Weblica: Scalable and Reproducible Training Environments for Visual Web Agents
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913099406114816 |
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| author | Kar, Oğuzhan Fatih Bachmann, Roman Gong, Yuanzheng Larsen, Anders Boesen Lindbo Dehghan, Afshin |
| author_facet | Kar, Oğuzhan Fatih Bachmann, Roman Gong, Yuanzheng Larsen, Anders Boesen Lindbo Dehghan, Afshin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06761 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Weblica: Scalable and Reproducible Training Environments for Visual Web Agents Kar, Oğuzhan Fatih Bachmann, Roman Gong, Yuanzheng Larsen, Anders Boesen Lindbo Dehghan, Afshin Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Weblica: Scalable and Reproducible Training Environments for Visual Web Agents |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2605.06761 |