The field of language models is moving towards more efficient and scalable architectures, with a focus on reducing parameter counts and improving computational efficiency. Researchers are exploring innovative architectural designs, tokenization techniques, and training strategies to achieve state-of-the-art performance with fewer resources. Notable advancements include the development of lightweight models that can be deployed on devices, as well as distributed training architectures that enable the scaling of model sizes with the number of participants. These innovations have the potential to make language models more accessible and widely applicable. Noteworthy papers include the Apple Intelligence Foundation Language Models, which introduces a novel Parallel-Track Mixture-of-Experts transformer, and the Supernova paper, which demonstrates that careful architectural design and tokenization innovation can achieve the performance of larger models while maintaining computational efficiency.