This AI Paper from UNC-Chapel Hill Proposes ReGAL: A Gradient-Free Method for Learning a Library of Reusable Functions via Code Refactorization

Optimizing code through abstraction in software development is not just a practice but a necessity. It leads to streamlined processes, where reusable components simplify tasks, increase code readability, and foster reuse. The development of generalizable abstractions, especially in automated program synthesis, stands at the forefront of current research endeavors. Traditionally, Large Language Models (LLMs) have […]

The post This AI Paper from UNC-Chapel Hill Proposes ReGAL: A Gradient-Free Method for Learning a Library of Reusable Functions via Code Refactorization appeared …

abstraction abstractions ai paper ai shorts applications artificial intelligence automated code components development editors pick free functions gradient hill leads library paper practice processes readability software software development staff tasks tech news technology through via