Categorical Flow Maps

• URL: http://arxiv.org/abs/2602.12233v1
• Date: Thu, 12 Feb 2026 18:10:46 GMT
• Title: Categorical Flow Maps
• Authors: Daan Roos, Oscar Davis, Floor Eijkelboom, Michael Bronstein, Max Welling, İsmail İlkan Ceylan, Luca Ambrogioni, Jan-Willem van de Meent,
• Abstract summary: Categorical Flow Maps is a flow-matching method for accelerated few-step generation of categorical data via self-distillation.<n>We achieve state-of-the-art few-step results on images, molecular graphs, and text, with strong performance even in single-step generation.
• Score: 42.309126712129384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Categorical Flow Maps, a flow-matching method for accelerated few-step generation of categorical data via self-distillation. Building on recent variational formulations of flow matching and the broader trend towards accelerated inference in diffusion and flow-based models, we define a flow map towards the simplex that transports probability mass toward a predicted endpoint, yielding a parametrisation that naturally constrains model predictions. Since our trajectories are continuous rather than discrete, Categorical Flow Maps can be trained with existing distillation techniques, as well as a new objective based on endpoint consistency. This continuous formulation also automatically unlocks test-time inference: we can directly reuse existing guidance and reweighting techniques in the categorical setting to steer sampling toward downstream objectives. Empirically, we achieve state-of-the-art few-step results on images, molecular graphs, and text, with strong performance even in single-step generation.

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