Summary of Online Iterative Reinforcement Learning From Human Feedback with General Preference Model, by Chenlu Ye et al.
Online Iterative Reinforcement Learning from Human Feedback with General Preference Model
by Chenlu Ye, Wei Xiong, Yuheng Zhang, Hanze Dong, Nan Jiang, Tong Zhang
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this paper, researchers explore Reinforcement Learning from Human Feedback (RLHF) in a novel context that doesn’t rely on predefined reward functions or oracles. Instead, they propose a mathematical framework that pits two Large Language Models (LLMs) against each other to determine the preferred policy. The framework is designed for general preference oracles and includes both offline learning from existing data and online learning with query access. The study demonstrates the effectiveness of this approach through empirical studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement Learning helps machines make good decisions by trying different actions and seeing what works best. In this paper, scientists are working on a way to teach machines using feedback from humans. They’re doing it in a special way that doesn’t require knowing what’s “good” or “bad” beforehand. Instead, they’re letting two big language models compete against each other to figure out the best approach. This method can learn with data we already have or while getting more information as it goes along. The results show this new approach is really effective. |
Keywords
* Artificial intelligence * Online learning * Reinforcement learning * Reinforcement learning from human feedback * Rlhf
