Deep Dive: Why is Autoregressive Modeling Harder than Diffusion in High-Dimensional Semantics?
kalomaze · x · 2026-08-08
Researcher @kalomaze provided an in-depth comparison highlighting the fundamental differences between Autoregressive (AR) models and Diffusion models (EDM diffusion).
- Advantage of Diffusion: In an iid training batch, the task is provably easier for samples near the low noise end, even at initialization before any optimizer step is taken.
- Disadvantage of AR: On the first generation step, the conditioning is essentially worthless, making the task equally hard everywhere.
He concluded that exact autoregression is doing something way harder. As 1D categorical semantics scale to higher-dimensional spaces, the theoretical performance might become significantly worse. However, few seem interested in natively modeling these high-dimensional semantics via novel factorizations.
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