Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching

Abstract:Flow matching and diffusion can be trained to predict different quantities, most commonly the data $x_1$, the source noise $x_0$, or the velocity~$v$. Although theoretically equivalent, these can lead to substantially different performances. We identify two main drivers for these differences: the source--data signal-to-noise ratio, and the information bottleneck induced by the neural architecture. We show that, beyond intrinsic data dimension, the factor affecting the optimal parametrization the most is a certain signal-to-noise ratio in each data covariance direction. From this analysis, we introduce new \emph{spectral hybrid} parameterizations that adapt across time and data covariance directions; we show that these are optimal for Gaussian data. We also show that architecture-induced compression changes which parameterization is easier to learn, with $v$-prediction being more sensitive to discarded directions than $x_1$-prediction. Experiments across architectures and source scales show that our spectral parameterizations are robust across regimes, can substantially accelerate optimization, while incurring essentially no additional training cost compared with standard parameterizations.

Machine Learning (cs.LG)

arXiv:2609.32432 [cs.LG]

(or arXiv:2609.32432v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.32432 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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