Nested Learning: The Illusion of Deep Learning Architectures – Paper Walkthrough

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This video explains the core ideas behind Nested Learning (NL), a new machine learning paradigm that reframes deep learning architectures as multi-level optimization systems with multi-time-scale memory. We explore how NL reinterprets optimizers as associative memory, introduces Deep Optimizers like DMGD, expands memory through Continuum Memory Systems, and demonstrates how the HOPE architecture outperforms Transformers and modern recurrent models for language modeling and reasoning.

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