Fine-tuning large language models (LLMs) is like teaching a brilliant but generic student to excel in a specific field. It takes time and resources. A new research paper introduces PMSS, or Pretrained Matrices Skeleton Selection, a faster and more efficient way to fine-tune these AI giants. Imagine trying to customize a massive, intricate machine. Traditional fine-tuning is like rebuilding the whole thing, while PMSS is like swapping out a few key components. It leverages the existing knowledge within the pre-trained model by selecting core “skeletons” of information. This allows for targeted adjustments without massive overhauls, making the process much faster and lighter. Researchers tested PMSS on complex tasks like reading comprehension (DROP benchmark), commonsense reasoning, and math problems. The results? PMSS outperformed other state-of-the-art fine-tuning methods across the board, even beating full fine-tuning in some cases. It achieved this with a remarkably smaller number of trainable parameters, meaning less computational overhead and faster processing. These findings open doors to wider LLM adoption. PMSS could enable quicker customization for specific industries, like healthcare or finance, allowing more efficient deployment of these powerful models even with limited resources. While the research focuses on existing tasks, future work may explore how PMSS handles other types of reasoning and even larger models. The challenge lies in integrating task-specific knowledge without losing the benefits of pre-trained information, a problem ripe for further exploration.