Google Nested Learning

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YouTube Excerpt: The Nested Learning (NL) paradigm views models as nested, multi-level optimization problems, each compressing its own context flow based on different Update Frequencies. This framework explains the emergence of In-Context Learning (ICL) in LLMs. Core NL principles led to Deep Optimizers (optimizers as associative memory) and the HOPE architecture, which utilizes a multi time-scale Continuum Memory System (CMS) for continual learning

The Nested Learning (NL) paradigm views models as nested, multi-level optimization problems, each compressing its own context flow based on...

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