Optimizing Assembly Code with LLMs Using Reinforcement Learning Outperforms Traditional Compilers

Large Language Models (LLMs) powered by reinforcement learning are now optimizing assembly code more efficiently than conventional compilers. This advancement matters because it enables faster, smaller, and more power-efficient machine code, transforming low-level programming approaches. Software developers and system programmers stand to benefit from these AI-driven improvements. Studies show reinforcement learning can iteratively enhance code in ways static compilers cannot.