三位一体RFT

Install Trinity-RFT with CPU-only backend

pip install -e ".[tinker]"

Run a simple example:

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.

Step 1: Installation

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

GPU Requirements

• Python: version 3.10 to 3.12 (inclusive)
• CUDA: version >= 12.8
• GPUs: At least one NVIDIA GPU with compute capability 8.0 or higher (e.g., RTX 30 series, A100, H100)

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

From Source (Recommended)

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

First, clone the repository:

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

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

Using Pre-built Docker Image (Recommended for Beginners)

```bash docker pull ghcr.io/agentscope-ai/trinity-rft:latest

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**

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

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**

🔨 Tutorials and Guidelines

[!TIP] Recommended Learning Paths 🆕 New users: Installation → Quick Start (GSM8K) → Configuration Guide → GPU Resource Guide 🔬 Algorithm researchers: Developer Guide → Algorithm Development Guide → CHORD Algorithm Example 🤖 Agent developers: Developer Guide → Workflow Development → General Multi-step Workflow Example

[!NOTE] For more tutorials, please refer to the Trinity-RFT documentation.

Quick Start

[!NOTE] This project is currently under active development. Comments and suggestions are welcome!

Minimal CPU-Only Quick Start

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

```bash

Contribution Guide

This project is currently under active development--star the repo to watch releases for the latest updates!

We welcome all kinds of contributions from the community, including:

• Documentation improvements
• Example workflows, algorithms, and data pipelines
• 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.

Create and activate environment

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

Step 3: configurations

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

[!NOTE] This is an experimental feature, and we will continue to improve it.

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.

<details>

<summary> Example: config manager GUI </summary>

</details>