Equation 19 · The Main Technical Approaches to AI Alignment, Compared
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They quantify how much of the gap this recovers with a metric called performance gap recovered, defined by its two boundary conditions: it equals one under perfect weak-to-strong generalization and zero when the weak-to-strong model does no better than the weak supervisor it learned from [ 12 ] . Writing for the weak supervisor, for the strong model fine-tuned on the weak model’s labels, and for the strong model fine-tuned on ground truth as an upper-bound ceiling, those two conditions pin down
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