Summary of Reinforcement Learning From Llm Feedback to Counteract Goal Misgeneralization, by Houda Nait El Barj et al.
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization
by Houda Nait El Barj, Theophile Sautory
First submitted to arxiv on: 14 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper introduces a novel approach to address goal misgeneralization in reinforcement learning (RL) by leveraging Large Language Model (LLM) feedback during training. The proposed method utilizes LLMs to analyze an RL agent’s policies, identify potential failure scenarios, and learn a reward model through the LLM preferences and feedback. This LLM-informed reward model is used to further train the RL agent on its original dataset. The approach demonstrates significant improvements in goal generalization, particularly when the true and proxy goals are somewhat distinguishable and behavioral biases are pronounced. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers aimed to improve reinforcement learning by using large language models to help agents learn better. They found that by analyzing an agent’s actions and identifying situations where it might get stuck, they could provide helpful feedback to guide the agent towards its original goal. This approach worked well in a maze navigation task, especially when the true goal was similar but not identical to a proxy goal. |
Keywords
* Artificial intelligence * Generalization * Large language model * Reinforcement learning
