Goodhart in the Planner: Sampling-Based Control Exploits Rewards Learned from Human Preferences
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Soovadeep Bakshi, Apurba Bose · arXiv: 2611.09670v1 · Summary page
Why cover this story this week?
A robot planner can optimize its way into failure
Searching harder for actions can make a robot worse when its objective is a reward model learned from human preferences. Across locomotion and manipulation tasks, researchers found that more aggressive sampling-based planning could exploit flaws in the learned score, cutting true return by up to 76% despite high accuracy on held-out preference labels. In a drawer-opening task with human labels, planning failed even though the cloned policy it started from succeeded. The paper also develops a theoretical rule for restraining optimization according to uncertainty in the scorer. Calibrated using true-return comparisons, that rule was the safest correction tested, though it could be overly cautious where search helped. The catch is consequential: comparisons from the same person whose judgments trained the scorer cannot expose biases shared by that person and the model. Better optimization is not automatically better behavior.
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