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TopicsWeak-to-Strong Generalization Mathematics Machine Learning

Forward-KL fine-tuning on weak supervision reduces strong-model error by at least its disagreement with the weak model.

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In general, since this condition cannot be guaranteed to hold universally, the inequality in

The Capabilities and Limitations of Weak-to-Strong Generalization: Generalization and Calibrationdeniespreprintextraction 0.65

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

Appendix B.5: for forward KL loss, the gain bound requires an extra weighted Itakura-Saito divergence condition, revealing a discrepancy from squared loss.

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