Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Grokking: When Tiny Datasets Teach Big Surprises

Imagine a simple set of rules, a tiny pile of examples, and a big model that first just remembers, then suddenly understands.
Researchers watch this happen on small, rule-made datasets where a model can go from random guessing to near perfect — but only after it already seemed to have overfitted.
At first the system just memorizes the training examples, and later it slowly learns the rule, a process people call grokking, it looks almost like a lightbulb turned on.

Smaller sets need a lot more training steps before the model will generalize, and that is strange, but true.
Studying these tiny tasks lets us peek into how big models learn beyond just copying data.
This might help make smarter, more trusting systems, and show why some models suddenly start to work on things they never saw before.
It's simple experiments with big lessons, and they still surprise even the experts, a little bit.

Read article comprehensive review in Paperium.net:
Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

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