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RynnValue shows that using temporal distance as a supervision target can outperform traditional preference-based methods in robotic learning, achieving higher accuracy and broader generalization.
The data pyramid framework reveals how the interplay of diverse data sources can unlock new capabilities in embodied agents, highlighting critical gaps in current methodologies.
Robots can learn faster and perform better by strategically choosing *when* to be unpredictable, achieving both multimodal expressivity and deterministic efficiency.
