Apple trains API-calling agents with synthetic trajectories and no live environment
apple · hf · 2026-07-21
What Apple proposes
Apple introduces an environment-free synthetic data pipeline for training API-calling agents.
How it works
- Given only API specs, an LLM generates tasks that are solvable with those APIs.
- A teacher agent solves the task while an LLM simulator produces coherent synthetic API responses conditioned on task context and interaction history.
- An LLM judge filters low-quality trajectories.
Why it matters
- Collecting agent trajectories usually requires fully implemented environments, executable APIs, and realistic backend data.
- This method removes that bottleneck and lets teams generate supervision without a live environment.
- Fine-tuning on the synthetic data improves performance on AppWorld and OfficeBench, including both retrieval-style and state-changing tasks.
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