Parallel Decoding for Diffusion Language Models

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49 papers16 papers in the last four weeks, up 220% on the four weeks before. 0.1% of all new papers.

Measured Coupling for Parallel Decoding in Diffusion Language Models
Nov 5, 2026Tal Oved, Lior WolfDiffusion Language Model Inference Parallel Decoding for Diffusion Language Models
Tel Aviv University
Masked diffusion as random sequential adsorption: an exactly solvable model for parallel decoding
Nov 5, 2026Adrian BauleMasked Diffusion Models Diffusion Language Models
School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK
Bidirectional Centered Score Matching for Discrete Diffusion Distillation
Nov 3, 2026Peng Tu, Baoyou Chen, Haoyuan Xia +3Diffusion Distillation Diffusion Language Model Distillation
Shanghai Academy of AI for Science · Fudan University · Shanghai Innovation Institute +2
PACT: Certifying What Is Written in Parallel Decoding of Diffusion Language Models
Nov 3, 2026Yanchao Li, Yusu Zhang, Jiaqing Xie +5Diffusion Language Model Decoding Parallel Decoding for Diffusion Language Models
Nanjing University · Shanghai Artificial Intelligence Laboratory
Multi-Draft On-Policy Distillation for Diffusion Drafters
Oct 27, 2026Petr Molodyk, Yuchen Zhu, Jaemoo Choi +2Speculative Decoding for Diffusion Language Models On-Policy Distillation
Georgia Institute of Technology
Birth, Death and Diff: Insertion-Based Sequence Generation with Corrections
Oct 25, 2026Durga Prasad Maram, Dhruvesh Patel, Tahira Naseem +4Diffusion Language Models Text Generation
University of Massachusetts Amherst · IBM Research
How To Build A Modern Diffusion Language Model
Oct 24, 2026Volodymyr Kuleshov, Marianne Arriola, Yair Schiff +2Diffusion Language Models Diffusion Language Model Inference
Cornell University, USA
SetCommit: Coordinated Token Commitments for Few-Step Diffusion Language Model Decoding
Oct 22, 2026Yuannuo Feng, Yubing Ye, Wenshuai Yao +4Diffusion Language Model Inference Diffusion Language Models
Beihang University · The University of Hong Kong · Peking University
Self-Decomposing Diffusion for Code Generation
Oct 20, 2026Lize Shao, Zichen Xie, Zhizhen Chen +2Code Generation LLM Code Generation
University of Virginia
TeCoP: Accelerating Diffusion Large Language Model Decoding via Tentative Context Pipelining
Oct 20, 2026Jui-Chien Tsou, Hsuan-Chi Liu, Wei-Chin Wang +2Diffusion Language Model Inference Diffusion Language Model Decoding
National Taiwan University
ReAnchor: Reusing Rejected Verifier States for Diffusion Drafting
Oct 19, 2026Sanghyun Lee, Wonjin Nam, Seungho Jang +4Speculative Decoding Speculative Decoding for Diffusion Language Models
KAIST · Seoul National University
Balance-Aware Pivot Selection for Parallel Diffusion LLM Decoding
Oct 19, 2026Yejin Lee, Hyundong Jin, Yo-Sub HanDiffusion Language Model Inference Parallel Decoding for Diffusion Language Models
Yonsei University, Seoul, Republic of Korea
Learning to Couple: Context-Adaptive Joint Denoising for Diffusion Language Models
Oct 19, 2026Zilei Shao, Ian Li, Heng Zhao +4Diffusion Language Model Inference Diffusion Language Models
University of California, Los Angeles · University of California, San Diego · University of Virginia +1
LED: Improving Masked Diffusion Language Models by Lookahead Editing
Oct 16, 2026Yuyan Zhou, Zeyuan Yang, Yuxuan Song +5Diffusion Language Model Decoding Masked Diffusion Models
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology · ByteDance Seed · Institute for AI Industry Research (AIR), Tsinghua University +1
Query-Efficient Rejection Sampling for Parallel Masked Diffusion
Oct 15, 2026Shiyuan Zhang, Xuheng Li, Qiwei Di +1Parallel Decoding for Diffusion Language Models Diffusion Sampling
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
One Block Ahead: Promoting Sampler State in Diffusion Language Models
Oct 14, 2026Yangyi Shen, Pin-Jui Ku, Nicolai Oswald +6Parallel Decoding for Diffusion Language Models Diffusion Language Model Decoding
Stanford University · NVIDIA
A Few More Tokens Ahead: Reinforcing Parallelism in Diffusion LLMs
Oct 13, 2026Jiahao Kuang, Yangfan Gao, Tao Gui +4Parallel Decoding for Diffusion Language Models Parallel Decoding
East China Normal University · Fudan University
Batch-Paced: Fixed-Budget Batch Decoding in Discrete Diffusion Language Models
Oct 13, 2026Shuai Zhang, Zhanchao Zhou, Huachuan Qiu +2Diffusion Language Model Decoding Inference-Time Compute Allocation
Zhejiang University · Westlake University
PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models
Oct 12, 2026Quentin Fruytier, Anton Xue, Sanjay Shakkottai +1Parallel Decoding for Diffusion Language Models Diffusion Language Model Inference
The University of Texas at Austin
Intent-Triggered Anchoring for Multi-Token Format Units in Parallel Decoding of Diffusion Language Models
Oct 12, 2026Junhyeok Lee, Kyu Sung ChoiParallel Decoding for Diffusion Language Models Language Model Decoding
Interdisciplinary Program in Cancer Biology, Seoul National University College of Medicine · Department of Radiology, Seoul National University Hospital · Department of Radiology, Seoul National University College of Medicine +1
ALoDLM: Adaptively Looped Diffusion Language Models
Oct 3, 2026Liancheng Fang, Zhuowei Li, Youngeun Kim +10Adaptive Computation for Diffusion Models Diffusion Language Models
University of Illinois Chicago · Amazon AGI · Korea University
Alpha Diffusion Language Models: Factorization Alone Is Not the Problem
Sep 29, 2026Nikita Gushchin, Dmitry Baranchuk, Alexander KorotinDiffusion Language Models Diffusion Language Model Training
Applied AI Institute, Moscow, Russia · AXXX, Russia · Yandex Research
Rethinking Soft Tokens for Parallel Decoding in Diffusion Language Models
Sep 29, 2026Kodai Kawamura, Kenji Kawaguchi, Anji LiuLanguage Model Decoding Diffusion Language Model Decoding
National University of Singapore
Reliable Parallel Decoding in Masked Diffusion Language Models
Sep 29, 2026Zhenghao He, Bohan Liu, Guangzhi Xiong +1Masked Diffusion Models Diffusion Language Model Decoding
Department of Computer Science, University of Virginia
Parallelism, critical windows, and separations among diffusion language models
Sep 17, 2026Sitan Chen, Liye WangMasked Diffusion Models Diffusion Sampling
SEAS, Harvard University · Tsinghua University
CForce: Boosting Parallel Decoding for dLLMs via Consistency Forcing
Aug 14, 2026Yuji Ren, Chenkai Xu, Zhuocheng Gong +2Parallel Decoding for Diffusion Language Models Diffusion Language Model Decoding
Shanghai Jiao Tong University · Ant Group
DiffusionGemma Technical Report
Jul 31, 2026DiffusionGemma Team, Adrien Ali Taïga, James Assiene +41Diffusion Language Model Decoding Blockwise Diffusion Language Models
Rethinking the Generation Order of Block Diffusion Language Models
Jul 27, 2026Kai Syun Hou, James KwokDiffusion Model Sampling Blockwise Diffusion Language Models
Department of Computer Science and Engineering The Hong Kong University of Science and Technology
Multi-Block Diffusion Language Models
Jun 28, 2026Yijie Jin, Jiajun Xu, Yuxuan Liu +8Blockwise Diffusion Language Models Diffusion Language Model Decoding
Shanghai Jiao Tong University · Xi’an Jiao Tong University · Huawei
Mean-Field Parallel Decoding for Discrete Diffusion Language Models
Jun 14, 2026Tamim Zoabi, Ameen Ali, Liran Ringel +1Parallel Decoding Diffusion Language Model Decoding
School of Electrical & Computer Engineering Tel Aviv University, Israel · School of Computer Science and AI Tel Aviv University, Israel · Department of Computer Science Technion, Israel Institute of Technology, Israel

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Measured Coupling for Parallel Decoding in Diffusion Language Models

Tal Oved, Lior Wolf

Tel Aviv University

Diffusion Language Model Inference Parallel Decoding for Diffusion Language Models

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Diffusion language models can write many tokens per step, but must choose which tokens are safe to commit together. Mean-field decoding penalizes pairs of positions whose predictions look alike. We replace this proxy with the coupling itself: how much committing one token changes the model's confidence at another. Cheap probes inside the main forward pass measure it, with no training or extra model, and it plugs into the same mean-field rule. On three models and four benchmarks, our method needs far fewer steps than mean-field decoding and is faster in most settings, also with a key-value cache, at an average accuracy cost of 1.1 points. Shuffling the couplings or replacing them with a uniform bonus loses this gain.