AI RESEARCH
Guiding Distribution Matching Distillation with Gradient-Based Reinforcement Learning
arXiv CS.LG
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ArXi:2604.19009v1 Announce Type: new Diffusion distillation, exemplified by Distribution Matching Distillation (DMD), has shown great promise in few-step generation but often sacrifices quality for sampling speed. While integrating Reinforcement Learning (RL) into distillation offers potential, a naive fusion of these two objectives relies on suboptimal raw sample evaluation. This sample-based scoring creates inherent conflicts with the distillation trajectory and produces unreliable rewards due to the noisy nature of early-stage generation.