[2605.06761] Weblica: Scalable and Reproducible Training Environments for Visual Web Agents
[Submitted on 7 May 2026]
Title:Weblica: Scalable and Reproducible Training Environments for Visual Web Agents
View a PDF of the paper titled Weblica: Scalable and Reproducible Training Environments for Visual Web Agents, by O\u{g}uzhan Fatih Kar and 4 other authors
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Abstract:The web is complex, open-ended, and constantly changing, making it challenging to scale training data for visual web agents. Existing data collection attempts remain limited to offline trajectories for supervised fine-tuning or a handful of simulated environments for RL training, thus failing to capture web diversity. We propose Weblica (Web Replica), a framework for constructing reproducible and scalable web environments. Our framework leverages 1) HTTP-level caching to capture and replay stable visual states while preserving interactive behavior and 2) LLM-based environment synthesis grounded in real-world websites and core web navigation skills. Using this framework, we scale RL training to thousands of diverse environments and tasks. Our best model, Weblica-8B, outperforms open-weight baselines of similar size across multiple web navigation benchmarks while using fewer inference steps, scales favorably with additional test-time compute, and is competitive with API models.
Comments: 28 pages, 19 figures
Subjects:
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2605.06761 [cs.AI]
(or arXiv:2605.06761v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.06761
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Oğuzhan Fatih Kar [view email] [v1] Thu, 7 May 2026 17:17:10 UTC (24,151 KB)
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