From Reasoning to Code: GRPO Optimization for Underrepresented Languages 事件

From Reasoning to Code: GRPO Optimization for Underrepresented Languages arXiv:2506.11027v3 Announce Type: replace-cross Abstract: Generating accurate and executable code using Large Language Models (LLMs) remains a significant challenge for underrepresented programming languages, such as Prolog and Lisp, due to the scarcity of public training data compared to high-resource languages like Python. This paper introduces a generalizable Reinforcement Learning (RL) approach that combines small-scal