In ML research, "grokking" is not used as a synonym for "generalization"; rather, it names a sometimes-observed delayed‑generalization training phenomenon in which training and held‑out performance do …

Jan 6, 2022 · In this paper we propose to study generalization of neural networks on small algorithmically generated datasets. In this setting, questions about data efficiency, memorization, …

Dec 7, 2016 · Grok may be the only English word that derives from Martian. Yes, we do mean the language of the planet Mars. No, we're not getting spacey; we've just ventured into the realm of …

May 15, 2025 · Grokking forces us to reconsider established practices in training neural networks. It challenges the validity of early stopping criteria and suggests that a model appearing to overfit might …

Jun 14, 2024 · One of the most intriguing is the phenomenon of grokking, where neural networks exhibit surprisingly delayed generalization, achieving high performance on unseen data long after they have...

Nov 26, 2025 · Grokking implies experiential, embodied learning, something beyond surface-level exposure. It hints of an orientation towards fluid intuition, rather than rigid knowing or memorization.

It’s important to note that grokking is a contingent phenomenon — it goes away if model size, weight decay, data size and other hyper parameters aren’t just right. With too little weight decay, the model …

Grokking, or delayed generalization, is a phenomenon where generalization in a deep neural network (DNN) occurs long after achieving near zero training error. Previous studies have reported the …

Jun 29, 2025 · In recent years, machine learning, especially deep learning, has made remarkable progress. A fascinating occurrence in this field is known as " Grokking." This term describes a …

Grokking refers to the surprising phenomenon of delayed generalization where neural networks, on certain learning problems, generalize long after overfitting their training set.