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Google researchers introduced "Nested Learning," a new machine learning paradigm that treats model architecture and optimization as unified, nested processes rather than separate components. The approach addresses catastrophic forgetting by enabling multi-timescale updates inspired by human neuroplasticity. Their proof-of-concept architecture, Hope, uses a Continuum Memory System that manages memory as a spectrum with modules updating at different rates. Hope outperforms standard transformers on language modeling and excels at needle-in-haystack tasks. The framework also introduces "Deep Optimizers" that use L2 regression loss instead of dot-product similarity, creating more robust momentum variants. This represents a shift toward continual learning systems that can acquire new skills without erasing existing knowledge.

Google Just Dropped Transformer 2.0: Meet "Nested Learning"

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