You don't talk about human analogs of grokking, and that makes sense for a technical paper like this. Nonetheless, grokking also seems to happen in humans, and

You don't talk about human analogs of grokking, and that makes sense for a technical paper like this. Nonetheless, grokking also seems to happen in humans, and everybody has had "Aha!" moments before. Can you maybe comment a bit on the relation to human learning? It seems clear that human grokking is not a process that purely depends on the number of training samples seen but also on the availability of hypotheses. People grok faster if you provide them with symbolic descriptions of what goes on. What are your thoughts on the representation and transfer of the resulting structure, e.g., via language/token streams?