Unverified50% confidenceOpinionExact time
Distillation 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.
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10/7/2026
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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.78% similarVerifiedIn offline distillation, training data is generated once by the teacher model and fixed, with the student model learning from a static dataset.77% 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.77% similarUnverifiedOutput distillation involves directly using text generated by a teacher model—such as dialogues and reasoning chains—as training data to fine-tune a student model, without requiring access to the teacher's internal parameters.77% similarUnverifiedModel distillation was first proposed by Hinton et al. in 2015; by having a small model learn the output distribution of a large model, the student model can often retain 80% to 95% of the teacher model's performance.76% similar
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