This paper introduces Test-Time Reinforcement Learning (TTRL), a novel method enabling Large Language Models (LLMs) to improve performance on unlabeled test data using Reinforcement Learning (RL). TTRL overcomes the lack of ground-truth labels by employing majority voting on multiple model outputs to estimate rewards, essentially allowing models to self-supervise their training. The research demonstrates that this approach leads to significant performance gains across various reasoning tasks and models, showing that LLMs can effectively self-evolve and learn from experience on unseen data, potentially reducing reliance on costly human annotations.