LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls
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 adaptively focus on a model's specific weaknesses 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 refines 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 operates within a cost-effective, open-source ecosystem, eliminating dependence on expensive closed-source APIs. Experiments show that our 8B model trained with LoopTool 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.
691555e2a1b06ca3cc813576
Kangning Zhang
691555e2a1b06ca3cc813577
Wenxiang Jiao
claimed_verified
2026-02-27T16:47:02.237Z
63db16330cc3bc12bc0b6f8f
https://cdn-avatars.huggingface.co/v1/production/uploads/63db16330cc3bc12bc0b6f8f/ld0JQIfX1SBlDVDOmw9VT.jpeg
Wenxiang Jiao
wxjiao
user
wxjiao
691555e2a1b06ca3cc813578
Kounianhua Du
691555e2a1b06ca3cc813579
Yuan Lu
691555e2a1b06ca3cc81357a
Weiwen Liu
691555e2a1b06ca3cc81357b
Weinan Zhang
691555e2a1b06ca3cc81357c
Lei Zhang
691555e2a1b06ca3cc81357d
Yong Yu
LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls
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 adaptively focus on a model's specific weaknesses 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 refines 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 operates within a cost-effective, open-source ecosystem, eliminating dependence on expensive closed-source APIs. Experiments show that our 8B model trained with LoopTool 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.
A fully automated data evolution framework, LoopTool, enhances tool-use capabilities of Large Language Models by iteratively refining data and model through a closed-loop process.
Large Language Models LoopTool Greedy Capability Probing Judgement-Guided Label Verification Error-Driven Data Expansion BFCL-v3 ACEBench