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Equation 7 · Part 2 · Claude, From First Principles: Training, Constitutional Methods, and What Actually Shapes a Response

Symbol x

r(x,y)r(x,y)
xx

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x 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

The reward term r(x,y) pulls the policy toward whatever the fitted preference model scores highly; the Kullback–Leibler penalty, weighted by β\beta , pulls it back toward the reference distribution. That second term is not a minor regulariser. It is the load-bearing assumption behind the entire method: remove it, and a policy optimised hard enough against an imperfect reward model will find outputs the reward model over-scores without those outputs actually being better, a failure usually called reward hacking. Casper and thirty-one co-authors, surveying RLHF across the field rather than defending any one lab’s implementation, catalogue this and related problems as fundamental rather than…

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