Derived from arXiv:2607.16900 - Environment-free Synthetic Data Generation for API-Calling Agents
Scanned 9/11/2026
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# Environment-free Synthetic Data Generation for API-Calling Agents
Derived from arXiv:2607.16900 - Environment-free Synthetic Data Generation for API-Calling Agents
## Core Concept
Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs and realistic, pre-populated backend databases, creating a major bottleneck for scalability. To overcome this, we propose an environment-free synthetic data generation approach that leverages LLMs as on-the-fly digital world models. Given only API specifications, our method genera...
## Key Insights
- Derived from arXiv:2607.16900
- Published: 2026-07-18
- Utility Score: 1.00
- Authors: Seanie Lee, Sanjoy Chowdhury, Chao Jiang et al.
## Activation
environment-free-synthetic-data-generation-for-api, 2607.16900
## References
- arXiv: https://arxiv.org/abs/2607.16900
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