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Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Shape space analysis reveals that traditional machine learning methods often fail to capture the rich geometric structures inherent in complex datasets, leading to missed insights in fields from biology to computer vision.
Forget uniform resampling – targeting high-entropy decision points in reasoning traces unlocks better performance than RL-trained models, all without extra training data or compute.
