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Loop-$μ$P: Residual Scaling and Hyperparameter Transfer in Looped Transformers
Looped Transformers have drawn growing attention because looping improves multi-step reasoning and increases the executed depth of a model without adding parameters. They achieve this by reusing a stack of $L$ layers for $N$ loops. However, increasing the loop count $N$ not only destabilizes trainin
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