ACTD: Anchor-Based Cross-Tokenizer Distillation with Residual Regularization

Abstract:Knowledge distillation effectively transfers reasoning capabilities from large language models to lightweight student models. To enable knowledge transfer across disparate model families, researchers increasingly explore cross-tokenizer distillation. However, cross-tokenizer distillation remains challenging due to vocabulary and sequence misalignment, while approximate vocabulary alignment can introduce additional noise into distillation. To address these challenges, we propose Anchor-Based Cross-Tokenizer Distillation with Residual Regularization (ACTD). ACTD bridges structural heterogeneity through vocabulary and sequence alignment, while mitigating alignment noise via a novel anchor loss with residual regularization. We further extend this framework to a multi-teacher setting. Evaluated across five reasoning benchmarks with three distinct teacher models, ACTD achieves state-of-the-art performance. Moreover, its multi-teacher extension outperforms the strongest single-teacher and multi-teacher baselines, further demonstrating the robustness of our method.

Computation and Language (cs.CL)

arXiv:2608.29662 [cs.CL]

(or arXiv:2608.29662v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.29662 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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