Go-Browse: Training Web Agents with Structured Exploration
On the map: LLM agents and tool use → Web, search, and research agents
Abstract (arXiv)
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%.
Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 26 of its 33 references that are in the map. First posted on arXiv 4 Jun 2025.
Builds on
26 of 33 references.
Closest ancestors · 14
Same direction, newest first
- 2025SkillWeaver: Web Agents can Self-Improve by Discovering and Honing Skills9 Apr
- 2025Inducing Programmatic Skills for Agentic Tasks9 Apr
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- 2025InSTA: Towards Internet-Scale Training For Agents10 Feb
- 2024Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation Model Internet Agents17 Dec
- 2024The BrowserGym Ecosystem for Web Agent Research6 Dec
- 2024ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data22 Nov
- 2024Beyond Browsing: API-Based Web Agents21 Oct
- 2024NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wild3 Oct
- 2024Agent Workflow Memory11 Sep
- 2024AutoWebGLM: A Large Language Model-based Web Navigating Agent4 Apr
2 more
Other references · 12
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
- 2024Qwen2 Technical Reportcited by 2,120
- 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
- 2018Exploration by Random Network Distillationcited by 993
- 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 639
- 2020First return, then explorecited by 266
- 2019Go-Explore: a New Approach for Hard-Exploration Problemscited by 212
- 2024OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesiscited by 163
- 2024Autonomous Evaluation and Refinement of Digital Agentscited by 137
- 2024Synatra: Turning Indirect Knowledge into Direct Demonstrations for Digital Agents at Scalecited by 43
- 2024BAGEL: Bootstrapping Agents by Guiding Exploration with Languagecited by 34
- 2025AutoEval: A Practical Framework for Autonomous Evaluation of Mobile Agentscited by 9
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Citing papers per year: 2025: 8; 2026: 25.
33 papers.
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