A common critique of AI models is that "a model is only as good as the data it's trained on," implying its performance is capped by the quality of the initial dataset. Evaluate this statement by contrasting two different training paradigms:

1. A paradigm where the model learns exclusively by imitating a fixed, pre-existing set of "correct" examples provided by human annotators.
2. A paradigm where the model can generate novel outputs not present in the initial data, which are then assessed and used as feedback to update the model's behavior.

In your evaluation, explain which paradigm is more likely to be constrained by the initial dataset and why the other might be able to discover policies or generate outputs that are superior to the examples it was initially shown.