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arXiv · 2602.01338

High-accuracy sampling for diffusion models and log-concave distributions

Fan Chen·Sinho Chewi·Constantinos Daskalakis·Alexander Rakhlin
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Abstract

We present algorithms for diffusion model sampling which obtain $\delta$-error in $\mathrm{polylog}(1/\delta)$ steps, given access to $\widetilde O(\delta)$-accurate score estimates in $L^2$. This is an exponential improvement over all previous results. Specifically, under minimal data assumptions, the complexity is $\widetilde O(d_\star \mathrm{polylog}(1/\delta))$ where $d_\star$ is the intrinsic dimension of the data. Further, under a non-uniform $L$-Lipschitz condition, the complexity reduces to $\widetilde O(L \mathrm{polylog}(1/\delta))$. Our approach also yields the first $\mathrm{polylog}(1/\delta)$ complexity sampler for general log-concave distributions using only gradient evaluations.

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Fan Chen, Sinho Chewi, Constantinos Daskalakis, Alexander Rakhlin. 2026-02-01. High-accuracy sampling for diffusion models and log-concave distributions. https://arxiv.org/abs/2602.01338

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