arXiv:2503.07154v3 Announce Type: replace Abstract: Generative pre-training is often framed through a false dichotomy between autoregressive models for discrete signals and diffusion models for continuous signals. We argue that the dichotomy is false because it conflates model family, data representation, training objective, and inference procedure. Autoregression is an inference procedure that expands a sequence through normalized conditional draws, while diffusion is a refinement procedure that repeatedly revises an existing state. The more useful contrast is therefore not autoregressive ver
The paper is a current release in the cs.LG category, representing ongoing academic advancements in foundational AI research, specifically regarding generative models.
A strategic reader should care because disambiguating core concepts in generative AI can lead to more efficient and powerful model development, impacting the future capabilities and costs of AI systems.
This re-framing changes the understanding of generative model architectures and inference, potentially guiding future research and development towards hybrid or more unified approaches.
- · AI researchers
- · Generative AI developers
- · Companies building on generative models
- · Companies with rigid or narrow generative AI strategies
- · Older, less adaptable AI research paradigms
Improved understanding and conceptual frameworks for generative pre-training algorithms.
Development of novel hybrid generative models that combine elements of autoregression and diffusion for superior performance.
Accelerated progress in areas like multi-modal generation and AI agent capabilities due to more effective underlying generative architectures.
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Read at arXiv cs.LG