This paper introduces TempR1, a novel temporal-aware multi-task reinforcement learning framework designed to significantly enhance the temporal understanding capabilities of Multimodal Large Language Models (MLLMs). By integrating diverse temporal tasks and tailored reward functions, TempR1 achieves state-of-the-art performance across various video understanding benchmarks and improves generalization.