With thousands of reinforcement learning papers published every year, only a small fraction end up genuinely shaping how the field moves forward. Understanding what separates a paper that generates lasting influence from one that gets cited a handful of times and forgotten reveals useful patterns for both researchers and readers trying to prioritize their attention. Novelty Alone Rarely Makes a...