Data Turnstile: A Scalable Open Framework for Function-Calling Data Generation

TL;DR - Data Turnstile is an open-source framework that turns user-supplied API specs into validated synthetic function-calling data, letting sub-billion-parameter models match or beat models 7–53× larger on tool-use benchmarks. It matters because it targets data quality — the main bottleneck for agentic small language models — rather than scale.

• Decomposes multi-turn tool-use into constrained stepwise generation with validation and error-feedback loops, giving control over API diversity, conversation complexity, and output correctness.
• On BFCL single-turn, a fine-tuned Qwen3-0.6B (no chain-of-thought) reaches 75.9% overall accuracy vs. 67.4% for the thinking-enabled base, approaching thinking-enabled Qwen3-1.7B (78.4%) and Qwen3-4B (79.9%).
• On the multi-turn τ²-bench Telecom domain, Turnstile-trained Qwen3-1.7B hits 31.1% pass^1 (4.7× over its 6.6% base), surpassing Qwen2.5-32B-Instruct (27.4%); Qwen3-0.6B reaches 24.6% (7× over base).
• Released with the framework: a dataset spanning 1,000+ APIs and 100K+ multi-turn interactions.