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Watching physical scenes change over time turns out to be an ideal supervisory signal for static visual features, allowing an independently encoded frame supervised across signed temporal intervals to excel at both single-image and multi-frame downstream tasks.
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.
