Equivariant Diffusion can have a relatively larger generalization regime than non-equivariant one

Equivariant diffusion models use known symmetries to guide denoising, but how symmetry shapes the course of learning remains unclear. Understanding this effect is important for explaining when training favors useful structure over less relevant variation. This paper identifies selective delay as a mechanism by which symmetry separates these stages of learning.Averaging over symmetry-related inputs can slow the learning of less relevant variation more strongly than the learning of the target's main structure. This increases the relative separation between the two stages,even when both become slower. We establish this mechanism in a tractable denoising model, characterize when it produces a wider learning regime under discrete and continuous symmetries, and verify its predictions numerically. These results connect symmetry to the timing of learning and provide a principle for choosing symmetries that favor task-relevant structure early in training.