Summary of Provable Multi-party Reinforcement Learning with Diverse Human Feedback, by Huiying Zhong et al.
Provable Multi-Party Reinforcement Learning with Diverse Human Feedback
by Huiying Zhong, Zhun Deng, Weijie J. Su, Zhiwei Steven Wu, Linjun Zhang
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces the theoretical study of reinforcement learning with human feedback (RLHF) that explicitly models the diverse preferences of multiple individuals. Traditional RLHF approaches can fail to capture and balance these preferences, leading to the need for novel methods. The authors propose incorporating meta-learning to learn multiple preferences and adopting different social welfare functions to aggregate these preferences across parties. They focus on the offline learning setting and establish sample complexity bounds, efficiency, and fairness guarantees for optimizing various welfare functions. The results show a separation between the sample complexities of multi-party RLHF and traditional single-party RLHF. Additionally, the paper explores a reward-free setting where individual preferences are no longer consistent with a reward model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is like teaching a computer to make good choices based on feedback from humans. Usually, this process involves getting input from multiple people who might have different opinions. This paper looks at how we can learn from these diverse viewpoints and create a single “reward function” that balances all the preferences. The authors show that traditional methods don’t work well when dealing with many different perspectives. To fix this, they suggest using something called meta-learning to learn about multiple preferences and combining them in a fair way. They also explore what happens when people’s preferences don’t fit neatly into a reward system. |
Keywords
* Artificial intelligence * Meta learning * Reinforcement learning * Rlhf
