Second Order Drifting Models
Drifting models are a recent class of one-step generative models that evolve the model distribution during training using a predefined sample-based drift field. Although they avoid iterative inference, their kernel-based drift fields induce frequency-dependent training dynamics: In the linearized regime, each Fourier mode of the density residual decays at a rate determined by the kernel spectrum, leading to slow recovery of fine-scale structure. We propose Second-Order Drifting Models, which lift drifting dynamics into phase space by augmenting generated samples with artificial velocity variables. We show that the resulting density perturbations obey accelerated second-order dynamics in F...
| Authors | Drake Brown, Yuhao Huang, Shih-Hsin Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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