SwiftCoder addresses a critical gap in AI code generation by teaching language models to write not just functional code, but efficient code.

• Creates a specialized dataset of high-quality, efficient code solutions
• Uses multiple LLMs to generate diverse candidate solutions
• Optimizes for both correctness (does it work?) and efficiency (does it work well?)
• Demonstrates that code efficiency can be significantly improved through targeted fine-tuning

For engineering teams, this research offers a pathway to AI coding assistants that align with professional software development standards, potentially reducing technical debt and improving system performance from the start.

SwiftCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning