Look Before You Leap: Autonomous Exploration for LLM Agents
Ziang Ye, Wentao Shi, Yuxin Liu, Yu Wang, Zhengzhou Cai, Yaorui Shi, Qi Gu, Xunliang Cai, Fuli Feng
LLM agents trained purely on task-completion rewards develop tunnel vision — they exploit prior knowledge rather than learning what's actually in the environment. This paper introduces "Exploration Checkpoint Coverage" as a metric to measure how broadly an agent discovers states, objects, and affordances, then trains agents with interleaved exploration and task-execution rollouts, each with its own verifiable reward signal. The resulting "Explore-then-Act" paradigm has agents spend an explicit interaction budget on information-gathering before attempting task resolution. Claimed improvement is that this generalizes better to unfamiliar environments than standard RL-trained agents.
No production traction yet — zero citations and the GitHub references are all paper-tracking aggregator repos with no implementation code. The paper is very recent and the approach is conceptually relevant to tool-using and embodied agents, but there's no open-source implementation or downstream adoption visible at this point.
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. To formalize and quantify this capability, we introduce Exploration 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-oriented reinforcement learning consistently exhibit narrow and repetitive behaviors that impede downstream performance. To address this limitation, we develop a training strategy that interleaves task-execution rollouts and exploration rollouts, with each type of rollout optimized by its corresponding verifiable reward. Building on this training strategy, we propose the Explore-then-Act paradigm, which decouples information-gathering from task execution: agents first utilize an interaction budget to 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.