Goodhart in the Planner: Sampling-Based Control Exploits Rewards Learned from Human Preferences
Soovadeep Bakshi (The University of Texas at Austin) and Apurba Bose (independent researcher) measure what happens to the true objective when robots search harder against a reward model learned from human preferences.
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Finding. On HalfCheetah, Hopper and Meta-World tasks, stronger MPPI search cuts true return by up to 76%, while the reward model's held-out label accuracy stays between 0.75 and 0.97.
Example. With human labels on drawer-open, the drawer opens in 0 of 20 planner runs, against 10 of 10 for the cloned policy the planner starts from. Best-of-N with a verifier trained on synthetic labels does worse than a single noisy action.
Predictor. True return roughly follows how far the planner's weights move from its proposals, measured as the square root of the KL divergence.
Theory. Chi-squared-regularized MPPI at a temperature set by the scorer's error spread on the proposals is worst-case optimal, and no positive gain can be certified once that spread exceeds the scorer's own.
Fix. Calibrated from 100 oracle-ranked proposal pairs, this rule succeeds in every run and keeps 86% to 94% of the cloned policy's return on plate-slide and drawer-open, though it is cautious where search helps.
Abstract
Robots increasingly choose actions by searching against a reward model learned from human preferences, in model-predictive control or best-of-$N$ selection; we measure what this does to the true objective. On HalfCheetah and Hopper with crowd-sourced labels, and on Meta-World drawer-open and plate-slide with synthetic labels and blind labels from the two authors, harder MPPI search cuts true return by up to 76%: with human labels the drawer opens in 0 of 20 runs against 10 of 10 for the cloned policy the planner starts from, while held-out label accuracy stays at 0.75-0.97. On plate-slide human labels are exploited less than synthetic ones, and on window-open search helps. True return roughly follows how far the planner's weights move from its proposals ($\sqrt{\mathrm{KL}}$) for both weighting rules we test, and best-of-$N$ with a verifier from synthetic labels falls below a single noisy action. We prove that $χ^2$-regularized MPPI at a temperature set by the scorer's error spread on the proposals is worst-case optimal, that no positive gain can be certified once that spread exceeds the scorer's own, and that an episode's return splits exactly into per-step terms, of which the guarantee covers the selection terms. Calibrated from 100 proposal pairs ranked by true return (an oracle) on start states the evaluation never uses, this rule is the safest fix we test: with each of five independent sets it succeeds in every arm run and keeps on average 86-94% of the cloned policy's return on plate-slide and drawer-open, though it is cautious where search helps. Calibration from a person's comparisons cannot detect a bias the scorer learned from that person; correcting for the pairs people cannot tell apart, or a second person, limits the damage.