This article introduces a new approach to reinforcement learning called Uniqueness-Aware Reinforcement Learning, aimed at improving how large language models (LLMs) solve complex reasoning tasks. By rewarding rare and effective solution strategies rather than common ones, the method enhances diversity and performance in problem-solving without sacrificing accuracy. The authors demonstrate its effectiveness across multiple benchmarks in mathematics, physics, and medical reasoning.