Qi-Ming Zhu

SIVA-RL: Sensitivity-Invariance Visual Alignment for Multimodal Reinforcement Learning

SIVA-RL is proposed, a Sensitivity-Invariance Visual Alignment framework that replaces operator-conditioned regularization with sample-wise, outcome-conditioned supervision and yields an 8.79 percentage-point gain on vision-dependent reasoning and up to 14.9% relative overall improvement across all four GRPO- and DAPO-based configurations.