Go-Browse: Training Web Agents with Structured Exploration

Apurva Gandhi; Graham Neubig

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arXiv · 2506.03533

Go-Browse: Training Web Agents with Structured Exploration

Apurva Gandhi·Graham Neubig

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Abstract

One of the fundamental problems in digital agents is their lack of understanding of their environment. For instance, a web browsing agent may get lost in unfamiliar websites, uncertain what pages must be visited to achieve its goals. To address this, we propose Go-Browse, a method for automatically collecting diverse and realistic web agent data at scale through structured exploration of web environments. Go-Browse achieves efficient exploration by framing data collection as a graph search, enabling reuse of information across exploration episodes. We instantiate our method on the WebArena benchmark, collecting a dataset of 10K successful task-solving trajectories and 40K interaction steps across 100 URLs. Fine-tuning a 7B parameter language model on this dataset achieves a success rate of 21.7% on the WebArena benchmark, beating GPT-4o mini by 2.4% and exceeding current state-of-the-art results for sub-10B parameter models by 2.9%.

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Apurva Gandhi, Graham Neubig. 2026-03-02. Go-Browse: Training Web Agents with Structured Exploration. https://arxiv.org/abs/2506.03533

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