The concept of training large language models (LLMs) on assembly code, as raised by @astrra.space, represents a significant step towards a new frontier in code generation and optimization. While LLMs have demonstrated remarkable capabilities in high-level programming languages, their potential in the low-level domain of assembly code remains largely unexplored. A recent research paper titled "Improving Assembly Code Performance with Large Language Models via Reinforcement Learning" sheds light on this very topic. The study reveals that through reinforcement learning, an LLM was trained to optimize assembly code, achieving an average speedup of 1.47x over the industry-standard gcc -O3 compiler while maintaining a 96.0% test pass rate. This demonstrates the immense potential of LLMs to serve as powerful tools for code optimization, capable of making fine-grained improvements that are often difficult to express in high-level languages. The primary challenge lies in the complexity and verbosity of assembly code, which requires a different approach to training and a more nuanced understanding of the underlying hardware. However, the potential benefits, including significant performance gains and the ability to generate highly optimized code for specific architectures, make this a promising area of research. As LLMs continue to evolve, their application in low-level code generation could revolutionize software development, leading to more efficient and performant applications.