Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
EGO is a framework for training compact generators that produce discrete outputs directly, solving the challenge of generating diverse candidates cheaply. It trains by matching proposal distributions to hard sample evaluations using distribution matching, structural constraints, and optional diversity rewards. This allows the generator to create high-yield, valid molecules and architectures with low inference costs.