Nested Learning: A new ML paradigm for continual learning
4 days ago
- #Nested Learning
- #continual learning
- #machine learning
- Introduction of Nested Learning, a new ML paradigm addressing catastrophic forgetting by treating models as nested optimization problems.
- Human brain's neuroplasticity is highlighted as the gold standard for continual learning, contrasting with current LLMs' limitations.
- Traditional approaches to combat catastrophic forgetting involve separate treatments of model architecture and optimization algorithms.
- Nested Learning bridges the gap by unifying architecture and optimization into interconnected, multi-level learning problems.
- Proof-of-concept architecture 'Hope' demonstrates superior performance in language modeling and memory management.
- Nested Learning reveals ML models as interconnected optimization problems with distinct context flows and update rates.
- Associative memory concepts are applied to training processes and architectural components like attention mechanisms.
- Deep optimizers and continuum memory systems (CMS) are introduced as improvements derived from Nested Learning principles.
- Hope architecture utilizes CMS blocks for unbounded levels of in-context learning and self-modification.
- Experiments show Hope's lower perplexity, higher accuracy, and superior memory management in long-context tasks.
- Nested Learning offers a foundation for developing self-improving AI with continual learning capabilities akin to the human brain.