Pruning + Distillation focuses on creating smaller, more efficient language models by removing redundant parameters (pruning) and transferring knowledge from larger models to smaller ones (distillation). This approach aims to reduce computational costs and resource requirements for deploying AI models, making them more accessible and faster for various applications.