Environment-free Synthetic Data Generation for API-Calling Agents

This post proposes an environment-free synthetic data generation approach for training API-calling LLM agents. It leverages LLMs as on-the-fly digital world models to generate diverse tasks, simulate coherent API responses, and filter trajectories, overcoming the bottleneck of requiring fully implemented environments. This method demonstrates significant performance gains on benchmarks like AppWorld and OfficeBench when fine-tuning models on the generated synthetic data.