@inproceedings{yang-etal-2026-loopcoder,
title = "{L}oop{C}oder: Scaling Code Intelligence via Looped Language Models",
author = "Yang, Jian and
Zhang, Wei and
Guo, Shuyue and
LI, Yizhi and
Chai, Linzheng and
Ye, Zhengmao and
Liu, Shukai and
Song, Yuyang and
Wu, Jiajun and
Liu, Che and
Zheng, Tianyu and
Wu, Siwei and
L, Leo and
Ma, Xudong and
Hao, Chuan and
Tao, Ran and
Xing, Yan and
Wang, Jianzhou and
Tang, Mingjie and
Liu, Aishan and
Li, Zhoujun and
Liu, Xianglong and
Lv, Weifeng and
Dai, Bryan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.796/",
pages = "16209--16223",
ISBN = "979-8-89176-395-1",
abstract = "While large language models (LLMs) have mastered syntax-level code generation, complex algorithmic reasoning remains a challenge, typically addressed by scaling model depth and parameter count. Universal Transformers (UT) offer a compelling alternative by introducing a recurrent inductive bias that aligns with the recursive nature of programming logic. However, training looped architectures at scale has historically been hindered by severe instability and optimization difficulties associated with backpropagation through time (BPTT). We present LoopCoder (40B-A80B) pre-trained on 12T+ code and general tokens, along with LoopCoder-Thinking and LoopCoder-Instruct variants{---}the first large-scale looped transformer for code, achieving comparable performance to standard dense architectures with more parameters. Unlike prior approaches that restrict recurrence to small-scale tasks, we implement a comprehensive looped training protocol spanning both pre-training and post-training phases. We initiate the model via dense-to-loop transformation, folding a pre-trained dense checkpoint to initialize a recurrent block, followed by rigorous looped pre-training and specialized post-training for instruction following and reasoning. Our results establish a robust recipe for scaling coding intelligence via recurrent computation, proving that dense checkpoints serve as an optimal foundation for evolving into dynamic, looped reasoners."
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<abstract>While large language models (LLMs) have mastered syntax-level code generation, complex algorithmic reasoning remains a challenge, typically addressed by scaling model depth and parameter count. Universal Transformers (UT) offer a compelling alternative by introducing a recurrent inductive bias that aligns with the recursive nature of programming logic. However, training looped architectures at scale has historically been hindered by severe instability and optimization difficulties associated with backpropagation through time (BPTT). We present LoopCoder (40B-A80B) pre-trained on 12T+ code and general tokens, along with LoopCoder-Thinking and LoopCoder-Instruct variants—the first large-scale looped transformer for code, achieving comparable performance to standard dense architectures with more parameters. Unlike prior approaches that restrict recurrence to small-scale tasks, we implement a comprehensive looped training protocol spanning both pre-training and post-training phases. We initiate the model via dense-to-loop transformation, folding a pre-trained dense checkpoint to initialize a recurrent block, followed by rigorous looped pre-training and specialized post-training for instruction following and reasoning. Our results establish a robust recipe for scaling coding intelligence via recurrent computation, proving that dense checkpoints serve as an optimal foundation for evolving into dynamic, looped reasoners.</abstract>
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%0 Conference Proceedings
%T LoopCoder: Scaling Code Intelligence via Looped Language Models
%A Yang, Jian
%A Zhang, Wei
%A Guo, Shuyue
%A LI, Yizhi
%A Chai, Linzheng
%A Ye, Zhengmao
%A Liu, Shukai
%A Song, Yuyang
%A Wu, Jiajun
%A Liu, Che
%A Zheng, Tianyu
%A Wu, Siwei
%A L, Leo
%A Ma, Xudong
%A Hao, Chuan
%A Tao, Ran
%A Xing, Yan
%A Wang, Jianzhou
%A Tang, Mingjie
%A Liu, Aishan
%A Li, Zhoujun
%A Liu, Xianglong
%A Lv, Weifeng
%A Dai, Bryan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yang-etal-2026-loopcoder
%X While large language models (LLMs) have mastered syntax-level code generation, complex algorithmic reasoning remains a challenge, typically addressed by scaling model depth and parameter count. Universal Transformers (UT) offer a compelling alternative by introducing a recurrent inductive bias that aligns with the recursive nature of programming logic. However, training looped architectures at scale has historically been hindered by severe instability and optimization difficulties associated with backpropagation through time (BPTT). We present LoopCoder (40B-A80B) pre-trained on 12T+ code and general tokens, along with LoopCoder-Thinking and LoopCoder-Instruct variants—the first large-scale looped transformer for code, achieving comparable performance to standard dense architectures with more parameters. Unlike prior approaches that restrict recurrence to small-scale tasks, we implement a comprehensive looped training protocol spanning both pre-training and post-training phases. We initiate the model via dense-to-loop transformation, folding a pre-trained dense checkpoint to initialize a recurrent block, followed by rigorous looped pre-training and specialized post-training for instruction following and reasoning. Our results establish a robust recipe for scaling coding intelligence via recurrent computation, proving that dense checkpoints serve as an optimal foundation for evolving into dynamic, looped reasoners.
%U https://aclanthology.org/2026.findings-acl.796/
%P 16209-16223
Markdown (Informal)
[LoopCoder: Scaling Code Intelligence via Looped Language Models](https://aclanthology.org/2026.findings-acl.796/) (Yang et al., Findings 2026)
ACL
- Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, and Bryan Dai. 2026. LoopCoder: Scaling Code Intelligence via Looped Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 16209–16223, San Diego, California, United States. Association for Computational Linguistics.