OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
<|reference_start|>OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework: 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}.<|reference_end|>
@article{hu2024openrlhf:,
    title={OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework},
    author={Jian Hu, Xibin Wu, Weixun Wang, Xianyu, Dehao Zhang, Yu Cao},
    journal={arXiv preprint arXiv:2405.11143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11143},
    primaryClass={cs.AI cs.CL cs.LG}
}