OpenRLHF Training Framework

About

OpenRLHF is a specialized skill designed to streamline the alignment of large language models (7B-70B+) through advanced reinforcement learning techniques. It leverages a distributed architecture powered by Ray and inference acceleration via vLLM to deliver training speeds up to 2× faster than standard alternatives. The framework provides production-ready implementations for PPO, GRPO, RLOO, and DPO, making it an essential tool for AI researchers and engineers looking to optimize model performance and stability with efficient GPU resource sharing and ZeRO-3 optimization.

Key Features

• Distributed training using Ray for multi-node scalability
• Optimized GPU memory management via ZeRO-3 and Hybrid Engine
• Support for multiple RLHF algorithms including PPO, GRPO, and DPO
• High-speed inference acceleration with vLLM integration
• 384 GitHub stars
• Efficient training of large models ranging from 7B to 70B+ parameters

Use Cases

• Aligning large language models with human preferences using PPO or DPO
• Scaling RLHF pipelines across multi-node GPU clusters for 70B+ models
• Implementing memory-efficient training without a critic model using GRPO