Asking Smarter Questions: Targeted Active Preference Learning for Underspecified Rewards

arXiv:2610.22285v1 Announce Type: cross Abstract: Reward learning typically assumes the human's input covers everything that matters for the task. In practice, humans can only attend to so much at once, so their teaching naturally captures some features---or aspects of the task---better than others. Prior work has shown how the robot can identify which features were poorly supervised directly from its teaching data. But the current solution is to ask the human to try again, putting the burden on someone who already struggled to convey those features. We propose shifting this burden to the robo

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