Improving Assembly Code Performance with Large Language Models via
  Reinforcement Learning
Authors: name: Anjiang Wei, name: Tarun Suresh, name: Huanmi Tan, name: Yinglun Xu, name: Gagandeep Singh, name: Ke Wang, name: Alex Aiken

Large language models (LLMs) have demonstrated strong performance across a
wide range of programming tasks, yet their potential for code optimization
remains underexplored. This work investigates whether LLMs can optimize the
performance of assembly code, where fine-grained control over execution enables
improvements that are difficult to express in high-level languages. We present
a reinforcement learning framework that trains LLMs using Proximal Policy
Optimization (PPO), guided by a reward function that considers both functional
correctness, validated through test cases, and execution performance relative
to the industry-standard compiler gcc -O3. To support this study, we introduce
a benchmark of 8,072 real-world programs. Our model, Qwen2.5-Coder-7B-PPO,
achieves 96.0% test pass rates and an average speedup of 1.47x over the gcc -O3
baseline, outperforming all 20 other models evaluated, including
Claude-3.7-sonnet. These results indicate that reinforcement learning can
unlock the potential of LLMs to serve as effective optimizers for assembly code
performance.