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Donate arxiv logo > cs > arXiv:2510.25741 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2510.25741 (cs) [Submitted on 29 Oct 2025 (v1), last revised 17 Nov 2025 (this version, v4)] Title: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 View a PDF of the paper titled Scaling Latent Reasoning via Looped Language Models, by Rui-Jie Zhu and 32 other authors View PDF HTML (experimental) Abstract:Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training data. We present and open-source Ouro, named after the recursive Ouroboros, a family of pre-trained Looped Language Models (LoopLM) that instead build reasoning into the pre-training phase through (i) iterative computation in latent space, (ii) an entropy-regularized objective for learned depth allocation, and (iii) scaling to 7.7T tokens. Ouro 1.4B and 2.6B models enjoy superior performance that match the results of up to 12B SOTA LLMs across a wide range of benchmarks. Through controlled experiments, we show this advantage stems not from increased knowledge capacity, but from superior knowledge manipulation capabilities. We also show that LoopLM yields reasoning traces more aligned with final outputs than explicit CoT. We hope our results show the potential of LoopLM as a novel scaling direction in the reasoning era. Our model is available here: this http URL. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2510.25741 [cs.CL] (or arXiv:2510.25741v4 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2510.25741 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Rui-Jie Zhu [view email] [v1] Wed, 29 Oct 2025 17:45:42 UTC (14,928 KB) [v2] Mon, 3 Nov 2025 06:54:49 UTC (9,619 KB) [v3] Fri, 14 Nov 2025 02:14:36 UTC (9,607 KB) [v4] Mon, 17 Nov 2025 20:03:56 UTC (9,607 KB) Full-text links: Access Paper: View a PDF of the paper titled Scaling Latent Reasoning via Looped Language Models, by Rui-Jie Zhu and 32 other authors * View PDF * HTML (experimental) * TeX Source license icon view license Current browse context: cs.CL < prev | next > new | recent | 2025-10 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar export BibTeX citation Loading... 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