@inproceedings{zhang-etal-2026-looptool,
title = "{L}oop{T}ool: Closing the Data{--}Training Loop for Robust {LLM} Tool Calls",
author = "Zhang, Kangning and
Liu, Weiwen and
Jiao, Wenxiang and
Du, Kounianhua and
Lu, Yuan and
Zhang, Weinan and
Yu, Yong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.968/",
pages = "21144--21164",
ISBN = "979-8-89176-390-6",
abstract = "Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipelines, where data generation and model training are executed as two separate, non-interactive processes. This approach fails to focus on the model{'}s specific weaknesses adaptively and allows noisy labels to persist, degrading training efficiency. We introduce \textbf{LoopTool}, a fully automated, model-aware data evolution framework that closes this loop by tightly integrating data synthesis and model training. LoopTool iteratively evolves both the data and the model through three synergistic modules: (1) \textit{Greedy Capability Probing (GCP)} diagnoses the model{'}s mastered and failed capabilities; (2) \textit{Judgement-Guided Label Verification (JGLV)} uses an open-source judge model to find and correct annotation errors, progressively purifying the dataset; and (3) \textit{Error-Driven Data Expansion (EDDE)} generates new, challenging samples based on identified failures. This closed-loop process is tightly integrated with reinforcement learning training and operates within a cost-efficient, open-source ecosystem, thereby eliminating reliance on costly APIs. Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. Our work demonstrates that closed-loop, self-refining data pipelines can dramatically enhance the tool-use capabilities of LLMs."
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<abstract>Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipelines, where data generation and model training are executed as two separate, non-interactive processes. This approach fails to focus on the model’s specific weaknesses adaptively and allows noisy labels to persist, degrading training efficiency. We introduce LoopTool, a fully automated, model-aware data evolution framework that closes this loop by tightly integrating data synthesis and model training. LoopTool iteratively evolves both the data and the model through three synergistic modules: (1) Greedy Capability Probing (GCP) diagnoses the model’s mastered and failed capabilities; (2) Judgement-Guided Label Verification (JGLV) uses an open-source judge model to find and correct annotation errors, progressively purifying the dataset; and (3) Error-Driven Data Expansion (EDDE) generates new, challenging samples based on identified failures. This closed-loop process is tightly integrated with reinforcement learning training and operates within a cost-efficient, open-source ecosystem, thereby eliminating reliance on costly APIs. Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. Our work demonstrates that closed-loop, self-refining data pipelines can dramatically enhance the tool-use capabilities of LLMs.</abstract>
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%0 Conference Proceedings
%T LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls
%A Zhang, Kangning
%A Liu, Weiwen
%A Jiao, Wenxiang
%A Du, Kounianhua
%A Lu, Yuan
%A Zhang, Weinan
%A Yu, Yong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-etal-2026-looptool
%X Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipelines, where data generation and model training are executed as two separate, non-interactive processes. This approach fails to focus on the model’s specific weaknesses adaptively and allows noisy labels to persist, degrading training efficiency. We introduce LoopTool, a fully automated, model-aware data evolution framework that closes this loop by tightly integrating data synthesis and model training. LoopTool iteratively evolves both the data and the model through three synergistic modules: (1) Greedy Capability Probing (GCP) diagnoses the model’s mastered and failed capabilities; (2) Judgement-Guided Label Verification (JGLV) uses an open-source judge model to find and correct annotation errors, progressively purifying the dataset; and (3) Error-Driven Data Expansion (EDDE) generates new, challenging samples based on identified failures. This closed-loop process is tightly integrated with reinforcement learning training and operates within a cost-efficient, open-source ecosystem, thereby eliminating reliance on costly APIs. Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. Our work demonstrates that closed-loop, self-refining data pipelines can dramatically enhance the tool-use capabilities of LLMs.
%U https://aclanthology.org/2026.acl-long.968/
%P 21144-21164
Markdown (Informal)
[LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls](https://aclanthology.org/2026.acl-long.968/) (Zhang et al., ACL 2026)
ACL
- Kangning Zhang, Weiwen Liu, Wenxiang Jiao, Kounianhua Du, Yuan Lu, Weinan Zhang, and Yong Yu. 2026. LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21144–21164, San Diego, California, United States. Association for Computational Linguistics.