The paper on Nested Learning argues that current deep learning models suffer from an “anterograde amnesia” problem—unable to gradually consolidate new experiences into long-term memory—and proposes a new framework where learning occurs across multiple nested optimization levels operating at different frequencies. Drawing inspiration from the brain’s multi‑scale processing,

👉 @ai_python ✍️

it reframes optimizers as memory systems and shows that architectures like Transformers and MLPs are fundamentally uniform when viewed through this lens.


The key promise is continual learning without catastrophic forgetting, shifting focus from stacking layers to designing nested optimization processes across time scales.

https://www.k-a.in/nl.html