1.3k citations · 2.4k across the 148 of their papers we have counts for
34 papers · 1 filter
Data-regularized Reinforcement Learning for Diffusion Models at Scale
Haotian Ye, Kaiwen Zheng, Jiashu Xu +15
Aligning generative diffusion models with human preferences via reinforcement learning (RL) is critical yet challenging. Most existing algorithms are often vulnerable to reward hac…
Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective
Jingyang Ou, Jiaqi Han, Minkai Xu +5
Reinforcement Learning (RL) has proven highly effective for autoregressive language models, but adapting these methods to diffusion large language models (dLLMs) presents fundament…
InfoTok: Adaptive Discrete Video Tokenizer via Information-Theoretic Compression
Haotian Ye, Qiyuan He, Jiaqi Han +12
Accurate and efficient discrete video tokenization is essential for long video sequences processing. Yet, the inherent complexity and variable information density of videos present…
MeanFlow Transformers with Representation Autoencoders
Zheyuan Hu, Chieh-Hsin Lai, Ge Wu +2
MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data. In practice, it is often used…
Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
Aniketh Iyengar, Jiaqi Han, Boris Ruf +3
The rapidly growing computational demands of diffusion models for image generation have raised significant concerns about energy consumption and environmental impact. While existin…
Generative Modeling Enables Molecular Structure Retrieval from Coulomb Explosion Imaging
Xiang Li, Till Jahnke, Rebecca Boll +12
Capturing the structural changes that molecules undergo during chemical reactions in real space and time is a long-standing dream and an essential prerequisite for understanding an…