ReGAL: Refactoring Programs to Discover Generalizable Abstractions

While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict programs one at a time, often repeating the same functionality. Generating redundant code from scratch is both inefficient and error-prone. To address this, we propose Refactoring for Generalizable Abstraction Learning (ReGAL), a gradient-free method for learning a library of reusable functions via code refactorization, i.e. restructuring code without changing its execution output. ReGAL learns from …

abstractions code cs.ai cs.cl cs.lg cs.se error global language language models large language large language models llms refactoring synthesis view