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Equation 7 · Part 3 · How Constitutional AI Actually Constrains a Model's Behavior

Symbol y_l

(x,yw,yl)(x, y_w, y_l)
yly_l

What this part means

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

Its job in the formula

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

The passage around this formula

Nothing in that loss distinguishes where a pair came from; a human-labeled winner and an AI-labeled winner are interchangeable once written down as (x, ywy_w, yly_l) . That is the precise, narrow sense in which Constitutional AI “differs mechanically from plain RLHF”: it changes the labeling function for one half of one dataset, not the loss, not the optimizer, not the use of a KL penalty against the supervised policy. For the soft-labeled AI comparisons specifically, where the feedback model outputs a probability p of preferring response yAy_A over yBy_B rather than a hard choice, the corresponding cross-entropy term is

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A subscript is a label attached below a symbol. It often selects a time step, component, category, or member of a sequence.

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