machine learning paradigm

Google Research’s Nested Learning paradigm reframes the age-old dichotomy of architecture vs optimiser into a unified, hierarchical system of nested learning loops. By deploying multiple modules updating at varied frequencies, the continuum memory system enables long-context retention and mitigates catastrophic forgetting. Their HOPE architecture exemplifies this, outperforming standard models in continual-learning tasks. For AI agents, this suggests a transition from static tools to evolving systems. The real frontier isn’t larger models — it’s learning better models.