arXiv Machine Learning

Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement

arXiv:2507. 08390v5 Announce Type: replace Abstract: Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training.

arXiv AI

Jun 2

Efficient Weighted Sampling via Score-based Generative Models

arXiv:2502. 04646v2 Announce Type: replace-cross Abstract: Weighted sampling -- sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function -- is a fundamental technique with wide-ranging applications in variance reduction, biased sampling, data augmentation, and more.

By Heasung Kim, Taekyun Lee, Hyeji Kim, Gustavo de Veciana

diffusion benchmarks

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 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

Jun 16

Semantic DLM+: Improving Diffusion Language Models through Bias-variance Trade-off in Transition Kernel Design

arXiv:2606. 15327v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have demonstrated strong scaling capacity as alternatives to autoregressive language models.

By Keyue Jiang, Yuxiang Wang, Yanan Zhao, Xiang Yu, Qifang Zhao, Bohan Tang, Baojian Zhou, Yanghua Xiao, Lin Qu, Xiaoxiao Xu

llms diffusion safety

arXiv AI

Jun 2

Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms

arXiv:2503. 07154v3 Announce Type: replace-cross Abstract: Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for continuous signals.

By Jiaming Song, Linqi Zhou

diffusion efficiency

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

Jul 28

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective

arXiv:2607. 24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diffusion, where every token remains editable during sampling.

By Xiaoyi Jiang, Jingyuan Li, Yixuan Jiang, Wei Liu, Yi Zhu, Zuoqiang Shi, Pipi Hu

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

Jul 9

Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF

arXiv:2607. 07693v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences.

By Eric Zhu, Abhinav Shrivastava, Soumik Mukhopadhyay

diffusion reinforcement-learning

arXiv Machine Learning

Jun 26

Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM

arXiv:2606. 26120v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirectional attention mechanisms.

By Tianyi Wu, Xiaoxi Sun, Yanhua Jiao, Yulin Li, Yixin Chen, YunHao Cao, YiQi Hu, Zhuotao Tian

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jul 28

Learning Sampling Parameters for Diffusion Models

arXiv:2607. 23488v1 Announce Type: new Abstract: Text-to-image diffusion models expose many inference-time sampling parameters, including prompts, negative prompts, classifier-free guidance scales, and noise schedules.

By Arisrei Lim, Yossi Gandelsman

llms diffusion reinforcement-learning multimodal

arXiv AI

Jul 15

Accelerating Masked Diffusion Large Language Models: A Survey of Efficient Inference Techniques

arXiv:2607. 12829v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a theoretical advantage in parallel generation over standard autoregressive models.

By Daehoon Gwak, Minhyung Lee, Junwoo Park, Jaegul Choo

llms diffusion efficiency benchmarks

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