Environment-free Synthetic Data Generation for API-Calling Agents

Apple ML Research · 17 hours ago

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.