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by Paul Christiano 292 days ago | Jessica Taylor likes this | link | parent

I meant that I may be able to sample pairs from some attack distribution without being able to harden my function against the attack distribution.

Suppose that I have a program \(\widetilde{f} \in [0, 1]\) which implements my desired reward function, except that it has a bunch of vulnerabilities \(\widetilde{a}_i\) on which it mistakenly outputs 1 (when it really should output 0). Suppose further that I am able to sample vulnerabilities roughly as effectively as my AI.

Then I can sample vulnerabilities \(\widetilde{a}\) and provide the pairs \((\widetilde{a}, -1)\) to train my reward function, along with a bunch of pairs \((a, \widetilde{f}(a))\) for actions \(a\) produced by the agent. This doesn’t quite work as stated but you could imagine learning \(f\) despite having no access to it.

(This is very similar to adversarial training / red teams).



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