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University of Science and Technology
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Static sample selection in reinforcement fine-tuning is a recipe for suboptimal updates鈥擠IEM adapts dynamically, leading to superior performance on reasoning tasks.
MuEvo not only evolves heuristic ensembles but also dynamically adapts component priorities, leading to superior performance in complex optimization tasks.
CoPES recovers 92% of the performance gains of traditional methods while slashing GPU memory requirements to less than one-eighth.
