Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.

The goal is to efficiently compute a near-optimal policy for a given user.

However, in this paper, we reveal that one-to-one mappings may overlook the complex relationship between pretrained and downstream labels.