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Convergence rates for generative drifting flows: fixed-scale obstructions and multihead acceleration

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

Drifting models offer a promising route to faster generative AI: they perform gradual transport during training, while generating new samples in a single step. This paper asks whether the underlying drifting process can converge rapidly to a target distribution under ideal conditions, before finite-data or optimization effects are introduced. We show that its convergence rate depends critically on how it handles spatial scale. With a single fixed resolution, fine-scale features of the target can become nearly invisible, leading to extremely slow convergence. We introduce a multihead approach t

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