Learning in this sense means it’s able to extrapolate to unseen data (I.e., it’s learned correlations that are inherent to the problem, not just inherent in the training data). Memorizing, on the other hand, implies that it will do very well on any examples in the domain of the training data, and will break (sometimes pretty hilariously) on unseen data.

The fact that refitting once a day improves real world performance actually makes me think that the problem/data they work on is highly non-stationary, not that the model is memorizing. If it was purely memorizing then the model would perform poorly on all non-training data, and would not work for even one day.

To your point about large datasets - the large datasets are what allow learning to take place. With the most common forms of models we have now, they will memorize when they only have a few examples, and only “learn” when the training data is large enough. There is work to improve learning from a handful of examples, but in many of these cases they require a model that was already trained on the domain in question, and then are specialized to a specific use-case.