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How useful is quantilization for mitigating specification-gaming?

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This week: “How useful is quantilization for mitigating specification-gaming?” by Ryan Carey. Paper available here, published in the ICLR 2019 Safe Machine Learning workshop

If we have a specification that does not perfectly reflect what we care about, there are ways to maximize it which we want to avoid. To mitigate reward hacking (or specification-gaming), we can perform “quantilization, a method that interpolates between imitating demonstrations, and optimizing the proxy objective. If the demonstrations are of adequate quality, and the proxy reward overestimates perfor- mance, then quantilization has better guaranteed performance than other strategies. However, if the proxy reward underestimates performance, then either imitation or optimization will offer the best guarantee.”

As always, there will be free pizza. The first half hour is for stragglers to finish reading.

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This talk is part of the Engineering Safe AI series.

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