Evolutionary Curriculum Learning Improves Biological Sequence Modeling

The paper introduces Evolutionary Curriculum Learning (ECL), which trains biological VAEs by progressively expanding the evolutionary distance of sequences exposed around sampled anchors, using a power-law schedule. Across two VAE architectures and two domains, ECL improves protein variant-effect prediction with EVE and RNA generation with RfamGen. For p53, mean ClinVar AUROC increases from 0.981 to 0.989; for PTEN, ECL reaches 1.000 in every seed versus an unstable baseline averaging 0.905. RNA covariance-model bit scores improve across three tested families, but the family-level evidence remains limited.