LPQLD reduces soft label storage in dataset distillation by 78-500x on ImageNet datasets via pruning with dynamic reuse and quantization with student-teacher alignment, while improving accuracy.
Improved knowledge distillation via teacher assistant
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CLPD improves LLM distillation for reasoning by combining explicit data curriculum with progressive teacher scheduling of increasing capacity.
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Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
LPQLD reduces soft label storage in dataset distillation by 78-500x on ImageNet datasets via pruning with dynamic reuse and quantization with student-teacher alignment, while improving accuracy.
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Curriculum Learning-Guided Progressive Distillation in Large Language Models
CLPD improves LLM distillation for reasoning by combining explicit data curriculum with progressive teacher scheduling of increasing capacity.