Study: Preference Reward Planners Harm True Objectives

The paper "Goodhart in the Planner" by Soovadeep Bakshi and Apurba Bose measures the effect of sampling planners that learn rewards from human preferences on true objectives: on HalfCheetah, Hopper (crowdsourced labels), and Meta-World's drawer-open and plate-slide (synthetic labels and blind labeling by the two authors), increasing MPPI search intensity instead reduced true return by up to 76%. In the human-labeled drawer-open task, the planner failed to open the drawer in all 20 runs, while its starting clone policy succeeded in all 10, and held-out label accuracy remained at 0.75–0.97. The authors' proposed correction is to calibrate using 100 candidate pairs ranked by true return (oracle) on initial states unused in evaluation, which they call the safest correction tested: across five independent calibration sets, every robotic arm run succeeded, retaining on average 86–94% of the clone policy's return on plate-slide and drawer-open, but it is conservative in scenarios where search itself is beneficial. The authors also note that calibrating with comparisons from the same person cannot detect bias the scorer learned from that person, and that removing candidate pairs that people find hard to distinguish or introducing a second person can limit the harm.

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