Insight-based Learning Efficiency

Grokking involves evolving mental models through observations, selecting for general circuits and leading to insight-based learning. Neural networks' grokking is significantly less efficient than humans' integration of insights, which are immediately impactful. Models require multiple exposures to diverse contexts to grasp concepts, unlike humans' rapid incorporation of new insights. Neural networks' gradient descent training method lacks the immediate, impactful learning characteristic of human insight-based learning, hindering efficient knowledge integration.