Unverified70% confidenceFactExact time
Through distillation, student models with only one-tenth the parameters of the teacher model can often retain 80%–95% of the teacher model's performance.
2
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70%
Confidence
Long-term
Relevance
9/10/2026
First Seen
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Related Claims
UnverifiedThe cost of distillation lies in the fact that the student model tends to inherit the teacher model's behavior within the training distribution, but its generalization ability on out-of-distribution difficult samples (rare words, strong noise, rare accents) is markedly weaker than the original large model.74% similarUnverifiedCompared to the original baseline model, the student model after joint distillation and quantization achieves a 98.21% improvement in inference speed.70% similarUnverifiedKnowledge distillation has the student network learn the soft probability distribution output by the teacher network, enabling the student model to retain core reasoning capabilities while reducing its parameter count by more than 10 times.68% similarUnverifiedCompared to the original baseline model, the student model after joint distillation and quantization achieves a 69% reduction in size.67% similarUnverifiedThe teacher model's prediction quality is not uniform across different time steps; at some time steps (especially at the endpoints where t is close to 0 or close to 1) the predictions themselves are relatively noisy.66% similar
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