Weak-to-strong generalization is nearly inevitable in linear logistic regression for most student-teacher pairs without any model capacity mismatch.
Super (ficial)- alignment: Strong models may deceive weak models in weak-to-strong generalization.arXiv preprint arXiv:2406.11431, 2024a
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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.
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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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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.