@inproceedings{hu-etal-2026-towards,
title = "Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures",
author = "Hu, Yi and
Gu, Jiaqi and
Wang, Ruxin and
Yao, Zijun and
Peng, Hao and
Wu, Xiaobao and
Chen, Jianhui and
Zhang, Muhan and
Pan, Liangming",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.889/",
pages = "19449--19466",
ISBN = "979-8-89176-390-6",
abstract = "Reinforcement learning (RL) has catalyzed the emergence of Large Reasoning Models (LRMs) that have pushed reasoning capabilities to new heights. While their performance has garnered significant excitement, exploring the internal mechanisms driving these behaviors has become an equally critical research frontier. This paper provides a comprehensive survey of the mechanistic understanding of LRMs, organizing recent findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. By synthesizing these insights, we aim to bridge the gap between black-box performance and mechanistic transparency. Finally, we discuss under-explored challenges to outline a roadmap for future mechanistic studies, including the need for applied interpretability, improved methodologies, and a unified theoretical framework."
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<abstract>Reinforcement learning (RL) has catalyzed the emergence of Large Reasoning Models (LRMs) that have pushed reasoning capabilities to new heights. While their performance has garnered significant excitement, exploring the internal mechanisms driving these behaviors has become an equally critical research frontier. This paper provides a comprehensive survey of the mechanistic understanding of LRMs, organizing recent findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. By synthesizing these insights, we aim to bridge the gap between black-box performance and mechanistic transparency. Finally, we discuss under-explored challenges to outline a roadmap for future mechanistic studies, including the need for applied interpretability, improved methodologies, and a unified theoretical framework.</abstract>
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%0 Conference Proceedings
%T Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures
%A Hu, Yi
%A Gu, Jiaqi
%A Wang, Ruxin
%A Yao, Zijun
%A Peng, Hao
%A Wu, Xiaobao
%A Chen, Jianhui
%A Zhang, Muhan
%A Pan, Liangming
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F hu-etal-2026-towards
%X Reinforcement learning (RL) has catalyzed the emergence of Large Reasoning Models (LRMs) that have pushed reasoning capabilities to new heights. While their performance has garnered significant excitement, exploring the internal mechanisms driving these behaviors has become an equally critical research frontier. This paper provides a comprehensive survey of the mechanistic understanding of LRMs, organizing recent findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. By synthesizing these insights, we aim to bridge the gap between black-box performance and mechanistic transparency. Finally, we discuss under-explored challenges to outline a roadmap for future mechanistic studies, including the need for applied interpretability, improved methodologies, and a unified theoretical framework.
%U https://aclanthology.org/2026.acl-long.889/
%P 19449-19466
Markdown (Informal)
[Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures](https://aclanthology.org/2026.acl-long.889/) (Hu et al., ACL 2026)
ACL
- Yi Hu, Jiaqi Gu, Ruxin Wang, Zijun Yao, Hao Peng, Xiaobao Wu, Jianhui Chen, Muhan Zhang, and Liangming Pan. 2026. Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 19449–19466, San Diego, California, United States. Association for Computational Linguistics.