Purify Before You Align: stripping local correlations improves diffusion alignment
LucaAmb · x · 2026-10-25
A new paper, Purify Before You Align, shows that more information in a representation isn't always better for diffusion models: removing locally predictable information yields substantially improved diffusion alignment. Sander Dieleman endorses the intuition — drop strong local correlations to surface weaker long-range ones — linking it to his 2019 work on hierarchical autoregressive image models with auxiliary decoders and masked-self prediction.
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