This article introduces Nested Learning, a machine learning paradigm that addresses catastrophic forgetting by treating models as interconnected optimization problems. It highlights how this approach can enhance continual learning and improve memory management in AI systems, demonstrated through a new architecture called Hope.

This article details the implementation of Google's Nested Learning (HOPE) architecture, focusing on its mechanism-level components and testing procedures. It provides guidance on installation, usage, and evaluation, including various training configurations and memory management strategies for machine learning models.