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Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents

By Yifan Song and others
Large Language Models (LLMs) have become integral components in various autonomous agent systems. In this study, we present an exploration-based trajectory optimization approach, referred to as ETO. This learning method is designed to enhance the performance of open LLM agents. Contrary to previous studies that exclusively train on successful expert... Show more
July 10, 2024
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Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
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