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The 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.
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10/22/2026
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UnverifiedThrough distillation, student models with only one-tenth the parameters of the teacher model can often retain 80%–95% of the teacher model's performance.74% 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.72% similarUnverifiedThe distillation loss often uses KL divergence to measure the difference between the student distribution and the teacher distribution.71% similarUnverifiedThe probability calibration of model outputs is often biased, especially on patterns that appear familiar within the training data distribution, where overly high confidence is given.69% 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.67% similar
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