Hugging Face Trending Papers July 2, 2026

ART for Diffusion Sampling: Continuous-Time Control and Actor-Critic Learning

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We study timestep allocation for score-based diffusion sampling, where a learned reverse-time dynamics is discretized on a finite grid. Uniform and hand-crafted schedules are standard choices, but they rely on fixed prescriptions and can therefore be suboptimal.

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arXiv AI

Jul 3

ART for Diffusion Sampling: Continuous-Time Control and Actor-Critic Learning

arXiv:2607. 02137v1 Announce Type: cross Abstract: We study timestep allocation for score-based diffusion sampling, where a learned reverse-time dynamics is discretized on a finite grid.

By Yilie Huang, Wenpin Tang, Xun Yu Zhou

diffusion reinforcement-learning

arXiv AI

2d ago

Adaptive Reparametrized Time for Score-Based Diffusion Sampling

arXiv:2607.02137v3 Announce Type: replace-cross Abstract: We study timestep allocation for score-based diffusion sampling, where a learned reverse-time dynamics is discretized on a finite grid. Unifo...

By Yilie Huang, Wenpin Tang, Xun Yu Zhou

diffusion reinforcement-learning

arXiv AI

2d ago

ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule

arXiv:2601.18681v3 Announce Type: replace-cross Abstract: We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid. Un...

By Yilie Huang, Wenpin Tang, Xunyu Zhou

diffusion reinforcement-learning

arXiv Machine Learning

Jun 2

Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning

arXiv:2602. 08689v2 Announce Type: replace Abstract: Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network.

By Constant Bourdrez, Alexandre V\'erine, Olivier Capp\'e

diffusion reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 16

Temporal Difference Learning for Diffusion Models

arXiv:2606. 15048v1 Announce Type: new Abstract: Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along the denoising trajectory.

By Qizhen Ying, Yangchen Pan, Victor Adrian Prisacariu, Junfeng Wen

diffusion reinforcement-learning 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

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