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TopicsWeak-to-Strong Generalization Machine Learning

Continued training on weak supervision can make a strong model overfit and perform no better than its weak supervisor.

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Note that the upper bounds derived in

The Capabilities and Limitations of Weak-to-Strong Generalization: Generalization and Calibrationpreprintextraction 0.80

Yao, Wei et al. · arXiv.org · Feb 3, 2025 · original

Section 4.3.2: as epochs increase, GPT-2-XL accuracy drops below GPT-2-Base (90.95%), attributed to overfitting to weak supervision; bounds do not guarantee the strong model outperforms the weak.

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