OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

TL;DR

OpenRLHF is a user-friendly, scalable, and high-performance open-source framework for Reinforcement Learning from Human Feedback, designed to improve accessibility and efficiency in training large language models.

Contribution

It introduces a simplified, well-structured RLHF framework built on popular tools, achieving faster training speeds and easier implementation compared to existing solutions.

Findings

Achieves 1.22x to 1.68x speedup over state-of-the-art frameworks

Requires fewer lines of code for implementation

Facilitates entry for researchers and practitioners

Abstract

Large Language Models (LLMs) fine-tuned via Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning with Verifiable Rewards (RLVR) significantly improve the alignment of human-AI values, further raising the upper bound of AI capabilities, particularly in reasoning-intensive, long-context Chain-of-Thought (CoT) tasks. However, existing frameworks commonly face challenges such as inference bottlenecks and complexity barriers, which restrict their accessibility to newcomers. To bridge this gap, we introduce \textbf{OpenRLHF}, a user-friendly, scalable, and easy-to-learn open-source RLHF framework built upon Ray, vLLM, DeepSpeed, and HuggingFace Transformers, featuring a simplified design, clear code structure, and comprehensive documentation to facilitate entry for researchers and practitioners. Experimental results show that OpenRLHF achieves superior training efficiency, with speedups ranging from 1.22x to 1.68x across different model sizes, compared to state-of-the-art frameworks. Additionally, it requires significantly fewer lines of code for implementation. OpenRLHF is publicly available at https://github.com/OpenRLHF/OpenRLHF, and has already been adopted by leading institutions to accelerate RLHF research and learning.