Improving Assembly Code Performance with Large Language Models via Reinforcement Learning

• URL: http://arxiv.org/abs/2505.11480v1
• Date: Fri, 16 May 2025 17:40:45 GMT
• Title: Improving Assembly Code Performance with Large Language Models via Reinforcement Learning
• Authors: Anjiang Wei, Tarun Suresh, Huanmi Tan, Yinglun Xu, Gagandeep Singh, Ke Wang, Alex Aiken,
• Abstract summary: Large language models (LLMs) have demonstrated strong performance across a wide range of programming tasks.<n>We present a reinforcement learning framework that trains LLMs using Proximal Policy Optimization (PPO)<n>Our model, Qwen2.5-Coder-7B-PPO, achieves 96.4% test pass rates and an average speedup of 1.47x over the gcc -O3 baseline.
• Score: 9.20863636863631
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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