@inproceedings{ye-etal-2026-feedback,
title = "Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments",
author = "Ye, Junjie and
Jiang, Changhao and
Du, Zhengyin and
Xu, Yufei and
Yao, Xuesong and
Xi, Zhiheng and
Fan, Xiaoran and
Zhang, Qi and
Gui, Tao and
Huang, Xuanjing and
Chen, Jiecao",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.109/",
pages = "2293--2323",
ISBN = "979-8-89176-395-1",
abstract = "Effective tool use is essential for large language models (LLMs) to interact with their environment. However, progress is limited by the lack of efficient reinforcement learning (RL) frameworks specifically designed for tool use, due to challenges in constructing stable training environments and designing verifiable reward mechanisms. To address this, we propose an automated environment construction pipeline, incorporating scenario decomposition, document generation, function integration, complexity scaling, and localized deployment. This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools. Additionally, we introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution. When combined with trajectory data collected from the constructed environments, this mechanism integrates seamlessly with standard RL algorithms to facilitate feedback-driven model training. Experiments on LLMs of varying scales demonstrate that our approach significantly enhances the models' tool-use performance without degrading their general capabilities. Our analysis suggests that these gains result from improved context understanding and reasoning, driven by updates to the lower-layer MLP parameters in models. Code and data are available at https://github.com/bytedance/FTRL."
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<abstract>Effective tool use is essential for large language models (LLMs) to interact with their environment. However, progress is limited by the lack of efficient reinforcement learning (RL) frameworks specifically designed for tool use, due to challenges in constructing stable training environments and designing verifiable reward mechanisms. To address this, we propose an automated environment construction pipeline, incorporating scenario decomposition, document generation, function integration, complexity scaling, and localized deployment. This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools. Additionally, we introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution. When combined with trajectory data collected from the constructed environments, this mechanism integrates seamlessly with standard RL algorithms to facilitate feedback-driven model training. Experiments on LLMs of varying scales demonstrate that our approach significantly enhances the models’ tool-use performance without degrading their general capabilities. Our analysis suggests that these gains result from improved context understanding and reasoning, driven by updates to the lower-layer MLP parameters in models. Code and data are available at https://github.com/bytedance/FTRL.</abstract>
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%0 Conference Proceedings
%T Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments
%A Ye, Junjie
%A Jiang, Changhao
%A Du, Zhengyin
%A Xu, Yufei
%A Yao, Xuesong
%A Xi, Zhiheng
%A Fan, Xiaoran
%A Zhang, Qi
%A Gui, Tao
%A Huang, Xuanjing
%A Chen, Jiecao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ye-etal-2026-feedback
%X Effective tool use is essential for large language models (LLMs) to interact with their environment. However, progress is limited by the lack of efficient reinforcement learning (RL) frameworks specifically designed for tool use, due to challenges in constructing stable training environments and designing verifiable reward mechanisms. To address this, we propose an automated environment construction pipeline, incorporating scenario decomposition, document generation, function integration, complexity scaling, and localized deployment. This enables the creation of high-quality training environments that provide detailed and measurable feedback without relying on external tools. Additionally, we introduce a verifiable reward mechanism that evaluates both the precision of tool use and the completeness of task execution. When combined with trajectory data collected from the constructed environments, this mechanism integrates seamlessly with standard RL algorithms to facilitate feedback-driven model training. Experiments on LLMs of varying scales demonstrate that our approach significantly enhances the models’ tool-use performance without degrading their general capabilities. Our analysis suggests that these gains result from improved context understanding and reasoning, driven by updates to the lower-layer MLP parameters in models. Code and data are available at https://github.com/bytedance/FTRL.
%U https://aclanthology.org/2026.findings-acl.109/
%P 2293-2323
Markdown (Informal)
[Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments](https://aclanthology.org/2026.findings-acl.109/) (Ye et al., Findings 2026)
ACL
- Junjie Ye, Changhao Jiang, Zhengyin Du, Yufei Xu, Xuesong Yao, Zhiheng Xi, Xiaoran Fan, Qi Zhang, Tao Gui, Xuanjing Huang, and Jiecao Chen. 2026. Feedback-Driven Tool-Use Improvements in Large Language Models via Automated Build Environments. In Findings of the Association for Computational Linguistics: ACL 2026, pages 2293–2323, San Diego, California, United States. Association for Computational Linguistics.