CORL Spotlight work: stop optimizing robot policies against a single overall reward
StanfordAILab · x · 2026-11-07
Presenting at CORL: Freeform Preference Learning and MEM (Spotlight). The core argument: trajectories differ along many axes (speed, precision, subtask completion), and collapsing them into a single overall reward loses structure, making the reward ambiguous and harder to optimize. Blog and paper linked.
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