Large language models (LLMs) are revolutionizing how we interact with technology, but their sheer size makes them resource-intensive. Knowledge distillation (KD) offers a way to compress these giants into smaller, more efficient models, but traditional methods falter when teacher and student models use different vocabularies (tokenizers). Imagine trying to teach someone a new skill when they speak a different language—it’s a similar challenge in the AI world. This is where cross-tokenizer knowledge distillation (CTKD) comes in. Existing CTKD methods often struggle to effectively transfer knowledge due to vocabulary mismatches. A new approach called Multi-Level Optimal Transport (MultiLevelOT) is changing the game. Instead of treating each word in isolation, MultiLevelOT considers both individual words and the entire sentence structure when transferring knowledge. Think of it as teaching not just vocabulary, but also grammar and context. This method uses clever mathematical tools (optimal transport and Sinkhorn distance) to align the “language” of the teacher and student models, even if their vocabularies differ. This allows for a much smoother and more effective transfer of knowledge. Experiments show that MultiLevelOT significantly outperforms existing CTKD methods across various tasks like question answering and summarization. This breakthrough paves the way for more efficient and deployable LLMs, potentially revolutionizing applications on resource-constrained devices like smartphones and bringing the power of advanced AI to a wider audience. The ability to effectively distill knowledge across different LLMs opens doors to exciting possibilities. Imagine combining the strengths of multiple specialized AI models into a single, compact model – a true AI all-star. While challenges remain, MultiLevelOT marks a significant step towards unlocking the full potential of LLMs, making them more accessible and impactful for everyone.