Unverified50% confidenceFactExact time
Teacher intervention pilot studies show that teacher preferences differ across tokens, and that through majority voting and KL-divergence-based selection, a better teacher can be identified for each token.
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50%
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Long-term
Relevance
10/30/2026
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Related Claims
UnverifiedToken-level supervision means that the teacher gives a distribution or label for each token position in the sequence, and the student learns by minimizing the difference from the teacher's distribution (such as KL divergence).76% similarUnverifiedUnlike directly applying cross-entropy (hard labels) to the correction segments, KL supervision uses the teacher's full soft distribution as a soft target, preserving the teacher's relative confidence across candidate tokens, yielding smoother signals and making it less likely for the student to overfit to a single answer.71% similarUnverifiedExisting 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.69% similar
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