Summary of Secrets Of Rlhf in Large Language Models Part Ii: Reward Modeling, by Binghai Wang et al.
Secrets of RLHF in Large Language Models Part II: Reward Modeling
by Binghai Wang, Rui Zheng, Lu Chen, Yan Liu, Shihan Dou, Caishuang Huang, Wei Shen, Senjie Jin, Enyu Zhou, Chenyu Shi, Songyang Gao, Nuo Xu, Yuhao Zhou, Xiaoran Fan, Zhiheng Xi, Jun Zhao, Xiao Wang, Tao Ji, Hang Yan, Lixing Shen, Zhan Chen, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 In this paper, researchers propose improvements to Reinforcement Learning from Human Feedback (RLHF), which is critical for aligning language models with human values. They develop reward models as proxies for human preferences to optimize reinforcement learning. The authors highlight the challenges of using these reward models in practical applications, including incorrect and ambiguous preference pairs in datasets, and difficulties generalizing to new examples. To address these issues, they suggest developing more robust and transferable reward models that can accurately capture human intent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are getting better at understanding what we mean, thanks to Reinforcement Learning from Human Feedback (RLHF). This helps keep responses helpful and harmless. The problem is that the rewards used to train language models aren’t perfect. Sometimes they’re wrong or unclear, which stops them from really understanding what people want. Also, reward models are good at some things but not others. They need to be able to handle new situations too. |
Keywords
* Artificial intelligence * Reinforcement learning * Reinforcement learning from human feedback * Rlhf
