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Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability

The paper investigates on-policy distillation (OPD) in cross-tokenizer settings, focusing on alignment coverage and supervision reliability. It finds that strict 1:1 token alignment covers most student-generated tokens despite vocabulary mismatches, and that restricting reverse KL to a top-16 subset of shared vocabulary achieves comparable accuracy to full shared-vocabulary OPD, while adding span supervision reduces accuracy.

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PublishedOctober 6, 2026Bingxi Hou, Guochao Jiang, Guofeng Quan
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WHY IT MAY MATTER

This work provides insights into improving distillation effectiveness by focusing on reliable supervision at strict alignment positions rather than broad coverage, which may help avoid conflicting training signals in cross-tokenizer settings.

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