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What is Trinity-RFT?

Trinity-RFT is a general-purpose, flexible framework designed for reinforcement fine-tuning (RFT) of large language models (LLMs). It aims to support diverse application scenarios and serves as a unified platform for exploring advanced reinforcement learning paradigms.

How to use Trinity-RFT?

To use Trinity-RFT, clone the repository from GitHub, set up your environment, and follow the installation instructions. You can configure the RFT process through a web interface or command line, and run the training process using provided examples.

Key features of Trinity-RFT?

• Unified RFT core supporting various training modes (synchronous/asynchronous, on-policy/off-policy).
• First-class agent-environment interaction handling lagged feedback and multi-turn interactions.
• Optimized data pipelines for active management of rollout tasks.
• User-friendly design with modular architecture and graphical interfaces for low-code usage.

Use cases of Trinity-RFT?

1. Adapting to new agent-environment scenarios.
2. Developing custom reinforcement learning algorithms.
3. Monitoring and tracking the learning process through graphical interfaces.

FAQ from Trinity-RFT?

• Is Trinity-RFT suitable for all types of reinforcement learning tasks?

Yes, Trinity-RFT is designed to be flexible and can be adapted to various RL tasks and scenarios.

• What are the system requirements for Trinity-RFT?

Trinity-RFT requires Python 3.10-3.12 and at least 2 GPUs for optimal performance.

• How can I contribute to Trinity-RFT?

Contributions are welcome! You can follow the contribution guide in the repository.