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arXiv · 2609.06124

SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use

Zichen Tian·Jinpeng Chen·Cheng Gong·Suiyun Zhang·Rui Liu
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Abstract

High-quality multi-turn tool-use data is essential for training agentic models, yet existing data synthesis methods often underrepresent the argument-level dependencies that are critical to long-horizon tool use. As a result, even when a model selects the correct tool, task execution may still fail because the model fills tool arguments with fabricated, stale, or weakly grounded values. To address this problem, we propose \textbf{State-Guided Data Synthesis with Argument Provenance (SAP)}. SAP combines state guidance, tool-argument provenance constraints, and turn-level validation to efficiently construct tool-use trajectories with long-range dependencies and high accuracy. Using data generated by SAP, we build SAP-4B, which is highly competitive even when compared with much larger models across multiple benchmarks. Source code, synthesized data, and trained weights are available at https://github.com/Zichen1024/SAP.

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Zichen Tian, Jinpeng Chen, Cheng Gong, Suiyun Zhang, Rui Liu. 2026-09-05. SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use. https://arxiv.org/abs/2609.06124

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