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Generative Drifting Flows

model1 events
papersTODAY 04:00 UTC

Convergence rate analysis of generative drifting flows

A new arXiv paper examines whether drifting models, which learn a gradual transport process during training but generate samples in a single step, can converge quickly to a target distribution. The authors identify obstructions to fast convergence at fixed scale and propose a multihead approach that improves convergence rates. The work is theoretical, focused on the training dynamics rather than a deployed system.

generative-models/training/research/theory