Look Before You Leap: Autonomous Exploration for LLM Agents
The paper discusses the challenges faced by large language model agents in unfamiliar environments due to premature exploitation. It introduces a new metric called Exploration Checkpoint Coverage to measure an agent's ability to explore and gather information. The authors propose a training strategy that separates exploration from task execution to enhance agent adaptability and performance.
- ▪Large language model agents often fail in unfamiliar environments due to a tendency to act on prior knowledge too soon.
- ▪The paper introduces Exploration Checkpoint Coverage as a metric to evaluate how well agents discover key states and objects.
- ▪A new training strategy is proposed that interleaves task-execution and exploration rollouts to improve agent performance.
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Computer Science > Artificial Intelligence arXiv:2605.16143 (cs) [Submitted on 15 May 2026] Title:Look Before You Leap: Autonomous Exploration for LLM Agents Authors:Ziang Ye, Wentao Shi, Yuxin Liu, Yu Wang, Zhengzhou Cai, Yaorui Shi, Qi Gu, Xunliang Cai, Fuli Feng View a PDF of the paper titled Look Before You Leap: Autonomous Exploration for LLM Agents, by Ziang Ye and 8 other authors View PDF HTML (experimental) Abstract:Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acquiring sufficient environment-specific information. We identify autonomous exploration as a critical yet underexplored capability for building adaptive agents.
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