Environment-free Synthetic Data Generation for API-Calling Agents
This paper proposes a method to generate synthetic data for training API-calling agents without the need for an actual environment. This is useful for scalability, as collecting high-quality data at scale can be difficult. Practitioners might care about this approach because it could speed up the development of AI agents that can interact with APIs.