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OpenRLHF/OpenRLHF

An easy-to-use, scalable, and high-performance open-source framework for Reinforcement Learning from Human Feedback (RLHF) based on Ray and vLLM.

Core Features

High-performance and scalable RLHF with Ray + vLLM distributed architecture.

Unified agent-based design paradigm for extensible RL pipelines.

Comprehensive RLHF pipeline capabilities including SFT, Reward Model, and RL Training.

Support for advanced RL algorithms like PPO, REINFORCE++, GRPO, and RLOO.

Integrated VLM (Vision-Language Model) RLHF support and asynchronous training.

Detailed Introduction

OpenRLHF is a pioneering open-source framework designed for Reinforcement Learning from Human Feedback (RLHF), offering a production-ready solution for training large language and vision-language models. It leverages a distributed architecture combining Ray and vLLM for unparalleled scalability and performance. With its unified agent-based design, OpenRLHF simplifies the development and deployment of complex RL pipelines, supporting a wide array of state-of-the-art RL algorithms and enabling both single-turn and multi-turn agent interactions. It addresses the critical need for efficient and extensible RLHF solutions in the rapidly evolving AI landscape.