We introduce CPPO, a Contrastive Perception Policy Optimization method for finetuning vision--language models (VLMs). Reliable perception is a core requirement for VLM-based agents that must reason and act in open-ended environments: faulty visual grounding cascades directly into faulty actions, hallucinated tool calls, and unsafe decisions. While reinforcement learning (RL) has significantly improved reasoning in language models, extending these advances to multimodal agents requires improving both perception and reasoning. Prior works address this challenge mainly through explicit perception rewards, which often require extra LLM judges, ground-truth annotations, or forced separation of perception from reasoning. CPPO addresses this limitation in a self-supervised manner by extending the RL objective with a Contrastive Perception Loss (CPL) that provides a direct learning signal for visual grounding. The contrastive objective encourages the model to become more sensitive to input visual information. To apply this signal effectively, CPPO identifies perception tokens using an entropy-shift mechanism in the model's output distributions under perturbed images and applies the contrastive loss selectively to those tokens during training. Experiments show that CPPO surpasses prior methods while avoiding extra models, making training more efficient and scalable, and yielding policies that are better suited to perception-critical agentic tasks.