LASER: LLM Agent with State-Space Exploration for Web Navigation
Authors: name: Kaixin Ma, name: Hongming Zhang, name: Hongwei Wang, name: Xiaoman Pan, name: Dong Yu

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.