WeightWatch flags a late collapse in generalization
A paper reports anti-grokking signals in weight matrices across two prolonged-training experiments: a three-layer MLP on a subset of MNIST and a transformer trained on modular addition.
Published on February 4, 2026, the report describes anti-grokking as a late collapse in generalization. The work explores whether model weights can reveal this loss during prolonged training. The cited experiments use a three-layer MLP on a subset of MNIST and a transformer trained on modular addition; the post says WeightWatcher detects signals in the weight matrices.
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