Nested Learning introduces a new approach to machine learning that redefines how models are structured and optimized. Instead of treating a model as a single continuous process, it views it as a series of interconnected optimization problems. This shift addresses the problem of “catastrophic forgetting,” where models lose proficiency on old tasks when learning new ones. By integrating the model architecture and optimization rules into a unified system, Nested Learning aims to create more efficient and capable AI. The concept draws inspiration from human neuroplasticity, highlighting how the brain adapts and retains knowledge. Current large language models (LLMs) struggle with this adaptability, often confined to their training data and immediate input context. Nested Learning proposes a “continuum memory system,” which allows memory components to operate at different update frequencies, enhancing the model's ability to manage long-term knowledge while still learning new tasks. The researchers validated Nested Learning with a model named Hope, a self-modifying architecture based on the Titans framework. Hope utilizes continuum memory to improve long-context memory management and demonstrates better performance in language modeling tasks, achieving lower perplexity and higher accuracy than existing models. Their experiments confirm that this new paradigm can significantly enhance the way AI learns and retains information over time, moving closer to human-like learning capabilities.