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Top-16 shared-token supervision matches full cross-tokenizer distillation

huggingface.co by Bingxi Hou et al. Editor score 8/10

Across three teacher–student pairs, strict token alignment covers most student tokens, and reverse-KL supervision on a student-selected top-16 shared-vocabulary subset matches full shared-vocabulary OPD accuracy. The result suggests a simpler, more reliable supervision path for distillation across tokenizers.

What
On-Policy Distillation trains a student on its own generations using teacher feedback, requiring alignment across different tokenizers.
Method
Strict 1:1 positions with a student-selected top-16 shared-vocabulary subset achieved accuracy comparable to full shared-vocabulary OPD.
Why it matters
For tokenizer-mismatched distillation, broader alignment coverage is not necessarily better; selective supervision may be more reliable.
align restrict select supervise studentgenerations strict 1:1groups sharedvocabulary top-16 subset reverse KL
Student generations are aligned and distilled using a restricted shared-vocabulary subset. Sketched from the article's own text.

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