Second-Order Drifting Models Accelerate Generative Training
A recent paper on arXiv presents Second-Order Drifting Models, which enhance the training speed of drifting models, a type of one-step generative model. Identified as arXiv:2608.07924, it details a method that integrates artificial velocity variables into generated samples, elevating drifting dynamics into phase space. This results in density perturbations that exhibit accelerated second-order dynamics in Fourier space, linking the approach to Nesterov acceleration from optimization theory. The authors demonstrate that in the linearized regime, the decay rate of each Fourier mode of the density residual is influenced by the kernel spectrum, which previously hindered the recovery of fine-scale structures. This innovative method, announced on August 26, 2026, offers significant advancements in generative modeling and machine learning.
Key facts
- The paper is titled 'Second Order Drifting Models'.
- It is available on arXiv with identifier 2608.07924.
- The announcement type is 'cross'.
- Drifting models are a class of one-step generative models.
- The method uses a predefined sample-based drift field.
- The proposed approach augments generated samples with artificial velocity variables.
- The method connects to Nesterov acceleration from optimization theory.
- The paper addresses spectral stiffness in first-order drifting models.
Entities
Institutions
- arXiv