3 papers
cs.LG2026
V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think
Bingda Tang, Yuhui Zhang, Xiaohan Wang +3
Aligning denoising generative models with human preferences or verifiable rewards remains a key challenge. While policy-gradient online reinforcement learning (RL) offers a princip…
cs.CV2026
Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders
Shengbang Tong, Boyang Zheng, Ziteng Wang +7
Representation Autoencoders (RAEs) have shown distinct advantages in diffusion modeling on ImageNet by training in high-dimensional semantic latent spaces. In this work, we investi…
cs.CV2025
Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis
Bingda Tang, Boyang Zheng, Xichen Pan +2
This paper does not describe a new method; instead, it provides a thorough exploration of an important yet understudied design space related to recent advances in text-to-image syn…