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Evidence record 2957 · automatically gathered

Weblica: Scalable and Reproducible Training Environments for Visual Web Agents

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 stabl

Record details

Published: 7 July 2026
Source: Apple Machine Learning Research
Category: Field notes
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (7 July 2026), “Weblica: Scalable and Reproducible Training Environments for Visual Web Agents,” evidence record 2957, https://ethics.ai/record/2957 (originally published by Apple Machine Learning Research).

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