Large language models (LLMs) have revolutionized how we interact with technology, but training these massive models is computationally expensive and environmentally taxing. What if there was a more efficient way to build powerful LLMs? Researchers are exploring a novel technique called pre-training distillation (PD), which could significantly reduce the resources needed to create cutting-edge AI. Think of it like tutoring: a seasoned teacher LLM guides a smaller student LLM, transferring its knowledge and accelerating the learning process. Instead of starting from scratch, the student benefits from the teacher's expertise, potentially achieving comparable performance with less training. This research dives deep into the mechanics of PD, experimenting with different approaches to optimize how knowledge is transferred. They examined how to best process the teacher LLM's output, which loss functions are most effective, and how the size of both the student and teacher models affects the outcome. They even experimented with having the teacher provide instruction in real-time. The findings are exciting: larger student models generally benefit more from this tutoring process. Surprisingly, however, a bigger teacher isn't always better. There seems to be an ideal size difference between teacher and student for optimal learning. This discovery opens doors to more efficient training methods, potentially allowing for the development of more powerful, accessible LLMs in the future. While there's still more to explore, pre-training distillation offers a promising path towards a more sustainable and efficient future for large language models.