In the early iterations of the sample-and-search process, designing an effective CV is challenging due to the limited data available for training the Autoencoder. To address this, we employed a warm-start training approach, which has been widely successful in convex optimization . Instead of training the model from scratch at each iteration, the parameters learned from the previous iteration are used as the starting point, and the model is trained to converge from these pre-initialized values (see SI for details)