Nested Learning: Unifying Architecture and Optimization for Continual AI

Nested Learning unifies model architecture and optimization into a hierarchy of interconnected, multi-timescale learning modules. This yields robust deep optimizers and continuum memory systems that better manage knowledge across short and long horizons. A self-modifying model, Hope, demonstrates superior language modeling and long-context performance, supporting the paradigm’s effectiveness.

Key Points

• Nested Learning reframes models as nested, multi-timescale optimization problems, unifying architecture and training rules as levels with distinct context flows and update frequencies.
• Backpropagation and attention are interpreted as associative memory mechanisms, revealing a common template for how components store and update information.
• Deep optimizers derived from a regression-style objective (e.g., L2 loss) yield more resilient variants of momentum and related updates than dot-product-based schemes.
• Continuum memory systems (CMS) organize memory as a spectrum of modules with different update rates, improving long-context handling and continual learning.
• Hope, a self-modifying recurrent architecture built on Titans and augmented with CMS, achieves better perplexity, accuracy, and long-context performance than contemporary baselines.

Sentiment

The overall sentiment of the discussion is one of initial curiosity and proactive engagement, marked by an immediate attempt to reproduce the work in open-source. It does not express strong agreement or disagreement with the article's claims, but rather a keen interest in practical exploration.

In Agreement

• An open-source reproduction effort has begun, indicating community interest in exploring and potentially validating the proposed Nested Learning paradigm.