Layer normalization has demonstrated remarkable effectiveness at preventing plasticity loss in continual and reinforcement learning (RL), though the precise reasons for this effectiveness remain mysterious.

We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores.

Recent works in V ariational Inference have examined alternative criteria to the commonly used exclusive Kullback-Leibler divergence.