diff --git a/README.md b/README.md
index 797b71ec0f..d0e319603a 100644
--- a/README.md
+++ b/README.md
@@ -42,7 +42,7 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
## 🚀 News
-* [2025-12] Trinity-RFT has supported [tinker](https://thinkingmachines.ai/tinker/) training backend, which enables model training on devices **without GPUs**.
+* [2025-12] [[Release Notes]](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.4.0) Trinity-RFT v0.4.0 released: added [Tinker](https://thinkingmachines.ai/tinker/) backend for users **without GPUs**, add more benchmarks, enhance online RL and more.
* [2025-12] Trinity-RFT powers the medical and health business of "Taobao Shangou", enabling the AI agent to understand vague symptoms, proactively ask follow-up questions, and provide precise recommendations ([News](https://tech.china.com.cn/sx/20251201/411376.shtml)).
* [2025-11] [[Release Notes](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.3)] Trinity-RFT v0.3.3 released: bug fixes.
* [2025-11] Introducing [Learn-to-Ask](https://github.com/modelscope/Trinity-RFT/tree/main/examples/learn_to_ask): a framework for training proactive dialogue agents from offline expert data ([paper](https://arxiv.org/pdf/2510.25441)).
@@ -70,7 +70,7 @@ Trinity-RFT provides functionalities for users with different backgrounds and ob
| Category | Tutorial / Guideline |
| --- | ----|
-| *Run diverse RFT modes* | + [Quick start: GRPO on GSM8k](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_reasoning_basic.html)
+ [Off-policy RFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_reasoning_advanced.html)
+ [Fully asynchronous RFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_async_mode.html)
+ [Offline learning by DPO or SFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_dpo.html) |
+| *Run diverse RFT modes* | + [Quick start: GRPO on GSM8k](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_reasoning_basic.html)
+ [Off-policy RFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_reasoning_advanced.html)
+ [Fully asynchronous RFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_async_mode.html)
+ [Offline learning by DPO or SFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_dpo.html)
+ [RFT without local GPU (Tinker Backend)](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_tinker_backend.html) |
| *Multi-step agentic RL* | + [Concatenated multi-turn workflow](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_multi_turn.html)
+ [General multi-step workflow](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_step_wise.html)
+ [ReAct workflow with an agent framework](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_react.html)
+ [Example: train a web-search agent](https://github.com/modelscope/Trinity-RFT/tree/main/examples/agentscope_websearch) |
| *Full-lifecycle data pipelines* | + [Rollout task mixing and selection](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/develop_selector.html)
+ [Online task curriculum](https://github.com/modelscope/Trinity-RFT/tree/main/examples/bots) (📝 [paper](https://arxiv.org/pdf/2510.26374))
+ [Research project: learn-to-ask](https://github.com/modelscope/Trinity-RFT/tree/main/examples/learn_to_ask) (📝 [paper](https://arxiv.org/pdf/2510.25441))
+ [Experience replay with prioritization](https://github.com/modelscope/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay)
+ [Advanced data processing & human-in-the-loop](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_data_functionalities.html) |
| *Algorithm development* | + [RL algorithm development with Trinity-RFT](https://modelscope.github.io/Trinity-RFT/en/main/tutorial/example_mix_algo.html) (📝 [paper](https://arxiv.org/pdf/2508.11408))
+ [Research project: group-relative REINFORCE](https://github.com/modelscope/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [paper](https://arxiv.org/abs/2509.24203))
+ Non-verifiable domains: [RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [trainable RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
diff --git a/README_zh.md b/README_zh.md
index 9a2ce35baa..9197031c22 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -41,6 +41,7 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
## 🚀 新闻
+* [2025-12] [[发布说明]](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.4.0) Trinity-RFT v0.4.0 发布：新增[Tinker](https://thinkingmachines.ai/tinker/) 后端以支持在 **无 GPU** 的设备上训练，增加更多基准测试，增强在线 RL 等功能。
* [2025-12] Trinity-RFT 已支持 [tinker](https://thinkingmachines.ai/tinker/) 训练后端，可在**无 GPU 的设备**上进行模型训练。
* [2025-12] Trinity-RFT 助力淘宝闪购医药健康业务，让 AI 智能体能够理解模糊症状、主动询问后续问题，并提供精准推荐（[新闻](https://tech.china.com.cn/sx/20251201/411376.shtml)）。
* [2025-11] [[发布说明](https://github.com/modelscope/Trinity-RFT/releases/tag/v0.3.3)] Trinity-RFT v0.3.3 发布：修复若干 Bug。
@@ -70,7 +71,7 @@ Trinity-RFT 面向不同背景和目标的用户提供相应功能：
| 类别 | 教程 / 指南 |
| --- | ----|
-| *运行各种 RFT 模式* | + [快速开始：在 GSM8k 上运行 GRPO](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_reasoning_basic.html)
+ [Off-policy RFT](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_reasoning_advanced.html)
+ [全异步 RFT](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_async_mode.html)
+ [通过 DPO 或 SFT 进行离线学习](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_dpo.html) |
+| *运行各种 RFT 模式* | + [快速开始：在 GSM8k 上运行 GRPO](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_reasoning_basic.html)
+ [Off-policy RFT](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_reasoning_advanced.html)
+ [全异步 RFT](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_async_mode.html)
+ [通过 DPO 或 SFT 进行离线学习](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_dpo.html)
+ [在无GPU环境下运行RFT训练（Tinker 后端）](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_tinker_backend.html) |
| *多轮智能体强化学习* | + [拼接多轮任务](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_multi_turn.html)
+ [通用多轮任务](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_step_wise.html)
+ [调用智能体框架中的 ReAct 工作流](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_react.html)
+ [例子：训练一个网络搜索智能体](https://github.com/modelscope/Trinity-RFT/tree/main/examples/agentscope_websearch) |
| *全生命周期的数据流水线* | + [Rollout 任务混合与选取](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/develop_selector.html)
+ [在线任务选择](https://github.com/modelscope/Trinity-RFT/tree/main/examples/bots) (📝 [论文](https://arxiv.org/pdf/2510.26374))
+ [研究项目：learn-to-ask](https://github.com/modelscope/Trinity-RFT/tree/main/examples/learn_to_ask) (📝 [论文](https://arxiv.org/pdf/2510.25441))
+ [经验回放机制](https://github.com/modelscope/Trinity-RFT/tree/main/examples/ppo_countdown_exp_replay)
+ [高级数据处理能力 & Human-in-the-loop](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_data_functionalities.html) |
| *强化学习算法开发* | + [使用 Trinity-RFT 进行 RL 算法开发](https://modelscope.github.io/Trinity-RFT/zh/main/tutorial/example_mix_algo.html) (📝 [论文](https://arxiv.org/pdf/2508.11408))
+ [研究项目: group-relative REINFORCE](https://github.com/modelscope/Trinity-RFT/tree/main/examples/rec_gsm8k) (📝 [论文](https://arxiv.org/abs/2509.24203))
+ 不可验证的领域: [RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_ruler), [可训练 RULER](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_gsm8k_trainable_ruler), [rubric-as-reward](https://github.com/modelscope/Trinity-RFT/tree/main/examples/grpo_rubric_as_reward) |
diff --git a/docs/sphinx_doc/source/index.rst b/docs/sphinx_doc/source/index.rst
index 265b7b59aa..e232fa8155 100644
--- a/docs/sphinx_doc/source/index.rst
+++ b/docs/sphinx_doc/source/index.rst
@@ -42,6 +42,7 @@ Welcome to Trinity-RFT's documentation!
tutorial/example_react.md
tutorial/example_search_email.md
tutorial/example_dpo.md
+ tutorial/example_tinker_backend.md
tutorial/example_megatron.md
tutorial/example_data_functionalities.md
tutorial/example_dataset_perspective.md
diff --git a/docs/sphinx_doc/source/tutorial/example_tinker_backend.md b/docs/sphinx_doc/source/tutorial/example_tinker_backend.md
new file mode 100644
index 0000000000..a1a3db5061
--- /dev/null
+++ b/docs/sphinx_doc/source/tutorial/example_tinker_backend.md
@@ -0,0 +1,214 @@
+# Tinker Backend
+
+```{note}
+This example demonstrates how to use Trinity-RFT with the [Tinker](https://thinkingmachines.ai/tinker/) backend, which enables model training on devices **without GPUs**.
+```
+
+## Setup Instructions
+
+### 1. API Key Configuration
+
+Before starting Ray, you must set the `TRINITY_API_KEY` environment variable to your Tinker API key to enable proper access to Tinker's API:
+
+```bash
+export TRINITY_API_KEY=your_tinker_api_key
+ray start --head
+```
+
+### 2. Configuration File
+
+Configure the Tinker backend in your YAML configuration file by setting the `model.tinker` parameters as shown below:
+
+```yaml
+model:
+ tinker:
+ enable: true
+ base_model: null
+ rank: 32
+ seed: null
+ train_mlp: true
+ train_attn: true
+ train_unembed: true
+```
+
+#### Explanation of Configuration Parameters
+
+- **`tinker`**: Tinker-specific configuration section. **Important**: When Tinker is enabled, any LoRA configuration settings (`model.lora_configs`) will be ignored.
+ - **`enable`**: Whether to activate the Tinker backend. Default: `false`
+ - **`base_model`**: Path to the base model for Tinker. If not specified (`null`), it defaults to the `model_path` defined elsewhere in your config
+ - **`rank`**: The LoRA rank that controls the size of the adaptation matrices. Default: `32`
+ - **`seed`**: Random seed for reproducible Tinker operations. If not specified (`null`), no specific seed is set
+ - **`train_mlp`**: Whether to train the MLP (feed-forward) layers. Default: `true`
+ - **`train_attn`**: Whether to train the attention layers. Default: `true`
+ - **`train_unembed`**: Whether to train the unembedding (output) layer. Default: `true`
+
+
+## Usage
+
+Once configured, Trinity-RFT works with the Tinker backend just like it does with the standard veRL backend. Start training with:
+
+```bash
+trinity run --config tinker.yaml # Replace with your actual config file path
+```
+
+### Important Limitations of the Tinker Backend
+
+1. **Entropy loss** is not consistent compared to veRL backends.
+2. **Algorithms requiring `compute_advantage_in_trainer=true` are NOT supported currently**, including:
+ - PPO (`algorithm.algorithm_type=ppo`)
+ - Reinforce++ (`algorithm.algorithm_type=reinforceplusplus`)
+ - RLOO (`algorithm.algorithm_type=rloo`)
+ - On-policy distillation (`algorithm.algorithm_type=on_policy_distill`)
+
+ Algorithms like `grpo`, `opmd`, `sft` are supported and we will support more algorithms in the future.
+
+3. **Multiple stages training** is not supported currently, we will add support for this in the future.
+
+> 💡 A complete example configuration file is available at [`tinker.yaml`](https://github.com/modelscope/Trinity-RFT/blob/main/examples/tinker/tinker.yaml).
+
+
+## Results on the Llama-3.2-3B Model
+
+We trained the **Llama-3.2-3B** model on the **GSM8K** dataset using both the **Tinker** and **veRL** backends. Below are the full configuration files used in our experiments.
+
+
+Click to expand: Tinker Backend Configuration
+
+```yaml
+mode: both
+project: Trinity-RFT-gsm8k
+group: alignment-tinker
+name: tinker-llama3.2-3B-off1
+checkpoint_root_dir: ${oc.env:TRINITY_CHECKPOINT_ROOT_DIR,./checkpoints}
+algorithm:
+ algorithm_type: grpo
+ repeat_times: 8
+ kl_loss_fn_args:
+ kl_coef: 0.0
+ optimizer:
+ lr: 1.0e-05
+model:
+ model_path: meta-llama/Llama-3.2-3B
+ max_prompt_tokens: 1024
+ max_response_tokens: 2048
+ custom_chat_template: "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"
+ tinker:
+ enable: true
+ base_model: meta-llama/Llama-3.2-3B
+cluster:
+ node_num: 1
+ gpu_per_node: 8
+buffer:
+ batch_size: 96
+ total_epochs: 1
+ explorer_input:
+ taskset:
+ name: taskset
+ storage_type: file
+ path: openai/gsm8k
+ split: train
+ subset_name: main
+ format:
+ prompt_key: question
+ response_key: answer
+ default_workflow_type: math_workflow
+ trainer_input:
+ experience_buffer:
+ name: experience_buffer
+ storage_type: queue
+explorer:
+ runner_per_model: 16
+ rollout_model:
+ engine_num: 4
+ seed: 42
+trainer:
+ save_interval: 100
+ grad_clip: 1.0
+monitor:
+ monitor_type: wandb
+synchronizer:
+ sync_method: checkpoint
+ sync_style: fixed
+ sync_interval: 1
+ sync_offset: 1
+ sync_timeout: 1200
+```
+
+Click to expand: veRL Backend Configuration (LoRA)
+
+```yaml
+mode: both
+project: Trinity-RFT-gsm8k
+group: alignment-tinker
+name: verl-llama3.2-3B-lora-off1
+checkpoint_root_dir: ${oc.env:TRINITY_CHECKPOINT_ROOT_DIR,./checkpoints}
+algorithm:
+ algorithm_type: grpo
+ repeat_times: 8
+ kl_loss_fn_args:
+ kl_coef: 0.0
+ optimizer:
+ lr: 1.0e-05
+data_processor: {}
+model:
+ model_path: meta-llama/Llama-3.2-3B
+ max_prompt_tokens: 1024
+ max_response_tokens: 2048
+ custom_chat_template: "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"
+ lora_configs:
+ - name: lora
+ lora_rank: 32
+ lora_alpha: 32
+cluster:
+ node_num: 1
+ gpu_per_node: 8
+buffer:
+ batch_size: 96
+ total_epochs: 1
+ explorer_input:
+ taskset:
+ name: taskset
+ storage_type: file
+ path: openai/gsm8k
+ split: train
+ subset_name: main
+ format:
+ prompt_key: question
+ response_key: answer
+ default_workflow_type: math_workflow
+ trainer_input:
+ experience_buffer:
+ name: experience_buffer
+ storage_type: queue
+explorer:
+ runner_per_model: 16
+ rollout_model:
+ engine_num: 4
+ tensor_parallel_size: 1
+ gpu_memory_utilization: 0.9
+ dtype: bfloat16
+ seed: 42
+trainer:
+ trainer_type: verl
+ save_interval: 100
+ grad_clip: 1.0
+monitor:
+ monitor_type: wandb
+synchronizer:
+ sync_method: checkpoint
+ sync_style: fixed
+ sync_interval: 1
+ sync_offset: 1
+ sync_timeout: 1200
+```
+
+点击展开：Tinker 后端配置
+
+```yaml
+mode: both
+project: Trinity-RFT-gsm8k
+group: alignment-tinker
+name: tinker-llama3.2-3B-off1
+checkpoint_root_dir: ${oc.env:TRINITY_CHECKPOINT_ROOT_DIR,./checkpoints}
+algorithm:
+ algorithm_type: grpo
+ repeat_times: 8
+ kl_loss_fn_args:
+ kl_coef: 0.0
+ optimizer:
+ lr: 1.0e-05
+model:
+ model_path: meta-llama/Llama-3.2-3B
+ max_prompt_tokens: 1024
+ max_response_tokens: 2048
+ custom_chat_template: "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"
+ tinker:
+ enable: true
+ base_model: meta-llama/Llama-3.2-3B
+cluster:
+ node_num: 1
+ gpu_per_node: 8
+buffer:
+ batch_size: 96
+ total_epochs: 1
+ explorer_input:
+ taskset:
+ name: taskset
+ storage_type: file
+ path: openai/gsm8k
+ split: train
+ subset_name: main
+ format:
+ prompt_key: question
+ response_key: answer
+ default_workflow_type: math_workflow
+ trainer_input:
+ experience_buffer:
+ name: experience_buffer
+ storage_type: queue
+explorer:
+ runner_per_model: 16
+ rollout_model:
+ engine_num: 4
+ seed: 42
+trainer:
+ save_interval: 100
+ grad_clip: 1.0
+monitor:
+ monitor_type: wandb
+synchronizer:
+ sync_method: checkpoint
+ sync_style: fixed
+ sync_interval: 1
+ sync_offset: 1
+ sync_timeout: 1200
+```
+
+点击展开：veRL 后端配置（LoRA）
+
+```yaml
+mode: both
+project: Trinity-RFT-gsm8k
+group: alignment-tinker
+name: verl-llama3.2-3B-lora-off1
+checkpoint_root_dir: ${oc.env:TRINITY_CHECKPOINT_ROOT_DIR,./checkpoints}
+algorithm:
+ algorithm_type: grpo
+ repeat_times: 8
+ kl_loss_fn_args:
+ kl_coef: 0.0
+ optimizer:
+ lr: 1.0e-05
+data_processor: {}
+model:
+ model_path: meta-llama/Llama-3.2-3B
+ max_prompt_tokens: 1024
+ max_response_tokens: 2048
+ custom_chat_template: "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n"
+ lora_configs:
+ - name: lora
+ lora_rank: 32
+ lora_alpha: 32
+cluster:
+ node_num: 1
+ gpu_per_node: 8
+buffer:
+ batch_size: 96
+ total_epochs: 1
+ explorer_input:
+ taskset:
+ name: taskset
+ storage_type: file
+ path: openai/gsm8k
+ split: train
+ subset_name: main
+ format:
+ prompt_key: question
+ response_key: answer
+ default_workflow_type: math_workflow
+ trainer_input:
+ experience_buffer:
+ name: experience_buffer
+ storage_type: queue
+explorer:
+ runner_per_model: 16
+ rollout_model:
+ engine_num: 4
+ tensor_parallel_size: 1
+ gpu_memory_utilization: 0.9
+ dtype: bfloat16
+ seed: 42
+trainer:
+ trainer_type: verl
+ save_interval: 100
+ grad_clip: 1.0
+monitor:
+ monitor_type: wandb
+synchronizer:
+ sync_method: checkpoint
+ sync_style: fixed
+ sync_interval: 1
+ sync_offset: 1
+ sync_timeout: 1200
+```
+
+