
+            process_uuid=process_uuid,
+            generation=args.replica_generation,
+        )
+        _fast_metrics_port = metrics_sidecar.port
+        try:
+            set_fast_metrics_writer(metrics_sidecar.writer)
+            asyncio.run(api_server.run_server(namespace))
+        finally:
+            _fast_metrics_port = None
+            set_fast_metrics_writer(None)
+            metrics_sidecar.close()
 
 
 if __name__ == "__main__":
diff --git a/vllm_runtime/src/art_vllm_runtime/dsv4_patches.py b/vllm_runtime/src/art_vllm_runtime/dsv4_patches.py
index 03e1f5a1b..772836e91 100644
--- a/vllm_runtime/src/art_vllm_runtime/dsv4_patches.py
+++ b/vllm_runtime/src/art_vllm_runtime/dsv4_patches.py
@@ -1,13 +1,23 @@
 """DSV4-specific monkey patches for the ART-owned vLLM runtime."""
 
+from copy import copy
 import functools
 import importlib
+import inspect
 from typing import Any
 
+from packaging.version import Version
+import torch
+
 
 def apply_dsv4_vllm_runtime_patches() -> None:
-    patch_layerwise_reload_shadow_attrs()
-    patch_dsv4_attn_sink_layerwise_reload()
+    model = _require_dsv4_vllm_0251_contract()
+    if getattr(model, "_art_dsv4_runtime_patched", False):
+        return
+    patch_dsv4_hash_moe_config()
+    patch_dsv4_dummy_hash_routes()
+    patch_dsv4_rope_config()
+    patch_dsv4_compress_ratio_config()
     patch_dsv4_mhc_pre_fixed_split()
     patch_dsv4_mhc_stable_transition()
     patch_dsv4_lora_support()
@@ -15,178 +25,184 @@ def apply_dsv4_vllm_runtime_patches() -> None:
     patch_dsv4_fast_path_lora()
     patch_dsv4_triton_moe_topk6_routing()
     patch_lora_linear_base_attr_proxy()
-    patch_marlin_lora_swiglu_limit()
+    model._art_dsv4_runtime_patched = True
 
 
-def _drop_reload_shadow_attrs(layer: Any, names: Any) -> None:
-    for name in names:
-        if (
-            name in getattr(layer, "__dict__", {})
-            and name not in layer._parameters
-            and name not in layer._buffers
-            and name not in layer._modules
-        ):
-            delattr(layer, name)
+def _require_dsv4_vllm_0251_contract() -> Any:
+    import vllm
 
+    if Version(vllm.__version__).base_version != "0.25.1":
+        raise RuntimeError(
+            "ART DSV4 runtime patches require vLLM 0.25.1 exactly; "
+            f"found {vllm.__version__}"
+        )
+    model = importlib.import_module("vllm.models.deepseek_v4.nvidia.model")
+    flashmla = importlib.import_module("vllm.models.deepseek_v4.nvidia.flashmla")
+    flashinfer = importlib.import_module(
+        "vllm.models.deepseek_v4.nvidia.flashinfer_sparse"
+    )
+    required = (
+        (model.DeepseekV4Model.load_weights, ("self", "weights")),
+        (model.DeepseekV4ForCausalLM.load_weights, ("self", "weights")),
+        (flashmla.DeepseekV4FlashMLAAttention._o_proj, ("self", "o", "positions")),
+        (
+            flashinfer.DeepseekV4FlashInferMLAAttention._o_proj,
+            ("self", "o", "positions"),
+        ),
+        (
+            flashinfer.DeepseekV4FlashInferSM120Attention._o_proj,
+            ("self", "o", "positions"),
+        ),
+    )
+    for function, expected in required:
+        actual = tuple(inspect.signature(function).parameters)
+        if actual != expected:
+            raise RuntimeError(
+                f"vLLM DSV4 patch contract changed for {function}: "
+                f"{actual} != {expected}"
+            )
+    routing = importlib.import_module("vllm.third_party.triton_kernels.routing")
+    if not hasattr(routing.SortTokens, "forward"):
+        raise RuntimeError("vLLM DSV4 routing patch target is unavailable")
+    return model
 
-def patch_layerwise_reload_shadow_attrs() -> None:
-    """Allow vLLM layerwise reload to restore processed DSV4 MegaMoE params.
 
-    DeepSeek V4 MegaMoE drops loader-side Parameters after transforming them for
-    DeepGEMM. Some vLLM builds leave same-name plain attributes behind; PyTorch
-    then rejects register_parameter during the next checkpoint-format reload.
-    """
-    from vllm.model_executor.model_loader.reload import layerwise, meta
+def patch_dsv4_hash_moe_config() -> None:
+    """Bridge the canonical hash-MoE layer list into vLLM's count field."""
+    from transformers import configuration_utils
+    from vllm.transformers_utils.configs.deepseek_v4 import DeepseekV4Config
 
-    if getattr(meta, "_art_reload_shadow_attrs_patched", False):
+    if "hash_moe" not in configuration_utils.ALLOWED_LAYER_TYPES:
+        configuration_utils.ALLOWED_LAYER_TYPES += ("hash_moe",)
+    original = DeepseekV4Config.__init__
+    if getattr(original, "__art_hash_moe_patched__", False):
         return
 
-    original_restore_layer_on_meta = meta.restore_layer_on_meta
-    original_place_kernel_tensors = layerwise._place_kernel_tensors
+    def __init__(self: Any, *args: Any, **kwargs: Any) -> None:
+        layer_types = list(kwargs.get("mlp_layer_types", ()) or ())
+        if layer_types:
+            num_hash_layers = next(
+                (
+                    index
+                    for index, layer_type in enumerate(layer_types)
+                    if layer_type != "hash_moe"
+                ),
+                len(layer_types),
+            )
+            if "hash_moe" in layer_types[num_hash_layers:]:
+                raise ValueError("DSV4 hash-MoE layers must form a contiguous prefix")
+            configured = kwargs.setdefault("num_hash_layers", num_hash_layers)
+            if int(configured) != num_hash_layers:
+                raise ValueError(
+                    "DSV4 num_hash_layers disagrees with mlp_layer_types: "
+                    f"{configured} != {num_hash_layers}"
+                )
+        original(self, *args, **kwargs)
 
-    def restore_layer_on_meta(layer: Any, info: Any) -> None:
-        restore_params, restore_buffers = info.restore_metadata
-        _drop_reload_shadow_attrs(layer, tuple(restore_params) + tuple(restore_buffers))
-        return original_restore_layer_on_meta(layer, info)
+    __init__.__art_hash_moe_patched__ = True  # type: ignore[attr-defined]
+    __init__.__art_original__ = original  # type: ignore[attr-defined]
+    DeepseekV4Config.__init__ = __init__
 
-    def _place_kernel_tensors(layer: Any, info: Any) -> None:
-        assert info.kernel_tensors is not None
-        parameters, buffers = info.kernel_tensors
-        _drop_reload_shadow_attrs(layer, tuple(parameters) + tuple(buffers))
-        return original_place_kernel_tensors(layer, info)
 
-    restore_layer_on_meta.__art_patched__ = True  # type: ignore[attr-defined]
-    _place_kernel_tensors.__art_patched__ = True  # type: ignore[attr-defined]
-    meta.restore_layer_on_meta = restore_layer_on_meta  # type: ignore[method-assign]
-    layerwise.restore_layer_on_meta = restore_layer_on_meta  # type: ignore[method-assign]
-    layerwise._place_kernel_tensors = _place_kernel_tensors  # type: ignore[method-assign]
-    setattr(meta, "_art_reload_shadow_attrs_patched", True)
+def patch_dsv4_dummy_hash_routes() -> None:
+    """Make dummy hash routes valid, deterministic replay inputs."""
+    from vllm.model_executor.models.utils import extract_layer_index
+    from vllm.models.deepseek_v4.nvidia.model import DeepseekV4MoE
 
+    original = DeepseekV4MoE.__init__
+    if getattr(original, "__art_dummy_hash_routes_patched__", False):
+        return
 
-def _import_dsv4_model_module() -> Any | None:
-    for module_name in (
-        "vllm.model_executor.models.deepseek_v4",
-        "vllm.models.deepseek_v4.nvidia.model",
-    ):
-        try:
-            return importlib.import_module(module_name)
-        except ImportError:
-            continue
-    return None
+    def __init__(self: Any, vllm_config: Any, prefix: str = "") -> None:
+        original(self, vllm_config, prefix)
+        table = self.gate.tid2eid
+        if vllm_config.load_config.load_format != "dummy" or table is None:
+            return
+        num_experts = int(self.n_routed_experts)
+        topk = int(table.shape[1])
+        if topk > num_experts:
+            raise ValueError(
+                f"DSV4 hash top-k exceeds expert count: {topk} > {num_experts}"
+            )
+        tokens = torch.arange(table.shape[0], dtype=table.dtype, device=table.device)
+        offsets = torch.arange(topk, dtype=table.dtype, device=table.device)
+        starts = tokens * (topk + 1) + (extract_layer_index(prefix) + 1) * topk
+        with torch.no_grad():
+            table.copy_((starts[:, None] + offsets).remainder(num_experts))
+
+    __init__.__art_dummy_hash_routes_patched__ = True  # type: ignore[attr-defined]
+    __init__.__art_original__ = original  # type: ignore[attr-defined]
+    DeepseekV4MoE.__init__ = __init__
+
+
+def patch_dsv4_rope_config() -> None:
+    """Bridge Transformers 5's per-attention RoPE sets into vLLM 0.25."""
+    attention = importlib.import_module("vllm.models.deepseek_v4.attention")
+    rope = importlib.import_module("vllm.models.deepseek_v4.common.rope")
+    original = rope.build_deepseek_v4_rope
+    if getattr(original, "__art_nested_rope_config_patched__", False):
+        return
 
+    def build_deepseek_v4_rope(
+        config: Any, *, compress_ratio: int, **kwargs: Any
+    ) -> Any:
+        parameter_sets = getattr(config, "rope_parameters", None)
+        if not isinstance(parameter_sets, dict) or not {
+            "main",
+            "compress",
+        }.issubset(parameter_sets):
+            return original(config, compress_ratio=compress_ratio, **kwargs)
+        compat_config = copy(config)
+        compat_config.rope_parameters = dict(
+            parameter_sets["compress" if compress_ratio > 1 else "main"]
+        )
+        return original(
+            compat_config,
+            compress_ratio=compress_ratio,
+            **kwargs,
+        )
 
-def patch_dsv4_attn_sink_layerwise_reload() -> None:
-    """Route DSV4 attention-sink loads through vLLM's layerwise loader.
+    build_deepseek_v4_rope.__art_nested_rope_config_patched__ = True  # type: ignore[attr-defined]
+    rope.build_deepseek_v4_rope = build_deepseek_v4_rope
+    attention.build_deepseek_v4_rope = build_deepseek_v4_rope
 
-    Merged-weight transfer uses vLLM checkpoint-format reload. During that path,
-    every loadable parameter must be applied through its `weight_loader`; direct
-    `copy_` into `attn_sink` bypasses layerwise accounting and finalize restores
-    the old kernel tensor. With `load_format=dummy`, that old tensor is the
-    initialized sink, not the checkpoint sink.
-    """
-    dsv4_model = _import_dsv4_model_module()
-    if dsv4_model is None:
-        return
-    from vllm.model_executor.models.utils import is_pp_missing_parameter
 
-    model_cls = getattr(dsv4_model, "DeepseekV4Model", None)
-    if model_cls is None:
+def _normalize_dsv4_compress_ratios(config: Any) -> None:
+    if getattr(config, "compress_ratios", None) is not None:
         return
-    original = model_cls.load_weights
-    if getattr(original, "__art_patched__", False):
+    layer_types = list(getattr(config, "layer_types", ()) or ())
+    num_layers = int(getattr(config, "num_hidden_layers", 0))
+    if len(layer_types) != num_layers:
+        raise ValueError(
+            "DSV4 layer_types must match num_hidden_layers: "
+            f"{len(layer_types)} != {num_layers}"
+        )
+    rates = dict(getattr(config, "compress_rates", {}) or {})
+    supported = {"sliding_attention", *rates}
+    unknown = sorted(set(layer_types) - supported)
+    if unknown:
+        raise ValueError(f"Unsupported DSV4 layer types: {unknown}")
+    config.compress_ratios = [
+        int(rates.get(layer_type, 0)) for layer_type in layer_types
+    ]
+
+
+def patch_dsv4_compress_ratio_config() -> None:
+    """Bridge Transformers 5 DSV4 config names into vLLM 0.25."""
+    attention = importlib.import_module("vllm.models.deepseek_v4.attention")
+    attention_cls = attention.DeepseekV4Attention
+    marker = "_art_compress_ratio_config_patched"
+    if getattr(attention_cls, marker, False):
         return
+    original = attention_cls.__init__
 
-    def load_weights(self: Any, weights: Any) -> set[str]:
-        stacked_params_mapping = [
-            ("gate_up_proj", "w1", 0),
-            ("gate_up_proj", "w3", 1),
-            ("attn.fused_wqa_wkv", "attn.wq_a", 0),
-            ("attn.fused_wqa_wkv", "attn.wkv", 1),
-            ("compressor.fused_wkv_wgate", "compressor.wkv", 0),
-            ("compressor.fused_wkv_wgate", "compressor.wgate", 1),
-        ]
-        params_dict = dict(self.named_parameters())
-        loaded_params: set[str] = set()
-
-        tp_size = dsv4_model.get_tensor_model_parallel_world_size()
-        tp_rank = dsv4_model.get_tensor_model_parallel_rank()
-        n_head = self.config.num_attention_heads
-        n_local_head = n_head // tp_size
-        head_rank_start = n_local_head * tp_rank
-        head_rank_end = n_local_head * (tp_rank + 1)
-        expert_mapping = self.get_expert_mapping()
-
-        for name, loaded_weight in weights:
-            for param_name, weight_name, shard_id in stacked_params_mapping:
-                if ".experts." in name:
-                    continue
-                if weight_name not in name:
-                    continue
-                name = name.replace(weight_name, param_name)
-
-                if is_pp_missing_parameter(name, self):
-                    break
-                param = params_dict[name]
-                param.weight_loader(param, loaded_weight, shard_id)
-                loaded_params.add(name)
-                break
-            else:
-                if ".experts." in name:
-                    if (
-                        "weight_scale" in name
-                        and loaded_weight.dtype == dsv4_model.torch.float8_e8m0fnu
-                    ):
-                        loaded_weight = loaded_weight.view(dsv4_model.torch.uint8)
-                    for mapping in expert_mapping:
-                        param_name, weight_name, expert_id, expert_shard_id = mapping
-                        if weight_name not in name:
-                            continue
-                        name_mapped = name.replace(weight_name, param_name)
-                        if is_pp_missing_parameter(name_mapped, self):
-                            continue
-                        param = params_dict[name_mapped]
-                        success = param.weight_loader(
-                            param,
-                            loaded_weight,
-                            name_mapped,
-                            shard_id=expert_shard_id,
-                            expert_id=expert_id,
-                            return_success=True,
-                        )
-                        if success:
-                            name = name_mapped
-                            break
-                    loaded_params.add(name_mapped)
-                    continue
-                if "attn_sink" in name:
-                    if is_pp_missing_parameter(name, self):
-                        continue
-                    param = params_dict[name]
-                    narrow_weight = loaded_weight[head_rank_start:head_rank_end]
-                    padded_weight = loaded_weight.new_full(
-                        tuple(param.shape), -float("inf")
-                    )
-                    padded_weight[: narrow_weight.shape[0]].copy_(narrow_weight)
-                    weight_loader = getattr(
-                        param, "weight_loader", dsv4_model.default_weight_loader
-                    )
-                    weight_loader(param, padded_weight)
-                    loaded_params.add(name)
-                    continue
-
-                if is_pp_missing_parameter(name, self):
-                    continue
-                param = params_dict[name]
-                weight_loader = getattr(
-                    param, "weight_loader", dsv4_model.default_weight_loader
-                )
-                weight_loader(param, loaded_weight)
-                loaded_params.add(name)
-
-        return loaded_params
+    def __init__(self: Any, vllm_config: Any, *args: Any, **kwargs: Any) -> None:
+        _normalize_dsv4_compress_ratios(vllm_config.model_config.hf_config)
+        original(self, vllm_config, *args, **kwargs)
 
-    load_weights.__art_patched__ = True  # type: ignore[attr-defined]
-    model_cls.load_weights = load_weights  # type: ignore[method-assign]
+    __init__.__art_original__ = original  # type: ignore[attr-defined]
+    attention_cls.__init__ = __init__
+    setattr(attention_cls, marker, True)
 
 
 def patch_dsv4_mhc_pre_fixed_split() -> None:
@@ -279,11 +295,9 @@ def patch_dsv4_lora_support() -> None:
     point this patch at the FlashInfer TRTLLM MXFP4 backend; that backend
     currently has no LoRA hooks.
     """
-    dsv4_model = _import_dsv4_model_module()
-    if dsv4_model is None:
-        return
-    model_cls = getattr(dsv4_model, "DeepseekV4ForCausalLM", None)
-    if model_cls is None or getattr(model_cls, "_art_dsv4_lora_patched", False):
+    dsv4_model = importlib.import_module("vllm.models.deepseek_v4.nvidia.model")
+    model_cls = dsv4_model.DeepseekV4ForCausalLM
+    if getattr(model_cls, "_art_dsv4_lora_patched", False):
         return
     model_cls.supports_lora = True
     model_cls.embedding_modules = {}
@@ -383,21 +397,6 @@ def _is_lora_wrapped_linear(module: Any) -> bool:
     )
 
 
-def _apply_lora_to_existing_linear_output(
-    module: Any,
-    x: Any,
-    output: Any,
-) -> Any:
-    if not _is_lora_wrapped_linear(module):
-        return output
-    wrapper = module.punica_wrapper
-    if getattr(wrapper, "no_lora", False):
-        return output
-    if getattr(wrapper, "indices_len", [None])[0] is None:
-        return output
-    return module._apply_lora_to_output(x, output)
-
-
 def _register_dsv4_lora_expand_fp32_output_op() -> None:
     if getattr(_register_dsv4_lora_expand_fp32_output_op, "_registered", False):
         return
@@ -1016,7 +1015,13 @@ def _apply_dsv4_wo_a_lora_fast(
         shrunk = wrapper.add_shrink(buffer, lora_input[group], wo_a.lora_a_stacked, 1.0)
         if not current_platform.can_update_inplace():
             buffer = shrunk
-        buffer = tensor_model_parallel_all_gather(buffer)
+        if wo_a.lora_config.fully_sharded_loras:
+            buffer = tensor_model_parallel_all_gather(buffer)
+        if buffer.shape[-1] != lora_b.shape[-1]:
+            raise RuntimeError(
+                "DSV4 wo_a LoRA rank mismatch after TP placement: "
+                f"A={buffer.shape[-1]} B={lora_b.shape[-1]}"
+            )
         expanded = wrapper.add_expand(
             z_flat,
             buffer,
@@ -1126,16 +1131,28 @@ def _dsv4_deep_gemm_fp8_o_proj_with_lora(
     return wo_b(z.flatten(1))
 
 
+def _dsv4_fp32_cos_sin_cache(rotary_emb: Any) -> Any:
+    cache = rotary_emb.cos_sin_cache
+    if cache.dtype != torch.float32:
+        cache = cache.float()
+        rotary_emb.cos_sin_cache = cache
+    return cache
+
+
 def _patch_dsv4_cuda_o_proj_lora(attn_cls: Any, o_proj_mod: Any) -> None:
     if getattr(attn_cls, "_art_wo_a_fast_path_lora_patched", False):
         return
+    original = attn_cls._o_proj
 
     def _o_proj(self: Any, o: Any, positions: Any) -> Any:
+        cos_sin_cache = _dsv4_fp32_cos_sin_cache(self.rotary_emb)
+        if not _is_active_lora_wrapped_linear(self.wo_a):
+            return original(self, o, positions)
         return _dsv4_deep_gemm_fp8_o_proj_with_lora(
             o_proj_mod,
             o,
             positions,
-            self.rotary_emb.cos_sin_cache,
+            cos_sin_cache,
             self.wo_a,
             self.wo_b,
             n_groups=self.n_local_groups,
@@ -1148,28 +1165,16 @@ def _o_proj(self: Any, o: Any, positions: Any) -> Any:
         )
 
     _o_proj.__art_patched__ = True  # type: ignore[attr-defined]
+    _o_proj.__art_original__ = original  # type: ignore[attr-defined]
     attn_cls._o_proj = _o_proj
     attn_cls._art_wo_a_fast_path_lora_patched = True
 
 
-def _patch_current_dsv4_fast_path_lora() -> bool:
-    try:
-        dsv4_attention = importlib.import_module("vllm.models.deepseek_v4.attention")
-    except ModuleNotFoundError:
-        return False
-
-    attention_cls = getattr(dsv4_attention, "DeepseekV4Attention", None)
-    if attention_cls is None:
-        return False
-
+def _patch_dsv4_fast_path_lora() -> None:
+    dsv4_attention = importlib.import_module("vllm.models.deepseek_v4.attention")
+    attention_cls = dsv4_attention.DeepseekV4Attention
     _patch_dsv4_compressor_fast_path_lora(attention_cls)
-
-    try:
-        o_proj_mod = importlib.import_module(
-            "vllm.models.deepseek_v4.nvidia.ops.o_proj"
-        )
-    except ModuleNotFoundError:
-        return True
+    o_proj_mod = importlib.import_module("vllm.models.deepseek_v4.nvidia.ops.o_proj")
 
     for module_name, class_name in (
         (
@@ -1180,15 +1185,13 @@ def _patch_current_dsv4_fast_path_lora() -> bool:
             "vllm.models.deepseek_v4.nvidia.flashinfer_sparse",
             "DeepseekV4FlashInferMLAAttention",
         ),
+        (
+            "vllm.models.deepseek_v4.nvidia.flashinfer_sparse",
+            "DeepseekV4FlashInferSM120Attention",
+        ),
     ):
-        try:
-            module = importlib.import_module(module_name)
-        except ModuleNotFoundError:
-            continue
-        attn_cls = getattr(module, class_name, None)
-        if attn_cls is not None:
-            _patch_dsv4_cuda_o_proj_lora(attn_cls, o_proj_mod)
-    return True
+        module = importlib.import_module(module_name)
+        _patch_dsv4_cuda_o_proj_lora(getattr(module, class_name), o_proj_mod)
 
 
 def patch_dsv4_fast_path_lora() -> None:
@@ -1203,119 +1206,7 @@ def patch_dsv4_fast_path_lora() -> None:
     """
     _register_dsv4_inv_rope_lora_input_op()
     _register_dsv4_lora_expand_fp32_output_op()
-    if _patch_current_dsv4_fast_path_lora():
-        return
-
-    dsv4_attn = importlib.import_module(
-        "vllm.model_executor.layers.deepseek_v4_attention"
-    )
-    wrapper_cls = getattr(dsv4_attn, "DeepseekV4MultiHeadLatentAttentionWrapper", None)
-    if wrapper_cls is None:
-        return
-    if getattr(wrapper_cls, "_art_fast_path_lora_patched", False):
-        return
-
-    original_attn_gemm_parallel_execute = wrapper_cls.attn_gemm_parallel_execute
-    original_forward = wrapper_cls.forward
-
-    def attn_gemm_parallel_execute(self: Any, hidden_states: Any) -> tuple[Any, ...]:
-        qr_kv, kv_score, indexer_kv_score, indexer_weights = (
-            original_attn_gemm_parallel_execute(self, hidden_states)
-        )
-        if self.compressor is not None:
-            kv_score = _apply_dsv4_compressor_lora_to_existing_output(
-                self.compressor.fused_wkv_wgate,
-                hidden_states,
-                kv_score,
-            )
-        if self.indexer is not None:
-            indexer_kv_score = _apply_dsv4_compressor_lora_to_existing_output(
-                self.indexer.compressor.fused_wkv_wgate,
-                hidden_states,
-                indexer_kv_score,
-            )
-        return qr_kv, kv_score, indexer_kv_score, indexer_weights
-
-    def forward(
-        self: Any,
-        positions: Any,
-        hidden_states: Any,
-        llama_4_scaling: Any | None = None,
-    ) -> Any:
-        if dsv4_attn.current_platform.is_rocm():
-            return original_forward(self, positions, hidden_states, llama_4_scaling)
-
-        num_tokens = hidden_states.shape[0]
-        o_padded = dsv4_attn.torch.empty(
-            (num_tokens, self.padded_heads, self.head_dim),
-            dtype=hidden_states.dtype,
-            device=hidden_states.device,
-        )
-
-        dsv4_attn.torch.ops.vllm.deepseek_v4_attention(
-            hidden_states,
-            positions,
-            o_padded,
-            self.layer_name,
-        )
-        o = o_padded[:, : self.n_local_heads, :]
-
-        wo_a_lora_input = None
-        if _is_active_lora_wrapped_linear(self.wo_a):
-            o_fp8, o_scale, wo_a_lora_input = (
-                _dsv4_fused_inv_rope_fp8_quant_with_lora_input(
-                    dsv4_attn,
-                    o,
-                    positions,
-                    self.rotary_emb.cos_sin_cache,
-                    n_groups=self.n_local_groups,
-                    heads_per_group=self.n_local_heads // self.n_local_groups,
-                    lora_dtype=self.wo_a.lora_a_stacked[0].dtype,
-                    nope_dim=self.nope_head_dim,
-                    rope_dim=self.rope_head_dim,
-                    tma_aligned_scales=self._tma_aligned_scales,
-                )
-            )
-        else:
-            o_fp8, o_scale = dsv4_attn.fused_inv_rope_fp8_quant(
-                o,
-                positions,
-                self.rotary_emb.cos_sin_cache,
-                n_groups=self.n_local_groups,
-                heads_per_group=self.n_local_heads // self.n_local_groups,
-                nope_dim=self.nope_head_dim,
-                rope_dim=self.rope_head_dim,
-                tma_aligned_scales=self._tma_aligned_scales,
-            )
-
-        z = dsv4_attn.torch.empty(
-            (num_tokens, self.n_local_groups, self.o_lora_rank),
-            device=o.device,
-            dtype=dsv4_attn.torch.bfloat16,
-        )
-        dsv4_attn.torch.ops.vllm.deepseek_v4_fp8_einsum(
-            o_fp8,
-            o_scale,
-            self.wo_a.weight,
-            self.wo_a.weight_scale_inv,
-            z,
-            "bhr,hdr->bhd",
-            list(self._einsum_recipe),
-        )
-        if wo_a_lora_input is not None:
-            z = _apply_dsv4_wo_a_lora_fast(
-                self.wo_a,
-                z,
-                lora_input=wo_a_lora_input,
-                n_local_groups=self.n_local_groups,
-            )
-        return self.wo_b(z.flatten(1))
-
-    attn_gemm_parallel_execute.__art_patched__ = True  # type: ignore[attr-defined]
-    forward.__art_patched__ = True  # type: ignore[attr-defined]
-    wrapper_cls.attn_gemm_parallel_execute = attn_gemm_parallel_execute
-    wrapper_cls.forward = forward
-    wrapper_cls._art_fast_path_lora_patched = True
+    _patch_dsv4_fast_path_lora()
 
 
 def _next_power_of_two(value: int) -> int:
@@ -1331,15 +1222,12 @@ def patch_dsv4_triton_moe_topk6_routing() -> None:
     the engine exits before serving starts. Keep the original indexing stride at
     192, but sort over a padded power-of-two vector and mask padded lanes.
     """
-    try:
-        import torch
-        import triton
-        import triton.language as tl
-        from vllm.third_party.triton_kernels.routing_details._expt_data import (
-            _expt_data_compute,
-        )
-    except ImportError:
-        return
+    import torch
+    import triton
+    import triton.language as tl
+    from vllm.third_party.triton_kernels.routing_details._expt_data import (
+        _expt_data_compute,
+    )
 
     @triton.jit
     def _routing_compute_indx_pow2(
@@ -1456,14 +1344,8 @@ def _combined_routing_compute_pow2(
                 BLOCK_SIZE_PADDED,
             )
 
-    for module_name in (
-        "vllm.third_party.triton_kernels.routing",
-        "triton_kernels.routing",
-    ):
-        try:
-            routing = importlib.import_module(module_name)
-        except ImportError:
-            continue
+    for module_name in ("vllm.third_party.triton_kernels.routing",):
+        routing = importlib.import_module(module_name)
         original_forward = routing.SortTokens.forward
         if getattr(original_forward, "__art_dsv4_topk6_pow2_patched__", False):
             continue
@@ -1610,64 +1492,3 @@ def patch_lora_linear_base_attr_proxy() -> None:
         if not hasattr(BaseLinearLayerWithLoRA, name):
             setattr(BaseLinearLayerWithLoRA, name, _base_layer_attr_proxy(name))
     BaseLinearLayerWithLoRA._art_base_attr_proxy_patched = True
-
-
-def patch_marlin_lora_swiglu_limit() -> None:
-    """Keep Marlin MoE LoRA active when DSV4 uses a SwiGLU clamp limit.
-
-    vLLM's Marlin LoRA path injects W13 LoRA inside the activation callback and
-    stores that activated cache for W2 LoRA. DSV4 sets ``gemm1_clamp_limit``;
-    upstream Marlin bypasses the callback in that case and calls the clamp op
-    directly, so W13 LoRA is skipped and W2 LoRA later misses ``cache2``. Route
-    the callback through the same clamp op while preserving Marlin execution.
-    """
-    try:
-        marlin_moe = importlib.import_module(
-            "vllm.model_executor.layers.fused_moe.fused_marlin_moe"
-        )
-    except ModuleNotFoundError:
-        return
-
-    from vllm.model_executor.layers.fused_moe.activation import MoEActivation
-    from vllm.model_executor.layers.fused_moe.utils import swiglu_limit_func
-
-    MarlinExperts = marlin_moe.MarlinExperts
-
-    original_apply = MarlinExperts.apply
-    if getattr(original_apply, "__art_patched__", False):
-        return
-
-    sentinel = object()
-
-    def apply(self: Any, *args: Any, **kwargs: Any) -> Any:
-        clamp_limit = getattr(self, "gemm1_clamp_limit", None)
-        if getattr(self, "_lora_context", None) is None or clamp_limit is None:
-            return original_apply(self, *args, **kwargs)
-
-        original_activation = self.activation
-        previous_activation = self.__dict__.get("activation", sentinel)
-        previous_clamp_limit = self.gemm1_clamp_limit
-
-        def activation_with_clamp(
-            activation: Any,
-            output: Any,
-            input: Any,
-        ) -> None:
-            if activation == MoEActivation.SILU:
-                swiglu_limit_func(output, input, clamp_limit)
-            else:
-                original_activation(activation, output, input)
-
-        self.activation = activation_with_clamp
-        self.gemm1_clamp_limit = None
-        try:
-            return original_apply(self, *args, **kwargs)
-        finally:
-            self.gemm1_clamp_limit = previous_clamp_limit
-            if previous_activation is sentinel:
-                delattr(self, "activation")
-            else:
-                self.activation = previous_activation
-
-    apply.__art_patched__ = True  # type: ignore[attr-defined]
-    MarlinExperts.apply = apply  # type: ignore[method-assign]
diff --git a/vllm_runtime/src/art_vllm_runtime/engine_core.py b/vllm_runtime/src/art_vllm_runtime/engine_core.py
new file mode 100644
index 000000000..ec142c39c
--- /dev/null
+++ b/vllm_runtime/src/art_vllm_runtime/engine_core.py
@@ -0,0 +1,25 @@
+"""Utilities that require replies from every vLLM engine core."""
+
+import asyncio
+from typing import Any
+
+
+async def query_engine_cores(
+    engine_client: Any, method: str, *args: Any
+) -> tuple[Any, ...]:
+    core = engine_client.engine_core
+    data_parallel_size = int(
+        engine_client.vllm_config.parallel_config.data_parallel_size
+    )
+    if data_parallel_size == 1:
+        return (await core.call_utility_async(method, *args),)
+
+    engines = getattr(core, "core_engines", ())
+    call = getattr(core, "_call_utility_async", None)
+    if len(engines) != data_parallel_size or not callable(call):
+        raise RuntimeError("vLLM client does not expose every DP engine core")
+    return tuple(
+        await asyncio.gather(
+            *(call(method, *args, engine=engine) for engine in engines)
+        )
+    )
diff --git a/vllm_runtime/src/art_vllm_runtime/fast_metrics.py b/vllm_runtime/src/art_vllm_runtime/fast_metrics.py
new file mode 100644
index 000000000..bb8a1fd4a
--- /dev/null
+++ b/vllm_runtime/src/art_vllm_runtime/fast_metrics.py
@@ -0,0 +1,380 @@
+"""Process-isolated HTTP serving for ART's scalar vLLM metrics."""
+
+from __future__ import annotations
+
+import argparse
+import ctypes
+import hashlib
+from http import HTTPStatus
+from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
+import json
+import math
+import mmap
+import os
+from pathlib import Path
+import secrets
+import select
+import socket
+import struct
+import subprocess
+import sys
+import threading
+from typing import Mapping, cast
+import zlib
+
+FAST_METRIC_NAMES = (
+    "prompt_tokens_total",
+    "prompt_tokens_computed_total",
+    "prompt_tokens_cached_total",
+    "prompt_tokens_local_cache_hit_total",
+    "prompt_tokens_external_kv_transfer_total",
+    "generation_tokens_total",
+    "prefix_cache_queries_total",
+    "prefix_cache_hits_total",
+    "external_prefix_cache_queries_total",
+    "external_prefix_cache_hits_total",
+    "num_preempted_reqs_total",
+    "policy_cache_salted_lora_requests_total",
+    "policy_cache_unsalted_lora_requests_total",
+    "policy_cache_waiting_requests_updated_total",
+    "policy_cache_started_waiting_requests_skipped_total",
+    "prefix_cache_hit_rate",
+    "external_prefix_cache_hit_rate",
+    "num_requests_running",
+    "num_requests_waiting",
+    "num_requests_waiting_capacity",
+    "num_requests_waiting_deferred",
+    "kv_cache_usage_perc",
+    "max_num_seqs",
+    "max_num_batched_tokens",
+    "max_num_scheduled_tokens",
+    "max_model_len",
+    "world_size",
+)
+
+_CONTROL = struct.Struct("<Q")
+_PAYLOAD = struct.Struct(f"<dQQ{len(FAST_METRIC_NAMES)}d")
+_SLOT_HEADER = struct.Struct("<QI")
+_SLOT_SIZE = _SLOT_HEADER.size + _PAYLOAD.size
+_STATE_SIZE = _CONTROL.size + 2 * _SLOT_SIZE
+
+
+def _memfd_create(name: str) -> int:
+    function = ctypes.CDLL(None, use_errno=True).memfd_create
+    function.argtypes = (ctypes.c_char_p, ctypes.c_uint)
+    function.restype = ctypes.c_int
+    fd = function(name.encode(), 1)  # MFD_CLOEXEC
+    if fd < 0:
+        error = ctypes.get_errno()
+        raise OSError(error, os.strerror(error))
+    return fd
+
+
+def _slot_offset(sequence: int) -> int:
+    return _CONTROL.size + (sequence & 1) * _SLOT_SIZE
+
+
+class FastMetricsSharedWriter:
+    def __init__(self) -> None:
+        self.fd = _memfd_create("art-fast-metrics")
+        os.ftruncate(self.fd, _STATE_SIZE)
+        self._mapping = mmap.mmap(self.fd, _STATE_SIZE)
+        self._sequence = 0
+        self._closed = False
+
+    def publish(
+        self,
+        *,
+        last_update_unix_s: float,
+        record_count: int,
+        engine_count: int,
+        metrics: Mapping[str, float],
+    ) -> None:
+        values = tuple(float(metrics[name]) for name in FAST_METRIC_NAMES)
+        if not math.isfinite(last_update_unix_s) or not all(
+            math.isfinite(value) for value in values
+        ):
+            raise ValueError("fast metrics must be finite")
+        payload = _PAYLOAD.pack(
+            last_update_unix_s,
+            record_count,
+            engine_count,
+            *values,
+        )
+        self._sequence += 1
+        offset = _slot_offset(self._sequence)
+        # Fill the inactive slot completely before publishing its sequence.
+        slot = _SLOT_HEADER.pack(self._sequence, zlib.crc32(payload)) + payload
+        self._mapping[offset : offset + _SLOT_SIZE] = slot
+        _CONTROL.pack_into(self._mapping, 0, self._sequence)
+
+    def close(self) -> None:
+        if self._closed:
+            return
+        self._closed = True
+        self._mapping.close()
+        os.close(self.fd)
+
+
+class _FastMetricsSharedReader:
+    def __init__(self, fd: int) -> None:
+        self._mapping = mmap.mmap(fd, _STATE_SIZE, access=mmap.ACCESS_READ)
+
+    def read(self) -> tuple[int, tuple[float | int, ...]]:
+        for _ in range(8):
+            sequence = _CONTROL.unpack_from(self._mapping)[0]
+            if sequence == 0:
+                continue
+            offset = _slot_offset(sequence)
+            slot_sequence, checksum = _SLOT_HEADER.unpack_from(self._mapping, offset)
+            payload = self._mapping[offset + _SLOT_HEADER.size : offset + _SLOT_SIZE]
+            if slot_sequence == sequence and zlib.crc32(payload) == checksum:
+                return sequence, _PAYLOAD.unpack(payload)
+        raise RuntimeError("fast metrics shared snapshot changed during every read")
+
+    def close(self) -> None:
+        self._mapping.close()
+
+
+class _FastMetricsHTTPServer(ThreadingHTTPServer):
+    allow_reuse_address = True
+    daemon_threads = True
+    block_on_close = False
+
+    def __init__(
+        self,
+        host: str,
+        port: int,
+        *,
+        token_hashes: tuple[bytes, ...],
+        reader: _FastMetricsSharedReader,
+        process_uuid: str,
+        generation: int,
+    ) -> None:
+        self._token_hashes = token_hashes
+        self._reader = reader
+        self._process_uuid = process_uuid
+        self._generation = generation
+        self._cache_lock = threading.Lock()
+        self._cached_sequence = 0
+        self._cached_body = b""
+        super().__init__((host, port), _FastMetricsRequestHandler)
+
+    def get_request(self) -> tuple[socket.socket, object]:
+        request, address = super().get_request()
+        request.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)
+        return request, address
+
+    def authorized(self, value: str | None) -> bool:
+        if not self._token_hashes:
+            return True
+        scheme, _, token = (value or "").partition(" ")
+        candidate = hashlib.sha256(token.encode()).digest()
+        matches = False
+        for expected in self._token_hashes:
+            matches |= secrets.compare_digest(candidate, expected)
+        return scheme.casefold() == "bearer" and matches
+
+    def snapshot_body(self) -> bytes:
+        sequence, values = self._reader.read()
+        with self._cache_lock:
+            if sequence > self._cached_sequence:
+                last_update_unix_s, record_count, engine_count, *metrics = values
+                content = {
+                    "schema_version": 1,
+                    "source": "art_vllm_runtime",
+                    "last_update_unix_s": last_update_unix_s,
+                    "record_count": record_count,
+                    "engine_count": engine_count,
+                    "metrics": dict(zip(FAST_METRIC_NAMES, metrics, strict=True)),
+                    "process_uuid": self._process_uuid,
+                    "generation": self._generation,
+                }
+                self._cached_body = json.dumps(
+                    content, allow_nan=False, separators=(",", ":")
+                ).encode()
+                self._cached_sequence = sequence
+            return self._cached_body
+
+
+class _FastMetricsRequestHandler(BaseHTTPRequestHandler):
+    protocol_version = "HTTP/1.1"
+
+    def do_GET(self) -> None:
+        server = cast(_FastMetricsHTTPServer, self.server)
+        if self.path.partition("?")[0] != "/art/metrics":
+            self._send_json(HTTPStatus.NOT_FOUND, b'{"error":"Not Found"}')
+        elif not server.authorized(self.headers.get("Authorization")):
+            self._send_json(HTTPStatus.UNAUTHORIZED, b'{"error":"Unauthorized"}')
+        else:
+            try:
+                body = server.snapshot_body()
+            except RuntimeError:
+                self._send_json(
+                    HTTPStatus.SERVICE_UNAVAILABLE,
+                    b'{"error":"Metrics unavailable"}',
+                )
+            else:
+                self._send_json(HTTPStatus.OK, body)
+
+    def _send_json(self, status: HTTPStatus, body: bytes) -> None:
+        self.send_response(status.value)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Content-Length", str(len(body)))
+        self.end_headers()
+        self.wfile.write(body)
+
+    def log_message(self, format: str, *args: object) -> None:
+        return None
+
+
+class FastMetricsSidecar:
+    def __init__(
+        self,
+        *,
+        process: subprocess.Popen[bytes],
+        writer: FastMetricsSharedWriter,
+        lifetime_fd: int,
+        port: int,
+    ) -> None:
+        self.process = process
+        self.writer = writer
+        self._lifetime_fd = lifetime_fd
+        self.port = port
+        self._closed = False
+
+    @classmethod
+    def start(
+        cls,
+        host: str,
+        tokens: list[str],
+        *,
+        process_uuid: str,
+        generation: int,
+        port: int = 0,
+        startup_timeout_s: float = 10.0,
+    ) -> FastMetricsSidecar:
+        writer = FastMetricsSharedWriter()
+        ready_read, ready_write = os.pipe()
+        lifetime_read, lifetime_write = os.pipe()
+        token_hashes = [hashlib.sha256(token.encode()).hexdigest() for token in tokens]
+        command = [
+            sys.executable,
+            "-E",
+            "-S",
+            str(Path(__file__).resolve()),
+            "--serve",
+            f"--host={host}",
+            f"--port={port}",
+            f"--state-fd={writer.fd}",
+            f"--ready-fd={ready_write}",
+            f"--lifetime-fd={lifetime_read}",
+            f"--process-uuid={process_uuid}",
+            f"--generation={generation}",
+            *(f"--token-sha256={value}" for value in token_hashes),
+        ]
+        process: subprocess.Popen[bytes] | None = None
+        try:
+            try:
+                process = subprocess.Popen(
+                    command,
+                    stdin=subprocess.DEVNULL,
+                    pass_fds=(writer.fd, ready_write, lifetime_read),
+                    env={"LC_ALL": "C"},
+                )
+            finally:
+                os.close(ready_write)
+                os.close(lifetime_read)
+        except BaseException:
+            os.close(ready_read)
+            os.close(lifetime_write)
+            writer.close()
+            raise
+        try:
+            ready, _, _ = select.select([ready_read], [], [], startup_timeout_s)
+            if not ready:
+                raise TimeoutError("fast metrics sidecar did not become ready")
+            message = os.read(ready_read, 64)
+            if not message:
+                returncode = None if process is None else process.poll()
+                raise RuntimeError(
+                    f"fast metrics sidecar exited before readiness: {returncode=}"
+                )
+            return cls(
+                process=cast(subprocess.Popen[bytes], process),
+                writer=writer,
+                lifetime_fd=lifetime_write,
+                port=int(message),
+            )
+        except BaseException:
+            os.close(lifetime_write)
+            writer.close()
+            if process is not None and process.poll() is None:
+                process.terminate()
+                process.wait()
+            raise
+        finally:
+            os.close(ready_read)
+
+    def close(self) -> None:
+        if self._closed:
+            return
+        self._closed = True
+        os.close(self._lifetime_fd)
+        try:
+            returncode = self.process.wait(timeout=5.0)
+        except subprocess.TimeoutExpired:
+            self.process.terminate()
+            self.process.wait()
+            raise RuntimeError("fast metrics sidecar did not stop after parent release")
+        finally:
+            self.writer.close()
+        if returncode != 0:
+            raise RuntimeError(f"fast metrics sidecar exited with status {returncode}")
+
+
+def _serve(args: argparse.Namespace) -> None:
+    reader = _FastMetricsSharedReader(args.state_fd)
+    server = _FastMetricsHTTPServer(
+        args.host,
+        args.port,
+        token_hashes=tuple(bytes.fromhex(value) for value in args.token_sha256),
+        reader=reader,
+        process_uuid=args.process_uuid,
+        generation=args.generation,
+    )
+
+    try:
+        os.write(args.ready_fd, str(server.server_port).encode())
+        os.close(args.ready_fd)
+        os.set_blocking(args.lifetime_fd, False)
+        server.timeout = 0.05
+        while True:
+            try:
+                if os.read(args.lifetime_fd, 1) == b"":
+                    break
+            except BlockingIOError:
+                pass
+            server.handle_request()
+    finally:
+        server.server_close()
+        reader.close()
+        os.close(args.lifetime_fd)
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--serve", action="store_true", required=True)
+    parser.add_argument("--host", required=True)
+    parser.add_argument("--port", type=int, required=True)
+    parser.add_argument("--state-fd", type=int, required=True)
+    parser.add_argument("--ready-fd", type=int, required=True)
+    parser.add_argument("--lifetime-fd", type=int, required=True)
+    parser.add_argument("--process-uuid", required=True)
+    parser.add_argument("--generation", type=int, required=True)
+    parser.add_argument("--token-sha256", action="append", default=[])
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    _serve(_parse_args())
diff --git a/vllm_runtime/src/art_vllm_runtime/glm52_patches.py b/vllm_runtime/src/art_vllm_runtime/glm52_patches.py
new file mode 100644
index 000000000..76a8f241d
--- /dev/null
+++ b/vllm_runtime/src/art_vllm_runtime/glm52_patches.py
@@ -0,0 +1,12 @@
+"""GLM-5.2 adaptations for the ART-owned vLLM runtime."""
+
+
+def apply_glm52_vllm_runtime_patches() -> None:
+    patch_glm52_lora_metadata()
+
+
+def patch_glm52_lora_metadata() -> None:
+    from vllm.model_executor.models.deepseek_v2 import GlmMoeDsaForCausalLM
+
+    GlmMoeDsaForCausalLM.is_3d_moe_weight = True
+    GlmMoeDsaForCausalLM.lora_skip_prefixes = ["indexer"]
diff --git a/vllm_runtime/src/art_vllm_runtime/lora_delta.py b/vllm_runtime/src/art_vllm_runtime/lora_delta.py
deleted file mode 100644
index 8952bb5d2..000000000
--- a/vllm_runtime/src/art_vllm_runtime/lora_delta.py
+++ /dev/null
@@ -1,250 +0,0 @@
-from collections.abc import Iterable
-from contextlib import contextmanager
-import math
-from typing import Any
-
-import torch
-
-ART_LORA_DELTA_UPDATE_KIND = "lora_delta"
-_LORA_A_SUFFIX = ".lora_A.weight"
-_LORA_B_SUFFIX = ".lora_B.weight"
-_GATE_UP_A_SUFFIX = ".base_layer.lora_A.weight"
-_GATE_UP_B_SUFFIX = ".base_layer.lora_B.weight"
-_PEFT_PREFIX = "base_model.model."
-_UNSUPPORTED_MERGED_DELTA_TARGETS_KEY = (
-    "art_merged_lora_delta_unsupported_target_modules"
-)
-
-
-def _lora_scaling(adapter_config: dict[str, Any]) -> float:
-    rank = int(adapter_config["r"])
-    alpha = float(adapter_config["lora_alpha"])
-    return alpha / math.sqrt(rank) if adapter_config.get("use_rslora") else alpha / rank
-
-
-def _checkpoint_base(base: str) -> str:
-    if base.startswith(_PEFT_PREFIX):
-        base = base.removeprefix(_PEFT_PREFIX)
-    return base.removesuffix(".base_layer")
-
-
-def _lora_delta(
-    *,
-    a_key: str,
-    b_key: str,
-    lora_tensors: dict[str, torch.Tensor],
-    previous_lora_tensors: dict[str, torch.Tensor] | None,
-    scaling: float,
-) -> torch.Tensor:
-    delta = lora_tensors[b_key].float().matmul(lora_tensors[a_key].float())
-    delta.mul_(scaling)
-    if previous_lora_tensors is None:
-        return delta
-    previous_delta = (
-        previous_lora_tensors[b_key]
-        .float()
-        .matmul(previous_lora_tensors[a_key].float())
-    )
-    return delta.sub_(previous_delta.mul_(scaling))
-
-
-def _unpack_expert_lora_b(tensor: torch.Tensor, *, rank: int) -> torch.Tensor:
-    num_experts = tensor.shape[1] // rank
-    return tensor.reshape(tensor.shape[0], rank, num_experts).permute(2, 0, 1)
-
-
-def _merged_delta_skips_experts(adapter_config: dict[str, Any]) -> bool:
-    targets = adapter_config.get(_UNSUPPORTED_MERGED_DELTA_TARGETS_KEY) or ()
-    return "experts" in set(targets)
-
-
-def _iter_lora_checkpoint_deltas(
-    lora_tensors: dict[str, torch.Tensor],
-    *,
-    adapter_config: dict[str, Any],
-    previous_lora_tensors: dict[str, torch.Tensor] | None,
-) -> Iterable[tuple[str, torch.Tensor]]:
-    rank = int(adapter_config["r"])
-    scaling = _lora_scaling(adapter_config)
-    skip_expert_deltas = _merged_delta_skips_experts(adapter_config)
-    consumed: set[str] = set()
-    for a_key in sorted(lora_tensors):
-        if a_key.endswith(_GATE_UP_A_SUFFIX):
-            prefix = a_key.removesuffix(_GATE_UP_A_SUFFIX)
-            b_key = prefix + _GATE_UP_B_SUFFIX
-            consumed.update((a_key, b_key))
-            if skip_expert_deltas:
-                continue
-            a_tensor = lora_tensors[a_key]
-            b_tensor = _unpack_expert_lora_b(lora_tensors[b_key], rank=rank)
-            previous_b = (
-                _unpack_expert_lora_b(previous_lora_tensors[b_key], rank=rank)
-                if previous_lora_tensors is not None
-                else None
-            )
-            checkpoint_prefix = _checkpoint_base(prefix)
-            for expert_id, b_expert in enumerate(b_tensor):
-                expert_a = a_tensor[expert_id * rank : (expert_id + 1) * rank]
-                delta = b_expert.float().matmul(expert_a.float()).mul_(scaling)
-                if previous_b is not None:
-                    assert previous_lora_tensors is not None
-                    previous_a = previous_lora_tensors[a_key][
-                        expert_id * rank : (expert_id + 1) * rank
-                    ]
-                    delta.sub_(
-                        previous_b[expert_id]
-                        .float()
-                        .matmul(previous_a.float())
-                        .mul_(scaling)
-                    )
-                gate_delta, up_delta = delta.chunk(2, dim=0)
-                yield f"{checkpoint_prefix}.{expert_id}.gate_proj.weight", gate_delta
-                yield f"{checkpoint_prefix}.{expert_id}.up_proj.weight", up_delta
-            continue
-        if not a_key.endswith(_LORA_A_SUFFIX):
-            continue
-        prefix = a_key.removesuffix(_LORA_A_SUFFIX)
-        b_key = prefix + _LORA_B_SUFFIX
-        consumed.update((a_key, b_key))
-        if prefix.endswith(".experts"):
-            if skip_expert_deltas:
-                continue
-            a_tensor = lora_tensors[a_key]
-            b_tensor = _unpack_expert_lora_b(lora_tensors[b_key], rank=rank)
-            previous_b = (
-                _unpack_expert_lora_b(previous_lora_tensors[b_key], rank=rank)
-                if previous_lora_tensors is not None
-                else None
-            )
-            checkpoint_prefix = _checkpoint_base(prefix)
-            for expert_id, b_expert in enumerate(b_tensor):
-                expert_a = a_tensor[expert_id * rank : (expert_id + 1) * rank]
-                delta = b_expert.float().matmul(expert_a.float()).mul_(scaling)
-                if previous_b is not None:
-                    assert previous_lora_tensors is not None
-                    previous_a = previous_lora_tensors[a_key][
-                        expert_id * rank : (expert_id + 1) * rank
-                    ]
-                    delta.sub_(
-                        previous_b[expert_id]
-                        .float()
-                        .matmul(previous_a.float())
-                        .mul_(scaling)
-                    )
-                yield f"{checkpoint_prefix}.{expert_id}.down_proj.weight", delta
-            continue
-        yield (
-            f"{_checkpoint_base(prefix)}.weight",
-            _lora_delta(
-                a_key=a_key,
-                b_key=b_key,
-                lora_tensors=lora_tensors,
-                previous_lora_tensors=previous_lora_tensors,
-                scaling=scaling,
-            ),
-        )
-    unexpected = sorted(set(lora_tensors) - consumed)
-    if unexpected:
-        raise RuntimeError(f"Unexpected LoRA tensor keys: {unexpected}")
-
-
-def _default_weight_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
-    if param.numel() == 1 and loaded_weight.numel() == 1:
-        param.data.copy_(loaded_weight.view(param.shape))
-        return
-    assert param.size() == loaded_weight.size(), (
-        f"Attempted to load weight ({loaded_weight.size()}) into parameter "
-        f"({param.size()})"
-    )
-    param.data.copy_(loaded_weight)
-
-
-def _call_weight_loader(
-    loader: Any,
-    loader_param: torch.Tensor,
-    loaded_weight: torch.Tensor,
-    *args: Any,
-    **kwargs: Any,
-) -> Any:
-    if not hasattr(loader_param, "load_merged_column_weight"):
-        owner = getattr(loader, "__self__", None)
-        legacy_loader = getattr(owner, "weight_loader", None)
-        if (
-            legacy_loader is not None
-            and legacy_loader is not loader
-            and getattr(loader, "__name__", "") == "weight_loader_v2"
-        ):
-            return legacy_loader(loader_param, loaded_weight, *args, **kwargs)
-    return loader(loader_param, loaded_weight, *args, **kwargs)
-
-
-def _additive_weight_loader(param: torch.Tensor, original_loader: Any) -> Any:
-    def load_delta(
-        loader_param: torch.Tensor,
-        loaded_weight: torch.Tensor,
-        *args: Any,
-        **kwargs: Any,
-    ) -> Any:
-        real_data = loader_param.data
-        scratch = torch.zeros_like(real_data)
-        loader_param.data = scratch
-        try:
-            result = _call_weight_loader(
-                original_loader,
-                loader_param,
-                loaded_weight,
-                *args,
-                **kwargs,
-            )
-        finally:
-            loader_param.data = real_data
-        if result is not False:
-            real_data.add_(scratch)
-        return result
-
-    return load_delta
-
-
-@contextmanager
-def _additive_weight_loaders(model: Any) -> Any:
-    originals: list[tuple[torch.Tensor, bool, Any]] = []
-    for param in model.parameters():
-        has_loader = hasattr(param, "weight_loader")
-        original_loader = getattr(param, "weight_loader", _default_weight_loader)
-        originals.append((param, has_loader, original_loader))
-        param.weight_loader = _additive_weight_loader(param, original_loader)  # type: ignore[attr-defined]
-    try:
-        yield
-    finally:
-        for param, has_loader, original_loader in originals:
-            if has_loader:
-                param.weight_loader = original_loader  # type: ignore[attr-defined]
-            else:
-                delattr(param, "weight_loader")
-
-
-def apply_lora_delta_update(
-    *,
-    model: Any,
-    lora_tensors: dict[str, torch.Tensor],
-    adapter_config: dict[str, Any],
-    previous_lora_tensors: dict[str, torch.Tensor] | None,
-) -> dict[str, torch.Tensor]:
-    if previous_lora_tensors is not None and set(lora_tensors) != set(
-        previous_lora_tensors
-    ):
-        raise RuntimeError(
-            "LoRA update key set changed: "
-            f"current={sorted(lora_tensors)} previous={sorted(previous_lora_tensors)}"
-        )
-    with torch.no_grad(), _additive_weight_loaders(model):
-        model.load_weights(
-            _iter_lora_checkpoint_deltas(
-                lora_tensors,
-                adapter_config=adapter_config,
-                previous_lora_tensors=previous_lora_tensors,
-            )
-        )
-    return {
-        name: tensor.detach().clone() for name, tensor in sorted(lora_tensors.items())
-    }
diff --git a/vllm_runtime/src/art_vllm_runtime/metrics.py b/vllm_runtime/src/art_vllm_runtime/metrics.py
index 0c8be3d8f..4aa22e429 100644
--- a/vllm_runtime/src/art_vllm_runtime/metrics.py
+++ b/vllm_runtime/src/art_vllm_runtime/metrics.py
@@ -8,12 +8,15 @@
 
 from vllm.v1.metrics.loggers import StatLoggerBase
 
+from art_vllm_runtime.fast_metrics import FastMetricsSharedWriter
+
 
 class _ArtRuntimeMetricsState:
     def __init__(self) -> None:
         self._lock = threading.Lock()
         self._record_count = 0
         self._last_update_unix_s = 0.0
+        self._writer: FastMetricsSharedWriter | None = None
         self._engine_gauges: dict[int, dict[str, float]] = {}
         self._engine_configs: dict[int, dict[str, float]] = {}
         self._counters = {
@@ -43,7 +46,7 @@ def configure(self, vllm_config: Any, *, engine_idx: int) -> None:
             ("max_num_seqs", scheduler_config, "max_num_seqs"),
             ("max_num_batched_tokens", scheduler_config, "max_num_batched_tokens"),
             ("max_model_len", model_config, "max_model_len"),
-            ("world_size", parallel_config, "world_size"),
+            ("world_size", parallel_config, "world_size_across_dp"),
         ):
             value = getattr(obj, attr, None)
             if isinstance(value, (int, float)):
@@ -59,6 +62,7 @@ def configure(self, vllm_config: Any, *, engine_idx: int) -> None:
             ]
         with self._lock:
             self._engine_configs[engine_idx] = engine_config
+            self._publish_locked()
 
     def record(
         self,
@@ -118,68 +122,82 @@ def record(
                 self._counters["num_preempted_reqs_total"] += float(
                     iteration_stats.num_preempted_reqs
                 )
+            self._publish_locked()
+
+    def _metrics_locked(self) -> dict[str, float]:
+        gauges = list(self._engine_gauges.values())
+        engine_configs = list(self._engine_configs.values())
+        metrics = dict(self._counters)
+        prefix_queries = metrics["prefix_cache_queries_total"]
+        external_prefix_queries = metrics["external_prefix_cache_queries_total"]
+        max_model_lens = [
+            item["max_model_len"] for item in engine_configs if "max_model_len" in item
+        ]
+        metrics.update(
+            {
+                "prefix_cache_hit_rate": (
+                    metrics["prefix_cache_hits_total"] / prefix_queries
+                    if prefix_queries > 0
+                    else 0.0
+                ),
+                "external_prefix_cache_hit_rate": (
+                    metrics["external_prefix_cache_hits_total"]
+                    / external_prefix_queries
+                    if external_prefix_queries > 0
+                    else 0.0
+                ),
+                "num_requests_running": sum(item["running"] for item in gauges),
+                "num_requests_waiting": sum(item["waiting"] for item in gauges),
+                "num_requests_waiting_capacity": sum(
+                    item["waiting_capacity"] for item in gauges
+                ),
+                "num_requests_waiting_deferred": sum(
+                    item["waiting_deferred"] for item in gauges
+                ),
+                "kv_cache_usage_perc": max(
+                    (item["kv_cache_usage"] for item in gauges), default=0.0
+                ),
+                "max_num_seqs": sum(
+                    item.get("max_num_seqs", 0.0) for item in engine_configs
+                ),
+                "max_num_batched_tokens": sum(
+                    item.get("max_num_batched_tokens", 0.0) for item in engine_configs
+                ),
+                "max_num_scheduled_tokens": sum(
+                    item.get("max_num_scheduled_tokens", 0.0) for item in engine_configs
+                ),
+                "max_model_len": max(max_model_lens, default=0.0),
+                "world_size": max(
+                    (item.get("world_size", 0.0) for item in engine_configs),
+                    default=0.0,
+                ),
+            }
+        )
+        return metrics
+
+    def _publish_locked(self) -> None:
+        if self._writer is not None:
+            self._writer.publish(
+                last_update_unix_s=self._last_update_unix_s,
+                record_count=self._record_count,
+                engine_count=len(self._engine_gauges),
+                metrics=self._metrics_locked(),
+            )
+
+    def set_writer(self, writer: FastMetricsSharedWriter | None) -> None:
+        with self._lock:
+            self._writer = writer
+            self._publish_locked()
 
     def snapshot(self) -> dict[str, Any]:
         with self._lock:
-            gauges = list(self._engine_gauges.values())
-            engine_configs = list(self._engine_configs.values())
-            metrics = dict(self._counters)
-            prefix_queries = metrics["prefix_cache_queries_total"]
-            external_prefix_queries = metrics["external_prefix_cache_queries_total"]
-            max_model_lens = [
-                item["max_model_len"]
-                for item in engine_configs
-                if "max_model_len" in item
-            ]
-            metrics.update(
-                {
-                    "prefix_cache_hit_rate": (
-                        metrics["prefix_cache_hits_total"] / prefix_queries
-                        if prefix_queries > 0
-                        else 0.0
-                    ),
-                    "external_prefix_cache_hit_rate": (
-                        metrics["external_prefix_cache_hits_total"]
-                        / external_prefix_queries
-                        if external_prefix_queries > 0
-                        else 0.0
-                    ),
-                    "num_requests_running": sum(item["running"] for item in gauges),
-                    "num_requests_waiting": sum(item["waiting"] for item in gauges),
-                    "num_requests_waiting_capacity": sum(
-                        item["waiting_capacity"] for item in gauges
-                    ),
-                    "num_requests_waiting_deferred": sum(
-                        item["waiting_deferred"] for item in gauges
-                    ),
-                    "kv_cache_usage_perc": max(
-                        (item["kv_cache_usage"] for item in gauges), default=0.0
-                    ),
-                    "max_num_seqs": sum(
-                        item.get("max_num_seqs", 0.0) for item in engine_configs
-                    ),
-                    "max_num_batched_tokens": sum(
-                        item.get("max_num_batched_tokens", 0.0)
-                        for item in engine_configs
-                    ),
-                    "max_num_scheduled_tokens": sum(
-                        item.get("max_num_scheduled_tokens", 0.0)
-                        for item in engine_configs
-                    ),
-                    "max_model_len": max(max_model_lens, default=0.0),
-                    "world_size": max(
-                        (item.get("world_size", 0.0) for item in engine_configs),
-                        default=0.0,
-                    ),
-                }
-            )
             return {
                 "schema_version": 1,
                 "source": "art_vllm_runtime",
                 "last_update_unix_s": self._last_update_unix_s,
                 "record_count": self._record_count,
                 "engine_count": len(self._engine_gauges),
-                "metrics": metrics,
+                "metrics": self._metrics_locked(),
             }
 
     def record_policy_cache_salt_audit(
@@ -194,6 +212,7 @@ def record_policy_cache_salt_audit(
         )
         with self._lock:
             self._counters[key] += 1.0
+            self._publish_locked()
 
     def record_policy_cache_waiting_update(
         self, *, updated: int, skipped_started: int
@@ -205,6 +224,7 @@ def record_policy_cache_waiting_update(
             self._counters["policy_cache_started_waiting_requests_skipped_total"] += (
                 float(skipped_started)
             )
+            self._publish_locked()
 
 
 _STATE = _ArtRuntimeMetricsState()
@@ -237,6 +257,10 @@ def get_art_metrics_snapshot() -> dict[str, Any]:
     return _STATE.snapshot()
 
 
+def set_fast_metrics_writer(writer: FastMetricsSharedWriter | None) -> None:
+    _STATE.set_writer(writer)
+
+
 def record_policy_cache_salt_audit(*, lora_request: bool, salted: bool) -> None:
     _STATE.record_policy_cache_salt_audit(lora_request=lora_request, salted=salted)
 
diff --git a/vllm_runtime/src/art_vllm_runtime/moe_lora_patches.py b/vllm_runtime/src/art_vllm_runtime/moe_lora_patches.py
new file mode 100644
index 000000000..c988ca398
--- /dev/null
+++ b/vllm_runtime/src/art_vllm_runtime/moe_lora_patches.py
@@ -0,0 +1,72 @@
+"""Correctness patches for vLLM's fused MoE LoRA kernels."""
+
+from typing import Any
+
+import torch
+
+
+def patch_local_3d_moe_dummy_lora() -> None:
+    """Reshape EP-local warmup adapters by their local expert count."""
+    from vllm.lora.model_manager import LoRAModelManager
+
+    original_create = LoRAModelManager.create_dummy_lora
+    if getattr(original_create, "__art_local_3d_moe_dummy_patched__", False):
+        return
+    original_stack = LoRAModelManager._stack_moe_lora_weights
+
+    def create_dummy_lora(self: Any, *args: Any, **kwargs: Any) -> Any:
+        lora_model = original_create(self, *args, **kwargs)
+        lora_model._art_local_3d_moe_lora = True
+        return lora_model
+
+    def stack_moe_lora_weights(
+        self: Any, lora_model: Any, module: Any, module_name: str
+    ) -> Any:
+        down = self._get_lora_layer_weights(lora_model, module_name)
+        gate_up = self._get_lora_layer_weights(lora_model, module_name + ".base_layer")
+        local_experts = module.w13_lora_a_stacked[0].shape[1]
+        if (
+            not getattr(lora_model, "_art_local_3d_moe_lora", False)
+            or local_experts == module.global_num_experts
+            or down is None
+            or gate_up is None
+            or not torch.is_tensor(down.lora_a)
+        ):
+            return original_stack(self, lora_model, module, module_name)
+        gate_up.lora_a = gate_up.lora_a.reshape(
+            local_experts, -1, gate_up.lora_a.shape[-1]
+        )
+        down.lora_a = down.lora_a.reshape(local_experts, -1, down.lora_a.shape[-1])
+        gate_up.lora_b = (
+            gate_up.lora_b.reshape(gate_up.lora_b.shape[0], -1, local_experts)
+            .permute(2, 0, 1)
+            .contiguous()
+        )
+        down.lora_b = (
+            down.lora_b.reshape(down.lora_b.shape[0], -1, local_experts)
+            .permute(2, 0, 1)
+            .contiguous()
+        )
+        down.lora_a = [gate_up.lora_a.contiguous(), down.lora_a.contiguous()]
+        down.lora_b = [gate_up.lora_b, down.lora_b]
+        return original_stack(self, lora_model, module, module_name)
+
+    create_dummy_lora.__art_local_3d_moe_dummy_patched__ = True  # type: ignore[attr-defined]
+    LoRAModelManager.create_dummy_lora = create_dummy_lora  # type: ignore[method-assign]
+    LoRAModelManager._stack_moe_lora_weights = stack_moe_lora_weights  # type: ignore[method-assign]
+
+
+def patch_small_batch_moe_lora_intermediate_dtype() -> None:
+    from vllm.lora.ops.triton_ops import fused_moe_lora_op
+
+    kernel = fused_moe_lora_op._fused_moe_lora_small_batch_kernel.fn
+    source = kernel.src
+    cast = "            rank_vec = rank_vec.to(out_ptr.dtype.element_ty)\n"
+    if cast in source:
+        return
+    anchor = (
+        "            # EXPAND: walk n_tiles_per_program consecutive output-N tiles\n"
+    )
+    if source.count(anchor) != 1:
+        raise RuntimeError("Unsupported vLLM small-batch MoE LoRA kernel source")
+    kernel._unsafe_update_src(source.replace(anchor, f"{cast}\n{anchor}"))
diff --git a/vllm_runtime/src/art_vllm_runtime/patches.py b/vllm_runtime/src/art_vllm_runtime/patches.py
index cef798784..7c9d9c560 100644
--- a/vllm_runtime/src/art_vllm_runtime/patches.py
+++ b/vllm_runtime/src/art_vllm_runtime/patches.py
@@ -1,108 +1,41 @@
 """Monkey patches and bootstrap contract for the ART-owned vLLM runtime."""
 
-import ctypes
-from functools import wraps
-import importlib
-import inspect
-import logging
 from typing import Any
 
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
 
 def apply_vllm_runtime_patches() -> None:
     from art_vllm_runtime.dsv4_patches import apply_dsv4_vllm_runtime_patches
     from art_vllm_runtime.gemma4_moe_lora_patch import (
         patch_gemma4_moe_lora_support,
     )
+    from art_vllm_runtime.glm52_patches import apply_glm52_vllm_runtime_patches
+    from art_vllm_runtime.moe_lora_patches import (
+        patch_local_3d_moe_dummy_lora,
+        patch_small_batch_moe_lora_intermediate_dtype,
+    )
     from art_vllm_runtime.policy_spans import patch_policy_token_spans
+    from art_vllm_runtime.qwen35_patches import apply_qwen35_vllm_runtime_patches
 
-    patch_transformers_v5_compat()
-    patch_flashinfer_oneshot_pdl_completion()
     patch_policy_token_spans()
     patch_gemma4_moe_lora_support()
     subclass_chat_completion_request()
-    patch_listen_for_disconnect()
-    patch_tool_parser_manager()
-    patch_nccl_unique_id_bootstrap()
+    patch_nonstreaming_chat_response_offload()
+    patch_local_3d_moe_dummy_lora()
+    patch_small_batch_moe_lora_intermediate_dtype()
+    apply_glm52_vllm_runtime_patches()
     apply_dsv4_vllm_runtime_patches()
-    patch_art_lora_delta_weight_update()
-    patch_gemma4_checkpoint_weight_update_reload()
-    patch_routed_experts_prefix_cache_sidecar()
-    from art_vllm_runtime.binary_routes import patch_binary_routed_experts_response
+    apply_qwen35_vllm_runtime_patches()
+    from art_vllm_runtime.binary_routes import (
+        patch_binary_routed_experts_response,
+        patch_pipeline_routed_experts,
+        patch_pipeline_routed_experts_validation,
+    )
 
+    patch_pipeline_routed_experts_validation()
+    patch_pipeline_routed_experts()
     patch_binary_routed_experts_response()
 
 
-def patch_flashinfer_oneshot_pdl_completion() -> None:
-    """Prevent one-shot fused all-reduce consumers from racing its output.
-
-    FlashInfer's one-shot algorithm has no internal synchronization after an
-    early PDL completion trigger, so completion must be signaled at kernel end.
-    This backports vLLM PR #45448 without changing the synchronized two-shot path.
-    """
-    import flashinfer.comm as flashinfer_comm
-
-    original = flashinfer_comm.allreduce_fusion
-    if getattr(original, "__art_oneshot_pdl_patched__", False):
-        return
-
-    @wraps(original)
-    def allreduce_fusion(*args: Any, **kwargs: Any) -> Any:
-        if kwargs.get("use_oneshot"):
-            kwargs["trigger_completion_at_end"] = True
-        return original(*args, **kwargs)
-
-    allreduce_fusion.__art_oneshot_pdl_patched__ = True  # type: ignore[attr-defined]
-    flashinfer_comm.allreduce_fusion = allreduce_fusion
-
-
-def patch_transformers_v5_compat() -> None:
-    _patch_rope_validation_ignore_keys()
-    _patch_qwen3_vl_moe_tie_word_embeddings()
-    _patch_gemma4_moe_experts_per_tok_alias()
-
-
-def _patch_rope_validation_ignore_keys() -> None:
-    from transformers.configuration_utils import PretrainedConfig
-
-    original = PretrainedConfig.convert_rope_params_to_dict
-    if getattr(original, "__art_patched__", False):
-        return
-
-    def patched(self: Any, ignore_keys_at_rope_validation: Any = None, **kwargs: Any):
-        if ignore_keys_at_rope_validation is not None:
-            ignore_keys_at_rope_validation = set(ignore_keys_at_rope_validation)
-        return original(
-            self,
-            ignore_keys_at_rope_validation=ignore_keys_at_rope_validation,
-            **kwargs,
-        )
-
-    patched.__art_patched__ = True  # type: ignore[attr-defined]
-    PretrainedConfig.convert_rope_params_to_dict = patched  # type: ignore[method-assign]
-
-
-def _patch_qwen3_vl_moe_tie_word_embeddings() -> None:
-    from transformers import Qwen3VLMoeTextConfig
-
-    setattr(Qwen3VLMoeTextConfig, "tie_word_embeddings", False)
-
-
-def _patch_gemma4_moe_experts_per_tok_alias() -> None:
-    from transformers import Gemma4TextConfig
-
-    if hasattr(Gemma4TextConfig, "num_experts_per_tok"):
-        return
-
-    def num_experts_per_tok(self: Any) -> Any:
-        return self.top_k_experts
-
-    Gemma4TextConfig.num_experts_per_tok = property(num_experts_per_tok)  # type: ignore[attr-defined]
-
-
 def subclass_chat_completion_request() -> None:
     from vllm.entrypoints.openai.chat_completion import protocol
 
@@ -118,588 +51,102 @@ class ChatCompletionRequest(protocol.ChatCompletionRequest):
     setattr(protocol, "_art_chat_completion_request_patched", True)
 
 
-def patch_listen_for_disconnect() -> None:
-    try:
-        api_utils = importlib.import_module("vllm.entrypoints.serve.utils.api_utils")
-    except ModuleNotFoundError:
-        api_utils = importlib.import_module("vllm.entrypoints.utils")
-
-    if getattr(api_utils, "_art_listen_for_disconnect_patched", False):
-        return
-
-    async def patched_listen_for_disconnect(request: Any) -> None:
-        try:
-            while True:
-                message = await request.receive()
-                if message["type"] == "http.disconnect":
-                    if getattr(
-                        request.app.state, "enable_server_load_tracking", False
-                    ) and hasattr(request.app.state, "server_load_metrics"):
-                        request.app.state.server_load_metrics -= 1
-                    break
-        except UnboundLocalError:
-            pass
-
-    api_utils.listen_for_disconnect = patched_listen_for_disconnect  # ty:ignore[invalid-assignment]
-    setattr(api_utils, "_art_listen_for_disconnect_patched", True)
+def patch_nonstreaming_chat_response_offload() -> None:
+    import asyncio
 
+    from starlette.responses import JSONResponse as StarletteJSONResponse
+    from starlette.responses import Response
+    from vllm.entrypoints.openai.chat_completion import api_router
+    from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionResponse
+    from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
 
-def patch_tool_parser_manager() -> None:
-    from vllm.entrypoints.openai.engine.protocol import DeltaMessage
-    from vllm.tool_parsers.abstract_tool_parser import ToolParserManager
-
-    original = ToolParserManager.get_tool_parser
-    if getattr(original, "__art_patched__", False):
+    marker = "_art_nonstreaming_response_offload_patched"
+    if getattr(OpenAIServingChat, marker, False):
         return
+    original = OpenAIServingChat.chat_completion_full_generator
 
-    def patched_get_tool_parser(name: str) -> type:
-        tool_parser_class = original(name)
-        current = tool_parser_class.extract_tool_calls_streaming
-        if getattr(current, "__art_patched__", False):
-            return tool_parser_class
-
-        def patch(
-            *args: Any,
-            **kwargs: Any,
-        ) -> Any:
-            return current(*args, **kwargs) or DeltaMessage()
-
-        patch.__art_patched__ = True  # type: ignore[attr-defined]
-        tool_parser_class.extract_tool_calls_streaming = patch  # ty:ignore[invalid-assignment]
-        return tool_parser_class
-
-    patched_get_tool_parser.__art_patched__ = True  # type: ignore[attr-defined]
-    ToolParserManager.get_tool_parser = patched_get_tool_parser  # ty:ignore[invalid-assignment]
-
-
-def _restore_nccl_unique_id_payload(
-    payload: object,
-    template: object | None,
-) -> object:
-    from vllm.distributed.device_communicators.pynccl_wrapper import ncclUniqueId
-
-    if not isinstance(payload, (bytes, bytearray)) or not isinstance(
-        template, ncclUniqueId
-    ):
-        return payload
-    raw = bytes(payload)
-    assert len(raw) == ctypes.sizeof(ncclUniqueId)
-    unique_id = ncclUniqueId()
-    ctypes.memmove(ctypes.byref(unique_id), raw, len(raw))
-    return unique_id
-
-
-def _normalize_nccl_comm_init_rank_unique_id(library: Any, unique_id: object) -> object:
-    if isinstance(unique_id, (bytes, bytearray)):
-        return library.unique_id_from_bytes(bytes(unique_id))
-    return unique_id
-
-
-def patch_nccl_unique_id_bootstrap() -> None:
-    from vllm.distributed.device_communicators.pynccl_wrapper import NCCLLibrary
-    from vllm.distributed.utils import StatelessProcessGroup
+    class PreencodedContent:
+        def __init__(self, body: bytes) -> None:
+            self.body = body
 
-    original_broadcast = StatelessProcessGroup.broadcast_obj
-    if not getattr(original_broadcast, "__art_patched__", False):
+    original_model_dump = ChatCompletionResponse.model_dump
 
-        def patched_broadcast(self: Any, obj: Any | None, src: int) -> Any:
-            return _restore_nccl_unique_id_payload(
-                original_broadcast(self, obj, src), obj
-            )
-
-        patched_broadcast.__art_patched__ = True  # type: ignore[attr-defined]
-        StatelessProcessGroup.broadcast_obj = patched_broadcast  # type: ignore[method-assign]
-
-    original_comm_init_rank = NCCLLibrary.ncclCommInitRank
-    if getattr(original_comm_init_rank, "__art_patched__", False):
-        return
+    def model_dump(self: Any, *args: Any, **kwargs: Any) -> Any:
+        cached = getattr(self, "_art_preencoded_content", None)
+        if cached is not None and not args and not kwargs:
+            return cached
+        return original_model_dump(self, *args, **kwargs)
 
-    def patched_comm_init_rank(
-        self: Any,
-        world_size: int,
-        unique_id: object,
-        rank: int,
+    async def build_response(
+        self: Any, request: Any, result_generator: Any, *args: Any, **kwargs: Any
     ) -> Any:
-        unique_id = _normalize_nccl_comm_init_rank_unique_id(self, unique_id)
-        return original_comm_init_rank(self, world_size, unique_id, rank)
-
-    patched_comm_init_rank.__art_patched__ = True  # type: ignore[attr-defined]
-    NCCLLibrary.ncclCommInitRank = patched_comm_init_rank  # type: ignore[method-assign]
-
-
-def _is_gemma4_conditional_worker(worker: Any) -> bool:
-    hf_config = worker.model_config.hf_config
-    return hf_config.architectures == ["Gemma4ForConditionalGeneration"]
-
-
-def patch_gemma4_checkpoint_weight_update_reload() -> None:
-    from vllm.v1.worker.gpu_worker import Worker
-
-    original_start_weight_update = Worker.start_weight_update
-    if getattr(original_start_weight_update, "__art_patched__", False):
-        return
-    original_finish_weight_update = Worker.finish_weight_update
-
-    def start_weight_update(
-        self: Any,
-        is_checkpoint_format: bool = True,
-    ) -> None:
-        if not is_checkpoint_format or not _is_gemma4_conditional_worker(self):
-            return original_start_weight_update(
-                self,
-                is_checkpoint_format=is_checkpoint_format,
-            )
-        self._check_weight_transfer_engine()
-        if self._weight_update_active:
-            raise RuntimeError(
-                "start_weight_update called while a weight update is "
-                "already active. Call finish_weight_update first."
-            )
-        self._is_checkpoint_format = True
-        self._weight_update_active = True
-
-    def finish_weight_update(self: Any) -> None:
-        if not _is_gemma4_conditional_worker(self):
-            return original_finish_weight_update(self)
-        self._check_weight_transfer_engine()
-        if not self._weight_update_active:
-            raise RuntimeError(
-                "start_weight_update must be called before finish_weight_update."
+        final_result = None
+        try:
+            async for result in result_generator:
+                final_result = result
+        except asyncio.CancelledError:
+            return self.create_error_response("Client disconnected")
+
+        async def materialize() -> Any:
+            async def replay_final_result():
+                if final_result is not None:
+                    yield final_result
+
+            result = await original(
+                self, request, replay_final_result(), *args, **kwargs
             )
-        if not self._is_checkpoint_format:
-            return original_finish_weight_update(self)
-        self._weight_update_active = False
-        self._is_checkpoint_format = True
-
-    start_weight_update.__art_patched__ = True  # type: ignore[attr-defined]
-    start_weight_update.__art_original__ = original_start_weight_update  # type: ignore[attr-defined]
-    finish_weight_update.__art_patched__ = True  # type: ignore[attr-defined]
-    finish_weight_update.__art_original__ = original_finish_weight_update  # type: ignore[attr-defined]
-    Worker.start_weight_update = start_weight_update  # type: ignore[method-assign]
-    Worker.finish_weight_update = finish_weight_update  # type: ignore[method-assign]
-
-
-def patch_art_lora_delta_weight_update() -> None:
-    import torch
-    from vllm.v1.worker.gpu_worker import Worker
-
-    from art_vllm_runtime.lora_delta import (
-        ART_LORA_DELTA_UPDATE_KIND,
-        apply_lora_delta_update,
-    )
-
-    original_update_weights = Worker.update_weights
-    if getattr(original_update_weights, "__art_lora_delta_patched__", False):
-        return
-
-    def update_weights(self: Any, update_info: dict) -> None:
-        if update_info.get("art_weight_update_kind") != ART_LORA_DELTA_UPDATE_KIND:
-            return original_update_weights(self, update_info)
-
-        self._check_weight_transfer_engine()
-        assert self.weight_transfer_engine is not None
-        if not self._weight_update_active:
-            raise RuntimeError(
-                "start_weight_update must be called before update_weights."
+            if not isinstance(result, ChatCompletionResponse):
+                return result
+            content = original_model_dump(result)
+            object.__setattr__(
+                result,
+                "_art_preencoded_content",
+                PreencodedContent(StarletteJSONResponse(content).body),
             )
+            return result
 
-        adapter_config = update_info["art_lora_config"]
-        transfer_update_info = dict(update_info)
-        del transfer_update_info["art_weight_update_kind"]
-        del transfer_update_info["art_lora_config"]
-        typed_update_info = self.weight_transfer_engine.parse_update_info(
-            transfer_update_info
+        return await asyncio.to_thread(
+            asyncio.run,
+            materialize(),
         )
-        lora_tensors: dict[str, torch.Tensor] = {}
 
-        def collect_lora_tensors(weights: list[tuple[str, torch.Tensor]]) -> None:
-            for name, tensor in weights:
-                if name in lora_tensors:
-                    raise RuntimeError(f"Duplicate LoRA tensor in update: {name}")
-                lora_tensors[name] = tensor.detach().contiguous().clone()
-
-        with torch.device(self.device):
-            self.weight_transfer_engine.receive_weights(
-                typed_update_info,
-                load_weights=collect_lora_tensors,
-            )
-            self._art_previous_lora_tensors = apply_lora_delta_update(
-                model=self.model_runner.model,
-                lora_tensors=lora_tensors,
-                adapter_config=adapter_config,
-                previous_lora_tensors=getattr(
+    class PreencodedJSONResponse(StarletteJSONResponse):
+        media_type = "application/json"
+
+        def render(self, content: Any) -> bytes:
+            if isinstance(content, bytes):
+                return content
+            return super().render(content)
+
+        def __init__(
+            self: Any,
+            content: Any,
+            status_code: int = 200,
+            headers: Any = None,
+            media_type: str | None = None,
+            background: Any = None,
+        ) -> None:
+            if isinstance(content, PreencodedContent):
+                Response.__init__(
                     self,
-                    "_art_previous_lora_tensors",
-                    None,
-                ),
-            )
-
-        torch.accelerator.synchronize()
-
-    update_weights.__art_lora_delta_patched__ = True  # type: ignore[attr-defined]
-    update_weights.__art_original__ = original_update_weights  # type: ignore[attr-defined]
-    Worker.update_weights = update_weights  # type: ignore[method-assign]
-
-
-def _lora_cache_key(lora_request: Any) -> tuple[Any, ...]:
-    if lora_request is None:
-        return ()
-    return (
-        getattr(lora_request, "adapter_id", None),
-        getattr(lora_request, "name", None),
-        getattr(lora_request, "path", None),
-    )
-
-
-def _request_token_ids(req_state: Any) -> list[int] | None:
-    prompt_token_ids = getattr(req_state, "prompt_token_ids", None)
-    if prompt_token_ids is None:
-        return None
-    return list(prompt_token_ids) + list(getattr(req_state, "output_token_ids", ()))
-
-
-def _route_block_key(
-    token_ids: list[int],
-    end: int,
-    lora_key: tuple[Any, ...],
-) -> tuple[Any, ...]:
-    return (lora_key, tuple(token_ids[:end]))
-
-
-def _runner_block_size(runner: Any) -> int:
-    kv_cache_config = getattr(runner, "kv_cache_config", None)
-    groups = getattr(kv_cache_config, "kv_cache_groups", None)
-    if groups and len(groups) == 1:
-        return int(groups[0].kv_cache_spec.block_size)
-    return int(getattr(runner.cache_config, "block_size", 16))
-
-
-def _request_snapshots(
-    runner: Any, ordered: dict[str, int]
-) -> dict[str, dict[str, Any]]:
-    snapshots: dict[str, dict[str, Any]] = {}
-    for req_id in ordered:
-        req_state = runner.requests.get(req_id)
-        if req_state is None:
-            continue
-        token_ids = _request_token_ids(req_state)
-        if token_ids is None:
-            continue
-        snapshots[req_id] = {
-            "token_ids": token_ids,
-            "lora_key": _lora_cache_key(getattr(req_state, "lora_request", None)),
-            "num_computed_tokens": int(getattr(req_state, "num_computed_tokens", 0)),
-        }
-    return snapshots
-
-
-def patch_routed_experts_prefix_cache_sidecar() -> None:
-    from vllm.model_executor.layers.fused_moe import routed_experts_capturer
-
-    if getattr(routed_experts_capturer, "_art_prefix_route_sidecar_patched", False):
-        return
-
-    host_cls = getattr(routed_experts_capturer, "_RoutedExpertsHostCache", None)
-    capturer_cls = getattr(routed_experts_capturer, "_RoutedExpertsCapturerReal", None)
-    if host_cls is None or capturer_cls is None:
-        return
-
-    original_host_init = host_cls.__init__
-    original_get_or_grow_buffer = host_cls.get_or_grow_buffer
-    original_free_request = host_cls.free_request
-    original_scatter_to_host = capturer_cls._scatter_to_host
-    original_get_routed_experts = capturer_cls.get_routed_experts
-    original_issue_routing_d2h_copy = routed_experts_capturer.issue_routing_d2h_copy
-
-    def host_init(self: Any, *args: Any, **kwargs: Any) -> None:
-        original_host_init(self, *args, **kwargs)
-        self._art_req_filled_masks: dict[str, np.ndarray] = {}
-        self._art_prefix_route_blocks: dict[tuple[Any, ...], np.ndarray] = {}
-        self._art_prefix_route_waiters: dict[
-            tuple[Any, ...], list[tuple[str, int, int]]
-        ] = {}
-        self._art_prefix_route_needs_by_req: dict[str, set[tuple[Any, ...]]] = {}
-        self._art_prefix_route_hydrated_tokens = 0
-        self._art_prefix_route_cache_misses = 0
-        self._art_prefix_route_cache_conflicts = 0
-
-    def get_or_grow_buffer(self: Any, req_id: str, max_pos: int) -> np.ndarray:
-        buf = original_get_or_grow_buffer(self, req_id, max_pos)
-        mask = self._art_req_filled_masks.get(req_id)
-        if mask is None:
-            self._art_req_filled_masks[req_id] = np.zeros(buf.shape[0], dtype=np.bool_)
-        elif mask.shape[0] < buf.shape[0]:
-            new_mask = np.zeros(buf.shape[0], dtype=np.bool_)
-            new_mask[: mask.shape[0]] = mask
-            self._art_req_filled_masks[req_id] = new_mask
-        return buf
-
-    def free_request(self: Any, req_id: str) -> None:
-        original_free_request(self, req_id)
-        self._art_req_filled_masks.pop(req_id, None)
-        for key in self._art_prefix_route_needs_by_req.pop(req_id, set()):
-            waiters = self._art_prefix_route_waiters.get(key)
-            if waiters is None:
-                continue
-            waiters = [waiter for waiter in waiters if waiter[0] != req_id]
-            if waiters:
-                self._art_prefix_route_waiters[key] = waiters
-            else:
-                self._art_prefix_route_waiters.pop(key, None)
-
-    def mark_filled(self: Any, req_id: str, positions: np.ndarray) -> None:
-        if positions.size == 0:
-            return
-        self.get_or_grow_buffer(req_id, int(positions.max()))
-        self._art_req_filled_masks[req_id][positions] = True
-
-    def require_filled(self: Any, req_id: str, seqlen: int) -> None:
-        mask = self._art_req_filled_masks.get(req_id)
-        if mask is None or mask.shape[0] < seqlen or not bool(mask[:seqlen].all()):
-            available = (
-                mask[:seqlen] if mask is not None else np.zeros(0, dtype=np.bool_)
-            )
-            missing = np.flatnonzero(~available)[:16].tolist()
-            raise RuntimeError(
-                "Routed expert capture is incomplete for request "
-                f"{req_id}: seqlen={seqlen}, first_missing_positions={missing}"
-            )
-
-    def fill_prefix_block(
-        self: Any,
-        req_id: str,
-        start: int,
-        end: int,
-        value: np.ndarray,
-        key: tuple[Any, ...] | None = None,
-    ) -> bool:
-        buf = self.get_or_grow_buffer(req_id, end - 1)
-        mask = self._art_req_filled_masks[req_id]
-        if bool(mask[start:end].all()):
-            if key is not None:
-                needs = self._art_prefix_route_needs_by_req.get(req_id)
-                if needs is not None:
-                    needs.discard(key)
-                    if not needs:
-                        self._art_prefix_route_needs_by_req.pop(req_id, None)
-            return False
-        buf[start:end] = value
-        mask[start:end] = True
-        self.update_filled_len(req_id, end - 1)
-        if key is not None:
-            needs = self._art_prefix_route_needs_by_req.get(req_id)
-            if needs is not None:
-                needs.discard(key)
-                if not needs:
-                    self._art_prefix_route_needs_by_req.pop(req_id, None)
-        return True
-
-    def store_prefix_block(
-        self: Any,
-        key: tuple[Any, ...],
-        value: np.ndarray,
-    ) -> None:
-        existing = self._art_prefix_route_blocks.get(key)
-        if existing is None:
-            existing = value.copy()
-            self._art_prefix_route_blocks[key] = existing
-        elif not np.array_equal(existing, value):
-            self._art_prefix_route_cache_conflicts += 1
-        hydrated = 0
-        for req_id, start, end in self._art_prefix_route_waiters.pop(key, []):
-            if self._art_fill_prefix_block(req_id, start, end, existing, key):
-                hydrated += end - start
-        if hydrated:
-            self._art_prefix_route_hydrated_tokens += hydrated
-            logger.info(
-                "Hydrated %s routed-expert prefix-cache tokens from materialized "
-                "route block",
-                hydrated,
-            )
-
-    def store_prefix_blocks(
-        self: Any,
-        req_id: str,
-        token_ids: list[int],
-        lora_key: tuple[Any, ...],
-        block_size: int,
-        max_pos_exclusive: int,
-    ) -> None:
-        if block_size <= 0:
-            return
-        upper = min(max_pos_exclusive, len(token_ids))
-        upper -= upper % block_size
-        if upper <= 0:
-            return
-        buf = self.get_buffer(req_id)
-        mask = self._art_req_filled_masks.get(req_id)
-        if buf is None or mask is None:
-            return
-        for end in range(block_size, upper + 1, block_size):
-            start = end - block_size
-            if end > mask.shape[0] or not bool(mask[start:end].all()):
-                continue
-            key = _route_block_key(token_ids, end, lora_key)
-            value = buf[start:end].copy()
-            self._art_store_prefix_block(key, value)
-
-    def need_cached_prefix(
-        self: Any,
-        req_id: str,
-        token_ids: list[int],
-        lora_key: tuple[Any, ...],
-        cached_len: int,
-        block_size: int,
-    ) -> None:
-        if block_size <= 0 or cached_len <= 0:
-            return
-        upper = min(cached_len, len(token_ids))
-        upper -= upper % block_size
-        if upper <= 0:
-            return
-        hydrated = 0
-        for end in range(block_size, upper + 1, block_size):
-            start = end - block_size
-            mask = self._art_req_filled_masks.get(req_id)
-            if (
-                mask is not None
-                and end <= mask.shape[0]
-                and bool(mask[start:end].all())
-            ):
-                continue
-            key = _route_block_key(token_ids, end, lora_key)
-            value = self._art_prefix_route_blocks.get(key)
-            if value is None:
-                needs = self._art_prefix_route_needs_by_req.setdefault(req_id, set())
-                if key not in needs:
-                    self._art_prefix_route_waiters.setdefault(key, []).append(
-                        (req_id, start, end)
-                    )
-                    needs.add(key)
-                    self._art_prefix_route_cache_misses += block_size
-                continue
-            if self._art_fill_prefix_block(req_id, start, end, value, key):
-                hydrated += block_size
-        if hydrated:
-            self._art_prefix_route_hydrated_tokens += hydrated
-            logger.info(
-                "Hydrated %s routed-expert prefix-cache tokens for request %s",
-                hydrated,
-                req_id,
-            )
-
-    def require_no_unmet_prefix_route_needs(self: Any, req_id: str) -> None:
-        needs = self._art_prefix_route_needs_by_req.get(req_id)
-        if needs:
-            raise RuntimeError(
-                "Routed expert capture is missing materialized prefix-cache "
-                f"route blocks for request {req_id}: unmet_blocks={len(needs)}"
-            )
-
-    def scatter_to_host(self: Any) -> None:
-        positions = self._pending_positions.copy()
-        scheduled = dict(self._pending_num_scheduled or {})
-        metadata = getattr(self, "_art_pending_route_metadata", None)
-        original_scatter_to_host(self)
-        host_cache = self.host_cache
-        if host_cache is None:
-            return
-        block_size = int((metadata or {}).get("block_size", 0))
-        snapshots = (metadata or {}).get("snapshots", {})
-        offset = 0
-        for req_id, n_tokens in scheduled.items():
-            pos = positions[offset : offset + n_tokens]
-            host_cache._art_mark_filled(req_id, pos)
-            snapshot = snapshots.get(req_id)
-            if snapshot is not None and pos.size:
-                host_cache._art_store_prefix_blocks(
-                    req_id,
-                    snapshot["token_ids"],
-                    snapshot["lora_key"],
-                    block_size,
-                    int(pos.max()) + 1,
+                    content.body,
+                    status_code=status_code,
+                    headers=headers,
+                    media_type=media_type or self.media_type,
+                    background=background,
                 )
-            offset += n_tokens
-        self._art_pending_route_metadata = None
-
-    def get_routed_experts(
-        self: Any,
-        req_id: str,
-        seqlen: int | None = None,
-        free_slot: bool = True,
-    ) -> np.ndarray | None:
-        if self.host_cache is not None:
-            filled = self.host_cache.get_filled_len(req_id)
-            effective_len = min(filled, seqlen) if seqlen is not None else filled
-            if effective_len > 0:
-                self.host_cache._art_require_no_unmet_prefix_route_needs(req_id)
-                self.host_cache._art_require_filled(req_id, effective_len)
-        return original_get_routed_experts(self, req_id, seqlen, free_slot)
-
-    def issue_routing_d2h_copy(
-        input_batch_req_ids: list[str],
-        num_scheduled_tokens: dict[str, int],
-        positions: Any,
-        positions_cpu: Any,
-    ) -> None:
-        capturer = routed_experts_capturer.get_global_experts_capturer()
-        host_cache = capturer.get_host_cache() if capturer is not None else None
-        frame = inspect.currentframe()
-        runner = frame.f_back.f_locals.get("self") if frame and frame.f_back else None
-        ordered = {
-            req_id: num_scheduled_tokens[req_id]
-            for req_id in input_batch_req_ids
-            if req_id in num_scheduled_tokens
-        }
-        metadata: dict[str, Any] | None = None
-        if host_cache is not None and runner is not None:
-            block_size = _runner_block_size(runner)
-            snapshots = _request_snapshots(runner, ordered)
-            for req_id, snapshot in snapshots.items():
-                host_cache._art_need_cached_prefix(
-                    req_id,
-                    snapshot["token_ids"],
-                    snapshot["lora_key"],
-                    snapshot["num_computed_tokens"],
-                    block_size,
+            else:
+                super().__init__(
+                    content,
+                    status_code=status_code,
+                    headers=headers,
+                    media_type=media_type,
+                    background=background,
                 )
-            metadata = {"block_size": block_size, "snapshots": snapshots}
-        original_issue_routing_d2h_copy(
-            input_batch_req_ids,
-            num_scheduled_tokens,
-            positions,
-            positions_cpu,
-        )
-        if capturer is not None and metadata is not None and sum(ordered.values()) > 0:
-            capturer._art_pending_route_metadata = metadata
-
-    host_cls.__init__ = host_init  # type: ignore[method-assign]
-    host_cls.get_or_grow_buffer = get_or_grow_buffer  # type: ignore[method-assign]
-    host_cls.free_request = free_request  # type: ignore[method-assign]
-    host_cls._art_mark_filled = mark_filled  # type: ignore[attr-defined]
-    host_cls._art_require_filled = require_filled  # type: ignore[attr-defined]
-    host_cls._art_fill_prefix_block = fill_prefix_block  # type: ignore[attr-defined]
-    host_cls._art_store_prefix_block = store_prefix_block  # type: ignore[attr-defined]
-    host_cls._art_store_prefix_blocks = store_prefix_blocks  # type: ignore[attr-defined]
-    host_cls._art_need_cached_prefix = need_cached_prefix  # type: ignore[attr-defined]
-    host_cls._art_require_no_unmet_prefix_route_needs = (  # type: ignore[attr-defined]
-        require_no_unmet_prefix_route_needs
-    )
-    capturer_cls._scatter_to_host = scatter_to_host  # type: ignore[method-assign]
-    capturer_cls.get_routed_experts = get_routed_experts  # type: ignore[method-assign]
-    from vllm.v1.worker import gpu_model_runner
-
-    gpu_model_runner_issue_routing_d2h_copy = getattr(
-        gpu_model_runner, "issue_routing_d2h_copy", None
-    )
-    if gpu_model_runner_issue_routing_d2h_copy is not original_issue_routing_d2h_copy:
-        raise RuntimeError(
-            "ART routed-expert prefix-cache patch expected "
-            "vllm.v1.worker.gpu_model_runner.issue_routing_d2h_copy to reference "
-            "vllm.model_executor.layers.fused_moe.routed_experts_capturer."
-            "issue_routing_d2h_copy. vLLM internals changed; update the patch."
-        )
 
-    routed_experts_capturer.issue_routing_d2h_copy = issue_routing_d2h_copy
-    gpu_model_runner.issue_routing_d2h_copy = issue_routing_d2h_copy
-    setattr(routed_experts_capturer, "_art_prefix_route_sidecar_patched", True)
+    setattr(build_response, "__art_offloaded__", True)
+    setattr(build_response, "__art_original__", original)
+    ChatCompletionResponse.model_dump = model_dump  # ty:ignore[invalid-assignment]
+    OpenAIServingChat.chat_completion_full_generator = build_response
+    api_router.JSONResponse = PreencodedJSONResponse  # ty:ignore[invalid-assignment]
+    setattr(OpenAIServingChat, marker, True)
diff --git a/vllm_runtime/src/art_vllm_runtime/policy_spans.py b/vllm_runtime/src/art_vllm_runtime/policy_spans.py
index 5e186d721..656094a50 100644
--- a/vllm_runtime/src/art_vllm_runtime/policy_spans.py
+++ b/vllm_runtime/src/art_vllm_runtime/policy_spans.py
@@ -9,7 +9,10 @@
 import asyncio
 from collections.abc import Mapping
 from contextlib import asynccontextmanager
+from contextvars import ContextVar
 from dataclasses import dataclass
+from functools import wraps
+import hashlib
 import importlib
 import re
 import sys
@@ -18,6 +21,7 @@
 import msgspec
 import numpy as np
 import torch
+from vllm.lora.request import LoRARequest
 
 POLICY_TOKEN_SPANS_FIELD = "policy_token_spans"
 ART_POLICY_TOKEN_SPANS_FIELD = "art_policy_token_spans"
@@ -25,10 +29,39 @@
 
 _CURRENT_ENGINE_POLICY_SPANS: dict[str, list[dict[str, Any]]] = {}
 _WORKER_LORA_POLICY_BY_ID: dict[int, dict[str, Any]] = {}
-_WORKER_LORA_UPDATE_SEQ = 0
 _POLICY_CACHE_SALT_PREFIX = "art_policy_cache_salt="
 _POLICY_CACHE_SALT_MARKER = f"|{_POLICY_CACHE_SALT_PREFIX}"
+_POLICY_CACHE_SALT_VERSION = "v1:"
 _LORA_UPDATE_COORDINATOR_FIELD = "_art_lora_update_coordinator"
+_EXECUTING_POLICY_CONTEXT_FIELD = "_art_executing_policy_context"
+_POLICY_EXECUTION_MARKER_FIELD = "_art_policy_execution_marker"
+_POLICY_HISTORY_BASE_FIELD = "_art_policy_history_before_current"
+_POLICY_CACHE_TRANSITIONS_FIELD = "_art_policy_cache_transitions"
+_POLICY_CACHE_TRANSITION_KEY = "art_policy_transition_v1"
+
+
+class _RequestAdmissionLease:
+    __slots__ = (
+        "closed",
+        "lora_request",
+        "lora_slot",
+        "owner",
+        "request_id",
+        "ticket",
+    )
+
+    def __init__(self) -> None:
+        self.closed = False
+        self.lora_request: Any | None = None
+        self.lora_slot: str | None = None
+        self.owner = asyncio.current_task()
+        self.request_id: str | None = None
+        self.ticket: _SlotAdmissionTicket | None = None
+
+
+_REQUEST_ADMISSION_LEASE: ContextVar[_RequestAdmissionLease | None] = ContextVar(
+    "art_request_admission_lease", default=None
+)
 
 _MODEL_RUNNER_OUTPUT_MODULES = (
     "vllm.v1.outputs",
@@ -45,17 +78,68 @@
 )
 
 
+class PolicyLoRARequest(LoRARequest, omit_defaults=True, array_like=True):  # type: ignore[call-arg]
+    """LoRA request carrying ART's exact executing-policy identity."""
+
+    policy_version: int = 0
+    update_seq: int = 0
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        if self.policy_version < 0 or self.update_seq < 0:
+            raise ValueError("policy_version and update_seq must be non-negative")
+
+
 def patch_policy_token_spans() -> None:
+    _patch_policy_cache_hashing()
     _patch_model_runner_output_type()
     _patch_engine_core_output_type()
     _patch_worker_policy_span_capture()
     _patch_scheduler_policy_span_transport()
     _patch_output_processor_policy_span_accumulation()
     _patch_openai_response_policy_spans()
-    _patch_lora_update_coordinator()
+    _patch_lora_alias_resolution()
     _patch_engine_request_admission()
     _patch_load_inplace_storage()
-    _patch_engine_waiting_cache_salt_utility()
+    _patch_policy_lora_update_rpc()
+
+
+def _patch_policy_cache_hashing() -> None:
+    from vllm.v1.core import block_pool, kv_cache_utils
+
+    original = kv_cache_utils.generate_block_hash_extra_keys
+    if getattr(original, "__art_policy_spans_patched__", False):
+        return
+
+    def generate_block_hash_extra_keys(
+        request: Any,
+        start_token_idx: int,
+        end_token_idx: int,
+        start_mm_idx: int,
+    ) -> tuple[tuple[Any, ...] | None, int]:
+        extra_keys, next_mm_idx = original(
+            request, start_token_idx, end_token_idx, start_mm_idx
+        )
+        transitions = tuple(
+            transition
+            for transition in getattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ())
+            if start_token_idx <= transition[0] < end_token_idx
+        )
+        if transitions:
+            extra_keys = (
+                (*extra_keys, (_POLICY_CACHE_TRANSITION_KEY, transitions))
+                if extra_keys
+                else ((_POLICY_CACHE_TRANSITION_KEY, transitions),)
+            )
+        return extra_keys, next_mm_idx
+
+    setattr(generate_block_hash_extra_keys, "__art_policy_spans_patched__", True)
+    setattr(
+        kv_cache_utils, "generate_block_hash_extra_keys", generate_block_hash_extra_keys
+    )
+    setattr(
+        block_pool, "generate_block_hash_extra_keys", generate_block_hash_extra_keys
+    )
 
 
 class _SlotAdmissionState:
@@ -64,8 +148,10 @@ class _SlotAdmissionState:
         "active_admissions",
         "blocked",
         "lora_request",
+        "next_update_seq",
+        "pending_update_seq",
+        "poisoned",
         "update_active",
-        "policy_version",
     )
 
     def __init__(self) -> None:
@@ -73,8 +159,42 @@ def __init__(self) -> None:
         self.active_admissions = 0
         self.blocked = False
         self.lora_request: Any | None = None
+        self.next_update_seq = 1
+        self.pending_update_seq: int | None = None
+        self.poisoned = False
         self.update_active = False
-        self.policy_version: int | None = None
+
+
+class _SlotAdmissionTicket:
+    __slots__ = ("lora_request", "released", "state")
+
+    def __init__(self, state: _SlotAdmissionState) -> None:
+        self.lora_request = state.lora_request
+        self.released = False
+        self.state = state
+
+    async def release(self) -> None:
+        async with self.state.condition:
+            if self.released:
+                return
+            self.state.active_admissions -= 1
+            self.released = True
+            self.state.condition.notify_all()
+
+
+async def _complete_task(task: asyncio.Task[Any]) -> asyncio.CancelledError | None:
+    interrupted: asyncio.CancelledError | None = None
+    while not task.done():
+        try:
+            await asyncio.shield(task)
+        except asyncio.CancelledError as error:
+            if task.cancelled():
+                break
+            interrupted = interrupted or error
+        except BaseException:
+            break
+    task.result()
+    return interrupted
 
 
 class LoraUpdateCoordinator:
@@ -86,63 +206,106 @@ def __init__(self) -> None:
     def _state(self, lora_slot: str) -> _SlotAdmissionState:
         return self._states.setdefault(lora_slot, _SlotAdmissionState())
 
-    @asynccontextmanager
-    async def admission(
-        self, lora_slot: str
-    ) -> AsyncIterator[tuple[int | None, Any | None]]:
+    async def acquire(self, lora_slot: str) -> _SlotAdmissionTicket:
         state = self._state(lora_slot)
         async with state.condition:
             await state.condition.wait_for(lambda: not state.blocked)
             state.active_admissions += 1
+            return _SlotAdmissionTicket(state)
+
+    @asynccontextmanager
+    async def admission(self, lora_slot: str) -> AsyncIterator[Any | None]:
+        ticket = await self.acquire(lora_slot)
         try:
-            yield state.policy_version, state.lora_request
+            yield ticket.lora_request
         finally:
-            async with state.condition:
-                state.active_admissions -= 1
-                state.condition.notify_all()
+            interrupted = await _complete_task(asyncio.create_task(ticket.release()))
+            if interrupted is not None:
+                raise interrupted
 
-    async def begin_update(self, lora_slot: str) -> None:
+    async def declare_initial(
+        self, lora_slot: str, lora_request: PolicyLoRARequest
+    ) -> None:
+        state = self._state(lora_slot)
+        async with state.condition:
+            if (
+                state.active_admissions
+                or state.update_active
+                or state.lora_request is not None
+            ):
+                raise RuntimeError(f"LoRA slot {lora_slot!r} is already active")
+            if lora_request.update_seq <= 0:
+                raise ValueError(
+                    "initial mutable LoRA policy requires a positive sequence"
+                )
+            if lora_request.lora_name != lora_slot:
+                raise ValueError("initial LoRA policy does not match its slot")
+            state.lora_request = lora_request
+            state.next_update_seq = lora_request.update_seq + 1
+
+    async def begin_update(self, lora_slot: str) -> int:
         state = self._state(lora_slot)
         async with state.condition:
-            acquired = False
+            await state.condition.wait_for(lambda: not state.update_active)
+            state.update_active = True
+            state.blocked = True
+            update_seq = state.next_update_seq
+            state.next_update_seq += 1
+            state.pending_update_seq = update_seq
             try:
-                await state.condition.wait_for(lambda: not state.update_active)
-                state.update_active = True
-                state.blocked = True
-                acquired = True
                 await state.condition.wait_for(lambda: state.active_admissions == 0)
             except BaseException:
-                if acquired:
-                    state.update_active = False
-                    state.blocked = False
-                    state.condition.notify_all()
+                state.update_active = False
+                state.blocked = state.poisoned
+                state.pending_update_seq = None
+                state.condition.notify_all()
                 raise
+            return update_seq
 
     async def commit_update(
         self,
         lora_slot: str,
-        policy_version: int,
-        lora_request: Any,
+        lora_request: PolicyLoRARequest,
     ) -> None:
         state = self._state(lora_slot)
         async with state.condition:
-            if not state.update_active:
-                raise RuntimeError(f"No active LoRA update for slot {lora_slot!r}")
-            state.policy_version = int(policy_version)
+            self._require_pending(state, lora_slot, lora_request.update_seq)
             state.lora_request = lora_request
             state.update_active = False
             state.blocked = False
+            state.poisoned = False
+            state.pending_update_seq = None
             state.condition.notify_all()
 
-    async def fail_update(self, lora_slot: str) -> None:
+    async def cancel_update(self, lora_slot: str, update_seq: int) -> None:
         state = self._state(lora_slot)
         async with state.condition:
+            self._require_pending(state, lora_slot, update_seq)
             state.update_active = False
-            # The workers may already hold new weights. Keep admission blocked
-            # until a retry completes publication and scheduler rehashing.
+            state.blocked = state.poisoned
+            state.pending_update_seq = None
+            state.condition.notify_all()
+
+    async def fail_update(self, lora_slot: str, update_seq: int) -> None:
+        state = self._state(lora_slot)
+        async with state.condition:
+            self._require_pending(state, lora_slot, update_seq)
+            state.update_active = False
+            # A worker may already hold new weights. This slot stays poisoned.
             state.blocked = True
+            state.poisoned = True
+            state.pending_update_seq = None
             state.condition.notify_all()
 
+    @staticmethod
+    def _require_pending(
+        state: _SlotAdmissionState, lora_slot: str, update_seq: int
+    ) -> None:
+        if not state.update_active or state.pending_update_seq != update_seq:
+            raise RuntimeError(
+                f"LoRA slot {lora_slot!r} has no update {update_seq} in progress"
+            )
+
 
 def lora_update_coordinator(models: Any, engine_client: Any) -> LoraUpdateCoordinator:
     coordinator = getattr(models, _LORA_UPDATE_COORDINATOR_FIELD, None)
@@ -155,6 +318,43 @@ def lora_update_coordinator(models: Any, engine_client: Any) -> LoraUpdateCoordi
     return coordinator
 
 
+async def declare_initial_lora_policy(
+    models: Any,
+    engine_client: Any,
+    *,
+    lora_slot: str,
+    policy_version: int,
+) -> None:
+    loaded = models.lora_requests.get(lora_slot)
+    if loaded is None:
+        raise RuntimeError(f"Initial LoRA slot {lora_slot!r} is not loaded")
+    request = PolicyLoRARequest(
+        lora_name=loaded.lora_name,
+        lora_int_id=loaded.lora_int_id,
+        lora_path=loaded.lora_path,
+        base_model_name=loaded.base_model_name,
+        tensorizer_config_dict=loaded.tensorizer_config_dict,
+        is_3d_lora_weight=loaded.is_3d_lora_weight,
+        policy_version=policy_version,
+        update_seq=1,
+    )
+    report = await engine_client.engine_core.call_utility_async(
+        "art_declare_loaded_lora_policy", policy_lora_request_payload(request)
+    )
+    if int(report.get("workers", 0)) <= 0:
+        raise RuntimeError("Initial LoRA policy declaration reached no workers")
+    models.lora_requests[lora_slot] = request
+    publish_lora_slot_policy(
+        models,
+        lora_slot=lora_slot,
+        policy_version=policy_version,
+        update_seq=request.update_seq,
+    )
+    await lora_update_coordinator(models, engine_client).declare_initial(
+        lora_slot, request
+    )
+
+
 def _patch_model_runner_output_type() -> None:
     import vllm.v1.outputs as outputs_mod
 
@@ -189,6 +389,50 @@ class ModelRunnerOutput(BaseModelRunnerOutput):  # type: ignore[misc, valid-type
     setattr(outputs_mod, "_art_policy_token_spans_model_runner_patched", True)
 
 
+def register_lora_alias(
+    models: Any,
+    *,
+    public_model_name: str,
+    lora_slot: str,
+) -> None:
+    aliases = getattr(models, "_art_lora_aliases", None)
+    if aliases is None:
+        aliases = {}
+        setattr(models, "_art_lora_aliases", aliases)
+    aliases[public_model_name] = lora_slot
+
+
+def publish_lora_slot_policy(
+    models: Any,
+    *,
+    lora_slot: str,
+    policy_version: int,
+    update_seq: int,
+) -> None:
+    identities = getattr(models, "_art_lora_slot_policy_identities", None)
+    if identities is None:
+        identities = {}
+        setattr(models, "_art_lora_slot_policy_identities", identities)
+    identities[lora_slot] = (int(policy_version), int(update_seq))
+
+
+def _resolve_lora_alias(models: Any, model_name: str | None) -> Any | None:
+    if not model_name:
+        return None
+    slot = getattr(models, "_art_lora_aliases", {}).get(model_name)
+    if not slot:
+        return None
+    return models.lora_requests.get(slot)
+
+
+def _slot_policy_identity(models: Any, lora_slot: str) -> tuple[int, int] | None:
+    identity = getattr(models, "_art_lora_slot_policy_identities", {}).get(lora_slot)
+    if identity is None:
+        return None
+    policy_version, update_seq = identity
+    return int(policy_version), int(update_seq)
+
+
 def _strip_policy_cache_salt(cache_salt: str | None) -> str | None:
     if not cache_salt:
         return None
@@ -200,13 +444,48 @@ def _strip_policy_cache_salt(cache_salt: str | None) -> str | None:
     return cache_salt
 
 
-def _policy_cache_salt(
+def _policy_history_from_cache_salt(cache_salt: str | None) -> str | None:
+    if not cache_salt:
+        return None
+    if cache_salt.startswith(_POLICY_CACHE_SALT_PREFIX):
+        value = cache_salt.removeprefix(_POLICY_CACHE_SALT_PREFIX)
+    else:
+        _base, marker, value = cache_salt.partition(_POLICY_CACHE_SALT_MARKER)
+        if not marker:
+            return None
+    if not value.startswith(_POLICY_CACHE_SALT_VERSION):
+        raise RuntimeError("Unsupported ART policy cache-salt format")
+    digest = value.removeprefix(_POLICY_CACHE_SALT_VERSION)
+    if len(digest) != 64 or any(char not in "0123456789abcdef" for char in digest):
+        raise RuntimeError("Malformed ART policy cache-salt digest")
+    return digest
+
+
+def _extend_policy_history(
+    previous_digest: str | None,
     *,
     lora_slot: str,
     policy_version: int,
+    update_seq: int,
+) -> str:
+    digest = hashlib.sha256()
+    digest.update(b"art-policy-history-v1\0")
+    if previous_digest is not None:
+        digest.update(bytes.fromhex(previous_digest))
+    digest.update(lora_slot.encode())
+    digest.update(b"\0")
+    digest.update(str(policy_version).encode())
+    digest.update(b"\0")
+    digest.update(str(update_seq).encode())
+    return digest.hexdigest()
+
+
+def _policy_cache_salt(
+    *,
+    history_digest: str,
     user_cache_salt: str | None,
 ) -> str:
-    policy_salt = f"{lora_slot}:{policy_version}"
+    policy_salt = f"{_POLICY_CACHE_SALT_VERSION}{history_digest}"
     if user_cache_salt:
         return f"{user_cache_salt}{_POLICY_CACHE_SALT_MARKER}{policy_salt}"
     return f"{_POLICY_CACHE_SALT_PREFIX}{policy_salt}"
@@ -217,12 +496,45 @@ def _set_policy_cache_salt(
     *,
     lora_slot: str,
     policy_version: int,
+    update_seq: int,
+    previous_digest: str | None = None,
 ) -> None:
-    user_cache_salt = _strip_policy_cache_salt(getattr(request, "cache_salt", None))
-    request.cache_salt = _policy_cache_salt(
+    current_salt = (
+        request.get("cache_salt")
+        if isinstance(request, dict)
+        else getattr(request, "cache_salt", None)
+    )
+    user_cache_salt = _strip_policy_cache_salt(current_salt)
+    cache_salt = _policy_cache_salt(
+        history_digest=_extend_policy_history(
+            previous_digest,
+            lora_slot=lora_slot,
+            policy_version=policy_version,
+            update_seq=update_seq,
+        ),
+        user_cache_salt=user_cache_salt,
+    )
+    if isinstance(request, dict):
+        request["cache_salt"] = cache_salt
+    else:
+        request.cache_salt = cache_salt
+
+
+def _apply_lora_alias_policy_cache_salt(
+    models: Any,
+    request: Any,
+    lora_request: Any,
+) -> None:
+    lora_slot = str(lora_request.lora_name)
+    identity = _slot_policy_identity(models, lora_slot)
+    if identity is None:
+        return
+    policy_version, update_seq = identity
+    _set_policy_cache_salt(
+        request,
         lora_slot=lora_slot,
         policy_version=policy_version,
-        user_cache_salt=user_cache_salt,
+        update_seq=update_seq,
     )
 
 
@@ -341,14 +653,40 @@ def add_adapter(self: Any, lora_request: Any) -> bool:
     for module_name in _GPU_MODEL_RUNNER_MODULES:
         module = importlib.import_module(module_name)
         gpu_model_runner_cls = module.GPUModelRunner
+
+        original_execute_model = gpu_model_runner_cls.execute_model
+        if not getattr(original_execute_model, "__art_policy_spans_patched__", False):
+
+            def make_execute_model(original: Any):
+                def execute_model(self: Any, *args: Any, **kwargs: Any) -> Any:
+                    output = original(self, *args, **kwargs)
+                    # The input batch is current only after execute_model, and the
+                    # next serial worker RPC may replace this adapter before sampling.
+                    context = _policy_context_from_runner(self)
+                    if getattr(self, "execute_model_state", None) is not None:
+                        setattr(self, _EXECUTING_POLICY_CONTEXT_FIELD, context)
+                    elif context and hasattr(output, "req_ids"):
+                        _attach_policy_spans_to_model_output(output, context)
+                    return output
+
+                return execute_model
+
+            execute_model = make_execute_model(original_execute_model)
+            execute_model.__art_policy_spans_patched__ = True  # type: ignore[attr-defined]
+            gpu_model_runner_cls.execute_model = execute_model  # type: ignore[method-assign]
+
         original_sample_tokens = gpu_model_runner_cls.sample_tokens
         if getattr(original_sample_tokens, "__art_policy_spans_patched__", False):
             continue
 
         def make_sample_tokens(original: Any):
             def sample_tokens(self: Any, *args: Any, **kwargs: Any) -> Any:
-                context = _policy_context_from_runner(self)
-                output = original(self, *args, **kwargs)
+                context = getattr(self, _EXECUTING_POLICY_CONTEXT_FIELD, None)
+                try:
+                    output = original(self, *args, **kwargs)
+                finally:
+                    if hasattr(self, _EXECUTING_POLICY_CONTEXT_FIELD):
+                        delattr(self, _EXECUTING_POLICY_CONTEXT_FIELD)
                 if context and output is not None:
                     if hasattr(output, "get_output"):
                         _attach_policy_span_context_to_sample_output(output, context)
@@ -395,26 +733,42 @@ def _patch_scheduler_policy_span_transport() -> None:
     from vllm.v1.core.sched.scheduler import Scheduler
 
     original_update = Scheduler.update_from_output
-    if getattr(original_update, "__art_policy_spans_patched__", False):
-        return
+    if not getattr(original_update, "__art_policy_spans_patched__", False):
 
-    def update_from_output(self: Any, scheduler_output: Any, model_runner_output: Any):
-        outputs_by_client = original_update(self, scheduler_output, model_runner_output)
-        spans_by_req = getattr(model_runner_output, ART_POLICY_TOKEN_SPANS_FIELD, None)
-        if not spans_by_req:
+        def update_from_output(
+            self: Any, scheduler_output: Any, model_runner_output: Any
+        ):
+            outputs_by_client = original_update(
+                self, scheduler_output, model_runner_output
+            )
+            spans_by_req = getattr(
+                model_runner_output, ART_POLICY_TOKEN_SPANS_FIELD, None
+            )
+            if not spans_by_req:
+                return outputs_by_client
+            for client_outputs in outputs_by_client.values():
+                for output in client_outputs.outputs:
+                    spans = spans_by_req.get(output.request_id)
+                    if not spans:
+                        continue
+                    output.art_policy_token_spans = _trim_step_spans(
+                        spans, len(output.new_token_ids)
+                    )
             return outputs_by_client
-        for client_outputs in outputs_by_client.values():
-            for output in client_outputs.outputs:
-                spans = spans_by_req.get(output.request_id)
-                if not spans:
-                    continue
-                output.art_policy_token_spans = _trim_step_spans(
-                    spans, len(output.new_token_ids)
-                )
-        return outputs_by_client
 
-    update_from_output.__art_policy_spans_patched__ = True  # type: ignore[attr-defined]
-    Scheduler.update_from_output = update_from_output  # type: ignore[method-assign]
+        update_from_output.__art_policy_spans_patched__ = True  # type: ignore[attr-defined]
+        Scheduler.update_from_output = update_from_output  # type: ignore[method-assign]
+
+    original_preempt = Scheduler._preempt_request
+    if getattr(original_preempt, "__art_policy_spans_patched__", False):
+        return
+
+    def _preempt_request(self: Any, request: Any, timestamp: float) -> None:
+        original_preempt(self, request, timestamp)
+        _rebase_preempted_request_policy_history(request)
+
+    _preempt_request.__art_policy_spans_patched__ = True  # type: ignore[attr-defined]
+    Scheduler._preempt_request = _preempt_request  # type: ignore[method-assign]
 
 
 def _patch_output_processor_policy_span_accumulation() -> None:
@@ -519,13 +873,15 @@ async def tracked_result_generator():
                 spans = spans_by_choice.get(choice.index)
                 if spans:
                     _set_pydantic_extra(choice, POLICY_TOKEN_SPANS_FIELD, spans)
+            if _resolve_lora_alias(self.models, getattr(request, "model", None)):
+                response.model = request.model
         return response
 
     chat_completion_full_generator.__art_policy_spans_patched__ = True  # type: ignore[attr-defined]
     OpenAIServingChat.chat_completion_full_generator = chat_completion_full_generator  # type: ignore[method-assign]
 
 
-def _patch_lora_update_coordinator() -> None:
+def _patch_lora_alias_resolution() -> None:
     try:
         module = importlib.import_module("vllm.entrypoints.openai.engine.serving")
         serving_base = module.OpenAIServing
@@ -545,70 +901,175 @@ def __init__(self: Any, *args: Any, **kwargs: Any) -> None:
         __init__.__art_lora_update_patched__ = True  # type: ignore[attr-defined]
         serving_base.__init__ = __init__
 
+    original_check = serving_base._check_model
+    if not getattr(original_check, "__art_policy_spans_patched__", False):
+
+        async def _check_model(self: Any, request: Any) -> Any:
+            lora_request = _resolve_lora_alias(
+                self.models, getattr(request, "model", None)
+            )
+            if lora_request is not None:
+                from art_vllm_runtime.metrics import record_policy_cache_salt_audit
+
+                _apply_lora_alias_policy_cache_salt(self.models, request, lora_request)
+                record_policy_cache_salt_audit(
+                    lora_request=True,
+                    salted=bool(getattr(request, "cache_salt", None)),
+                )
+                return None
+            return await original_check(self, request)
+
+        _check_model.__art_policy_spans_patched__ = True  # type: ignore[attr-defined]
+        serving_base._check_model = _check_model
+
+    original_maybe = serving_base._maybe_get_adapters
+    if getattr(original_maybe, "__art_policy_spans_patched__", False):
+        return
+
+    def _maybe_get_adapters(
+        self: Any,
+        request: Any,
+        supports_default_mm_loras: bool = False,
+    ) -> Any:
+        lora_request = _resolve_lora_alias(self.models, getattr(request, "model", None))
+        if lora_request is not None:
+            _apply_lora_alias_policy_cache_salt(self.models, request, lora_request)
+            return lora_request
+        return original_maybe(
+            self,
+            request,
+            supports_default_mm_loras=supports_default_mm_loras,
+        )
+
+    _maybe_get_adapters.__art_policy_spans_patched__ = True  # type: ignore[attr-defined]
+    serving_base._maybe_get_adapters = _maybe_get_adapters
+
 
 def _patch_engine_request_admission() -> None:
     from vllm.v1.engine.async_llm import AsyncLLM
 
-    original = AsyncLLM.add_request
-    if getattr(original, "__art_lora_update_patched__", False):
+    # Long-prompt input processing is policy-independent; only serialize the
+    # complete final fanout and engine enqueue with in-place weight updates.
+    original_add_request = AsyncLLM.add_request
+    original_enqueue = AsyncLLM._add_request
+    if getattr(original_add_request, "__art_lora_update_patched__", False):
         return
 
-    async def add_request(
+    @wraps(original_add_request)
+    async def add_request(self: Any, *args: Any, **kwargs: Any) -> Any:
+        lease = _RequestAdmissionLease()
+        token = _REQUEST_ADMISSION_LEASE.set(lease)
+        try:
+            result = await original_add_request(self, *args, **kwargs)
+            if lease.ticket is not None:
+                await lease.ticket.release()
+            return result
+        except BaseException as error:
+            await _cleanup_failed_admission(self, lease.request_id, lease.ticket, error)
+            raise
+        finally:
+            lease.closed = True
+            _REQUEST_ADMISSION_LEASE.reset(token)
+
+    async def _add_request(
         self: Any,
-        request_id: str,
-        prompt: Any,
-        params: Any,
-        arrival_time: float | None = None,
-        lora_request: Any | None = None,
-        **kwargs: Any,
+        request: Any,
+        prompt: str | None,
+        parent_req: Any,
+        index: int,
+        queue: Any,
     ) -> Any:
+        lease = _REQUEST_ADMISSION_LEASE.get()
+        if lease is not None and (
+            lease.closed or lease.owner is not asyncio.current_task()
+        ):
+            lease = None
+        request_id = parent_req.request_id if parent_req else request.request_id
+        if lease is not None:
+            if lease.request_id not in (None, request_id):
+                raise RuntimeError("One admission lease received multiple requests")
+        lora_request = request.lora_request
         coordinator = getattr(self, _LORA_UPDATE_COORDINATOR_FIELD, None)
         if coordinator is None or lora_request is None:
-            return await original(
-                self,
-                request_id,
-                prompt,
-                params,
-                arrival_time=arrival_time,
-                lora_request=lora_request,
-                **kwargs,
+            if lease is not None:
+                lease.request_id = request_id
+            return await original_enqueue(
+                self, request, prompt, parent_req, index, queue
             )
         lora_slot = str(lora_request.lora_name)
-        async with coordinator.admission(lora_slot) as (
-            policy_version,
-            current_lora_request,
-        ):
-            if current_lora_request is not None:
-                lora_request = current_lora_request
-            if policy_version is not None:
-                _set_policy_cache_salt(
-                    params,
-                    lora_slot=lora_slot,
-                    policy_version=policy_version,
+        if lease is None:
+            ticket = await coordinator.acquire(lora_slot)
+            try:
+                _bind_admitted_lora(request, lora_slot, ticket.lora_request)
+                result = await original_enqueue(
+                    self, request, prompt, parent_req, index, queue
                 )
-                if hasattr(prompt, "cache_salt"):
-                    _set_policy_cache_salt(
-                        prompt,
-                        lora_slot=lora_slot,
-                        policy_version=policy_version,
-                    )
-            return await original(
-                self,
-                request_id,
-                prompt,
-                params,
-                arrival_time=arrival_time,
-                lora_request=lora_request,
-                **kwargs,
-            )
+                await ticket.release()
+                return result
+            except BaseException as error:
+                await _cleanup_failed_admission(self, request_id, ticket, error)
+                raise
+        if lease.lora_slot is None:
+            lease.ticket = await coordinator.acquire(lora_slot)
+            lease.lora_request = lease.ticket.lora_request
+            lease.lora_slot = lora_slot
+        elif lease.lora_slot != lora_slot:
+            raise RuntimeError("One request fanout resolved to multiple LoRA slots")
+        _bind_admitted_lora(request, lora_slot, lease.lora_request)
+        lease.request_id = request_id
+        return await original_enqueue(self, request, prompt, parent_req, index, queue)
 
     add_request.__art_lora_update_patched__ = True  # type: ignore[attr-defined]
+    _add_request.__art_lora_update_patched__ = True  # type: ignore[attr-defined]
     AsyncLLM.add_request = add_request  # type: ignore[method-assign]
+    AsyncLLM._add_request = _add_request  # type: ignore[method-assign]
+
+
+async def _cleanup_failed_admission(
+    engine: Any,
+    request_id: str | None,
+    ticket: _SlotAdmissionTicket | None,
+    primary: BaseException,
+) -> None:
+    async def cleanup() -> None:
+        try:
+            if request_id is not None:
+                await engine.abort(request_id, internal=True)
+        finally:
+            if ticket is not None:
+                await ticket.release()
+
+    try:
+        await _complete_task(asyncio.create_task(cleanup()))
+    except BaseException as error:
+        raise BaseExceptionGroup(
+            "request admission and cleanup both failed", [primary, error]
+        ) from None
+
+
+def _bind_admitted_lora(
+    request: Any,
+    lora_slot: str,
+    lora_request: Any | None,
+) -> None:
+    if lora_request is None:
+        if lora_slot.endswith(":active"):
+            raise RuntimeError(
+                f"Mutable LoRA slot {lora_slot!r} has no declared policy identity"
+            )
+        lora_request = request.lora_request
+    if isinstance(lora_request, PolicyLoRARequest):
+        request.lora_request = lora_request
+        _set_policy_cache_salt(
+            request,
+            lora_slot=lora_slot,
+            policy_version=lora_request.policy_version,
+            update_seq=lora_request.update_seq,
+        )
 
 
 def _patch_load_inplace_storage() -> None:
     from vllm.entrypoints.openai.models.serving import OpenAIServingModels
-    from vllm.lora.request import LoRARequest
 
     original = OpenAIServingModels.load_lora_adapter
     if getattr(original, "__art_policy_spans_patched__", False):
@@ -619,78 +1080,378 @@ async def load_lora_adapter(
         request: Any,
         base_model_name: str | None = None,
     ) -> Any:
+        if request.load_inplace and request.lora_name in self.lora_requests:
+            raise RuntimeError(
+                "Existing LoRA slots must be updated through /art/in_flight_lora_update"
+            )
         result = await original(self, request, base_model_name=base_model_name)
         lora_request = self.lora_requests.get(request.lora_name)
         if lora_request is not None and lora_request.load_inplace:
-            normalized = LoRARequest(
-                lora_name=lora_request.lora_name,
-                lora_int_id=lora_request.lora_int_id,
-                lora_path=lora_request.lora_path,
-                base_model_name=lora_request.base_model_name,
-                tensorizer_config_dict=lora_request.tensorizer_config_dict,
-                load_inplace=False,
-                is_3d_lora_weight=lora_request.is_3d_lora_weight,
+            self.lora_requests[request.lora_name] = _normalized_lora_request(
+                lora_request
             )
-            self.lora_requests[request.lora_name] = normalized
         return result
 
     load_lora_adapter.__art_policy_spans_patched__ = True  # type: ignore[attr-defined]
     OpenAIServingModels.load_lora_adapter = load_lora_adapter  # type: ignore[method-assign]
 
 
-def _patch_engine_waiting_cache_salt_utility() -> None:
+def _patch_policy_lora_update_rpc() -> None:
     from vllm.v1.engine.core import EngineCore
+    from vllm.v1.worker.worker_base import WorkerBase
+
+    if not hasattr(WorkerBase, "art_load_lora_policy"):
+
+        def art_load_lora_policy(self: Any, payload: dict[str, Any]) -> dict[str, Any]:
+            lora_request = _policy_lora_request_from_payload(payload)
+            previous = _WORKER_LORA_POLICY_BY_ID.get(lora_request.lora_int_id)
+            loaded = self.add_lora(lora_request)
+            if not self.pin_lora(lora_request.lora_int_id):
+                raise RuntimeError("Loaded policy LoRA could not be pinned")
+            current = _record_worker_lora_policy(lora_request)
+            return {
+                "loaded": bool(loaded),
+                "previous": None if previous is None else dict(previous),
+                "current": dict(current),
+            }
 
-    if hasattr(EngineCore, "art_update_waiting_lora_cache_salt"):
-        return
+        WorkerBase.art_load_lora_policy = art_load_lora_policy  # type: ignore[attr-defined]
 
-    def art_update_waiting_lora_cache_salt(
-        self: Any,
-        lora_slot: str,
-        policy_version: int,
-    ) -> dict[str, int]:
-        return _update_waiting_lora_cache_salt(
-            self.scheduler,
-            lora_slot=str(lora_slot),
-            policy_version=int(policy_version),
-        )
+    if not hasattr(WorkerBase, "art_declare_loaded_lora_policy"):
+
+        def art_declare_loaded_lora_policy(
+            self: Any, payload: dict[str, Any]
+        ) -> dict[str, Any]:
+            lora_request = _policy_lora_request_from_payload(
+                payload, load_inplace=False
+            )
+            previous = _WORKER_LORA_POLICY_BY_ID.get(lora_request.lora_int_id)
+            if lora_request.lora_int_id not in self.list_loras() or previous is None:
+                raise RuntimeError(
+                    f"LoRA {lora_request.lora_int_id} is not loaded on this worker"
+                )
+            for field in ("lora_slot", "lora_path"):
+                expected = getattr(
+                    lora_request,
+                    "lora_name" if field == "lora_slot" else "lora_path",
+                )
+                if previous[field] != expected:
+                    raise RuntimeError(
+                        f"Loaded LoRA {field} is {previous[field]!r}, expected {expected!r}"
+                    )
+            if not self.pin_lora(lora_request.lora_int_id):
+                raise RuntimeError("Initial policy LoRA could not be pinned")
+            current = _record_worker_lora_policy(lora_request)
+            return {
+                "loaded": True,
+                "previous": dict(previous),
+                "current": dict(current),
+            }
+
+        WorkerBase.art_declare_loaded_lora_policy = art_declare_loaded_lora_policy  # type: ignore[attr-defined]
+
+    if not hasattr(EngineCore, "art_apply_lora_policy_update"):
+
+        def art_apply_lora_policy_update(
+            self: Any, payload: dict[str, Any]
+        ) -> dict[str, int]:
+            return _apply_policy_lora_update(self, payload)
+
+        EngineCore.art_apply_lora_policy_update = art_apply_lora_policy_update  # type: ignore[attr-defined]
+
+    if not hasattr(EngineCore, "art_declare_loaded_lora_policy"):
+
+        def art_declare_loaded_lora_policy(
+            self: Any, payload: dict[str, Any]
+        ) -> dict[str, int]:
+            request = _policy_lora_request_from_payload(payload, load_inplace=False)
+            acknowledgements = self.collective_rpc(
+                "art_declare_loaded_lora_policy", args=(payload,)
+            )
+            _validate_worker_lora_update(request, acknowledgements)
+            return {"workers": len(acknowledgements)}
 
-    EngineCore.art_update_waiting_lora_cache_salt = art_update_waiting_lora_cache_salt  # type: ignore[attr-defined]
+        EngineCore.art_declare_loaded_lora_policy = art_declare_loaded_lora_policy  # type: ignore[attr-defined]
 
 
-def _update_waiting_lora_cache_salt(
+def _apply_policy_lora_update(
+    engine_core: Any, payload: dict[str, Any]
+) -> dict[str, int]:
+    if not engine_core.is_scheduler_paused():
+        raise RuntimeError("Policy LoRA updates require a paused scheduler")
+    lora_request = _policy_lora_request_from_payload(payload)
+    started = {
+        request.request_id
+        for request in engine_core.scheduler.requests.values()
+        if _request_uses_lora_slot(request, lora_request.lora_name)
+        and _request_has_executed(request)
+    }
+    _validate_continued_policy_update(engine_core.scheduler, started)
+    try:
+        acknowledgements = engine_core.collective_rpc(
+            "art_load_lora_policy", args=(payload,)
+        )
+        previous = _validate_worker_lora_update(lora_request, acknowledgements)
+        return _transition_scheduler_policy_history(
+            engine_core.scheduler,
+            lora_request=_policy_lora_request_from_payload(payload, load_inplace=False),
+            previous_policy=previous,
+            started_request_ids=started,
+        )
+    except BaseException:
+        # Never let a core that may have partially changed workers schedule again.
+        engine_core.pause_scheduler("abort", True)
+        raise
+
+
+def _transition_scheduler_policy_history(
     scheduler: Any,
     *,
-    lora_slot: str,
-    policy_version: int,
+    lora_request: PolicyLoRARequest,
+    previous_policy: Mapping[str, Any] | None,
+    started_request_ids: set[str],
 ) -> dict[str, int]:
+    _validate_continued_policy_update(scheduler, started_request_ids)
     updated = 0
-    skipped_started = 0
-    for queue_name in ("waiting", "skipped_waiting"):
-        queue = getattr(scheduler, queue_name, None)
-        if queue is None:
+    continued = 0
+    for request in scheduler.requests.values():
+        if not _request_uses_lora_slot(request, lora_request.lora_name):
             continue
-        for request in list(queue):
-            lora_request = getattr(request, "lora_request", None)
-            if lora_request is None or str(lora_request.lora_name) != lora_slot:
-                continue
-            if int(getattr(request, "num_computed_tokens", 0) or 0) != 0:
-                skipped_started += 1
-                continue
-            _set_policy_cache_salt(
-                request,
-                lora_slot=lora_slot,
-                policy_version=policy_version,
+        previous_digest = getattr(request, _POLICY_HISTORY_BASE_FIELD, None)
+        if request.request_id in started_request_ids:
+            continued += 1
+            previous_digest = _policy_history_from_cache_salt(request.cache_salt)
+            if previous_digest is None:
+                if previous_policy is None:
+                    raise RuntimeError(
+                        f"Started request {request.request_id!r} has no policy identity"
+                    )
+                if int(previous_policy["update_seq"]) != 0:
+                    raise RuntimeError(
+                        f"Started request {request.request_id!r} lost policy history"
+                    )
+                previous_digest = _extend_policy_history(
+                    None,
+                    lora_slot=str(previous_policy["lora_slot"]),
+                    policy_version=int(previous_policy["policy_version"]),
+                    update_seq=0,
+                )
+        request.lora_request = lora_request
+        setattr(request, _POLICY_HISTORY_BASE_FIELD, previous_digest)
+        _set_policy_cache_salt(
+            request,
+            lora_slot=lora_request.lora_name,
+            policy_version=lora_request.policy_version,
+            update_seq=lora_request.update_seq,
+            previous_digest=previous_digest,
+        )
+        computed_tokens = int(getattr(request, "num_computed_tokens", 0) or 0)
+        if computed_tokens:
+            if computed_tokens > request.num_tokens:
+                raise RuntimeError(
+                    f"Started request {request.request_id!r} has "
+                    f"{computed_tokens} computed tokens but only {request.num_tokens} tokens"
+                )
+            history_digest = _policy_history_from_cache_salt(request.cache_salt)
+            assert history_digest is not None
+            transitions: list[tuple[int, str]] = list(
+                getattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ())
+            )
+            transition = (computed_tokens, history_digest)
+            if transitions and transitions[-1][0] == computed_tokens:
+                transitions[-1] = transition
+            else:
+                if transitions and transitions[-1][0] > computed_tokens:
+                    raise RuntimeError("Policy cache transitions are not monotonic")
+                transitions.append(transition)
+            setattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, tuple(transitions))
+
+            # Requests hash their entire known prompt eagerly. Preserve only the
+            # blocks whose KV was computed before this weight transition.
+            first_changed_block = computed_tokens // _scheduler_hash_block_size(
+                scheduler
             )
+            del request.block_hashes[first_changed_block:]
+        else:
+            setattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ())
             request.block_hashes.clear()
-            request.update_block_hashes()
-            updated += 1
+        request.update_block_hashes()
+        setattr(
+            request,
+            _POLICY_EXECUTION_MARKER_FIELD,
+            (
+                computed_tokens,
+                int(getattr(request, "num_preemptions", 0) or 0),
+                len(getattr(request, "output_token_ids", ())),
+            ),
+        )
+        updated += 1
     return {
-        "updated_waiting_requests": updated,
-        "skipped_started_waiting_requests": skipped_started,
+        "updated_requests": updated,
+        "continued_requests": continued,
     }
 
 
+def _validate_continued_policy_update(
+    scheduler: Any, started_request_ids: set[str]
+) -> None:
+    if not started_request_ids:
+        return
+    if getattr(scheduler, "connector", None) is not None:
+        raise RuntimeError(
+            "Mutable policy updates cannot continue requests with a KV connector"
+        )
+    for request_id in started_request_ids:
+        request = scheduler.requests[request_id]
+        if getattr(request, "mm_features", None):
+            raise RuntimeError(
+                "Mutable policy updates cannot continue multimodal requests"
+            )
+
+
+def _rebase_preempted_request_policy_history(request: Any) -> None:
+    if not getattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ()):
+        return
+    lora_request = request.lora_request
+    policy_version = getattr(lora_request, "policy_version", None)
+    update_seq = getattr(lora_request, "update_seq", None)
+    if policy_version is None or update_seq is None:
+        raise RuntimeError(
+            f"Preempted request {request.request_id!r} lost its policy identity"
+        )
+    setattr(request, _POLICY_HISTORY_BASE_FIELD, None)
+    _set_policy_cache_salt(
+        request,
+        lora_slot=str(lora_request.lora_name),
+        policy_version=int(policy_version),
+        update_seq=int(update_seq),
+    )
+    setattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ())
+    request.block_hashes.clear()
+    request.update_block_hashes()
+    setattr(
+        request,
+        _POLICY_EXECUTION_MARKER_FIELD,
+        (
+            int(getattr(request, "num_computed_tokens", 0) or 0),
+            int(getattr(request, "num_preemptions", 0) or 0),
+            len(getattr(request, "output_token_ids", ())),
+        ),
+    )
+
+
+def _scheduler_hash_block_size(scheduler: Any) -> int:
+    block_size = int(scheduler.kv_cache_manager.block_pool.hash_block_size)
+    if block_size <= 0:
+        raise RuntimeError("vLLM reported a non-positive KV hash block size")
+    return block_size
+
+
+def _policy_lora_request_from_payload(
+    payload: Mapping[str, Any], *, load_inplace: bool = True
+) -> PolicyLoRARequest:
+    return PolicyLoRARequest(
+        lora_name=str(payload["lora_name"]),
+        lora_int_id=int(payload["lora_int_id"]),
+        lora_path=str(payload["lora_path"]),
+        base_model_name=payload.get("base_model_name"),
+        tensorizer_config_dict=payload.get("tensorizer_config_dict"),
+        load_inplace=load_inplace,
+        is_3d_lora_weight=bool(payload.get("is_3d_lora_weight", False)),
+        policy_version=int(payload["policy_version"]),
+        update_seq=int(payload["update_seq"]),
+    )
+
+
+def policy_lora_request_payload(lora_request: PolicyLoRARequest) -> dict[str, Any]:
+    return {
+        "lora_name": lora_request.lora_name,
+        "lora_int_id": lora_request.lora_int_id,
+        "lora_path": lora_request.lora_path,
+        "base_model_name": lora_request.base_model_name,
+        "tensorizer_config_dict": lora_request.tensorizer_config_dict,
+        "is_3d_lora_weight": lora_request.is_3d_lora_weight,
+        "policy_version": lora_request.policy_version,
+        "update_seq": lora_request.update_seq,
+    }
+
+
+def _normalized_lora_request(lora_request: Any) -> LoRARequest:
+    request_type = (
+        PolicyLoRARequest
+        if isinstance(lora_request, PolicyLoRARequest)
+        else LoRARequest
+    )
+    policy_fields = (
+        {
+            "policy_version": lora_request.policy_version,
+            "update_seq": lora_request.update_seq,
+        }
+        if request_type is PolicyLoRARequest
+        else {}
+    )
+    return request_type(
+        lora_name=lora_request.lora_name,
+        lora_int_id=lora_request.lora_int_id,
+        lora_path=lora_request.lora_path,
+        base_model_name=lora_request.base_model_name,
+        tensorizer_config_dict=lora_request.tensorizer_config_dict,
+        load_inplace=False,
+        is_3d_lora_weight=lora_request.is_3d_lora_weight,
+        **policy_fields,
+    )
+
+
+def _validate_worker_lora_update(
+    lora_request: PolicyLoRARequest,
+    acknowledgements: list[Mapping[str, Any]],
+) -> Mapping[str, Any] | None:
+    if not acknowledgements:
+        raise RuntimeError("Policy LoRA update returned no worker acknowledgements")
+    expected = {
+        "policy_version": lora_request.policy_version,
+        "lora_slot": lora_request.lora_name,
+        "lora_path": lora_request.lora_path,
+        "update_seq": lora_request.update_seq,
+    }
+    previous: Mapping[str, Any] | None = None
+    previous_set = False
+    for rank, acknowledgement in enumerate(acknowledgements):
+        if not acknowledgement.get("loaded"):
+            raise RuntimeError(f"Worker rank {rank} did not load the policy LoRA")
+        current = acknowledgement.get("current")
+        if current != expected:
+            raise RuntimeError(
+                f"Worker rank {rank} acknowledged {current!r}, expected {expected!r}"
+            )
+        rank_previous = acknowledgement.get("previous")
+        if previous_set and rank_previous != previous:
+            raise RuntimeError("Policy LoRA workers started from different policies")
+        previous = rank_previous
+        previous_set = True
+    return previous
+
+
+def _request_uses_lora_slot(request: Any, lora_slot: str) -> bool:
+    lora_request = getattr(request, "lora_request", None)
+    return lora_request is not None and str(lora_request.lora_name) == lora_slot
+
+
+def _request_has_executed(request: Any) -> bool:
+    computed_tokens = int(getattr(request, "num_computed_tokens", 0) or 0)
+    preemptions = int(getattr(request, "num_preemptions", 0) or 0)
+    output_tokens = len(getattr(request, "output_token_ids", ()))
+    marker = getattr(request, _POLICY_EXECUTION_MARKER_FIELD, None)
+    if marker is not None:
+        baseline_computed_tokens, baseline_preemptions, baseline_output_tokens = marker
+        return bool(
+            computed_tokens > baseline_computed_tokens
+            or preemptions > baseline_preemptions
+            or output_tokens > baseline_output_tokens
+        )
+    return bool(computed_tokens or output_tokens or preemptions)
+
+
 def _policy_context_from_runner(runner: Any) -> dict[str, dict[str, Any]]:
     input_batch = getattr(runner, "input_batch", None)
     if input_batch is None:
@@ -725,34 +1486,52 @@ def _policy_metadata_for_lora_request(lora_request: Any | None) -> dict[str, Any
     state = _WORKER_LORA_POLICY_BY_ID.get(lora_request.lora_int_id)
     if state is None:
         state = _record_worker_lora_policy(lora_request)
+    if state["lora_slot"].endswith(":active") and state["update_seq"] == 0:
+        raise RuntimeError(
+            f"Mutable LoRA slot {state['lora_slot']!r} has no declared policy identity"
+        )
     return state
 
 
 def _record_worker_lora_policy(lora_request: Any) -> dict[str, Any]:
-    global _WORKER_LORA_UPDATE_SEQ
-    _WORKER_LORA_UPDATE_SEQ += 1
-    policy_version = _policy_version_from_lora_request(lora_request)
+    policy_version = getattr(lora_request, "policy_version", None)
+    update_seq = getattr(lora_request, "update_seq", None)
+    if policy_version is None:
+        policy_version = _immutable_policy_version_from_lora_name(
+            str(lora_request.lora_name)
+        )
+        update_seq = int(policy_version or 0)
     state = {
         "policy_version": int(policy_version or 0),
         "lora_slot": str(lora_request.lora_name),
-        "update_seq": _WORKER_LORA_UPDATE_SEQ,
+        "lora_path": str(lora_request.lora_path),
+        "update_seq": int(update_seq or 0),
     }
     _WORKER_LORA_POLICY_BY_ID[int(lora_request.lora_int_id)] = state
     return state
 
 
-def _policy_version_from_lora_request(lora_request: Any) -> int | None:
-    for pattern, value in (
-        (r"@(\d+)$", getattr(lora_request, "lora_name", "")),
-        (
-            r"^(?:step[_-]?)?(\d+)$",
-            getattr(lora_request, "lora_path", "").rstrip("/").split("/")[-1],
-        ),
-    ):
-        match = re.search(pattern, value)
-        if match:
-            return int(match.group(1))
-    return None
+def get_worker_lora_states(lora_ids: set[int]) -> tuple[dict[str, Any], ...]:
+    states = []
+    for lora_id in sorted(lora_ids):
+        state = _WORKER_LORA_POLICY_BY_ID.get(lora_id)
+        if state is None:
+            raise RuntimeError(f"loaded LoRA {lora_id} has no ART worker state")
+        states.append(
+            {
+                "lora_id": lora_id,
+                "lora_name": state["lora_slot"],
+                "lora_path": state["lora_path"],
+                "policy_version": state["policy_version"],
+                "update_seq": state["update_seq"],
+            }
+        )
+    return tuple(states)
+
+
+def _immutable_policy_version_from_lora_name(lora_name: str) -> int | None:
+    match = re.search(r"@(\d+)$", lora_name)
+    return int(match.group(1)) if match else None
 
 
 def _attach_policy_spans_to_model_output(
diff --git a/vllm_runtime/src/art_vllm_runtime/qwen35_patches.py b/vllm_runtime/src/art_vllm_runtime/qwen35_patches.py
new file mode 100644
index 000000000..e719c2a41
--- /dev/null
+++ b/vllm_runtime/src/art_vllm_runtime/qwen35_patches.py
@@ -0,0 +1,148 @@
+"""Qwen3.5 compatibility patches for the ART-owned vLLM runtime."""
+
+from typing import Any, Literal
+
+
+def patch_blackwell_gdn_prefill_backend() -> None:
+    """Keep vLLM 0.25.1's FlashInfer GDN off Qwen3.5 on SM10x."""
+    from vllm.model_executor.layers.mamba.gdn import qwen_gdn_linear_attn
+
+    current = qwen_gdn_linear_attn._resolve_gdn_prefill_backend
+    if getattr(current, "__art_blackwell_cutedsl_patched__", False):
+        return
+    original = current
+
+    def resolve(
+        vllm_config: Any,
+    ) -> tuple[str, Literal["triton", "flashinfer", "cutedsl"]]:
+        requested, active = original(vllm_config)
+        model_type = str(vllm_config.model_config.hf_text_config.model_type)
+        if (
+            model_type.startswith("qwen3_5")
+            and requested == "auto"
+            and active == "flashinfer"
+            and qwen_gdn_linear_attn.current_platform.is_device_capability_family(100)
+        ):
+            return requested, "cutedsl"
+        return requested, active
+
+    setattr(resolve, "__art_blackwell_cutedsl_patched__", True)
+    setattr(resolve, "__art_original__", original)
+    setattr(qwen_gdn_linear_attn, "_resolve_gdn_prefill_backend", resolve)
+
+
+def patch_trtllm_monolithic_route_capture() -> None:
+    import torch
+    from vllm.model_executor.layers.fused_moe.activation import MoEActivation
+    from vllm.model_executor.layers.fused_moe.experts.trtllm_bf16_moe import (
+        TrtLlmBf16Experts,
+    )
+    from vllm.model_executor.layers.fused_moe.runner.moe_runner import MoERunner
+    from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
+        activation_to_flashinfer_int,
+    )
+    from vllm.v1.worker.gpu_model_runner import GPUModelRunner
+
+    original_apply = TrtLlmBf16Experts.apply
+    if not getattr(original_apply, "__art_route_capture_patched__", False):
+
+        def apply(
+            self: Any,
+            hidden_states: torch.Tensor,
+            w1: torch.Tensor,
+            w2: torch.Tensor,
+            router_logits: torch.Tensor,
+            activation: Any,
+            global_num_experts: int,
+            expert_map: torch.Tensor | None,
+            a1q_scale: torch.Tensor | None,
+            apply_router_weight_on_input: bool,
+            num_expert_group: int | None = None,
+            e_score_correction_bias: torch.Tensor | None = None,
+            routed_scaling_factor: float | None = None,
+            topk_group: int | None = None,
+        ) -> torch.Tensor:
+            capture = getattr(self, "_art_route_capture", None)
+            if capture is None:
+                return original_apply(
+                    self,
+                    hidden_states,
+                    w1,
+                    w2,
+                    router_logits,
+                    activation,
+                    global_num_experts,
+                    expert_map,
+                    a1q_scale,
+                    apply_router_weight_on_input,
+                    num_expert_group,
+                    e_score_correction_bias,
+                    routed_scaling_factor,
+                    topk_group,
+                )
+
+            del expert_map, a1q_scale, apply_router_weight_on_input
+            import flashinfer
+
+            assert activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL]
+            replay_out = capture[0]
+            output = flashinfer.fused_moe.trtllm_bf16_moe(
+                routing_logits=router_logits,
+                routing_bias=e_score_correction_bias,
+                hidden_states=hidden_states,
+                gemm1_weights=w1,
+                gemm2_weights=w2,
+                num_experts=global_num_experts,
+                top_k=self.topk,
+                n_group=num_expert_group,
+                topk_group=topk_group,
+                intermediate_size=self.intermediate_size_per_partition,
+                local_expert_offset=self.ep_rank * self.local_num_experts,
+                local_num_experts=self.local_num_experts,
+                routed_scaling_factor=routed_scaling_factor,
+                routing_method_type=self.routing_method_type,
+                activation_type=activation_to_flashinfer_int(activation),
+                routing_replay_out=replay_out,
+            )
+            capture[1](replay_out[: hidden_states.shape[0]])
+            return output
+
+        setattr(apply, "__art_route_capture_patched__", True)
+        setattr(apply, "__art_original__", original_apply)
+        TrtLlmBf16Experts.apply = apply  # type: ignore[method-assign]
+
+    original_bind = GPUModelRunner._bind_routed_experts_capturer
+    if getattr(original_bind, "__art_trtllm_route_capture_patched__", False):
+        return
+
+    def bind(self: Any, capturer: Any) -> None:
+        original_bind(self, capturer)
+        for module in self.compilation_config.static_forward_context.values():
+            if not isinstance(module, MoERunner):
+                continue
+            kernel = module.routed_experts.quant_method.moe_kernel
+            if kernel is None or not kernel.is_monolithic:
+                continue
+            experts = kernel.impl.fused_experts
+            if not isinstance(experts, TrtLlmBf16Experts):
+                continue
+            capture_fn = getattr(module.router, "capture_fn", None)
+            if capture_fn is None:
+                continue
+            experts._art_route_capture = (  # type: ignore[attr-defined]
+                torch.empty(
+                    (capturer.device_buffer.shape[0], experts.topk),
+                    dtype=torch.int16,
+                    device=capturer.device_buffer.device,
+                ),
+                capture_fn,
+            )
+
+    setattr(bind, "__art_trtllm_route_capture_patched__", True)
+    setattr(bind, "__art_original__", original_bind)
+    GPUModelRunner._bind_routed_experts_capturer = bind  # type: ignore[method-assign]
+
+
+def apply_qwen35_vllm_runtime_patches() -> None:
+    patch_blackwell_gdn_prefill_backend()
+    patch_trtllm_monolithic_route_capture()
diff --git a/vllm_runtime/tests/test_binary_routes.py b/vllm_runtime/tests/test_binary_routes.py
new file mode 100644
index 000000000..6a3e9ef04
--- /dev/null
+++ b/vllm_runtime/tests/test_binary_routes.py
@@ -0,0 +1,112 @@
+from __future__ import annotations
+
+import json
+import unittest
+
+from art_vllm_runtime import binary_routes
+import numpy as np
+
+from art.vllm_route_transport import decode_routed_experts_response
+
+
+class BinaryRoutesProtocolTest(unittest.TestCase):
+    def test_exact_expert_count_and_dtype_roundtrip(self) -> None:
+        response = json.dumps(
+            {
+                "id": "route-test",
+                "choices": [],
+                "created": 0,
+                "model": "test-model",
+                "object": "chat.completion",
+            }
+        ).encode()
+        for num_experts, dtype, values in (
+            (256, np.uint8, [[[0, 255]]]),
+            (257, np.uint16, [[[0, 256]]]),
+        ):
+            body = binary_routes.encode_routed_experts_response(
+                response,
+                {0: np.asarray(values, dtype=dtype)},
+                num_experts=num_experts,
+            )
+            decoded_response, routes = decode_routed_experts_response(body)
+
+            self.assertEqual(decoded_response.id, "route-test")
+            self.assertEqual(routes[0].num_experts, num_experts)
+            self.assertEqual(routes[0].dtype, np.dtype(dtype))
+            np.testing.assert_array_equal(routes[0], values)
+
+    def test_rejects_expert_count_beyond_uint16_protocol(self) -> None:
+        with self.assertRaisesRegex(RuntimeError, r"\[1, 65536\]"):
+            binary_routes.encode_routed_experts_response(
+                b"{}",
+                {0: np.zeros((1, 1, 1), dtype=np.uint16)},
+                num_experts=65_537,
+            )
+
+    def test_capture_registers_vllm_authoritative_route_layout(self) -> None:
+        text_config = type(
+            "TextConfig",
+            (),
+            {"num_hidden_layers": 2, "mlp_layer_types": ["dense", "sparse"]},
+        )()
+        model_config = type(
+            "ModelConfig",
+            (),
+            {
+                "get_num_experts": lambda _self: 257,
+                "hf_text_config": text_config,
+            },
+        )()
+        previous = (
+            binary_routes._REGISTERED_NUM_EXPERTS,
+            binary_routes._REGISTERED_PADDING_LAYERS,
+        )
+        try:
+            binary_routes._REGISTERED_NUM_EXPERTS = None
+            binary_routes._REGISTERED_PADDING_LAYERS = None
+            binary_routes._register_model_route_layout(model_config)
+            with binary_routes.capture_routed_experts() as routes:
+                self.assertEqual(routes.num_experts, 257)
+                self.assertEqual(routes.padding_layers, (0,))
+        finally:
+            (
+                binary_routes._REGISTERED_NUM_EXPERTS,
+                binary_routes._REGISTERED_PADDING_LAYERS,
+            ) = previous
+
+    def test_resolves_only_registered_padding_layers(self) -> None:
+        routes = binary_routes._CapturedRoutes(num_experts=8, padding_layers=(0, 1, 2))
+        values = np.zeros((2, 5, 2), dtype=np.uint8)
+        values[:, 3, :] = (2, 5)
+        values[:, 4, :] = (1, 7)
+        routes[0] = values
+
+        response = json.dumps(
+            {
+                "id": "route-test",
+                "choices": [],
+                "created": 0,
+                "model": "test-model",
+                "object": "chat.completion",
+            }
+        ).encode()
+        body = binary_routes.encode_routed_experts_response(response, routes)
+        _, decoded = decode_routed_experts_response(body)
+
+        expected = np.broadcast_to((0, 1), (2, 3, 2))
+        np.testing.assert_array_equal(decoded[0][:, :3, :], expected)
+        np.testing.assert_array_equal(decoded[0][:, 3:, :], values[:, 3:, :])
+
+    def test_rejects_missing_capture_on_routed_layer(self) -> None:
+        routes = binary_routes._CapturedRoutes(num_experts=8, padding_layers=(0, 1, 2))
+        values = np.zeros((1, 5, 2), dtype=np.uint8)
+        values[:, 3, :] = (2, 5)
+        routes[0] = values
+
+        with self.assertRaisesRegex(RuntimeError, "must be distinct"):
+            binary_routes.encode_routed_experts_response(b"{}", routes)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/vllm_runtime/tests/test_dedicated_server.py b/vllm_runtime/tests/test_dedicated_server.py
new file mode 100644
index 000000000..4c91f23d3
--- /dev/null
+++ b/vllm_runtime/tests/test_dedicated_server.py
@@ -0,0 +1,158 @@
+from http.client import HTTPConnection
+import json
+import os
+from types import SimpleNamespace
+
+from art_vllm_runtime import dedicated_server
+from art_vllm_runtime.fast_metrics import FAST_METRIC_NAMES, FastMetricsSidecar
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+import pytest
+from starlette.datastructures import URL
+
+_PAYLOAD: dict[str, object] = {
+    "schema_version": 1,
+    "source": "art_vllm_runtime",
+    "last_update_unix_s": 1.0,
+    "record_count": 1,
+    "engine_count": 1,
+    "metrics": {
+        **dict.fromkeys(FAST_METRIC_NAMES, 0.0),
+        "num_requests_running": 2.0,
+        "prompt_tokens_total": 3.0,
+    },
+    "process_uuid": "runtime-process",
+    "generation": 4,
+}
+
+
+def _get(
+    connection: HTTPConnection, *, token: str | None = None
+) -> tuple[int, int, dict[str, object]]:
+    headers = {"Authorization": f"Bearer {token}"} if token else {}
+    connection.request("GET", "/art/metrics", headers=headers)
+    response = connection.getresponse()
+    return response.status, response.version, json.loads(response.read())
+
+
+def _start_sidecar(*, tokens: list[str], port: int = 0) -> FastMetricsSidecar:
+    sidecar = FastMetricsSidecar.start(
+        "127.0.0.1",
+        tokens,
+        process_uuid="runtime-process",
+        generation=4,
+        port=port,
+    )
+    sidecar.writer.publish(
+        last_update_unix_s=1.0,
+        record_count=1,
+        engine_count=1,
+        metrics=_PAYLOAD["metrics"],  # type: ignore[arg-type]
+    )
+    return sidecar
+
+
+def test_fast_metrics_listener_auth_keepalive_and_scalar_payload() -> None:
+    sidecar = _start_sidecar(tokens=["first", "second"])
+    assert sidecar.process.pid != os.getpid()
+    connection = HTTPConnection("127.0.0.1", sidecar.port, timeout=1.0)
+    try:
+        assert _get(connection)[0] == 401
+        reused_socket = connection.sock
+        status, version, payload = _get(connection, token="second")
+        assert (status, version) == (200, 11)
+        assert connection.sock is reused_socket
+        assert _get(connection, token="second")[0] == 200
+        assert connection.sock is reused_socket
+        assert payload == _PAYLOAD
+        metrics = payload["metrics"]
+        assert isinstance(metrics, dict)
+        assert all(type(value) in {int, float} for value in metrics.values())
+    finally:
+        connection.close()
+        sidecar.close()
+    assert sidecar.process.poll() == 0
+
+
+def test_fast_metrics_listener_reads_updated_shared_snapshot() -> None:
+    sidecar = _start_sidecar(tokens=[])
+    connection = HTTPConnection("127.0.0.1", sidecar.port, timeout=1.0)
+    try:
+        metrics = dict(_PAYLOAD["metrics"])  # type: ignore[arg-type]
+        metrics["num_requests_running"] = 7.0
+        sidecar.writer.publish(
+            last_update_unix_s=2.0,
+            record_count=2,
+            engine_count=1,
+            metrics=metrics,
+        )
+        _, _, payload = _get(connection)
+        assert payload["record_count"] == 2
+        assert payload["last_update_unix_s"] == 2.0
+        assert payload["metrics"]["num_requests_running"] == 7.0  # type: ignore[index]
+    finally:
+        connection.close()
+        sidecar.close()
+
+
+def test_fast_metrics_listener_reports_unpublished_snapshot() -> None:
+    sidecar = FastMetricsSidecar.start(
+        "127.0.0.1", [], process_uuid="runtime-process", generation=4
+    )
+    connection = HTTPConnection("127.0.0.1", sidecar.port, timeout=1.0)
+    try:
+        status, _, payload = _get(connection)
+        assert status == 503
+        assert payload == {"error": "Metrics unavailable"}
+    finally:
+        connection.close()
+        sidecar.close()
+
+
+def test_fast_metrics_listener_stops_and_restarts_on_same_port() -> None:
+    sidecar = _start_sidecar(tokens=[])
+    port = sidecar.port
+    sidecar.close()
+    assert sidecar.process.poll() == 0
+
+    restarted = _start_sidecar(tokens=[], port=port)
+    connection = HTTPConnection("127.0.0.1", port, timeout=1.0)
+    try:
+        assert _get(connection)[0] == 200
+    finally:
+        connection.close()
+        restarted.close()
+    assert restarted.process.poll() == 0
+
+
+def test_fast_metrics_url_uses_controller_routable_host(monkeypatch) -> None:
+    monkeypatch.setattr(dedicated_server, "_fast_metrics_port", 43123)
+    monkeypatch.setitem(dedicated_server._runtime_state, "nnodes", 2)
+    request = SimpleNamespace(url=URL("https://10.20.30.40:8000/art/capabilities"))
+    assert (
+        dedicated_server._fast_metrics_url(request)
+        == "http://10.20.30.40:43123/art/metrics"
+    )
+
+    for host in ("0.0.0.0", "127.0.0.1", "[::]"):
+        request = SimpleNamespace(url=URL(f"http://{host}:8000/art/capabilities"))
+        with pytest.raises(RuntimeError, match="unroutable host"):
+            dedicated_server._fast_metrics_url(request)
+
+
+def test_runtime_sleep_route_returns_engine_validation_error(monkeypatch) -> None:
+    from vllm.entrypoints.openai import api_server
+
+    monkeypatch.setattr(api_server, "build_app", lambda *args, **kwargs: FastAPI())
+    monkeypatch.setattr(api_server, "_art_runtime_routes_patched", False, raising=False)
+    dedicated_server._patch_art_runtime_routes()
+    app = api_server.build_app()
+
+    class Engine:
+        async def sleep(self, *, level: int, mode: str) -> None:
+            raise ValueError(f"invalid {level=} {mode=}")
+
+    app.state.engine_client = Engine()
+    response = TestClient(app).post("/sleep?level=1&mode=wait")
+    assert response.status_code == 400
+    assert response.json() == {"error": "invalid level=1 mode='wait'"}
diff --git a/vllm_runtime/uv.lock b/vllm_runtime/uv.lock
index edae59eea..6cb2d7995 100644
--- a/vllm_runtime/uv.lock
+++ b/vllm_runtime/uv.lock
@@ -1,16 +1,23 @@
 version = 1
 revision = 3
 requires-python = "==3.12.*"
+resolution-markers = [
+    "sys_platform == 'darwin' and extra != 'extra-16-art-vllm-runtime-cuda12' and extra == 'extra-16-art-vllm-runtime-cuda13'",
+    "sys_platform != 'darwin' and extra != 'extra-16-art-vllm-runtime-cuda12' and extra == 'extra-16-art-vllm-runtime-cuda13'",
+    "sys_platform == 'darwin' and extra == 'extra-16-art-vllm-runtime-cuda12' and extra != 'extra-16-art-vllm-runtime-cuda13'",
+    "sys_platform != 'darwin' and extra == 'extra-16-art-vllm-runtime-cuda12' and extra != 'extra-16-art-vllm-runtime-cuda13'",
+    "extra != 'extra-16-art-vllm-runtime-cuda12' and extra != 'extra-16-art-vllm-runtime-cuda13'",
+]
+conflicts = [[
+    { package = "art-vllm-runtime", extra = "cuda12" },
+    { package = "art-vllm-runtime", extra = "cuda13" },
+]]
 
 [manifest]
 overrides = [
-    { name = "flashinfer-python", specifier = "==0.6.12" },
-    { name = "numpy", specifier = "<2" },
-    { name = "nvidia-nccl-cu12", marker = "sys_platform == 'linux'", specifier = "==2.28.9" },
-    { name = "torch", url = "https://download.pytorch.org/whl/test/cu128/torch-2.11.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl" },
-    { name = "torchaudio", url = "https://download.pytorch.org/whl/test/cu128/torchaudio-2.11.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl" },
-    { name = "torchvision", url = "https://download.pytorch.org/whl/test/cu128/torchvision-0.26.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl" },
+    { name = "flashinfer-python", specifier = "==0.6.13" },
     { name = "transformers", specifier = "==5.12.1" },
+    { name = "xgrammar", specifier = "==0.2.3" },
 ]
 
 [[package]]
@@ -27,16 +34,19 @@ name = "aiohttp"
 version = "3.13.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "aiohappyeyeballs" },
-    { name = "aiosignal" },
-    { name = "attrs" },
-    { name = "frozenlist" },
-    { name = "multidict" },
-    { name = "propcache" },
-    { name = "yarl" },
+    { name = "aiohappyeyeballs", marker = "sys_platform != 'darwin'" },
+    { name = "aiosignal", marker = "sys_platform != 'darwin'" },
+    { name = "attrs", marker = "sys_platform != 'darwin'" },
+    { name = "frozenlist", marker = "sys_platform != 'darwin'" },
+    { name = "multidict", marker = "sys_platform != 'darwin'" },
+    { name = "propcache", marker = "sys_platform != 'darwin'" },
+    { name = "yarl", marker = "sys_platform != 'darwin'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/77/9a/152096d4808df8e4268befa55fba462f440f14beab85e8ad9bf990516918/aiohttp-3.13.5.tar.gz", hash = "sha256:9d98cc980ecc96be6eb4c1994ce35d28d8b1f5e5208a23b421187d1209dbb7d1", size = 7858271, upload-time = "2026-03-31T22:01:03.343Z" }
 wheels = [
+    { url = "https://files.pythonhosted.org/packages/be/6f/353954c29e7dcce7cf00280a02c75f30e133c00793c7a2ed3776d7b2f426/aiohttp-3.13.5-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:023ecba036ddd840b0b19bf195bfae970083fd7024ce1ac22e9bba90464620e9", size = 748876, upload-time = "2026-03-31T21:57:36.319Z" },
+    { url = "https://files.pythonhosted.org/packages/f5/1b/428a7c64687b3b2e9cd293186695affc0e1e54a445d0361743b231f11066/aiohttp-3.13.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:15c933ad7920b7d9a20de151efcd05a6e38302cbf0e10c9b2acb9a42210a2416", size = 499557, upload-time = "2026-03-31T21:57:38.236Z" },
+    { url = "https://files.pythonhosted.org/packages/29/47/7be41556bfbb6917069d6a6634bb7dd5e163ba445b783a90d40f5ac7e3a7/aiohttp-3.13.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ab2899f9fa2f9f741896ebb6fa07c4c883bfa5c7f2ddd8cf2aafa86fa981b2d2", size = 500258, upload-time = "2026-03-31T21:57:39.923Z" },
     { url = "https://files.pythonhosted.org/packages/67/84/c9ecc5828cb0b3695856c07c0a6817a99d51e2473400f705275a2b3d9239/aiohttp-3.13.5-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a60eaa2d440cd4707696b52e40ed3e2b0f73f65be07fd0ef23b6b539c9c0b0b4", size = 1749199, upload-time = "2026-03-31T21:57:41.938Z" },
     { url = "https://files.pythonhosted.org/packages/f0/d3/3c6d610e66b495657622edb6ae7c7fd31b2e9086b4ec50b47897ad6042a9/aiohttp-3.13.5-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:55b3bdd3292283295774ab585160c4004f4f2f203946997f49aac032c84649e9", size = 1721013, upload-time = "2026-03-31T21:57:43.904Z" },
     { url = "https://files.pythonhosted.org/packages/49/a0/24409c12217456df0bae7babe3b014e460b0b38a8e60753d6cb339f6556d/aiohttp-3.13.5-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c2b2355dc094e5f7d45a7bb262fe7207aa0460b37a0d87027dcf21b5d890e7d5", size = 1781501, upload-time = "2026-03-31T21:57:46.285Z" },
@@ -49,6 +59,8 @@ wheels = [
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     { url = "https://files.pythonhosted.org/packages/84/63/7749337c90f92bc2cb18f9560d67aa6258c7060d1397d21529b8004fcf6f/aiohttp-3.13.5-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:888e78eb5ca55a615d285c3c09a7a91b42e9dd6fc699b166ebd5dee87c9ccf14", size = 1732427, upload-time = "2026-03-31T21:58:06.337Z" },
+    { url = "https://files.pythonhosted.org/packages/98/de/cf2f44ff98d307e72fb97d5f5bbae3bfcb442f0ea9790c0bf5c5c2331404/aiohttp-3.13.5-cp312-cp312-win32.whl", hash = "sha256:8bd3ec6376e68a41f9f95f5ed170e2fcf22d4eb27a1f8cb361d0508f6e0557f3", size = 433534, upload-time = "2026-03-31T21:58:08.712Z" },
+    { url = "https://files.pythonhosted.org/packages/aa/ca/eadf6f9c8fa5e31d40993e3db153fb5ed0b11008ad5d9de98a95045bed84/aiohttp-3.13.5-cp312-cp312-win_amd64.whl", hash = "sha256:110e448e02c729bcebb18c60b9214a87ba33bac4a9fa5e9a5f139938b56c6cb1", size = 460446, upload-time = "2026-03-31T21:58:10.945Z" },
 ]
 
 [[package]]
@@ -56,8 +68,8 @@ name = "aiosignal"
 version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "frozenlist" },
-    { name = "typing-extensions" },
+    { name = "frozenlist", marker = "sys_platform != 'darwin'" },
+    { name = "typing-extensions", marker = "sys_platform != 'darwin'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007, upload-time = "2025-07-03T22:54:43.528Z" }
 wheels = [
@@ -87,14 +99,14 @@ name = "anthropic"
 version = "0.92.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "anyio" },
-    { name = "distro" },
-    { name = "docstring-parser" },
-    { name = "httpx" },
-    { name = "jiter" },
-    { name = "pydantic" },
-    { name = "sniffio" },
-    { name = "typing-extensions" },
+    { name = "anyio", marker = "sys_platform != 'darwin'" },
+    { name = "distro", marker = "sys_platform != 'darwin'" },
+    { name = "docstring-parser", marker = "sys_platform != 'darwin'" },
+    { name = "httpx", marker = "sys_platform != 'darwin'" },
+    { name = "jiter", marker = "sys_platform != 'darwin'" },
+    { name = "pydantic", marker = "sys_platform != 'darwin'" },
+    { name = "sniffio", marker = "sys_platform != 'darwin'" },
+    { name = "typing-extensions", marker = "sys_platform != 'darwin'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/01/2d/fc5c5a369db977efbaa646d77ba42b38a6de4e95789884032b0e2e3fc834/anthropic-0.92.0.tar.gz", hash = "sha256:d1e792ed0692379452a1af6b266df495e973c3695cd0aace2a108b838393cbc4", size = 652420, upload-time = "2026-04-08T16:55:35.37Z" }
 wheels = [
@@ -119,14 +131,16 @@ name = "apache-tvm-ffi"
 version = "0.1.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "sys_platform != 'darwin'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/6f/60/1e787a0b5ebf318483235be2a689ee367173983067e441b8379564f667c0/apache_tvm_ffi-0.1.9.tar.gz", hash = "sha256:d2d402587e8906de0a07f4746aa78f3d452c7efe3625d4bb39ac2ad693bce530", size = 2513731, upload-time = "2026-02-27T19:28:06.602Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/b5/23/1b7dc5f0807f83098183a57db6ee85b2c93b646d74a6e03781c9208aaeb0/apache_tvm_ffi-0.1.9-cp312-abi3-win_amd64.whl", hash = "sha256:d1dcf4c041d5ec05e3da1d545800c33cdbb95c113baa7705085ff79fa262752b", size = 1973200, upload-time = "2026-02-27T19:27:32.367Z" },
 ]
 
 [[package]]
@@ -134,19 +148,46 @@ name = "art-vllm-runtime"
 version = "0.1.0"
 source = { editable = "." }
 dependencies = [
-    { name = "nvidia-nccl-cu12", marker = "sys_platform == 'linux'" },
+    { name = "openai" },
     { name = "pydantic" },
     { name = "transformers" },
-    { name = "vllm", marker = "sys_platform == 'linux'" },
+]
+
+[package.optional-dependencies]
+cuda12 = [
+    { name = "nvidia-nccl-cu12", marker = "sys_platform == 'linux'" },
+    { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
+    { name = "torchaudio", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.26.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform == 'linux'" },
+    { name = "vllm", version = "0.25.1+cu129", source = { url = "https://github.com/vllm-project/vllm/releases/download/v0.25.1/vllm-0.25.1%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl" }, marker = "sys_platform == 'linux'" },
+]
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+
 [[package]]
 name = "cuda-tile"
 version = "1.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions" },
+    { name = "typing-extensions", marker = "sys_platform != 'darwin'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/f3/49/4592bc94ca05a07c7947ea114fd12734c8497f2daffee9faa79a03e39fb5/cuda_tile-1.3.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:375316b64c51ee7cfadb2f170a30c1547bc41eb39f1e233a6556713857d2e81f", size = 245744, upload-time = "2026-04-20T15:52:09.621Z" },
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+    { url = "https://files.pythonhosted.org/packages/db/6f/d2fd16c2b0d878021dc703eea5f8fe09599d6b04bdc2531a36fc617751fd/cuda_tile-1.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:93e20ed31e46e5bf704fb31d13e1c08338d2177838798876f7ee9ec4384b75ba", size = 240923, upload-time = "2026-04-20T15:52:14.939Z" },
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 tileiras = [
-    { name = "nvidia-cuda-nvcc" },
-    { name = "nvidia-cuda-tileiras" },
-    { name = "nvidia-nvvm" },
+    { name = "nvidia-cuda-nvcc", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-cuda-tileiras", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-nvvm", marker = "sys_platform != 'darwin'" },
 ]
 
 [[package]]
 name = "cuda-toolkit"
 version = "12.8.1"
 source = { registry = "https://pypi.org/simple" }
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 [package.optional-dependencies]
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 ]
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 ]
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-    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cufft-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-16-art-vllm-runtime-cuda12') or (sys_platform == 'win32' and extra == 'extra-16-art-vllm-runtime-cuda12') or (extra == 'extra-16-art-vllm-runtime-cuda12' and extra == 'extra-16-art-vllm-runtime-cuda13')" },
 ]
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+    { name = "nvidia-cufile-cu12", marker = "(sys_platform == 'linux' and extra == 'extra-16-art-vllm-runtime-cuda12') or (extra == 'extra-16-art-vllm-runtime-cuda12' and extra == 'extra-16-art-vllm-runtime-cuda13')" },
 ]
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 ]
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 ]
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 ]
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 ]
 nvjitlink = [
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 ]
 nvrtc = [
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 ]
 nvtx = [
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+[package.optional-dependencies]
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+]
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+]
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@@ -428,8 +595,8 @@ name = "depyf"
 version = "0.20.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "astor" },
-    { name = "dill" },
+    { name = "astor", marker = "sys_platform != 'darwin'" },
+    { name = "dill", marker = "sys_platform != 'darwin'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/88/35/83fb0178212279aa0af031031905804c6de5618435d229f41ed21bb9ad2c/depyf-0.20.0.tar.gz", hash = "sha256:fb7683bd72c44f67b56029df2c47721e9a02ffa4d7b19095f1c54c4ebf797a98", size = 6168761, upload-time = "2025-10-13T12:33:38.589Z" }
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@@ -495,8 +662,8 @@ name = "email-validator"
 version = "2.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "dnspython" },
-    { name = "idna" },
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+    { name = "idna", marker = "sys_platform != 'darwin'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/f5/22/900cb125c76b7aaa450ce02fd727f452243f2e91a61af068b40adba60ea9/email_validator-2.3.0.tar.gz", hash = "sha256:9fc05c37f2f6cf439ff414f8fc46d917929974a82244c20eb10231ba60c54426", size = 51238, upload-time = "2025-08-26T13:09:06.831Z" }
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@@ -508,11 +675,11 @@ name = "fastapi"
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-    { name = "pydantic" },
-    { name = "starlette" },
-    { name = "typing-extensions" },
-    { name = "typing-inspection" },
+    { name = "annotated-doc", marker = "sys_platform != 'darwin'" },
+    { name = "pydantic", marker = "sys_platform != 'darwin'" },
+    { name = "starlette", marker = "sys_platform != 'darwin'" },
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-    { name = "pydantic-extra-types" },
-    { name = "pydantic-settings" },
-    { name = "python-multipart" },
-    { name = "uvicorn", extra = ["standard"] },
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+    { name = "jinja2", marker = "sys_platform != 'darwin'" },
+    { name = "pydantic-extra-types", marker = "sys_platform != 'darwin'" },
+    { name = "pydantic-settings", marker = "sys_platform != 'darwin'" },
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 [[package]]
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 dependencies = [
-    { name = "rich-toolkit" },
-    { name = "typer" },
-    { name = "uvicorn", extra = ["standard"] },
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+    { name = "typer", marker = "sys_platform != 'darwin'" },
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@@ -547,8 +714,8 @@ wheels = [
 
 [package.optional-dependencies]
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-    { name = "uvicorn", extra = ["standard"] },
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 [[package]]
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-    { name = "rignore" },
-    { name = "sentry-sdk" },
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+    { name = "rich-toolkit", marker = "sys_platform != 'darwin'" },
+    { name = "rignore", marker = "sys_platform != 'darwin'" },
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     { name = "python-json-logger" },
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     { name = "sentencepiece" },
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 ]
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+
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+    { name = "lm-format-enforcer", marker = "sys_platform != 'darwin'" },
+    { name = "mcp", marker = "sys_platform != 'darwin'" },
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+    { name = "model-hosting-container-standards", marker = "sys_platform != 'darwin'" },
+    { name = "msgspec", marker = "sys_platform != 'darwin'" },
+    { name = "ninja", marker = "sys_platform != 'darwin'" },
+    { name = "numba", marker = "sys_platform != 'darwin'" },
+    { name = "numpy", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-cudnn-frontend", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-cutlass-dsl", marker = "sys_platform != 'darwin'" },
+    { name = "nvtx", marker = "sys_platform != 'darwin'" },
+    { name = "openai", marker = "sys_platform != 'darwin'" },
+    { name = "openai-harmony", marker = "sys_platform != 'darwin'" },
+    { name = "opencv-python-headless", marker = "sys_platform != 'darwin'" },
+    { name = "opentelemetry-api", marker = "sys_platform != 'darwin'" },
+    { name = "opentelemetry-exporter-otlp", marker = "sys_platform != 'darwin'" },
+    { name = "opentelemetry-sdk", marker = "sys_platform != 'darwin'" },
+    { name = "opentelemetry-semantic-conventions-ai", marker = "sys_platform != 'darwin'" },
+    { name = "outlines-core", marker = "sys_platform != 'darwin'" },
+    { name = "partial-json-parser", marker = "sys_platform != 'darwin'" },
+    { name = "pillow", marker = "sys_platform != 'darwin'" },
+    { name = "prometheus-client", marker = "sys_platform != 'darwin'" },
+    { name = "prometheus-fastapi-instrumentator", marker = "sys_platform != 'darwin'" },
+    { name = "protobuf", marker = "sys_platform != 'darwin'" },
+    { name = "psutil", marker = "sys_platform != 'darwin'" },
+    { name = "py-cpuinfo", marker = "sys_platform != 'darwin'" },
+    { name = "pybase64", marker = "sys_platform != 'darwin'" },
+    { name = "pydantic", marker = "sys_platform != 'darwin'" },
+    { name = "pynvvideocodec", marker = "sys_platform != 'darwin'" },
+    { name = "python-json-logger", marker = "sys_platform != 'darwin'" },
+    { name = "pyyaml", marker = "sys_platform != 'darwin'" },
+    { name = "pyzmq", marker = "sys_platform != 'darwin'" },
+    { name = "quack-kernels", marker = "sys_platform != 'darwin'" },
+    { name = "regex", marker = "sys_platform != 'darwin'" },
+    { name = "requests", marker = "sys_platform != 'darwin'" },
+    { name = "safetensors", marker = "sys_platform != 'darwin'" },
+    { name = "sentencepiece", marker = "sys_platform != 'darwin'" },
+    { name = "setproctitle", marker = "sys_platform != 'darwin'" },
+    { name = "setuptools", marker = "sys_platform != 'darwin'" },
+    { name = "six", marker = "sys_platform != 'darwin'" },
+    { name = "starlette", marker = "sys_platform != 'darwin'" },
+    { name = "tiktoken", marker = "sys_platform != 'darwin'" },
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+    { name = "tokenizers", marker = "sys_platform != 'darwin'" },
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+    { name = "torchaudio", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform != 'darwin'" },
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+    { name = "torchvision", version = "0.26.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "sys_platform != 'darwin'" },
+    { name = "tqdm", marker = "sys_platform != 'darwin'" },
+    { name = "transformers", marker = "sys_platform != 'darwin'" },
+    { name = "typing-extensions", marker = "sys_platform != 'darwin'" },
+    { name = "watchfiles", marker = "sys_platform != 'darwin'" },
+    { name = "xgrammar", marker = "sys_platform != 'darwin'" },
+]
+wheels = [
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 ]
 
 [package.metadata]
@@ -2670,24 +3525,24 @@ requires-dist = [
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     { name = "diskcache", specifier = "==5.6.3" },
     { name = "einops" },
-    { name = "fastapi", extras = ["standard"], specifier = ">=0.115.0" },
+    { name = "fastapi", extras = ["standard"], specifier = ">=0.133.0,<0.137.0" },
     { name = "fastsafetensors", specifier = ">=0.3.2" },
     { name = "fastsafetensors", marker = "extra == 'fastsafetensors'", specifier = ">=0.3.2" },
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-    { name = "flashinfer-python", specifier = "==0.6.12" },
-    { name = "gguf", specifier = ">=0.17.0" },
-    { name = "helion", marker = "extra == 'helion'", specifier = "==1.0.0" },
-    { name = "humming-kernels", extras = ["cu12"], specifier = "==0.1.4" },
+    { name = "flashinfer-cubin", specifier = "==0.6.13" },
+    { name = "flashinfer-python", specifier = "==0.6.13" },
+    { name = "helion", marker = "extra == 'helion'", specifier = "==1.1.0" },
+    { name = "humming-kernels", extras = ["cu12"], specifier = "==0.1.10" },
     { name = "ijson" },
     { name = "instanttensor", marker = "extra == 'instanttensor'", specifier = ">=0.1.5" },
+    { name = "jsonschema", specifier = ">=4.23.0" },
     { name = "lark", specifier = "==1.2.2" },
     { name = "llguidance", marker = "platform_machine == 'aarch64' or platform_machine == 'arm64' or platform_machine == 'ppc64le' or platform_machine == 'x86_64'", specifier = ">=1.7.0,<1.8.0" },
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     { name = "mistral-common", extras = ["audio"], marker = "extra == 'audio'" },
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     { name = "nvidia-cutlass-dsl", specifier = "==4.5.2" },
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     { name = "openai-harmony", specifier = ">=0.0.3" },
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-    { name = "prometheus-fastapi-instrumentator", specifier = ">=7.0.0" },
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     { name = "psutil" },
     { name = "py-cpuinfo" },
     { name = "pybase64" },
     { name = "pydantic", specifier = ">=2.12.0" },
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     { name = "python-json-logger" },
     { name = "pyyaml" },
     { name = "pyzmq", specifier = ">=25.0.0" },
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     { name = "soundfile", marker = "extra == 'audio'" },
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     { name = "torchaudio", specifier = "==2.11.0" },
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+provides-extras = ["zen", "bench", "tensorizer", "fastsafetensors", "instanttensor", "runai", "audio", "video", "flashinfer", "helion", "grpc", "otel", "extra-quant"]
 
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 [[package]]