arXiv Machine Learning

Fork-dLLM: Avoiding the Flexibility Trap in Diffusion Language Models

arXiv Machine Learning

Aug 12

A Tale of Two Temperatures: Simple, Efficient, and Diverse Sampling from Diffusion Language Models

arXiv:2604. 09921v2 Announce Type: replace Abstract: Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs).

By Theo X. Olausson, Metod Jazbec, Xi Wang, Armando Solar-Lezama, Christian A. Naesseth, Stephan Mandt, Eric Nalisnick

llms diffusion

arXiv AI

Jun 3

$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction

arXiv:2604. 18995v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction.

By Zhenbang Du, Kejing Xia, Xinrui Zhong, Yonggan Fu, Nicolai Oswald, Binfei Ji, Brucek Khailany, Pavlo Molchanov, Yingyan Lin

llms diffusion fine-tuning efficiency

arXiv AI

Jun 2

BlockBatch: Multi-Scale Consensus Decoding for Efficient Diffusion Language Model Inference

arXiv:2605. 29233v2 Announce Type: replace-cross Abstract: Diffusion language models (dLLMs) generate text by iteratively denoising multiple token positions in parallel, offering an attractive alternative to strictly autoregressive decoding.

By Xiaoyou Wu, Cheng-Jhih Shih, Binfei Ji, Yong Liu, Yingyan Celine Lin

llms diffusion

arXiv Machine Learning

Sep 25

Enabling Approximate Joint Sampling in Diffusion LMs

The paper introduces a lightweight single‑layer sampler that allows masked diffusion language models to approximate joint sampling of multiple tokens in a single full‑model forward pass. By training the sampler to mimic exact joint sampling from a frozen diffusion model, the authors enable parallel unmasking of tokens while maintaining a close match to the true joint distribution. Experiments on Dream‑7B and Llada‑7B models show that unmasking four tokens per denoising step yields a MAUVE score of 0.87, a substantial improvement over the marginal baseline of 0.31.

By Parikshit Bansal, Sujay Sanghavi

llms diffusion

arXiv Machine Learning

Jun 3

Learning Unmasking Policies for Diffusion Language Models

arXiv:2512. 09106v4 Announce Type: replace Abstract: Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during inference.

By Metod Jazbec, Theo X. Olausson, Louis B\'ethune, Pierre Ablin, Michael Kirchhof, Jo\~ao Monteiro, Victor Turrisi, Jason Ramapuram, Marco Cuturi

llms diffusion reinforcement-learning efficiency benchmarks

arXiv AI

Sep 16

Early-Bird Decoding: Accelerating Diffusion LLMs with Learnable Block Sizes and Parallel Sampling

arXiv:2609.16450v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a promising parallel decoding paradigm as an alternative to autoregressive generation through iterative...

By Lixuan Wei, Wei Zhou, Jianwen Wu, Yipeng Shen, Meiling Wang, Haoran You

llms diffusion efficiency benchmarks

arXiv AI

Jul 24

DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding

arXiv:2607. 20467v1 Announce Type: new Abstract: While parallel decoding is central to the efficiency of Diffusion Large Language Models (dLLMs), current strategies are often hindered by overly conservative confidence thresholds.

By Yanhua Jiao, Tianyi Wu, Xiaoxi Sun, Yulin Li, HuiLing Zhen, Libo Qin, Baotian Hu, Zhuotao Tian, Min Zhang

llms diffusion benchmarks

arXiv Machine Learning

Jul 8

Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge

arXiv:2606. 08048v1 Announce Type: cross Abstract: Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressive (AR) models.

By Juntong Shi, Brian L. Trippe, Jure Leskovec, Stefano Ermon, Minkai Xu

llms diffusion

arXiv AI

Sep 1

DIP: Dynamic In-Context Planner For Diffusion Language Models

arXiv:2601.03199v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. Existing In-Context...

By Yang Li, Han Meng, Chenan Wang, Zhenyu Bi, Xuan Wang, Haipeng Chen

llms diffusion benchmarks

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 Machine Learning

Aug 10

Retrofitting Linear Attention into Diffusion Language Models

arXiv:2608. 06628v1 Announce Type: new Abstract: Diffusion language models (dLLMs) offer a promising alternative to autoregressive models by accelerating inference through parallel decoding.

By Jinha Kim, Younghun Roh, Jaeyeon Kim

llms diffusion efficiency benchmarks

arXiv AI

Jul 24

Understanding and Accelerating the Training of Masked Diffusion Language Models

arXiv:2605. 13026v2 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling.

By Chunsan Hong, Sanghyun Lee, Chieh-Hsin Lai, Satoshi Hayakawa, Yuhta Takida, Yuki Mitsufuji, Seungryong Kim, Jong Chul Ye

llms diffusion benchmarks safety

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