Graph Neural Algorithmic Reasoning Reframed as a Reinforcement Learning Problem

A revised arXiv paper argues that neural algorithmic reasoning, which typically trains networks to imitate classic algorithms via supervised learning, is limited by its reliance on post-processing to produce valid outputs. The authors propose reformulating the task as a reinforcement learning problem, aiming to let models build correct solutions directly rather than repairing them afterward. The work appears in the cs.LG and cs.AI listings as a replacement submission.