Large language models (LLMs) have been successfully adapted for interactive
decision-making tasks like web navigation. While achieving decent performance,
previous methods implicitly assume a forward-only execution mode for the model,
where they only provide oracle trajectories as in-context examples to teach the
model how to reason in the interactive environment. Consequently, the model
could not handle more challenging scenarios not covered in the in-context
examples, e.g., mistakes, leading to sub-optimal performance. To address this
issue, we propose to model the interactive task as state space exploration,
where the LLM agent transitions among a pre-defined set of states by performing
actions to complete the task. This formulation enables flexible back-tracking,
allowing the model to easily recover from errors. We evaluate our proposed LLM
Agent with State-Space ExploRation (LASER) on the WebShop task. Experimental
results show that our LASER agent significantly outperforms previous methods
and closes the gap with human performance on the web navigation task.

大语言模型在网页导航等交互决策任务中已成功应用，我们提出了一种基于状态空间探索的交互式任务建模方法，通过动作与状态转换来完成任务，实现了灵活的回溯以提高性能。实验结果表明，我们的 LASER 代理在网页导航任务中明显优于以往的方法，并且与人类表现之间的差距缩小。