FasterPy: LLM-Based Python Code Optimization
Published:Dec 28, 2025 07:43
•1 min read
•ArXiv
Analysis
This paper introduces FasterPy, a framework leveraging Large Language Models (LLMs) to optimize Python code execution efficiency. It addresses the limitations of traditional rule-based and existing machine learning approaches by utilizing Retrieval-Augmented Generation (RAG) and Low-Rank Adaptation (LoRA) to improve code performance. The use of LLMs for code optimization is a significant trend, and this work contributes a practical framework with demonstrated performance improvements on a benchmark dataset.
Key Takeaways
- •FasterPy is a framework for optimizing Python code execution efficiency using LLMs.
- •It utilizes Retrieval-Augmented Generation (RAG) and Low-Rank Adaptation (LoRA).
- •The framework is evaluated on the Performance Improving Code Edits (PIE) benchmark.
- •The authors provide a publicly available tool and experimental results.
Reference
“FasterPy combines Retrieval-Augmented Generation (RAG), supported by a knowledge base constructed from existing performance-improving code pairs and corresponding performance measurements, with Low-Rank Adaptation (LoRA) to enhance code optimization performance.”