Apple ML Research generates API-agent training traces without a real environment
Apple ML Research · rss · 2026-07-21
Apple ML Research proposes an environment-free synthetic data generation method for training API-calling LLM agents.
The core problem is that collecting high-quality agent trajectories normally requires fully implemented environments, executable APIs, and realistic backend databases, which makes data collection a major bottleneck.
Their approach uses LLMs as on-the-fly digital world models. Given only API specifications, the method generates trajectories that mimic interactions between an agent and a stateful environment.
In short, the paper tries to remove the dependency on fully built environments while still producing usable training traces for agent systems.
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