Forward-Value Discretization: Faster Stochastic Diffusion Sampling Without High-Order Approximation

arXiv:2611.14328v1 Announce Type: cross Abstract: Stochastic diffusion samplers often require substantially more neural function evaluations (NFEs) than deterministic samplers to achieve comparable sample quality. Although higher-order discretization accelerates deterministic sampling, its benefits for stochastic sampling remains unclear. We establish matching upper and worst-case lower bounds showing that stochastic samplers with higher-order approximations remain first-order convergent, even with exact score functions. Thus, increasing the approximation order does not generally improve the c

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