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About this paper
Minghao Chen, Yihang Li, Yanting Yang, Shiyu Yu, Binbin Lin, Xiaofei He
The framework "AutoManual" enables Large Language Model (LLM) agents to autonomously construct and continuously refine environmental instruction manuals through interactive learning. This approach dynamically optimizes diverse rule types, achieving a 97.4% success rate on ALFWorld and 94.5% on MiniWoB++ using GPT-4-turbo, surpassing previous methods with minimal human demonstrations.
Key insights
- Online, iterative interaction for rule extraction and verification is essential for building robust and generalizable environmental understanding.
- Categorizing knowledge into distinct, dynamically managed rule types enables agents to learn underlying principles, effectively overcoming the "Path Dependence problem."
- Compiling learned rules into human-readable manuals serves dual purposes: improving interpretability for humans and enhancing planning guidance for other LLM agents.
Summary by alphaXiv.
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