Data Turnstile: A Scalable Open Framework for Function-Calling Data Generation
Small language models (SLMs) are attractive for agentic deployment due to low latency, reduced cost, and on-device privacy, yet they struggle with tool-use tasks where training data is scarce and noisy. Unlike larger models, SLMs cannot compensate for low-quality supervision through sheer capacity, making data quality the critical bottleneck. We present Data Turnstile, an open-source framework that takes user-defined API specifications and generates high-quality synthetic training data for function calling. Turnstile decomposes multi-turn tool-use interactions into constrained, stepwise generation with validation and error-feedback loops, providing fine-grained control over API diversity,...
| Authors | Goutham Ramakrishnan, Megha Sharma |
|---|---|
| Primary category | cs.CL |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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