[2606.09456] Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families

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Computer Science > Machine Learning

arXiv:2606.09456 (cs)

[Submitted on 8 Jun 2026]

Title:Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families

Authors:Yifan Niu, Han Xiao, Dongyi Liu, Zelong Wang, Dihong Gong, Yasheng Wang, Jia Li<br>View a PDF of the paper titled Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families, by Yifan Niu and 6 other authors

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Abstract:On-Policy Distillation (OPD) has become a core technique in the post-training of Large Language Models (LLMs) for transferring knowledge from domain experts to student models. However, existing OPD distillation methods require teacher and student models to share the same tokenizer, restricting the applicability of OPD within the model series. Current mainstream practice typically employs Supervised Fine-Tuning (SFT) on teacher-generated responses for cross-tokenizer distillation, which fails to capture the rich knowledge embedded in the teacher's probability distribution. In this work, we enable the standard on-policy distillation method to operate across model families, ensuring that high-fidelity token-level signals can propagate across different tokenizers with a precise token-mapping algorithm. Extensive experiments show that cross-tokenizer OPD is significantly more compute-efficient than baselines on various benchmarks. Our results unlock a broader range of teacher-student pairs for OPD, opening up new avenues for adapting and enhancing interactions between LLMs.

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Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.09456 [cs.LG]

(or<br>arXiv:2606.09456v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.09456

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Yifan Niu [view email]<br>[v1]<br>Mon, 8 Jun 2026 13:12:01 UTC (585 KB)

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