@inproceedings{yu-etal-2026-loopcd,
title = "{L}oop{CD}: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models",
author = "Yu, Byeongho and
So, Junhyuk and
Park, Eunhyeok",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1367/",
pages = "31185--31199",
ISBN = "979-8-89176-459-0",
abstract = "Looped Language Models (LoopLMs) perform ``latent reasoning'' by recursively refining internal latent representations with shared weights, offering a more effective alternative to explicit verbal reasoning. Despite their effectiveness, we find that LoopLMs remain prone to loop instability: excessive iterations can lead to errors in later iterations, producing ``hard'' localized uncertain tokens that contribute to reasoning errors. To address this, we propose LoopCD, loop-wise contrastive decoding that enhances the reasoning performance of LoopLMs by intervening on these tokens at inference time. Specifically, we exploit the internal dynamics of LoopLMs and contrast the logits from earlier iterations with logits from the last refined iteration to form the final sampling distribution. We find that this strategy is highly efficient, introducing only negligible inference overhead and requiring no additional training, while effectively improving reasoning performance by naturally refining reasoning-critical hard tokens. Extensive experiments show that our method improves the performance of recent representative LoopLMs across various reasoning tasks."
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<abstract>Looped Language Models (LoopLMs) perform “latent reasoning” by recursively refining internal latent representations with shared weights, offering a more effective alternative to explicit verbal reasoning. Despite their effectiveness, we find that LoopLMs remain prone to loop instability: excessive iterations can lead to errors in later iterations, producing “hard” localized uncertain tokens that contribute to reasoning errors. To address this, we propose LoopCD, loop-wise contrastive decoding that enhances the reasoning performance of LoopLMs by intervening on these tokens at inference time. Specifically, we exploit the internal dynamics of LoopLMs and contrast the logits from earlier iterations with logits from the last refined iteration to form the final sampling distribution. We find that this strategy is highly efficient, introducing only negligible inference overhead and requiring no additional training, while effectively improving reasoning performance by naturally refining reasoning-critical hard tokens. Extensive experiments show that our method improves the performance of recent representative LoopLMs across various reasoning tasks.</abstract>
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%0 Conference Proceedings
%T LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models
%A Yu, Byeongho
%A So, Junhyuk
%A Park, Eunhyeok
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yu-etal-2026-loopcd
%X Looped Language Models (LoopLMs) perform “latent reasoning” by recursively refining internal latent representations with shared weights, offering a more effective alternative to explicit verbal reasoning. Despite their effectiveness, we find that LoopLMs remain prone to loop instability: excessive iterations can lead to errors in later iterations, producing “hard” localized uncertain tokens that contribute to reasoning errors. To address this, we propose LoopCD, loop-wise contrastive decoding that enhances the reasoning performance of LoopLMs by intervening on these tokens at inference time. Specifically, we exploit the internal dynamics of LoopLMs and contrast the logits from earlier iterations with logits from the last refined iteration to form the final sampling distribution. We find that this strategy is highly efficient, introducing only negligible inference overhead and requiring no additional training, while effectively improving reasoning performance by naturally refining reasoning-critical hard tokens. Extensive experiments show that our method improves the performance of recent representative LoopLMs across various reasoning tasks.
%U https://aclanthology.org/2026.emnlp-main.1367/
%P 31185-31199
Markdown (Informal)
[LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models](https://aclanthology.org/2026.emnlp-main.1367/) (Yu et al., EMNLP 2026)
ACL