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Existing few-step distillation methods fall roughly into two categories: one relies on an additional score model to estimate the teacher distribution, and the other introduces adversarial losses to bring the student's output distribution closer to the teacher's.
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10/14/2026
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UnverifiedThe student model's training objective is a weighted combination of a conventional loss against true hard labels and a distillation loss against the teacher's soft labels, with the distillation loss generally measured by KL divergence.77% similarUnverifiedThe paper observes that supervision signals from a fixed teacher become increasingly less worthwhile as the student model's capabilities improve72% similarUnverifiedThere are two main technical approaches behind a tiered product line: training independent models with different parameter counts, or compressing the capabilities of a large model into a smaller student model through knowledge distillation.69% similarUnverifiedOffline distillation trains the student using a static dataset pre-generated by the teacher model, which leads to the problem of covariate shift.69% similar
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