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Xt+1  =  Distill(AmplifyH(Xt))X_{t+1} \;=\; \mathrm{Distill}\big(\mathrm{Amplify}_H(X_t)\big)

Why this formula appears here

IDA is debate’s closest theoretical relative, aimed at the same underlying worry from a different angle: rather than trusting a single judge to adjudicate an argument about a hard problem, it never asks any one evaluator to face the hard problem directly. Christiano, Shlegeris, and Amodei describe a scheme they call Amplify: a composite system, written AmplifyH(X)\mathrm{Amplify}_H(X) , “consisting of H and several copies of X working together to solve a problem,” in which H — standing in for a human overseer — breaks a hard question into useful subquestions, has several copies of the current trained agent X answer each subquestion, and then combines the subanswers into a response to the original…

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Xt+1X_{t+1}

Symbol X_t+1

XtX_t+1 is the next indexed value computed from the current indexed quantity and the update terms shown on the right.

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Xt+1  =  Distill(AmplifyH(Xt)),X_{t+1} \;=\; \mathrm{Distill}\big(\mathrm{Amplify}_H(X_t)\big),

Equation 16 · AI Safety

The Main Technical Approaches to AI Alignment, Compared

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

IDA is debate’s closest theoretical relative, aimed at the same underlying worry from a different angle: rather than trusting a single judge to adjudicate an argument about a hard problem, it never asks any one evaluator to face the hard problem directly. Christiano, Shlegeris, and Amodei describe a scheme they call Amplify: a composite system, written AmplifyH(X)\mathrm{Amplify}_H(X) , “consisting of H and several copies of X working together to solve a problem,” in which H — standing in for a human overseer — breaks a hard question into useful subquestions, has several copies of the current trained agent X answer each subquestion, and then combines the subanswers into a response to the original…

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