TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning

Enhancing temporal understanding of MLLMs is essential for long-form video analysis, supporting tasks such as temporal localization and time-sensitive question answering. While reinforcement learning (RL) has been explored for temporal reasoning, existing approaches are often limited to specific tasks or datasets, hindering generalization across diverse temporal scenarios. To address this, we present TempR1, an RL-based method that strengthens MLLMs' temporal comprehension through multi-task training. We construct a multi-task corpus covering diverse temporal structures and build upon the Group Relative Policy Optimization (GRPO) algorithm for stable cross-task optimization. Temporal tasks are categorized into three interval-instance correspondence types, with tailored localization rewards designed for each. This design enables TempR1 to capture fine-grained temporal dependencies and adapt to different temporal patterns. Experiments show that TempR1 achieves state-of-the-art performance on multiple benchmarks. Joint optimization across complementary tasks further produces strong synergy, improving both generalization and single-task performance, thereby providing a scalable paradigm for temporal reasoning in MLLMs.