Improving Assembly Code Performance with Large Language Models via
  Reinforcement Learning
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.
682b5efc970fc73c32ddc4e4
Anjiang Wei
682b5efc970fc73c32ddc4e5
Tarun Suresh
claimed_verified
2025-06-05T07:29:10.961Z
65e7bb35e5e78134ab049942
/avatars/3c0972f0d59e51ebb5c218ee736d4458.svg
Tarun Suresh
user
tarsur909
682b5efc970fc73c32ddc4e6
Huanmi Tan
682b5efc970fc73c32ddc4e7
Yinglun Xu
682b5efc970fc73c32ddc4e8
Gagandeep Singh
682b5efc970fc73c32ddc4e9
Ke Wang
682b5efc970fc73c32ddc4ea
Alex Aiken
Improving Assembly Code Performance with Large Language Models via
  Reinforcement Learning
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.
Reinforcement learning using Proximal Policy Optimization trains large language models to optimize assembly code performance, achieving higher speedups and test pass rates compared to standard compilers.
reinforcement learning Proximal Policy Optimization LLMs assembly code functional correctness execution performance gcc -O3 benchmark Qwen2.5-Coder-7B-PPO Claude-3.7-sonnet