Overview
OpenRLHF streamlines the entire RLHF pipeline—supervised fine-tuning, reward-model training, and policy optimization—into a single Ray-driven, highly parallel workflow. It integrates vLLM for fast token generation and DeepSpeed/ZeRO-3 for memory-efficient training.
Key Capabilities
- Distributed actor–critic architecture with Ray
- Hybrid Engine that co-locates inference and training workloads
- Built-in PPO, GRPO, REINFORCE++ and async agent-based RL
- One-click scripts for multi-node, multi-GPU clusters
- Detailed docs and tutorials for rapid onboarding
