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Robots can learn from fleet-wide failures and successes to autonomously improve their policies, achieving near-perfect reliability without any new human corrections.
Even top-performing robotic value models struggle with nuanced instruction grounding and history-dependent progress tracking, exhibiting inconsistent value judgments across valid execution orders.
Distilling LLM agents by supervising semantic actions, not token sequences, unlocks significant performance gains and bypasses tokenization constraints.