This June 2025 paper introduces a novel methodology called Test-Time Reinforcement Learning (TTRL), which enables Large Language Models (LLMs) to improve their performance on reasoning tasks using unlabeled test data. The core innovation addresses the challenge of reward estimation without ground-truth labels by employing Test-Time Scaling (TTS) practices, specifically majority voting, to generate effective pseudo-labels and rule-based rewards. TTRL facilitates the self-evolution of LLMs during inference, demonstrating substantial performance gains—up to a 211% boost on challenging mathematical benchmarks like AIME 2024—and even surpassing the performance ceiling of the initial majority voting signal. This unsupervised online learning approach is shown to be compatible with different reinforcement learning algorithms and effective across various models, suggesting a path toward continually learning AI systems less reliant on extensive human annotation. Source: https://arxiv.org/pdf/2504.16084