Dataset distillation creates a tiny synthetic training set that, when used with a fixed network initialization, produces models whose performance approximates that of models trained on the full original dataset.
Active learning for convolutional neural networks: A core-set approach
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Random sampling matches active preference learning on win-rate gains in online DPO yet both degrade benchmark performance, making active selection's overhead hard to justify.
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Dataset Distillation
Dataset distillation creates a tiny synthetic training set that, when used with a fixed network initialization, produces models whose performance approximates that of models trained on the full original dataset.
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Random Is Hard to Beat: Active Selection in online DPO with Modern LLMs
Random sampling matches active preference learning on win-rate gains in online DPO yet both degrade benchmark performance, making active selection's overhead hard to justify.