Distribution Matching Distillation Meets Reinforcement Learning

Distribution Matching Distillation (DMD) facilitates efficientinference by distilling multi-step diffusion models into few-step variants.Concurrently, Reinforcement Learning (RL) has emerged as a vital toolfor aligning generative models with human preferences. While both repre-sent critical post-training stages for large-scale diffusion models, existingstudies typically treat them as independent, sequential processes, leav-ing a systematic framework for their unification largely unexplored. Inthis work, we demonstrate that jointly optimizing these two objectivesyields mutual benefits: RL enables more preference-aware and control-lable distillation rather than uniformly compressing the full data dis-tribution, while DMD serves as an effective regularizer to mitigate re-ward hacking during RL training. Building on these insights, we proposeDMDR, a unified framework that incorporates Reward-Tilted Distribu-tion Matching optimization alongside two dynamic distillation trainingstrategies in the initial stage, followed by the joint DMD and RL opti-mization in the second stage. Extensive experiments demonstrate thatDMDR achieves state-of-the-art visual quality and prompt adherenceamong few-step generation methods, even surpassing the performance ofits multi-step teacher model.