The field of Large Language Models (LLMs) is moving towards improving efficiency and reasoning capabilities. Researchers are exploring innovative methods to optimize LLM performance, including automatic kernel generation and self-taught action deliberation. These approaches aim to reduce the need for manual tuning and improve the models' ability to reason and make decisions. Furthermore, there is a growing interest in understanding the underlying algorithms and computations that LLMs use to solve problems, with a focus on developing a more principled understanding of these models. Notable papers in this area include AutoTriton, which uses reinforcement learning to generate high-performance kernels, and SAND, which enables LLM agents to deliberate over candidate actions before committing to one. TeaR is also noteworthy, as it teaches LLMs to reason better through careful data curation and reinforcement learning. The position paper on AlgEval highlights the need for a systematic understanding of the algorithms learned by LLMs, proposing a framework for uncovering algorithmic primitives and their composition.