arXiv AI June 2, 2026 By Heasung Kim, Taekyun Lee, Hyeji Kim, Gustavo de Veciana

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 10

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.

By Meihua Dang, Jiaqi Han, Minkai Xu, Kai Xu, Akash Srivastava, Stefano Ermon

llms diffusion

arXiv AI

Jun 10

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

arXiv:2601. 08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data.

By Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia

diffusion fine-tuning benchmarks safety

arXiv AI

Jul 17

Integration Matters: Rollout-Based Training for Constrained Diffusion Models

arXiv:2607. 14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution.

By Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg, Frank Wood

diffusion fine-tuning

arXiv AI

Aug 12

SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models

arXiv:2506. 13058v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation.

By Hu Yu, Hao Luo, Xueyang Fu, Jie Huang, Fan Wang, Feng Zhao

diffusion

arXiv Machine Learning

Jul 7

Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

arXiv:2607. 04775v1 Announce Type: cross Abstract: Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications.

By Stanislas Strasman (SU, LPSM), Sobihan Surendran (SU, LPSM), Sylvain Le Corff (SU, LPSM)

diffusion

arXiv Machine Learning

Jun 26

Stochastic Gradient Optimization with Model-Assisted Sampling

arXiv:2606. 27171v1 Announce Type: new Abstract: This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization.

By Jonne Pohjankukka, Jukka Heikkonen

benchmarks

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Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

Integration Matters: Rollout-Based Training for Constrained Diffusion Models

SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models

Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

Stochastic Gradient Optimization with Model-Assisted Sampling