Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910002953846784 |
|---|---|
| author | Hu, Yi Gu, Jiaqi Wang, Ruxin Yao, Zijun Peng, Hao Wu, Xiaobao Chen, Jianhui Zhang, Muhan Pan, Liangming |
| author_facet | Hu, Yi Gu, Jiaqi Wang, Ruxin Yao, Zijun Peng, Hao Wu, Xiaobao Chen, Jianhui Zhang, Muhan Pan, Liangming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_19928 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures Hu, Yi Gu, Jiaqi Wang, Ruxin Yao, Zijun Peng, Hao Wu, Xiaobao Chen, Jianhui Zhang, Muhan Pan, Liangming Computation and Language 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. |
| title | Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.19928 |