Summary of Rlhf Workflow: From Reward Modeling to Online Rlhf, by Hanze Dong et al.
RLHF Workflow: From Reward Modeling to Online RLHF
by Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We present a technical report on Online Iterative Reinforcement Learning from Human Feedback (RLHF), which outperforms its offline counterpart by a large margin in recent large language model (LLM) literature. This report aims to fill the gap between existing open-source RLHF projects and online iterative learning settings. We construct preference models using diverse open-source datasets, approximating human feedback. Theoretical insights and algorithmic principles are discussed, followed by a practical implementation. Our trained LLM achieves state-of-the-art performance on LLM chatbot benchmarks (AlpacaEval-2, Arena-Hard, MT-Bench) and academic benchmarks (HumanEval, TruthfulQA). We demonstrate supervised fine-tuning (SFT) and iterative RLHF can achieve impressive results using fully open-source datasets. Our models, curated datasets, and code guidebooks are publicly available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This report is about a new way to make language models learn from humans even when they’re not physically present. It’s called Online Iterative Reinforcement Learning from Human Feedback (RLHF). Right now, most RLHF projects can only learn offline, but this report shows how to do online learning too. The authors use special preference models and datasets to help the model learn from human feedback. They also discuss some important ideas behind RLHF and show how it works in practice. The results are impressive – the model is really good at answering questions and generating text! You can check out more information on GitHub. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Online learning » Reinforcement learning from human feedback » Rlhf » Supervised
