AI BriefingEnvironment-free Synthetic Data Generation Technique for API-Calling Agents
Key point
This proposes a technique that generates training data for agents using only API specifications, without a real execution environment.
Details
Training LLM agents that call APIs requires vast amounts of high-quality trajectory data. However, building an environment with actually executable APIs and databases for this purpose creates a major bottleneck in terms of scalability.
To address this, this research proposes an Environment-free synthetic data generation approach that uses an LLM as a real-time digital world model. This approach operates based solely on API Specifications, without building a separate physical environment.
The core of this methodology is as follows:
- LLM-based Simulation: The LLM acts as a stateful environment that interacts with the agent.
- Trajectory Generation: It generates the data trajectories needed for training by mimicking the interaction between the agent and the environment.
- Scalability: It enables rapid acquisition of large-scale training data without building complex backend infrastructure.
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