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Soft-label distillation is a form of knowledge distillation in which the teacher model outputs a full probability distribution (soft labels) at each position, and the student model learns by minimizing the difference from the teacher distribution (such as KL divergence).
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10/22/2026
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VerifiedHard distillation refers to the student model directly learning the final output labels or generated text of the teacher model, while soft distillation requires the student model to learn the complete probability distribution (logits) output by the teacher model.80% similarVerifiedKnowledge distillation transfers the full probability distribution information output by the teacher model via soft labels, producing an implicit data augmentation effect.79% similarVerifiedModel distillation softens the teacher model's softmax outputs via temperature scaling, allowing information from low-probability classes to be transferred to the student model.76% similarVerifiedDistillation can be viewed as an implicit, data-constrained form of model merging—distilling a student using the averaged outputs of multiple teacher models is effectively performing a soft merge at the level of output distributions.69% similarUnverifiedReasoning distillation is a branch of knowledge distillation, referring to using reasoning processes generated by a large teacher model (rather than only the final answer) to train a smaller student model so that it acquires step-by-step reasoning ability.66% similar
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