{
  "id": 2957,
  "url": "https://machinelearning.apple.com/research/weblica-visual-web-agents",
  "title": "Weblica: Scalable and Reproducible Training Environments for Visual Web Agents",
  "summary": "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",
  "authors": null,
  "category": "org",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-07T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-apple-machine-learning-research",
  "source_name": "Apple Machine Learning Research",
  "source_homepage": "https://machinelearning.apple.com",
  "ethics_ai_record_url": "https://ethics.ai/record/2957",
  "original_url": "https://machinelearning.apple.com/research/weblica-visual-web-agents",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}