EasyVideoR1: Easier RL for Video Understanding

ArXi:2604.16893v1 Announce Type: cross Reinforcement learning from verifiable rewards (RLVR) has nstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve into natively multimodal architectures, extending RLVR to video understanding becomes increasingly important yet remains largely unexplored, due to the diversity of video task types, the computational overhead of repeatedly decoding and preprocessing high-dimensional visual inputs, and the difficulty of reproducible evaluation across numerous sensitive hyperparameters.