Preprint
Aug 2026
Designing Reinforcement Learning for Diffusion Models: A Unified Path-Space View
The empirical gap between these method families is identified as a variance-reduction effect rather than a difference in RL principle, and a multi-sample KDE value-gradient estimator that reuses rollout groups, together with scale-bounded weight families that retain stable existing recipes while excluding singular ones is proposed.
Yi-Xian Xu, Yuanrui Zhang, Shengjie Luo et al.
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