Summary of Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-prone Language Models, by Muhan Lin et al.
Navigating Noisy Feedback: Enhancing Reinforcement Learning with Error-Prone Language Models
by Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Simon Stepputtis, Joseph Campbell, Katia Sycara
First submitted to arxiv on: 22 Oct 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 The proposed paper investigates ways to improve reinforcement learning (RL) from large language model feedback, which can reduce human effort but often yields poor performance due to hallucination and other errors. The authors identify limitations in current approaches and introduce a simple method using potential-based shaping functions. They theoretically show that inconsistent rankings lead to uninformative rewards, but their method empirically improves convergence speed and policy returns even with significant ranking errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make RL more efficient by learning from large language models instead of humans. Current methods have limitations, like poor performance due to errors. The authors suggest a new way to get feedback using “potential-based shaping functions.” They show that this approach can still work well even with some errors. |
Keywords
» Artificial intelligence » Hallucination » Large language model » Reinforcement learning
