Grokking in machine learning refers to a phenomenon where a model, after a period where learning appears stagnant, suddenly achieves a rapid and significant improvement in understanding or performing its task. This abrupt shift in performance is akin to a sudden "clicking" into place of the model's understanding, analogous to a human experiencing a moment of clarity after struggling with a concept. The term draws on Robert Heinlein's science fiction, where "to grok" means to understand something deeply and fully.

The intriguing aspect of grokking is that it challenges traditional expectations in statistical learning, which typically anticipate gradual improvements through incremental learning. Instead, grokking suggests a different dynamic where models might initially seem not to learn or improve, only to later exhibit a swift elevation in capability. This has been observed particularly in cases involving complex problem-solving tasks or when models employ strategies that delay evident learning until a critical mass of knowledge or capability is reached.

Despite its potential implications for advancing AI efficiency and effectiveness, the precise mechanisms behind grokking are not yet well understood. Researchers continue to explore this behavior, hoping to glean insights that could lead to more predictable and controllable outcomes in AI training processes.

For a more detailed overview, read Towards Understanding Grokking: An Effective Theory of Representation Learning by Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, and Mike Williams