Weak-to-strong generalization is nearly inevitable in linear logistic regression for most student-teacher pairs without any model capacity mismatch.
arXiv preprint arXiv:2501.19105 , year=
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In the linear-width regime, the second GD step yields a spiked random matrix whose number of outliers is floor(alpha2 / (1/2 - alpha1)), and batch reuse enables learning directions with information exponent greater than one under suitable alpha scalings.
Pre-training provides a geometric warm start in a single-index model that enables weak-to-strong generalization up to a supervisor-limited bound, with empirical phase-transition evidence in LLMs.
citing papers explorer
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Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models)
Weak-to-strong generalization is nearly inevitable in linear logistic regression for most student-teacher pairs without any model capacity mismatch.
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Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent
In the linear-width regime, the second GD step yields a spiked random matrix whose number of outliers is floor(alpha2 / (1/2 - alpha1)), and batch reuse enables learning directions with information exponent greater than one under suitable alpha scalings.
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On the Blessing of Pre-training in Weak-to-Strong Generalization
Pre-training provides a geometric warm start in a single-index model that enables weak-to-strong generalization up to a supervisor-limited bound, with empirical phase-transition evidence in LLMs.