@inproceedings{wu-etal-2026-templaterl,
title = "{T}emplate{RL}: Structured Template-Guided Reinforcement Learning for {LLM} Reasoning",
author = "Wu, Jinyang and
Liao, Chonghua and
Feng, Mingkuan and
Zhang, Shuai and
Wen, Zhengqi and
Luo, Haoran and
Yang, Ling and
Xu, Huazhe and
Tao, Jianhua",
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.198/",
doi = "10.18653/v1/2026.findings-acl.198",
pages = "4050--4080",
ISBN = "979-8-89176-395-1",
abstract = "Reinforcement learning (RL) has emerged as an effective paradigm for enhancing model reasoning. However, existing RL methods like GRPO often rely on unstructured self-sampling to fit scalar rewards, often producing inefficient rollouts that fail to capture transferable problem-solving strategies. To address these limitations, we propose **TemplateRL**, a structured template-guided RL framework that augments policy optimization with explicit template guidance. Our approach first constructs a problem-solving template library via MCTS on a small seed set, then seamlessly integrates this high-level structured guidance into RL training. By guiding rollout generation to align with proven template structures, TemplateRL significantly improves high-quality trajectory hit rates while reducing ineffective exploration. This structure-guided design steers the policy toward validated strategic patterns, stabilizing training dynamics, and enhancing RL sampling efficiency. Notably, the explicit template library is interpretable, editable, and supports online updates-enabling continuous updates during both training and inference. Extensive experiments demonstrate that TemplateRL outperforms GRPO by 99{\%} on AIME and 41{\%} on AMC, with superior stability on weak models and remarkable cross-domain generalization, highlighting its potential for broader tasks."
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<abstract>Reinforcement learning (RL) has emerged as an effective paradigm for enhancing model reasoning. However, existing RL methods like GRPO often rely on unstructured self-sampling to fit scalar rewards, often producing inefficient rollouts that fail to capture transferable problem-solving strategies. To address these limitations, we propose **TemplateRL**, a structured template-guided RL framework that augments policy optimization with explicit template guidance. Our approach first constructs a problem-solving template library via MCTS on a small seed set, then seamlessly integrates this high-level structured guidance into RL training. By guiding rollout generation to align with proven template structures, TemplateRL significantly improves high-quality trajectory hit rates while reducing ineffective exploration. This structure-guided design steers the policy toward validated strategic patterns, stabilizing training dynamics, and enhancing RL sampling efficiency. Notably, the explicit template library is interpretable, editable, and supports online updates-enabling continuous updates during both training and inference. Extensive experiments demonstrate that TemplateRL outperforms GRPO by 99% on AIME and 41% on AMC, with superior stability on weak models and remarkable cross-domain generalization, highlighting its potential for broader tasks.</abstract>
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%0 Conference Proceedings
%T TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning
%A Wu, Jinyang
%A Liao, Chonghua
%A Feng, Mingkuan
%A Zhang, Shuai
%A Wen, Zhengqi
%A Luo, Haoran
%A Yang, Ling
%A Xu, Huazhe
%A Tao, Jianhua
%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 wu-etal-2026-templaterl
%X Reinforcement learning (RL) has emerged as an effective paradigm for enhancing model reasoning. However, existing RL methods like GRPO often rely on unstructured self-sampling to fit scalar rewards, often producing inefficient rollouts that fail to capture transferable problem-solving strategies. To address these limitations, we propose **TemplateRL**, a structured template-guided RL framework that augments policy optimization with explicit template guidance. Our approach first constructs a problem-solving template library via MCTS on a small seed set, then seamlessly integrates this high-level structured guidance into RL training. By guiding rollout generation to align with proven template structures, TemplateRL significantly improves high-quality trajectory hit rates while reducing ineffective exploration. This structure-guided design steers the policy toward validated strategic patterns, stabilizing training dynamics, and enhancing RL sampling efficiency. Notably, the explicit template library is interpretable, editable, and supports online updates-enabling continuous updates during both training and inference. Extensive experiments demonstrate that TemplateRL outperforms GRPO by 99% on AIME and 41% on AMC, with superior stability on weak models and remarkable cross-domain generalization, highlighting its potential for broader tasks.
%R 10.18653/v1/2026.findings-acl.198
%U https://aclanthology.org/2026.findings-acl.198/
%U https://doi.org/10.18653/v1/2026.findings-acl.198
%P 4050-4080
Markdown (Informal)
[TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning](https://aclanthology.org/2026.findings-acl.198/) (Wu et al., Findings 2026)
ACL
- Jinyang Wu, Chonghua Liao, Mingkuan Feng, Shuai Zhang, Zhengqi Wen, Haoran Luo, Ling Yang, Huazhe Xu, and Jianhua Tao. 2026. TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning. In Findings of the Association for Computational Linguistics: ACL 2026, pages 4050–4080, San Diego, California, United States. Association for Computational Linguistics.