  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.
