RLHF Workflow: From Reward Modeling to Online RLHF
<|reference_start|>RLHF Workflow: From Reward Modeling to Online RLHF: We present the workflow of Online Iterative Reinforcement Learning from Human Feedback (RLHF) in this technical report, which is widely reported to outperform its offline counterpart by a large margin in the recent large language model (LLM) literature. However, existing open-source RLHF projects are still largely confined to the offline learning setting. In this technical report, we aim to fill in this gap and provide a detailed recipe that is easy to reproduce for online iterative RLHF. In particular, since online human feedback is usually infeasible for open-source communities with limited resources, we start by constructing preference models using a diverse set of open-source datasets and use the constructed proxy preference model to approximate human feedback. Then, we discuss the theoretical insights and algorithmic principles behind online iterative RLHF, followed by a detailed practical implementation. Our trained LLM, LLaMA-3-8B-SFR-Iterative-DPO-R, achieves impressive performance on LLM chatbot benchmarks, including AlpacaEval-2, Arena-Hard, and MT-Bench, as well as other academic benchmarks such as HumanEval and TruthfulQA. We have shown that supervised fine-tuning (SFT) and iterative RLHF can obtain state-of-the-art performance with fully open-source datasets. Further, we have made our models, curated datasets, and comprehensive step-by-step code guidebooks publicly available. Please refer to https://github.com/RLHFlow/RLHF-Reward-Modeling and https://github.com/RLHFlow/Online-RLHF for more detailed information.<|reference_end|>
@article{dong2024rlhf,
    title={RLHF Workflow: From Reward Modeling to Online RLHF},
    author={Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou,
  Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang},
    journal={arXiv preprint arXiv:2405.07863},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07863},
    primaryClass={cs.LG cs.AI cs.CL stat.ML}
}