ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool learning
Xingshan Zeng, Weiwen Liu, Xu Huang, Zezhong Wang, Lingzhi Wang, Liangyou Li, Yasheng Wang, Lifeng Shang
Abstract
Tool learning, which allows Large Language Models (LLMs) to leverage external tools for solving complex user tasks, has emerged as a promising avenue for extending model capabilities. However, existing approaches primarily focus on data synthesis for fine-tuning LLMs to invoke tools effectively, largely ignoring how to fully stimulate the potential of the model. In this paper, we propose ToolACE-R, a novel framework that includes both model-aware iterative training and adaptive refinement for tool learning. ToolACE-R features a model-aware iterative training procedure that progressively adjust training samples based on the model’s evolving capabilities to maximize its potential. Additionally, it incorporates self-refinement training corpus which emphasizes LLM
BibTeX
@inproceedings{aaai2026_toolacermodelawa,
title = {ToolACE-R: Model-aware Iterative Training and Adaptive Refinement for Tool learning},
author = {Xingshan Zeng and Weiwen Liu and Xu Huang and Zezhong Wang and Lingzhi Wang and Liangyou Li and Yasheng Wang and Lifeng Shang and Xin Jiang and Ruiming Tang and Qun Liu},
booktitle = {AAAI 2026},
year = {2026}
}