Normalizing Trajectory Models: Diffusion-based models decompose sampling
Diffusion-based models decompose sampling into many small Gaussian denoising steps, an assumption that breaks down when generation is compressed to a few.
By Dillip Chowdary • Oct 08, 2026 • Source: Apple Machine Learning Research
Normalizing Trajectory Models: what actually changed
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Apple Machine Learning Research reports: Normalizing Trajectory Models. Diffusion-based models decompose sampling into many small Gaussian denoising steps, an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few-step methods address this through distillation, consistency training, or adversarial objectives, but sacrifice the likelihood…
Normalizing Trajectory Models: why it matters now
For primary quotes and complete technical detail, see Apple Machine Learning Research's original report linked above.
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Author
Dillip Chowdary
Writes Tech Bytes coverage of AI, engineering, and the tools that actually ship. Editor of Tech Pulse Daily.
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