Summary of The Accuracy Paradox in Rlhf: When Better Reward Models Don’t Yield Better Language Models, by Yanjun Chen et al.
The Accuracy Paradox in RLHF: When Better Reward Models Don’t Yield Better Language Models
by Yanjun Chen, Dawei Zhu, Yirong Sun, Xinghao Chen, Wei Zhang, Xiaoyu Shen
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper explores the relationship between reward models and language model performance in reinforcement learning from human feedback. It challenges the common assumption that stronger reward models lead to better language models, finding instead that moderately accurate reward models can outperform highly accurate ones on tasks like relevance, factuality, and completeness using the QA-FEEDBACK dataset and Longformer-based reward models. The study’s findings open up new research avenues into key factors driving model performance and reward model choice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how computers learn to understand human language by getting feedback from people. It looks at whether making the feedback more accurate always makes the computer better. The surprising answer is no – sometimes, just okay feedback can make the computer do a better job than super accurate feedback. This means scientists need to think more about what kind of feedback works best and why. |
Keywords
» Artificial intelligence » Language model » Reinforcement learning from human feedback
