arXiv AI October 27, 2026 By Volodymyr Kuleshov, Marianne Arriola, Yair Schiff, Gilad Turok, Guanghan Wang

How To Build A Modern Diffusion Language Model

The article "How To Build A Modern Diffusion Language Model" reviews the recent advances in large language models (LLMs) and highlights their limitations, such as sequential token generation that underutilizes GPU hardware and prevents error correction. It explains how discrete diffusion models overcome these issues by generating tokens in parallel through iterative denoising, enabling high-speed inference and controllable generation. The paper surveys key concepts, architectural components, and applications of diffusion LLMs in biology and language, and discusses their implications for future scaling laws.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 5

A Survey on Diffusion Language Models

arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.

By Tianyi Li, Mingda Chen, Bowei Guo, Zhiqiang Shen

llms diffusion nlp efficiency multimodal benchmarks

arXiv AI

Jun 19

Diffusion Language Models: An Experimental Analysis

arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.

By Thomas Bertolani, Davide Bucciarelli, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi

llms diffusion benchmarks

arXiv Machine Learning

Sep 4

Unlocking Lossless Speedups in LLMs via Discrete Diffusion

arXiv:2609. 04010v1 Announce Type: new Abstract: Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation.

By Subham Sekhar Sahoo, Lingjie Chen, Khiem Pham, Jonathan Geuter, Chaitanya Dwivedi, Varad Pimpalkhute, Yash Akhauri, Alexander Moreno, Mikhail Yurochkin, Zhenting Wang, Mostafa Elhoushi, Nolan Dey, Shane Bergsma, Joel Hestness, John Thickstun, Eric Xing, Zhengzhong Liu

llms agents diffusion efficiency benchmarks

arXiv Machine Learning

Jun 18

Sumi: Open Uniform Diffusion Language Model from Scratch

arXiv:2606. 19005v1 Announce Type: cross Abstract: Diffusion models have become a promising alternative to autoregressive models.

By Mengyu Ye, Keito Kudo, Wataru Ikeda, Ryosuke Matsuda, Keisuke Sakaguchi, Jun Suzuki

llms diffusion benchmarks

arXiv Machine Learning

Aug 4

Just on Time: Token-Level Early Stopping for Diffusion Language Models

arXiv:2602. 11133v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens reach stability long before the final denoising step.

By Zakhar Kohut, Severyn Shykula, Mykola Vysotskyi, Serhii Dmytryshyn, Dmytro Khamula, Michal Zakrzewski, Damian Rynczak, Jacek Ma{\l}ecki, Taras Rumezhak, Volodymyr Karpiv

llms diffusion nlp fine-tuning benchmarks

arXiv Computation and Language

Aug 25

Accelerating Diffusion Language Models via Structured Suffix Modeling

The paper introduces a training‑free structured suffix modeling technique to accelerate Diffusion Language Models (DLMs). It partitions the suffix into local, middle, and tail regions, retaining varying numbers of tokens per region and incorporating previous decoding results into current token representations. Experiments on three DLMs show significant speedups—up to 72.81× in long‑sequence inference—while often improving performance, and the method is compatible with existing acceleration strategies.

By Zifeng Cheng, Keda Li, Zhiwei Jiang, Cong Wang, Fei Shen, Qing Gu

llms diffusion efficiency benchmarks

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