Erdem Bıyık

Grounding reward learning in natural language rationales makes policies 2x more robust to spurious correlations and distribution shifts.

Learning robotic reward functions from a million trajectories reveals that comparing entire trajectories, not just individual frames, unlocks better generalization and learning from suboptimal data.