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One Model, Two Paths: Balancing Single-Pass and Looped Performance in Language Models
Repeating Transformer layers can improve language-model performance without adding parameters. A pretrained model can be adapted to support both single-pass and looped inference by reusing middle-layer weights for additional computation. Because both paths share weights, training to improve looped p
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