@inproceedings{qi-etal-2026-carl,
title = "{CARL}: Constraint-Aware Reinforcement Learning for Planning with {LLM}s",
author = "Qi, Qiuyi and
Zhang, Jinjian and
Bao, Mutian and
Liang, Tian and
Li, Guocong and
Liu, Dongnan and
Zhou, Wei and
Liu, Jie and
Kong, Ming and
Mo, Linjian and
Zhang, Feng and
Zhu, Qiang",
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.1069/",
pages = "21257--21281",
ISBN = "979-8-89176-395-1",
abstract = "Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications. This deficiency arises from a lack of systematic mechanisms to incorporate constraint information during the generation process. While existing approaches attempt to mitigate this by relying on external tools or task decomposition, they fail to enhance the model{'}s intrinsic constraint awareness. To address this, we propose Constraint-Aware Reinforcement Learning (CARL), a novel RL framework designed to strengthen LLMs' intrinsic focus on constraints. CARL introduces a constraint-aware reward by comparing the model{'}s output distributions under constrained and unconstrained inputs, encouraging constraint focus and penalizing neglect.Compatible with various RL frameworks and requiring no external solvers or top models, CARL enables scalable, end-to-end constraint-aware planning. Extensive experiments on BlocksWorld, TravelPlanner, and T-Eval demonstrate that CARL significantly outperforms standard Reinforcement Fine-Tuning (RFT) baselines and state-of-the-art reasoning models, exhibiting a markedly increased focus on constraints."
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<abstract>Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications. This deficiency arises from a lack of systematic mechanisms to incorporate constraint information during the generation process. While existing approaches attempt to mitigate this by relying on external tools or task decomposition, they fail to enhance the model’s intrinsic constraint awareness. To address this, we propose Constraint-Aware Reinforcement Learning (CARL), a novel RL framework designed to strengthen LLMs’ intrinsic focus on constraints. CARL introduces a constraint-aware reward by comparing the model’s output distributions under constrained and unconstrained inputs, encouraging constraint focus and penalizing neglect.Compatible with various RL frameworks and requiring no external solvers or top models, CARL enables scalable, end-to-end constraint-aware planning. Extensive experiments on BlocksWorld, TravelPlanner, and T-Eval demonstrate that CARL significantly outperforms standard Reinforcement Fine-Tuning (RFT) baselines and state-of-the-art reasoning models, exhibiting a markedly increased focus on constraints.</abstract>
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%0 Conference Proceedings
%T CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs
%A Qi, Qiuyi
%A Zhang, Jinjian
%A Bao, Mutian
%A Liang, Tian
%A Li, Guocong
%A Liu, Dongnan
%A Zhou, Wei
%A Liu, Jie
%A Kong, Ming
%A Mo, Linjian
%A Zhang, Feng
%A Zhu, Qiang
%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 qi-etal-2026-carl
%X Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their reliability in real-world applications. This deficiency arises from a lack of systematic mechanisms to incorporate constraint information during the generation process. While existing approaches attempt to mitigate this by relying on external tools or task decomposition, they fail to enhance the model’s intrinsic constraint awareness. To address this, we propose Constraint-Aware Reinforcement Learning (CARL), a novel RL framework designed to strengthen LLMs’ intrinsic focus on constraints. CARL introduces a constraint-aware reward by comparing the model’s output distributions under constrained and unconstrained inputs, encouraging constraint focus and penalizing neglect.Compatible with various RL frameworks and requiring no external solvers or top models, CARL enables scalable, end-to-end constraint-aware planning. Extensive experiments on BlocksWorld, TravelPlanner, and T-Eval demonstrate that CARL significantly outperforms standard Reinforcement Fine-Tuning (RFT) baselines and state-of-the-art reasoning models, exhibiting a markedly increased focus on constraints.
%U https://aclanthology.org/2026.findings-acl.1069/
%P 21257-21281
Markdown (Informal)
[CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs](https://aclanthology.org/2026.findings-acl.1069/) (Qi et al., Findings 2026)
ACL
- Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, and Qiang Zhu. 2026. CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 21257–21281, San Diego, California, United States. Association for Computational Linguistics.