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arXiv · 1810.08575

Supervising strong learners by amplifying weak experts

Paul Christiano·Buck Shlegeris·Dario Amodei
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Abstract

Many real world learning tasks involve complex or hard-to-specify objectives, and using an easier-to-specify proxy can lead to poor performance or misaligned behavior. One solution is to have humans provide a training signal by demonstrating or judging performance, but this approach fails if the task is too complicated for a human to directly evaluate. We propose Iterated Amplification, an alternative training strategy which progressively builds up a training signal for difficult problems by combining solutions to easier subproblems. Iterated Amplification is closely related to Expert Iteration (Anthony et al., 2017; Silver et al., 2017), except that it uses no external reward function. We present results in algorithmic environments, showing that Iterated Amplification can efficiently learn complex behaviors.

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Paul Christiano, Buck Shlegeris, Dario Amodei. 2018-10-19. Supervising strong learners by amplifying weak experts. https://arxiv.org/abs/1810.08575

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