Dataset distillation can be formulated as a bi-level meta-learning problem where the outer loop optimizes the metadataset and the inner loop trains a model on the distilled data.

The goal is to find the global optimal arm, and agents are able to pull any arm; however, they can only observe the reward when the selected arm is local.