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Training & Fine-tuningMedium· Maximal Update Parameterization· Failure Modes
Under what conditions does μP fail to transfer the lear...
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See plansThere are three primary failure modes for μP. First, learnable gains in normalization layers introduce scale-dependent implicit learning rate amplification. Second, high weight decay (typically above 0.1-0.3) disrupts the update-size invariant. Third, because the per-layer correction is derived specifically for the Adam optimizer, it does not generalize to other optimization algorithms without re-derivation.