Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine

Aaron Fainman; Gabriela Kadlecová; Maciej Gryka; Bartosz Kruszczyński; Usman Zafar; Cédric Archambeau; Aaron Klein; David Salinas; Selim Nowicki; Jacek Golebiowski

arXiv · 2610.07070

Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine

Aaron Fainman·Gabriela Kadlecová·Maciej Gryka·Bartosz Kruszczyński·Usman Zafar·Cédric Archambeau·Aaron Klein·David Salinas·Selim Nowicki·Jacek Golebiowski

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Abstract

Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calling, and fine-tuning them needs per-API data that rarely exists. Existing synthesis methods are too expensive for high-scale fine-tuning, as they often require mock operational environments for different domains and multiple LLM calls per generated conversation turn. We introduce a fully automated, lightweight synthesis framework that models each API as a finite-state machine, representing the system as abstract states that determine when each tool may be called, producing state-valid sequences of tools; sequences are translated into complete examples with a single LLM call. Rather than optimize diversity, we set a target distribution over the number of turns, the tool sequence and task complexity. We measure data quality by fine-tuning SLMs on generated trajectories, showing that our FSM-based generation significantly improves downstream accuracy over an unmutated baseline and, against existing works, reaches 70.7% full accuracy over 63.4% and 53.7% with 3.6-6.6$\times$ fewer tokens.

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Aaron Fainman, Gabriela Kadlecová, Maciej Gryka, Bartosz Kruszczyński, Usman Zafar, Cédric Archambeau, Aaron Klein, David Salinas, Selim Nowicki, Jacek Golebiowski. 2026-10-05. Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine. https://arxiv.org/abs/2610.07070

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