Environment-free Synthetic Data Generation for API-Calling Agents
Apple ML Research
Researchers developed a method to generate synthetic training data for API-calling language model agents without needing functional environments, using LLMs to simulate API responses based only on API specifications. The approach was evaluated on AppWorld and OfficeBench benchmarks, showing that models fine-tuned on the synthetic data achieved significant performance improvements. This removes the requirement for pre-built environments with executable APIs and populated databases, enabling scalable training of agents across diverse API ecosystems.
Why it matters
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 generates trajectories mimicking interactions between an agent and a stateful environment. Specifically…
