Summary of Better Than Your Teacher: Llm Agents That Learn From Privileged Ai Feedback, by Sanjiban Choudhury et al.
Better than Your Teacher: LLM Agents that learn from Privileged AI Feedback
by Sanjiban Choudhury, Paloma Sodhi
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 LEAP framework iteratively fine-tunes large language models (LLMs) using feedback from AI expert teachers. This approach enables LLM agents to continually improve their decision-making abilities, even without access to privileged information at test time. The key innovation is equipping expert teachers with a privileged state that provides precise guidance to student agents. LEAP outperforms baselines in diverse decision-making benchmarks, including text-based games, web navigation, and interactive coding. This framework allows weak LLM models to exceed the performance of strong teacher models and enables self-improvement using privileged versions of themselves. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LEAP is a new way for large language models (LLMs) to get better at making decisions. Right now, these models can make great choices, but they don’t always learn from their mistakes. LEAP fixes this by giving “teachers” special information that helps them guide the LLMs to improve. This leads to better results in games, navigating websites, and even coding. It’s like a teacher helping a student get better at math. |
