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.
More from coding & agent
- alphaXiv open-sources OpenResearch to run parallel research agents with any model — alphaXiv · 2026-09-11
- MathModelAgent gains traction: auto-solves math modeling and writes a submission-ready paper — jihe520 · 2026-09-11
- DeskcommCRM: open-source AI sales CRM with native agents and WhatsApp hits 1k stars — melgarafael · 2026-09-11
- hyperresearch: agent-driven knowledge base that turns web research into a searchable wiki — jordan-gibbs · 2026-09-11
- Forter's 13 lessons from its agent sprint: skip custom RAG, lean on mature enterprise search — bibryam · 2026-09-11
- Two real 'company brains' opened up live: Gorgias' in-house Cortex vs Slite — femke_plantinga · 2026-09-11