OpenRLHF

Why we included this project

OpenRLHF is aimed at teams who want to align a model with reinforcement learning rather than just supervised fine-tuning, and who would rather not build the distributed plumbing themselves. It combines Ray for scheduling, vLLM for generation, and DeepSpeed for memory-efficient training, so the same code path handles PPO, GRPO, RLOO, DAPO, or REINFORCE++ runs against large models, including multimodal vision-language ones, on multi-GPU hardware. The agent-based design also supports multi-turn, environment-interacting training loops with custom reward functions, which is where many real RLHF workloads end up. Practical value comes from runnable example scripts that cover common scenarios instead of bare concepts, so a small team can get a working pipeline without re-deriving the whole distributed training stack. It is a training framework, not an inference service, so expect to supply your own GPUs and experiment-management workflow.