[2603.28460] $R_{dm}$: Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.28460 (cs) [Submitted on 30 Mar 2026] Title:$R_{dm}$: Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation Authors:Linqian Fan, Peiqin Sun, Tiancheng Wen, Shun Lu, Chengru Song View a PDF of the paper titled $R_{dm}$: Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation, by Linqian Fan and 4 other authors View PDF HTML (experimental) Abstract:Diffusion models achieve state-of-the-art generative performance but are fundamentally bottlenecked by their slow iterative sampling process. While diffusion distillation techniques enable high-fidelity few-step generation, traditional objectives often restrict the student's performance by anchoring it solely to the teacher. Recent approaches have attempted to break this ceiling by integrating Reinforcement Learning (RL), typically through a simple summation of distillation and RL objectives. In this work, we propose a novel paradigm by reconceptualizing distribution matching as a reward, denoted as $R_{dm}$. This unified perspective bridges the algorithmic gap between Diffusion Matching Distillation (DMD) and RL, providing several key benefits. (1) Enhanced optimization stability: we introduce Group Normalized Distribution Matching (GNDM), which adapts standard RL group normalization to stabilize $R_{dm}$ estimation. By leveraging group-mean statistics, GNDM establishes a more robust and effective optimization d...