How is loss propagated in stacked classifiers? In a 2-stage stacked classifier the first model takes the input data and outputs feature vectors, which are then fed into a second model as input. The second model learns the mapping between the output of the first model and the data labels.
But what mapping does the first model learn? It is given the input data, but how can it learn learn relevant features if it itself is not seeing the labels? There are no "correct" feature vectors that the first model can calculate loss off of.
Is it possible to propagate
the loss from the second model to the first model somehow?

Yes, it is possible, but you need a single model that does that.

the first model takes the input data and outputs feature vectors, which are then fed into a second model as input

This sounds like a description of a standard two-layer neural network. A neural network is an example of such a model where the latter layers learn from the outputs created by the previous layers and propagate the errors downstream.
