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University of Wisconsin-Madison
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Generative data augmentation can enhance defect detection in TEM images, even with small labeled datasets, revealing nuanced improvements based on data splits.
LLMs can slash over 80% of their chain-of-thought tokens with a minor accuracy boost, thanks to a new RL-based method that targets the "Minimal Sufficient Length" of reasoning.
LLMs can escape the trap of converging on popular but incorrect answers in unsupervised RLVR by temporarily "unlearning" and exploring diverse response options.
