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Agent builders rely on model priors trained by non-experts, facing unknown-unknown risks
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A software engineer argues that agent builders cannot evaluate the risks of model priors, which are shaped by non-expert rewards and lead to compounding misalignments.
For engineers building agents, this highlights that the model's default behaviors are not trustworthy in domains outside their expertise. The article warns that misalignments compound over time and that there is no universal definition of a permissible shortcut, making alignment an irreducible complexity problem.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
Model priors are trained by non-expert rewards, leading to behaviors experts consider bad.
Agent builders rely on these priors for domains they cannot evaluate, creating unknown-unknown risks.
Misalignments compound over time, and there is no universal definition of permissible shortcuts, making alignment unsolved.
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