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TopicsWeak-to-Strong Generalization Machine Learning

In weak-to-strong generalization, stronger weak supervisors can improve strong-model performance.

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These findings suggest that (1) achieving the minimal optimization objective in W2SG limits the strong model’s ability to significantly outperform its weak supervisor, and (2) selecting a stronger weak model can enhance the performance of the strong model.

The Capabilities and Limitations of Weak-to-Strong Generalization: Generalization and Calibrationpreprintextraction 0.75

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

Introduction; supported by GPT-2 and Pythia experiments on CAI-Harmless reward modeling.

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