Yes there are certainly datasets you can train on a dataset effectively without repetition; this is more common in areas like reinforcement learning where you a

Yes there are certainly datasets you can train on a dataset effectively without repetition; this is more common in areas like reinforcement learning where you are simulating every training point. While theoretically possible you have the same random seed to produce the same training point, in practice that wont happen without bugs but some training data will be similar. Augmentation can also ensure you dont see the exact training data more than one. However, this doesn't guarantee you will be able to train an amazing model. You are limited by properties like model capacity, label noise, poor feature representation, optimizer+learning rate, etc. In the limit empirically there will be some dataset size after which you get diminishing returns on improving model performance.