AI Agent · Apple Machine Learning
Environment-free Synthetic Data Generation for API-Calling Agents
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Environment-free Synthetic Data Generation for API-Calling Agents.
Key facts
- Authors Seanie Lee, Sanjoy Chowdhury, Chao Jiang, Cheng-Yu Hsieh, Ting-Yao Hu, Alexander T Toshev, Oncel Tuzel, Raviteja Vemulapalli
- Their results establish LLM-based API simulation as a practical, scalable solution for training agents across diverse API ecosystems
- Environment-free Synthetic Data Generation for API-Calling Agents
- Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories
Summary
Authors Seanie Lee, Sanjoy Chowdhury, Chao Jiang, Cheng-Yu Hsieh, Ting-Yao Hu, Alexander T Toshev, Oncel Tuzel, Raviteja Vemulapalli. Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. A teacher agent then iteratively solves each task while an LLM simulator generates coherent synthetic API responses conditioned on the task context and simulation history. Their results establish LLM-based API simulation as a practical, scalable solution for training agents across diverse API ecosystems.