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Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences, of Space Integrated Information System
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GUPO reveals that accounting for gradient uncertainty can dramatically improve policy optimization in post-training LLMs, leading to more effective reasoning capabilities.
Over-smoothing in time series forecasts can be mitigated by a novel framework that preserves distinct dynamic modes, leading to more accurate and diverse predictions.
