arXiv:2605.07711v2 Announce Type: replace Abstract: On-policy distillation (OPD) is a standard tool for transferring teacher behavior to a smaller student, but it implicitly assumes that teacher and student predictions are comparable token by token, an assumption that fails whenever the two models tokenize the same text differently. Under heterogeneous tokenizers, exact shared-token matching silently discards a large fraction of the teacher signal at precisely the positions where vocabularies disagree. We propose \textbf{\underline{Sim}ple \underline{C}ross-\underline{T}okenizer OPD (SimCT)},
The rapid advancement and deployment of diverse large language models necessitate more efficient and robust distillation methods to create smaller, specialized models.
Improving on-policy distillation under heterogeneous tokenizers is crucial for optimizing the performance and efficiency of AI models, particularly as AI applications become more diverse.
This research introduces a method to recover lost supervision in model distillation, enabling more effective knowledge transfer between AI models with different underlying tokenization schemes.
- · AI developers
- · Companies deploying specialized AI models
- · AI research community
More efficient and performant smaller AI models will emerge, capable of handling diverse tasks.
This could lead to a broader adoption of specialized, compact AI solutions across various industries due to lower computational overhead.
The reduced resource requirements might democratize access to advanced AI capabilities, fostering innovation beyond well-resourced labs.
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Read at arXiv cs.CL