  As large language models (LLMs) continue to grow by scaling laws,
reinforcement learning from human feedback (RLHF) has gained significant
attention due to its outstanding performance. However, unlike pretraining or
fine-tuning a single model, scaling reinforcement learning from human feedback
(RLHF) for training large language models poses coordination challenges across
four models. We present OpenRLHF, an open-source framework enabling efficient
RLHF scaling. Unlike existing RLHF frameworks that co-locate four models on the
same GPUs, OpenRLHF re-designs scheduling for the models beyond 70B parameters
using Ray, vLLM, and DeepSpeed, leveraging improved resource utilization and
diverse training approaches. Integrating seamlessly with Hugging Face, OpenRLHF
provides an out-of-the-box solution with optimized algorithms and launch
scripts, which ensures user-friendliness. OpenRLHF implements RLHF, DPO,
rejection sampling, and other alignment techniques. Empowering state-of-the-art
LLM development, OpenRLHF's code is available at
\url{https://github.com/OpenRLHF/OpenRLHF}.
