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Results show that looping can improve Transformers even under budget matching, offering a practical recipe that turns depth reuse into measurable gains.
Together, the perplexity analysis indicates improved continuation predictability, while the controlled pre-training experiments suggest that this augmentation can improve model performance without changing the standard pre-training objective.
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