[MereInterest]

Not a data scientist, but my understanding is that restricting the set of training data for the initial training run often results in poorer inference due to a smaller data set. If you’re training early layers of a model, you’re often recognizing rather abstract features, such as boundaries between different colors.

That said, there is a benefit to fine-tuning a model on a reduced data set after the initial training. The initial training with the larger dataset means that it doesn’t get entirely lost in the smaller dataset.