**摘要**
Pretrained diffusion models serve as frozen teachers feeding downstream pipelines such as text-to-3D, single-step distillation, and data attribution. The teacher gradients these pipelines consume are Monte Carlo (MC) expectations over noise levels and Gaussian noise samples; their estimator variance dominates compute cost because each draw requires expensive upstream work (rendering, simulation, e
👤 作者: Jesse Bettencourt, Xindi Wu, Matan Atzmon, James Lucas, Jonathan Lorraine

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🔗 **[Variance Reduction for Expectations with Diffusion Teachers](https://arxiv.org/abs/2605.21489v1)**

> Variance Reduction for Expectations with Diffusion Teachers
🏷️ 来源: ArXiv cs.AI
⏱️ 2026-05-22 08:00