Environment-free Synthetic Data Generation for API-Calling Agents
Researchers introduce a method for generating synthetic training data for API-calling large language model (LLM) agents without the need for executable environments. Their approach uses LLMs to simulate both agent interactions and API responses based solely on API specifications, producing high-quality trajectories. Evaluations on the AppWorld and OfficeBench benchmarks show that models fine-tuned on this synthetic data achieve significant performance improvements.
Why it matters: This work enables scalable and practical training of API-calling agents by removing the need for real or simulated environments, addressing a key bottleneck in the field.
Full story at: arXiv AI/ML ↗