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Unlocking Feature Learning in Gated Delta Networks at Scale

Arxiv CS.LG (machine learning)

The paper investigates feature learning in Gated Delta Networks at scale, introducing a theoretical framework using Maximal Update Parametrization (μP) to enable efficient training of sub-quadratic LLMs. It provides insights into hyperparameter transfer and scaling laws for this architecture.

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7 days ago
Unlocking Feature Learning in Gated Delta Networks at Scale — AIBriefs