arXiv:2606.09456v1 Announce Type: new 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
The rapid advancement and proliferation of LLMs are driving a need for more efficient and flexible methods to transfer knowledge between diverse model architectures.
This breakthrough addresses a significant technical barrier in LLM distillation, potentially leading to more robust and adaptable AI models that can leverage knowledge from across different AI ecosystems.
The ability to perform On-Policy Distillation across different tokenizer families removes a major constraint, enabling cross-model-series knowledge transfer without relying on less effective supervised fine-tuning.
- · AI developers
- · LLM researchers
- · Companies using diverse LLM architectures
- · AI platforms
- · Monolithic AI ecosystems
- · Manual knowledge transfer methods
Improved efficiency and performance in distilling knowledge to smaller or specialized LLMs from larger, more capable ones.
Accelerated development of domain-specific or resource-efficient AI models by leveraging advanced 'teacher' models regardless of tokenizer compatibility.
Potential for a more fragmented yet interconnected AI landscape, where knowledge can flow more freely between different foundational models and their derivatives.
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Read at arXiv cs.LG