Nested Learning: Decoding Deep Architecture and Memory

The paper introduces Nested Learning (NL), a new paradigm that reframes machine learning models as an integrated system of multi-level, nested optimization problems, each possessing a distinct "context flow." This research addresses fundamental challenges in deep learning, particularly the inability of Large Language Models (LLMs) to achieve continual learning post-deployment, drawing inspiration from human memory consolidation processes. NL demonstrates that standard optimizers are essentially associative memory modules that compress gradients, an insight used to design more powerful Deep Optimizers. Building on the framework of varying update frequencies, the authors propose a Continuum Memory System (CMS) alongside a self-modifying sequence model. Combining these components results in the HOPE architecture, which exhibits strong performance across language modeling and common-sense reasoning benchmarks, surpassing established models like the Transformer in various scaling regimes.