stat.MLMar 22, 2026

Generalized Discrete Diffusion from Snapshots

Authors: Oussama Zekri, Théo Uscidda, Nicolas Boullé, Anna Korba

Organizations: CREST, ENSAE, Institut Polytechnique de Paris, Paris, France · Department of Mathematics, Imperial College London, London, SW7 2AZ, UK

Abstract

We introduce Generalized Discrete Diffusion from Snapshots (GDDS), a unified framework for discrete diffusion modeling that supports arbitrary noising processes over large discrete state spaces. Our formulation encompasses all existing discrete diffusion approaches, while allowing significantly greater flexibility in the choice of corruption dynamics. The forward noising process relies on uniformization and enables fast arbitrary corruption. For the reverse process, we derive a simple evidence lower bound (ELBO) based on snapshot latents, instead of the entire noising path, that allows efficient training of standard generative modeling architectures with clear probabilistic interpretation. Our experiments on large-vocabulary discrete generation tasks suggest that the proposed framework outperforms existing discrete diffusion methods in terms of training efficiency and generation quality, and beats autoregressive models for the first time at this scale. We provide the code along with a blog post on the project page : https://oussamazekri.fr/gdds.

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