Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved the reasoning capability of large vision-language models (LVLMs). Most RLVR methods for LVLMs follow an outcome-driven paradigm, using the correctness of final answer as supervision. However, in multimodal reasoning, this supervision overlooks a critical question: whether a successful reasoning trajectory is actually grounded in visual evidence. To address this limitation, we propose Visual Evidence-Calibrated Policy Optimization (VEPO), which turns visual grounding into an explicit trajectory-level credit calibration signal. VEPO first measures token-level visual responses by comparing the model’s likelihood under the original and perturbed visual inputs. It then estimates a visual background for the rollout-group and retains responses above the background as sparse visual anchors into a rollout-level evidence score. This score calibrates outcome-driven optimization and guides the policy toward successful trajectories with stronger visual support. Experiments on six multimodal reasoning benchmarks show that VEPO achieves strong overall performance against RLVR baselines without requiring additional annotations, demonstrating its scalability for multimodal policy optimization.