@inproceedings{lin-etal-2026-efficient-diffusion,
title = "Efficient Diffusion Language Models: A Comprehensive Survey",
author = "Lin, Haokun and
Jia, Xinle and
Liu, Shaozhen and
Xia, Shujun and
Huang, Weitao and
Xu, Haobo and
LI, Junyang and
Xiao, Yicheng and
Xing, Xingrun and
Guo, Ziyu and
Zhang, Renrui and
Li, Qi and
Wu, Yichen and
Wang, Renzhen and
Qi, Xiaojuan and
Meng, Deyu and
Shan, Caifeng and
Li, Hongsheng and
Sun, Zhenan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1074/",
pages = "22711--22727",
ISBN = "979-8-89176-462-0",
abstract = "Diffusion language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) language models, offering competitive performance with a fundamentally different generation paradigm. Instead of producing tokens strictly left-to-right, dLLMs iteratively refine partially masked sequences, enabling bidirectional context modeling and supporting parallel token updates. This design creates new opportunities for accelerating generation and improving controllability. However, practical deployment of dLLMs remains challenging due to the high cost of training at scale and the lack of mature inference optimizations such as cache-friendly decoding and robust parallel sampling. In this work, we provide a comprehensive overview of recent progress on efficient dLLMs. To reflect how efficiency bottlenecks arise across the full lifecycle of dLLMs, spanning training, decoding, and deployment, we organize existing approaches into five categories: Training, Inference, Context, Framework, and Multimodality. For each category, we summarize representative methods and highlight their motivations, key techniques, and empirical trade-offs. Finally, we discuss open challenges and outline promising directions for future research toward high-throughput, robust, and practically deployable diffusion language models."
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<abstract>Diffusion language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) language models, offering competitive performance with a fundamentally different generation paradigm. Instead of producing tokens strictly left-to-right, dLLMs iteratively refine partially masked sequences, enabling bidirectional context modeling and supporting parallel token updates. This design creates new opportunities for accelerating generation and improving controllability. However, practical deployment of dLLMs remains challenging due to the high cost of training at scale and the lack of mature inference optimizations such as cache-friendly decoding and robust parallel sampling. In this work, we provide a comprehensive overview of recent progress on efficient dLLMs. To reflect how efficiency bottlenecks arise across the full lifecycle of dLLMs, spanning training, decoding, and deployment, we organize existing approaches into five categories: Training, Inference, Context, Framework, and Multimodality. For each category, we summarize representative methods and highlight their motivations, key techniques, and empirical trade-offs. Finally, we discuss open challenges and outline promising directions for future research toward high-throughput, robust, and practically deployable diffusion language models.</abstract>
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%0 Conference Proceedings
%T Efficient Diffusion Language Models: A Comprehensive Survey
%A Lin, Haokun
%A Jia, Xinle
%A Liu, Shaozhen
%A Xia, Shujun
%A Huang, Weitao
%A Xu, Haobo
%A LI, Junyang
%A Xiao, Yicheng
%A Xing, Xingrun
%A Guo, Ziyu
%A Zhang, Renrui
%A Li, Qi
%A Wu, Yichen
%A Wang, Renzhen
%A Qi, Xiaojuan
%A Meng, Deyu
%A Shan, Caifeng
%A Li, Hongsheng
%A Sun, Zhenan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lin-etal-2026-efficient-diffusion
%X Diffusion language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) language models, offering competitive performance with a fundamentally different generation paradigm. Instead of producing tokens strictly left-to-right, dLLMs iteratively refine partially masked sequences, enabling bidirectional context modeling and supporting parallel token updates. This design creates new opportunities for accelerating generation and improving controllability. However, practical deployment of dLLMs remains challenging due to the high cost of training at scale and the lack of mature inference optimizations such as cache-friendly decoding and robust parallel sampling. In this work, we provide a comprehensive overview of recent progress on efficient dLLMs. To reflect how efficiency bottlenecks arise across the full lifecycle of dLLMs, spanning training, decoding, and deployment, we organize existing approaches into five categories: Training, Inference, Context, Framework, and Multimodality. For each category, we summarize representative methods and highlight their motivations, key techniques, and empirical trade-offs. Finally, we discuss open challenges and outline promising directions for future research toward high-throughput, robust, and practically deployable diffusion language models.
%U https://aclanthology.org/2026.findings-emnlp.1074/
%P 22711-22727
Markdown (Informal)
[Efficient Diffusion Language Models: A Comprehensive Survey](https://aclanthology.org/2026.findings-emnlp.1074/) (Lin et al., Findings 2026)
ACL
- Haokun Lin, Xinle Jia, Shaozhen Liu, Shujun Xia, Weitao Huang, Haobo Xu, Junyang LI, Yicheng Xiao, Xingrun Xing, Ziyu Guo, Renrui Zhang, Qi Li, Yichen Wu, Renzhen Wang, Xiaojuan Qi, Deyu Meng, Caifeng Shan, Hongsheng Li, and Zhenan Sun. 2026. Efficient Diffusion Language Models: A Comprehensive Survey. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 22711–22727, Budapest, Hungary. Association for Computational Linguistics.