Explaining Grokking Through Circuit Efficiency" is a research paper exploring novel predictions about grokking in neural networks, providing significant evidence in favor of its explanation. The authors demonstrate two surprising behaviors: ungrokking, where a network regresses from perfect to low test accuracy, and semi-grokking, where a network shows delayed generalization to partial rather than perfect test accuracy. The paper discusses the concept of "circuits" within neural networks, which refer to modules that can learn multiple different ways of achieving low loss in parallel. The authors argue that efficiency is independent of training size and that there is a crossover point beyond which the network's performance improves dramatically. They also propose a novel prediction about grokking, which they show is supported by their analysis. The paper highlights the importance of understanding generalization and the challenges associated with it, particularly in the context of large language models like GPT-4. The authors discuss potential applications and open questions related to grokking and efficiency in neural networks.