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Evidence record 12576 · automatically gathered

Environment-free Synthetic Data Generation for API-Calling Agents

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

Record details

Published: 21 July 2026
Source: Apple Machine Learning Research
Category: Field notes
Topics: Agents & autonomy · Environment
Retrieved: 22 July 2026

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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 (21 July 2026), “Environment-free Synthetic Data Generation for API-Calling Agents,” evidence record 12576, https://ethics.ai/record/12576 (originally published by Apple Machine Learning Research).

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