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Tianjin University
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WarpSAC achieves up to a 96.4% success rate in complex tasks by dynamically adjusting its learning stabilizers based on data availability.
Planning performance can be dramatically improved by aligning latent embedding geometry with task-relevant state representations, as shown by SCALE's consistent outperformance of LeWM.
