I have been curious about this as well, and I am not in the field, so I ask ChatGPT about how it can best learn new information without having to fully retrain the model and all the compute involved in that.
It suggest incremental learning, where its parameters are updated or an additional layer is added to the underlying neural network. Adding a layer involves "adding a new set of nodes or neurons to the existing architecture of the model".
This is far less computationally expensive than retraining the full model, because "adding new layers allows the model to incorporate new information without modifying the existing layers, which can preserve the previously learned knowledge".
You can make of that what you will, after all it was generated by ChatGPT itself. But it seems there are techniques to have these models learn new information without having to do another very costly training run.