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

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 could be found in: http://ouro-llm.github.io.