Traditional solutions prioritizing easy or hard samples lack the flexibility to handle such a variety simultaneously. Recent work has proposed a more reasonable data selection principle by examining the data's impact on the model's generalization loss.

Efficient methods that reliably quantify a deep neural network (DNN)'s predictive uncertainty are important for industrial-scale, real-world applications, which include examples such as object recognition in autonomous driving [22], ad click prediction in online advertising [76], and intent understanding inaconversational system [84].

These selections serve as subgoals that indicate subtasks and guide policy.

An alternative approach, Generative Adversarial Networks (GANs), has become popular across severaldomains, particularly Computer Vision, owing tobreakthrough realism intheimages they output[e.g.,19,65]. This is the case in NLP where, unlike computer vision, a measure of likelihood called perplexityhas been theprevailing metric fortraining and evaluating language models fordecades.