Look Before You Leap: Autonomous Exploration for LLM Agents
Summary
This paper identifies autonomous exploration as a critical capability for LLM agents and proposes the Explore-then-Act paradigm, which decouples information gathering from task execution to improve adaptability and real-world performance. It also introduces Exploration Checkpoint Coverage as a verifiable metric for evaluating exploration breadth.
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Paper page - Look Before You Leap: Autonomous Exploration for LLM Agents
Source: https://huggingface.co/papers/2605.16143
Abstract
Agents trained with standard reinforcement learning exhibit narrow behaviors due to premature exploitation, but systematic exploration training improves adaptability and real-world performance.
Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acquiring sufficientenvironment-specific information. We identifyautonomous explorationas a critical yet underexplored capability for building adaptive agents. To formalize and quantify this capability, we introduceExploration Checkpoint Coverage, a verifiable metric that measures how broadly an agent discovers key states, objects, and affordances. Our systematic evaluation reveals that agents trained with standard task-orientedreinforcement learningconsistently exhibit narrow and repetitive behaviors that impede downstream performance. To address this limitation, we develop a training strategy that interleaves task-execution rollouts andexplorationrollouts, with each type of rollout optimized by its correspondingverifiable reward. Building on this training strategy, we propose theExplore-then-Act paradigm, which decouples information-gathering fromtask execution: agents first utilize aninteraction budgetto acquire grounded environmental knowledge, then leverage it for task resolution. Our results demonstrate that learning to systematically explore is imperative for building generalizable and real-world-ready agents.
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