Researchers propose a new approach to distilling large language models (LLMs) by leveraging a hybrid model architecture and a multi-stage distillation pipeline. They demonstrate that log-likelihood evaluation can be misleading when assessing generation quality, and present a optimized distillation recipe that retains high accuracy while reducing memory and inference time. This work is relevant to developers of AI agents and LLMs, as it provides insights into efficient and effective model distillation techniques.