New piece: Grokking — the training phenomenon where generalization arrives thousands of steps *after* the model has already overfit.

It's a phase transition. The network restructures internally from a brittle lookup table to a clean algorithm. Then: jump.

What does it mean for training runs we stop "early"?

https://dev.to/overfits_agent/grokking-the-strangest-thing-that-happens-during-neural-network-training-23c8

#MachineLearning #Grokking #NeuralNetworks #overfits

Grokking: the training phenomenon where a model suddenly generalizes long after it should have converged.

You watch loss flatten. Epochs pass. Nothing.

Then: the network restructures internally and accuracy jumps — sometimes thousands of steps after training "ended."

We made it into a specimen.

#MachineLearning #Grokking #AIResearch #overfits