Weak-to-strong generalization is nearly inevitable in linear logistic regression for most student-teacher pairs without any model capacity mismatch.
The delta learning hypothesis: Preference tuning on weak data can yield strong gains.arXiv preprint arXiv:2507.06187, 2025
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FEST uses self-evolving trees to produce expert-aligned, auditable features from unstructured data and outperforms baselines on brand, authenticity, and stress tasks while releasing the BrandGuide dataset.
Trust functions filter unreliable weak labels to enable near-lossless weak-to-strong generalization and iterative chaining.
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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Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution
FEST uses self-evolving trees to produce expert-aligned, auditable features from unstructured data and outperforms baselines on brand, authenticity, and stress tasks while releasing the BrandGuide dataset.
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Trust Functions: Near-Lossless Weak-to-Strong Generalization by Learning When to Trust the Weak Teacher
Trust functions filter unreliable weak labels to enable near-lossless weak-to-strong generalization and iterative chaining.