EPISODE · Jun 30, 2025 · 59 MIN
Reinforcement Learning for Assembly Code Optimization with LLMs
from Neural intel Pod · host Neuralintel.org
The provided source explores enhancing assembly code performance using large language models (LLMs) through reinforcement learning (RL). It introduces a novel RL framework that trains LLMs with Proximal Policy Optimization (PPO), guided by a reward function that balances functional correctness and execution speedupcompared to the industry-standard gcc -O3 compiler. To facilitate this research, a benchmark of 8,072 real-world programs was developed. The resulting model, Qwen2.5-Coder-7B-PPO, significantly outperforms 20 other models, achieving a 96.0% test pass rate and an average 1.47x speedup, demonstrating LLMs' potential as effective assembly code optimizers.
What this episode covers
The provided source explores enhancing assembly code performance using large language models (LLMs) through reinforcement learning (RL). It introduces a novel RL framework that trains LLMs with Proximal Policy Optimization (PPO), guided by a reward function that balances functional correctness and execution speedupcompared to the industry-standard gcc -O3 compiler. To facilitate this research, a benchmark of 8,072 real-world programs was developed. The resulting model, Qwen2.5-Coder-7B-PPO, significantly outperforms 20 other models, achieving a 96.0% test pass rate and an average 1.47x speedup, demonstrating LLMs' potential as effective assembly code optimizers.
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Reinforcement Learning for Assembly Code Optimization with LLMs
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