# Scaling Latent Reasoning via Looped Language Models

Authors: Rui-Jie Zhu, Zixuan Wang, Kai Hua, Tianyu Zhang, Ziniu Li, Haoran Que, Boyi Wei, Zixin Wen, Fan Yin, He Xing, Lu Li, Jiajun Shi, Kaijing Ma, Shanda Li, Taylor Kergan, Andrew Smith, Xingwei Qu, Mude Hui, Bohong Wu, Qiyang Min, Hongzhi Huang, Xun Zhou, Wei Ye, Jiaheng Liu, Jian Yang, Yunfeng Shi, Chenghua Lin, Enduo Zhao, Tianle Cai, Ge Zhang, Wenhao Huang, Yoshua Bengio, Jason Eshraghian.

Scales looped language models that reuse layers across recurrent depth and studies how performance changes with additional internal computation.

First public: 2025-10-29.
ArXiv initial submission: 2025-10-29.

[Original paper](https://arxiv.org/abs/2510.25741) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2510.25741/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2510.25741/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2510.25741/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2510.25741/record.json)
[Code](https://ouro-llm.github.io/)

## Approaches

- [Recurrent reasoning](https://latent-implicit-thinking.github.io/topics/recurrent-reasoning/): Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.