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 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.

翻译：本技术报告介绍了在线迭代式人类反馈强化学习（RLHF）的工作流程。近期大语言模型（LLM）研究普遍报告表明，该方法性能大幅优于离线学习方式。然而，现有开源RLHF项目仍主要局限于离线学习设置。本技术报告旨在填补这一空白，提供一套易于复现的在线迭代RLHF详细方案。具体而言，由于在线获取人类反馈对于资源有限的开源社区通常不可行，我们首先利用多样化的开源数据集构建偏好模型，并使用构建的代理偏好模型来近似人类反馈。随后，我们探讨在线迭代RLHF背后的理论洞见与算法原理，并给出详细的实践实现方案。我们训练得到的LLM在AlpacaEval-2、Arena-Hard、MT-Bench等聊天机器人基准测试，以及HumanEval、TruthfulQA等学术基准测试中均取得了令人瞩目的性能表现。我们证明了监督微调（SFT）与迭代RLHF能够仅通过完全开源数据集获得最先进的性能。此外，我们已将训练模型、精选数据集及完整的逐步代码指南公开。更多详细信息请参阅https://github.com/RLHFlow/RLHF-Reward-Modeling 与 https://github.com/RLHFlow/Online-RLHF。