Weak-to-strong generalization is nearly inevitable in linear logistic regression for most student-teacher pairs without any model capacity mismatch.
Weak-to-strong generalization even in random feature networks, provably.arXiv preprint arXiv:2503.02877
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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.
In ridgeless regression with low intrinsic dimension, discrepancy between weak and strong models reduces W2S generalization variance by dim(V_s)/N in the discrepant subspace while inheriting it in the overlap.
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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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.
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Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension
In ridgeless regression with low intrinsic dimension, discrepancy between weak and strong models reduces W2S generalization variance by dim(V_s)/N in the discrepant subspace while inheriting it in the overlap.