all that grokking really means is that the 'correct', generalizable solution is often simpler than the overfit 'memorize all the datapoints' solution, so if you apply some sort of regularization to a model that you overfit, the regularization will make the memorized solution unstable and you will eventually tunnel over to the 'correct' solution
actual DNNs nowadays are usually not obviously overfit because they are trained on only one epoch
1) Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs https://arxiv.org/abs/1802.10026
2) Visualizing the Loss Landscape of Neural Nets https://arxiv.org/abs/1712.09913
There's also a very interesting body of work on merging trained models, such as by interpolating between points in weight space, which relates to the concept of "basins" of similar solutions. Skim the intro of this if you're interested in learning more: https://arxiv.org/abs/2211.08403
Reviewing the literature, I see the concept is more commonly referred to as "flat/wide minima"; e.g., https://www.pnas.org/doi/10.1073/pnas.1908636117