@inproceedings{mu-etal-2026-efficienttool,
title = "{E}fficient{T}ool: A Cost-Effective Aligning Framework for Tool-Conditioned Agents in {SME} Scenarios",
author = "Mu, Yuanqi and
Bingfeng.Pi and
Xia, Defei and
Lei.Zuo and
Zhang, Yongqi",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.56/",
pages = "825--843",
ISBN = "979-8-89176-394-4",
abstract = "Large language models (LLMs) are increasingly adopted in downstream industries, yet aligning proprietary agents remains challenging due to limited high-quality data and hardware constraints in small and medium-sized enterprises (SMEs).We propose EfficientTool, a cost-effective, tool-conditioned alignment framework forming a closed loop over data collection, iterative training, and deployment-oriented evaluation.EfficientTool adopts a self-evolving bootstrapping-based Trajectory Collection Pipeline for high-quality trajectory generation, followed by iterative Model Training Pipeline using tool-conditioned parameter-efficient fine-tuning (PEFT).We evaluate the model with Interaction and Evaluation Pipeline in public and private benchmarks, and deploy for an internal enterprise agent.Results show that EfficientTool effectively aligns model in SME scenarios while preserving general tool-calling capability."
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%0 Conference Proceedings
%T EfficientTool: A Cost-Effective Aligning Framework for Tool-Conditioned Agents in SME Scenarios
%A Mu, Yuanqi
%A Xia, Defei
%A Zhang, Yongqi
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%A Bingfeng.Pi
%A Lei.Zuo
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F mu-etal-2026-efficienttool
%X Large language models (LLMs) are increasingly adopted in downstream industries, yet aligning proprietary agents remains challenging due to limited high-quality data and hardware constraints in small and medium-sized enterprises (SMEs).We propose EfficientTool, a cost-effective, tool-conditioned alignment framework forming a closed loop over data collection, iterative training, and deployment-oriented evaluation.EfficientTool adopts a self-evolving bootstrapping-based Trajectory Collection Pipeline for high-quality trajectory generation, followed by iterative Model Training Pipeline using tool-conditioned parameter-efficient fine-tuning (PEFT).We evaluate the model with Interaction and Evaluation Pipeline in public and private benchmarks, and deploy for an internal enterprise agent.Results show that EfficientTool effectively aligns model in SME scenarios while preserving general tool-calling capability.
%U https://aclanthology.org/2026.acl-industry.56/
%P 825-843
Markdown (Informal)
[EfficientTool: A Cost-Effective Aligning Framework for Tool-Conditioned Agents in SME Scenarios](https://aclanthology.org/2026.acl-industry.56/) (Mu et al., ACL 2026)
ACL