arXiv:2605.09235v2 Announce Type: replace Abstract: One-step generative modeling has emerged as a leading approach for amortizing the inference cost of diffusion and flow-matching models. Among distillation-free methods, MeanFlow training is notoriously unstable, with non-decreasing loss and unbounded gradient variance. In this work, we establish a theory that attributes this pathology to a misuse of the conditional velocity field. We show that the conditional velocity plays two distinct statistical roles in the loss: both as an unbiased regression target and as a Monte Carlo control variate i
The rapid advancement in generative AI models, particularly diffusion models, has highlighted the need for more stable and efficient training methods.
This research addresses a fundamental instability in generative AI model training, potentially leading to more robust and scalable AI development.
A theoretical understanding of MeanFlow training instability could lead to improved optimization techniques and more reliable one-step generative models.
- · AI researchers
- · Generative AI developers
- · AI model deployers
- · Inefficient AI training methods
One-step generative models may become more stable and easier to train.
Reduced computational costs and faster iteration cycles for developing new AI models could accelerate innovation.
More reliable and efficient generative AI could lead to broader adoption across various industries, including content creation and scientific research.
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