Nested Learning: The Illusion of Deep Learning Architectures
12 days ago
- #deep learning
- #continual learning
- #machine learning
- The paper introduces Nested Learning (NL), a new theoretical paradigm that reframes machine learning models as an integrated system of nested, multi-level optimization problems.
- NL reveals that existing deep learning methods learn by compressing context, offering a 'white-box' view of model dynamics.
- Three core contributions: (1) Deep Optimizers, which reinterpret optimizers like SGD with Momentum as learnable, multi-level memory modules; (2) Continuum Memory System (CMS), generalizing memory into a hierarchy of blocks updating at different time scales; (3) HOPE, a self-modifying sequence architecture combining these principles.
- NL addresses the static nature of Large Language Models (LLMs), providing a blueprint for continual learning, self-improvement, and higher-order reasoning.
- HOPE architecture demonstrates superior performance over Transformers and Titans, showcasing the potential of NL principles.
- NL transitions AI design from heuristic architecture stacking to explicit engineering of multi-timescale memory systems.
- The paper highlights limitations, including computational complexity at scale, but opens vast future directions for nested optimization and continual learning.