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Turnslide framework synthesizes multi-turn data for small language models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Turnslide, a novel framework for synthesizing multi-turn conversational data to improve small language models' (SLMs) tool-calling capabilities. This automated system models APIs as finite-state machines, generating state-valid tool sequences with a single LLM call. Fine-tuning SLMs on data generated by Turnslide significantly boosted downstream accuracy compared to baselines, achieving higher full accuracy with substantially fewer tokens. AI

IMPACT This research could lead to more capable and efficient small language models for tool-use applications.

RANK_REASON The cluster contains an academic paper detailing a new method for data synthesis for language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Turnslide framework synthesizes multi-turn data for small language models

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18 / 100
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The cluster contains an academic paper detailing a new method for data synthesis for language models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Aaron Fainman, Gabriela Kadlecov\'a, Maciej Gryka, Bartosz Kruszczy\'nski, Usman Zafar, C\'edric Archambeau, Aaron Klein, David Salinas, Selim Nowicki, Jacek Golebiowski ·

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

    arXiv:2610.07070v1 Announce Type: new 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 synthes…

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