Practical advantages of nested learning over standard continual learning

Ascertain whether nested learning provides practical advantages over standard continual learning methods for incremental adaptation of genome-wide regulatory genomics models, and characterize the conditions under which these advantages manifest.

Background

Nested learning has been proposed to frame adaptation as a hierarchy of coupled optimization processes. While promising conceptually, its tangible benefits relative to well-studied continual learning strategies (e.g., replay and regularization) have not been demonstrated in regulatory genomics contexts.

References

More recently, nested learning frames adaptation as a hierarchy of coupled optimization processes, though their practical advantages over standard continual learning methods remain unclear.

— Toward Interpretable and Generalizable AI in Regulatory Genomics  (2602.01230 - Nagai et al., 1 Feb 2026) in Section “Continual Learning Across Genomic Assays”