Summary of Prototypical Reward Network For Data-efficient Rlhf, by Jinghan Zhang et al.
Prototypical Reward Network for Data-Efficient RLHF
by Jinghan Zhang, Xiting Wang, Yiqiao Jin, Changyu Chen, Xinhao Zhang, Kunpeng Liu
First submitted to arxiv on: 6 Jun 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 proposed framework Proto-RM leverages prototypical networks to enhance reward models under limited human feedback, enabling stable and reliable structural learning from fewer samples. This improves the adaptability and accuracy of Large Language Models (LLMs) in interpreting human preferences. The approach is tested on various datasets, demonstrating significant improvements in performance compared to traditional methods, while requiring less data. This research offers a promising direction for optimizing the fine-tuning of language models under restricted feedback conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Proto-RM uses prototypical networks to improve reward models with limited human feedback. This makes it easier and faster to fine-tune Large Language Models (LLMs) to understand what people want. The researchers tested this approach on many datasets and found that it worked better than other methods, using much less data. |
Keywords
» Artificial intelligence » Fine tuning
