Hugging Face Trending Papers July 2, 2026

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

Read the original on Hugging Face Trending Papers →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

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 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

arXiv Machine Learning

5d ago

Variance Reduction Based Experience Replay for Policy Optimization

arXiv:2602. 05379v2 Announce Type: replace-cross Abstract: Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and accelerate policy optimization.

By Hua Zheng, Wei Xie, M. Ben Feng, Keilung Choy

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 16

DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising

arXiv:2606. 15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories.

By Paolo Giaretta, Zeyang Li, Navid Azizan

diffusion multimodal benchmarks safety

Related stories

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

Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning

Temporal Difference Learning for Diffusion Models

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

Variance Reduction Based Experience Replay for Policy Optimization

DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising