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Trinity-RFT is a general-purpose, flexible and user-friendly framework for LLM reinforcement fine-tuning (RFT). It decouples RFT into three components that work in coordination:

• Explorer generates experience data via agent-environment interaction;
• Trainer updates model weights by minimizing losses on the data;
• Buffer pipelines data processing throughout the RFT lifecycle.

Trinity-RFT provides functionalities for users with different backgrounds and objectives:

• 🤖 Agent application developers: Train LLM-powered agents and improve their capabilities in specific domains [tutorial]
• 🧠 Reinforcement learning researchers: Design, implement and validate new RL algorithms using compact, plug-and-play modules that allow non-invasive customization [tutorial]
• 📊 Data engineers: Create RFT datasets and build data pipelines for cleaning, augmentation, and human-in-the-loop scenarios [tutorial]

• [2025-12] [Release Notes] Trinity-RFT v0.4.0 released: added 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).
• [2025-11] [Release Notes] Trinity-RFT v0.3.3 released: bug fixes.
• [2025-11] Introducing Learn-to-Ask: a framework for training proactive dialogue agents from offline expert data (paper).
• [2025-11] Introducing BOTS: online RL task selection for efficient LLM fine-tuning (paper).
• [2025-09] Our paper reveals a novel off-policy interpretation for group-relative REINFORCE and its variants like GRPO and AsymRE (implementation).
• [2025-08] Introducing CHORD: dynamic SFT + RL integration for advanced LLM fine-tuning (paper).

• Flexible RFT Modes:

  • Supports synchronous/asynchronous, on-policy/off-policy, and online/offline RL.
  • Rollout and training can run separately and scale independently across devices.
  • Boost sample and time efficiency by experience replay.
  

• Agentic RL Support:

  • Supports both concatenated and general multi-step agentic workflows.
  • Able to directly train agent applications developed using agent frameworks like AgentScope.
  

• Full-Lifecycle Data Pipelines:

  • Enables pipeline processing of rollout tasks and experience samples.
  • Active data management (prioritization, cleaning, augmentation, etc.) throughout the RFT lifecycle.
  • Native support for multi-task joint learning and online task curriculum construction.
  

• User-Friendly Design:

  • Plug-and-play modules and decoupled architecture, facilitating easy adoption and development.
  • Rich graphical user interfaces enable low-code usage.
  

• Quick Start
  • Minimal CPU-Only Quick Start
  • Step 1: installation
  • Step 2: prepare dataset and model
  • Step 3: configurations
  • Step 4: run the RFT process
• Contribution Guide
• Acknowledgements
• Citation

If you do not have access to a GPU, you can still try Trinity-RFT using the Tinker backend.

# Create and activate environment
python3.10 -m venv .venv
source .venv/bin/activate

# Install Trinity-RFT with CPU-only backend
pip install -e ".[tinker]"

Run a simple example:

trinity run --config examples/tinker/tinker.yaml

This example is designed to run on CPU-only machines. See the complete Tinker training example for more details.

To run Trinity-RFT on GPU machines instead, please follow the steps below.

Before installing, make sure your system meets the following requirements:

• Python: version 3.10 to 3.12 (inclusive)
• CUDA: version >= 12.8
• GPUs: at least 2 GPUs

Recommended for first-time users:

• If you have no GPU → Use Tinker backend
• If you want simple setup → Use Docker
• If you want development & contribution → Use Conda / venv

If you plan to customize or contribute to Trinity-RFT, this is the best option.

First, clone the repository:

git clone https://github.com/modelscope/Trinity-RFT
cd Trinity-RFT

Then, set up environment via one of the following options:

Using Pre-built Docker Image (Recommended for Beginners)

docker pull ghcr.io/modelscope/trinity-rft:latest

# Run the container, replacing <path_to_your_data_and_checkpoints> with your actual path
docker run -it \
  --gpus all \
  --shm-size="64g" \
  --rm \
  -v $PWD:/workspace \
  -v <path_to_your_data_and_checkpoints>:/data \
  ghcr.io/modelscope/trinity-rft:latest

This image has used uv to install all GPU-related dependencies of Trinity-RFT. The virtual environment will be automatically activated upon entering the container (you can also manually activate it via source /opt/venv/bin/activate if needed). You can use uv pip install to add extra packages as necessary.

Using Conda

conda create -n trinity python=3.12
conda activate trinity

pip install -e ".[vllm,flash_attn]"

# If you have no GPU, comment out the line above and uncomment this instead:
# pip install -e ".[tinker]"

# If you encounter issues when installing flash-attn, try:
# pip install flash-attn==2.8.1 --no-build-isolation
pip install -e ".[dev]"  # for development like linting and debugging

Using venv

python3.10 -m venv .venv
source .venv/bin/activate

pip install -e ".[vllm,flash_attn]"

# If you have no GPU, comment out the line above and uncomment this instead:
# pip install -e ".[tinker]"

# If you encounter issues when installing flash-attn, try:
# pip install flash-attn==2.8.1 --no-build-isolation

pip install -e ".[dev]"  # for development like linting and debugging

Using uv

uv sync --extra vllm --extra dev --extra flash_attn

# If you have no GPU, try to use Tinker instead:
# uv sync --extra tinker --extra dev

If you just want to use the package without modifying the code:

pip install trinity-rft
pip install flash-attn==2.8.1

Or with uv:

uv pip install trinity-rft
uv pip install flash-attn==2.8.1

For training with Megatron-LM, please refer to Megatron-LM Backend.

Trinity-RFT supports most datasets and models from Huggingface and ModelScope.

Prepare the model in the local directory $MODEL_PATH/{model_name}:

# Using Huggingface
huggingface-cli download {model_name} --local-dir $MODEL_PATH/{model_name}
# Using Modelscope
modelscope download {model_name} --local_dir $MODEL_PATH/{model_name}

For more details about model downloading, see Huggingface or ModelScope.

Prepare the dataset in the local directory $DATASET_PATH/{dataset_name}:

# Using Huggingface
huggingface-cli download {dataset_name} --repo-type dataset --local-dir $DATASET_PATH/{dataset_name}
# Using Modelscope
modelscope download --dataset {dataset_name} --local_dir $DATASET_PATH/{dataset_name}

For more details about dataset downloading, see Huggingface or ModelScope.

Trinity-RFT provides a web interface for configuring your RFT process.

To launch the web interface for minimal configurations, you can run

trinity studio --port 8080

Then you can configure your RFT process in the web page and generate a config file. You can save the config file for later use or run it directly as described in the following section.

Advanced users can also edit the config file directly. We provide example config files in examples.

For complete GUI features, please refer to the monorepo for Trinity-Studio.

Start a ray cluster:

# On master node
ray start --head
# On worker nodes
ray start --address=<master_address>

(Optional) You may use Wandb / TensorBoard / MLFlow for better monitoring. Please refer to this documentation for the corresponding configurations. For example, to log in to Wandb:

export WANDB_API_KEY=<your_api_key>
wandb login

For command-line users, run the RFT process:

trinity run --config <config_path>

Example — fine-tuning Qwen2.5-1.5B-Instruct on GSM8k with GRPO:

trinity run --config examples/grpo_gsm8k/gsm8k.yaml

For studio users, click "Run" in the web interface.

This project is currently under active development, and we welcome contributions from the community!

We welcome contributions of all kinds, including:

• Documentation improvements
• Example workflows
• Bug fixes and performance optimizations

If you're new to the project, documentation and example updates are a great place to start.

See CONTRIBUTING.md for detailed contribution guidelines, as well as our good-first-issue list.

This project is built upon many excellent open-source projects, including:

• verl, FSDP and Megatron-LM for LLM training;
• vLLM for LLM inference;
• Data-Juicer for data processing pipelines;
• AgentScope for agentic workflow;
• Ray for distributed systems;
• we have also drawn inspirations from RL frameworks like OpenRLHF, TRL, ChatLearn and rLLM;
• ......

@misc{trinity-rft,
      title={Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models},
      author={Xuchen Pan and Yanxi Chen and Yushuo Chen and Yuchang Sun and Daoyuan Chen and Wenhao Zhang and Yuexiang Xie and Yilun Huang and Yilei Zhang and Dawei Gao and Yaliang Li and Bolin Ding and Jingren Zhou},
      year={2025},
      eprint={2505.17826},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.17826},
}