Reward Models Must Cover Multiple Performance Dimensions
chelseabfinn · x · 2026-07-03
Stanford robotics and RL researcher Chelsea Finn notes that, in the long run, reward models need to capture all aspects of performance—including success, quality, and speed. They should also incorporate task-specific granular dimensions, such as whether groceries are packed evenly, apples are bruised, or furniture is scratched, providing richer supervision signals for robot learning.
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