dit-57f550c5·1 events·first seen Aliases: DiT
A new arXiv paper demonstrates that small forward-marginal L2 error in score matching does not certify numerical stability for discretized reverse-time diffusion samplers. The authors construct adversarial examples where Euler-Maruyama discretizations converge weakly yet all Wasserstein distances diverge, and show this failure can occur within a fixed finite neural architecture. A positive result is also provided: projecting the learned denoiser onto a bounded convex set containing the data support restores Wasserstein convergence under mild regularity. Experiments with a DiT-style network confirm the instability and its suppression via denoiser projection.