Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario
Empirical studies of trained models often report a transient regime in which signal is detectable in a finite gradient descent time window before overfitting dominates. We provide an analytically tractable random-matrix model that reproduces this phenomenon for gradient flow in a linear teacher--student setting. In this framework, learning occurs when an isolated eigenvalue separates from a noisy bulk, before eventually disappearing in the overfitting regime. The key ingredient is anisotropy in the input covariance, which induces fast and slow directions in the learning dynamics. In a two-block covariance model, we derive the full time-dependent bulk spectrum of the symmetrized weight mat...
| Authors | Florentin Coeurdoux, Grégoire Ferré, Jean-Philippe Bouchaud |
|---|---|
| Primary category | stat.ML |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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