Cross-Tokenizer LLM Distillation through a Byte-Level Interface

Avyav Kumar Singh; Yen-Chen Wu; Alexandru Cioba; Alberto Bernacchia; Davide Buffelli

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arXiv · 2604.07466

Cross-Tokenizer LLM Distillation through a Byte-Level Interface

Avyav Kumar Singh·Yen-Chen Wu·Alexandru Cioba·Alberto Bernacchia·Davide Buffelli

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Abstract

Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains a largely unsolved problem. Existing approaches rely on heuristic strategies to align mismatched vocabularies, introducing considerable complexity. In this paper, we propose a simple but effective baseline called Byte-Level Distillation (BLD) which enables CTD by operating at a common interface across tokenizers: the byte level. In more detail, we convert the teacher's output distribution to byte-level probabilities, attach a lightweight byte-level decoder head to the student, and distill through this shared byte-level interface. Despite its simplicity, BLD performs competitively with--and on several benchmarks surpasses--significantly more sophisticated CTD methods, across a range of distillation tasks with models from 1B to 8B parameters. Our results suggest that the byte level is a natural common ground for cross-tokenizer knowledge transfer, while also highlighting that consistent improvements across all tasks and benchmarks remain elusive, underscoring that CTD is still an open problem.

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Avyav Kumar Singh, Yen-Chen Wu, Alexandru Cioba, Alberto Bernacchia, Davide Buffelli. 2026-04-08. Cross-Tokenizer LLM Distillation through a Byte-Level Interface. https://arxiv.org/abs/2604.07466

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