Put differently, the oracle policy selects the set of unlabeled examples that maximizes the target metric of our model on a set sampled from the same distribution as the test set.
the oracle policy enjoys extremely favorable conditions compared to a trained policy, and we expect it to provide an upper bound on the performance of π θ .