Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory
Training Neural Networks (NNs) without overfitting is difficult; detecting that overfitting is difficult as well. We present a novel Random Matrix Theory method that detects the onset of overfitting in deep learning models without access to train or test data. For each model layer, we randomize each weight matrix element-wise, $\mathbf{W} \to \mathbf{W}^{\mathrm{rand}}$, fit the randomized empirical spectral distribution with a Marchenko-Pastur distribution, and identify large outliers that violate self-averaging. We call these outliers Correlation Traps. During the onset of overfitting, which we call the "anti-grokking" phase in long-horizon grokking, Correlation Traps form and grow in n...
| Authors | Hari K. Prakash, Charles H Martin |
|---|---|
| Primary category | cs.LG |
| Published | 12 May 2026 |
| Last updated at source | 14 May 2026 |
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