Observing a set of features while training is same as seeing it one time. How?
This really depends on the type of algorithm you are using. With iterative approaches like stochastic gradient descent this is exactly what does happen. With tree based methods it will not matter except that the interpretation of your hyperparameters surrounding regularization changes by artifically creating a bigger dataset.