@inproceedings{tian-etal-2026-sap,
title = "{SAP}: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use",
author = "Tian, Zichen and
Chen, Jinpeng and
Gong, Cheng and
Zhang, Suiyun and
Liu, Rui",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1013/",
pages = "21400--21422",
ISBN = "979-8-89176-462-0",
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 **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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<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 **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.</abstract>
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%0 Conference Proceedings
%T SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use
%A Tian, Zichen
%A Chen, Jinpeng
%A Gong, Cheng
%A Zhang, Suiyun
%A Liu, Rui
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F tian-etal-2026-sap
%X 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 **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.
%U https://aclanthology.org/2026.findings-emnlp.1013/
%P 21400-21422
Markdown (Informal)
[SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use](https://aclanthology.org/2026.findings-emnlp.1013/) (Tian et al., Findings 2026)
ACL