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arXiv · 2605.17244

Drift Flow Matching

Chenrui Ma·Xi Xiao·Lin Zhao·Tianyang Wang·Ferdinando Fioretto·Yanning Shen
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Abstract

Iterative generative models such as Flow Matching and Diffusion models have demonstrated strong test-time scaling behavior, where additional inference computation can improve generation quality. In contrast, Drift Models offer efficient one-step generation, but their direct generation paradigm limits such flexibility. In this work, we propose Drift Flow Matching (DFM), a framework that connects drifting generative modeling with flow-based iterative generation. DFM preserves the efficiency of direct transport maps while enabling generation to be refined through multiple inference steps when desired. This bridges the gap between one-step Drift Models and multi-step Flow Matching methods, and provides a novel generative paradigm that can adapt sampling computation to different quality--efficiency requirements. Extensive experiments across different tasks and datasets demonstrate the effectiveness and generality of the proposed framework.

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Chenrui Ma, Xi Xiao, Lin Zhao, Tianyang Wang, Ferdinando Fioretto, Yanning Shen. 2026-05-17. Drift Flow Matching. https://arxiv.org/abs/2605.17244

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