First-Order Stationarity of Reverse Diffusions

Abstract:Recent literature has shown a strong connection between optimization and sampling. We develop the corresponding first-order theory for diffusion models. First, the SDE-based reverse-time flows of overdamped and underdamped Langevin diffusions contract relative Fisher divergences at explicit exponential rates whenever the stationary potential of the forward process is strongly convex---a condition on the noising process one chooses, not on the data. This is a unique advantage of SDE-based reverse diffusion, absent in the reverse process based on ODEs. Second, we incorporate discretization and establish averaged first-order stationarity bounds---the sampling analog of averaged gradient-norm guarantees in nonconvex optimization---for samplers of both overdamped and underdamped diffusion models. As in nonconvex optimization, the convexity-free certificate is local: it guarantees score consistency, not global mode weights.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2609.31612 [stat.ML]

(or arXiv:2609.31612v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2609.31612 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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