Theoretical Blind Spot of Discrete Diffusion: Fails to Learn Joint Probability Distributions
kalomaze · x · 2026-08-10
Developer kalomaze points out a fundamental flaw in discrete diffusion models like MDLM: learning a set of independent conditionals is entirely different from learning a true joint probability distribution.
He gives an example that token combinations like |New| |Carolina| do not represent a real state, but parallel sampling of decoupled token conditionals is completely blind to this logical inconsistency.
Related event: Developer Highlights Theoretical Blind Spot in Discrete Diffusion Models(3 posts)→
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