Diffusion vs. AR Models: Disparity in Initial Step Difficulty
kalomaze · x · 2026-08-08
An AI researcher on X discussed the fundamental differences in conditioning mechanisms between diffusion models and Autoregressive (AR) models.
The author points out that for EDM diffusion models, the task is provably easier for certain samples in an IID training batch near the low noise end, even at initialization before any optimizer step is taken. This is because diffusion models can condition on every possible log-normal degree of intermediate exposure to the data manifold.
In contrast, true categorical AR models face equal difficulty everywhere on the first step. AR models essentially must "carve the joint" in a very tabula rasa way, meaning their conditioning is essentially worthless on the first step due to relying solely on prior conditioning.
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