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Executable families support repeatable benchmark renewal, with validation improving task quality and selection shaping the delivered challenge, with validation improving task quality and selection shaping the delivered challenge.
A key consequence is that the optimal learning rate depends only on the number of unique layers $L$, not on the loop count $N, enabling direct hyperparameter transfer from small to large $N$ without retuning.
This work presents a pipeline that unfolds each paper into a multi-turn generation trajectory in which a teacher model reconstructs the writing process of the whole paper: a writing request, a global plan, and pre-writing deliberation for each section.
Results show that looping can improve Transformers even under budget matching, offering a practical recipe that turns depth reuse into measurable gains.
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