@inproceedings{jin-etal-2026-towards,
title = "Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models",
author = "Jin, Dian and
Han, Kairong and
Li, Baohong and
Dong, Xinpeng and
Hu, Zijing and
Shan, Nuanqiao and
Wu, Fei and
Kuang, Kun",
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.292/",
pages = "6710--6728",
ISBN = "979-8-89176-459-0",
abstract = "Diffusion Language Models (DLMs) have attracted significant attention for their strong reasoning ability. However, under a bidirectional attention mechanism, DLMs operate over an exponentially large exploration space compared to autoregressive models (ARMs), making it challenging to focus on reasoning-guiding tokens under random masking. We define causal shortcuts as token chains that cover the full sequence and provide explicit guidance towards correct reasoning trajectories. We analyze the effects of causal shortcuts on the reasoning accuracy and convergence speed of DLMs, and find that they largely improve answer convergence efficiency and generation accuracy. Motivated by this, we propose a Causal Shortcut Learning (CSL) Framework for DLMs. Specifically, we introduce a step-by-step token extraction procedure to extract causal shortcuts from data, and apply parallel prioritized masking on these tokens during training to enable efficient and accurate convergence to correct answers via causal shortcuts. Extensive experiments across multiple reasoning benchmarks and two base models demonstrate that CSL consistently outperforms existing SFT-variant baselines, achieving an average improvement of 1.92{\%} over SFT-only models, and up to 4.20{\%} on MATH-500. The code is available at the https://github.com/ZJUDianJin/Causal-Shortcuts-Learning."
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<abstract>Diffusion Language Models (DLMs) have attracted significant attention for their strong reasoning ability. However, under a bidirectional attention mechanism, DLMs operate over an exponentially large exploration space compared to autoregressive models (ARMs), making it challenging to focus on reasoning-guiding tokens under random masking. We define causal shortcuts as token chains that cover the full sequence and provide explicit guidance towards correct reasoning trajectories. We analyze the effects of causal shortcuts on the reasoning accuracy and convergence speed of DLMs, and find that they largely improve answer convergence efficiency and generation accuracy. Motivated by this, we propose a Causal Shortcut Learning (CSL) Framework for DLMs. Specifically, we introduce a step-by-step token extraction procedure to extract causal shortcuts from data, and apply parallel prioritized masking on these tokens during training to enable efficient and accurate convergence to correct answers via causal shortcuts. Extensive experiments across multiple reasoning benchmarks and two base models demonstrate that CSL consistently outperforms existing SFT-variant baselines, achieving an average improvement of 1.92% over SFT-only models, and up to 4.20% on MATH-500. The code is available at the https://github.com/ZJUDianJin/Causal-Shortcuts-Learning.</abstract>
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%0 Conference Proceedings
%T Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models
%A Jin, Dian
%A Han, Kairong
%A Li, Baohong
%A Dong, Xinpeng
%A Hu, Zijing
%A Shan, Nuanqiao
%A Wu, Fei
%A Kuang, Kun
%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 jin-etal-2026-towards
%X Diffusion Language Models (DLMs) have attracted significant attention for their strong reasoning ability. However, under a bidirectional attention mechanism, DLMs operate over an exponentially large exploration space compared to autoregressive models (ARMs), making it challenging to focus on reasoning-guiding tokens under random masking. We define causal shortcuts as token chains that cover the full sequence and provide explicit guidance towards correct reasoning trajectories. We analyze the effects of causal shortcuts on the reasoning accuracy and convergence speed of DLMs, and find that they largely improve answer convergence efficiency and generation accuracy. Motivated by this, we propose a Causal Shortcut Learning (CSL) Framework for DLMs. Specifically, we introduce a step-by-step token extraction procedure to extract causal shortcuts from data, and apply parallel prioritized masking on these tokens during training to enable efficient and accurate convergence to correct answers via causal shortcuts. Extensive experiments across multiple reasoning benchmarks and two base models demonstrate that CSL consistently outperforms existing SFT-variant baselines, achieving an average improvement of 1.92% over SFT-only models, and up to 4.20% on MATH-500. The code is available at the https://github.com/ZJUDianJin/Causal-Shortcuts-Learning.
%U https://aclanthology.org/2026.emnlp-main.292/
%P 6710-6728
Markdown (Informal)
[Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models](https://aclanthology.org/2026.emnlp-main.292/) (Jin et al., EMNLP 2026)
ACL
- Dian Jin, Kairong Han, Baohong Li, Xinpeng Dong, Zijing Hu, Nuanqiao Shan, Fei Wu, and Kun Kuang. 2026. Towards Efficient Reasoning: Learning Causal Shortcuts for Diffusion Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 6710–6728, Budapest, Hungary. Association for Computational Linguistics.