3 papers
cs.LG2026
Refining Few-Step Text-to-Multiview Diffusion via Reinforcement Learning
Ziyi Zhang, Li Shen, Deheng Ye +5
Text-to-multiview (T2MV) diffusion models have shown great promise in generating multiple views of a scene from a single text prompt. While few-step backbones enable real-time T2MV…
cs.LG2026
Aligning Few-Step Diffusion Models with Dense Reward Difference Learning
Ziyi Zhang, Li Shen, Sen Zhang +6
Few-step diffusion models enable efficient high-resolution image synthesis but struggle to align with specific downstream objectives due to limitations of existing reinforcement le…
cs.LG2025
Robust Policy Expansion for Offline-to-Online RL under Diverse Data Corruption
Longxiang He, Deheng Ye, Junbo Tan +2
Pretraining a policy on offline data followed by fine-tuning through online interactions, known as Offline-to-Online Reinforcement Learning (O2O RL), has emerged as a promising par…