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Equation 19 · Part 1 · The Main Technical Approaches to AI Alignment, Compared

Symbol f_w

fwf_w
fwf_w

What this part means

the writing.

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fwf_w is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Where the article explains it

Writing fwf_w for the weak supervisor, fw→sf_{w\to s} for the strong model fine-tuned on the weak model’s labels, and fs→sf_{s\to s} for the strong model fine-tuned on ground truth as an upper-bound ceiling, those two conditions pin down

The passage around this formula

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 fwf_w for the weak supervisor, fw→sf_{w\to s} for the strong model fine-tuned on the weak model’s labels, and fs→sf_{s\to s} for the strong model fine-tuned on ground truth as an upper-bound ceiling, those two conditions pin down

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