{
  "id": 12576,
  "url": "https://machinelearning.apple.com/research/environment-free",
  "title": "Environment-free Synthetic Data Generation for API-Calling Agents",
  "summary": "Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs and realistic, pre-populated backend databases, creating a major bottleneck for scalability. To overcome this, we propose an environment-free synthetic data generation approach that leverages LLMs as on-the-fly digital world models. Given only API specifications, our method genera",
  "authors": null,
  "category": "org",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-21T00:00:00.000Z",
  "fetched_at": "2026-07-22T05:10:49.469Z",
  "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/12576",
  "original_url": "https://machinelearning.apple.com/research/environment-free",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}