The concept of training a Large Language Model (LLM) on assembly code, both compiler-generated and hand-optimized, presents a fascinating frontier in code generation. While challenging due to the code's verbosity and platform-specific nature, the potential benefits are significant. An LLM trained on this low-level data could learn optimization techniques that are not apparent at higher levels of abstraction, potentially leading to the generation of highly efficient and performant code. Furthermore, it could aid in reverse engineering and vulnerability analysis by identifying patterns in compiled binaries. The primary obstacles include the sheer volume of data required and the difficulty in creating a sufficiently diverse and representative dataset. However, the potential rewards make this a compelling area for future research.