Refresh or Realize? Compute Allocation in Drifting Models

arXiv:2609.32298 · cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Regularization — 5× in this paper · explained in 3 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Gradient Flow — 4× in this paper · explained in 2 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Flow Matching — 2× in this paper · explained in 8 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Second Order Drifting Models
• Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences
• Generative Modeling via Drifting
• Kernel-Gradient Drifting Models
• On the Wasserstein Gradient Flow Interpretation of Drifting Models
• A Unified View of Score-Based and Drifting Models
• A Long-Short Flow-Map Perspective for Drifting Models
• Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective

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