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Equation 11 · The Main Technical Approaches to AI Alignment, Compared

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HH

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HH

Symbol H

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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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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 question [ 11 ] . The trained agent is then updated to imitate that amplified process directly: “the agent X then learns from AmplifyH(X)\mathrm{Amplify}_H(X) in the same way that it would traditionally learn from H alone” [ 11 ] . Run repeatedly, the described process is naturally read as the recursion

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