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This paper derives scaling rules for Gated Delta Networks using Maximal Update Parametrization (μP), enabling zero-shot hyperparameter transfer across model widths for efficient sub-quadratic LLM architectures. Experiments confirm stable learning-rate transfer under both AdamW and SGD, whereas standard parametrization fails.