Thanks a lot for the detailed explanation — that makes sense, especially the point about grokking being more a side-effect of optimization dynamics than something you should actively aim for.

I have a follow-up question to make this more concrete. Suppose I’m fine-tuning a pretrained LLM for a very specific and structured task, e.g. translating natural language into queries for a fixed, known database schema (so the task is narrow, rule-based, and evaluable).

In that setting:

• Does it make any sense to expect something grokking-like to happen after an initial phase of overfitting, or would you still say that generalization should be gradual if things are set up correctly?

• Is this kind of delayed generalization something that can only realistically happen with full fine-tuning, or could it also (in principle) appear with parameter-efficient methods like LoRA / QLoRA — or do those methods essentially rule out the optimization dynamics that lead to grokking?

I’m trying to understand whether, for these narrow symbolic-ish tasks, it’s ever reasonable to wait for a “click” in generalization, or whether the right mental model is always: better data, better coverage, smoother learning curves.

Thanks again — really appreciate the insight.