From Illusion to Intention: Visual Rationale Learning for Reliable Evidence Acquisition

“Thinking with images” has recently emerged as a promisingdirection for vision-language models, where models actively interact withimages via iterative zoom-ins to acquire visual evidence for answering.Yet, prevailing frameworks ignore the quality of intermediate zoom-ins,boosting metrics but leading to an action–outcome disconnect. This fail-ure mode creates the illusion of thinking with images, where modelsappear visually grounded while relying on context-agnostic or redun-dant crops. We address this problem by introducing Visual RationaleLearning (ViRL), an end-to-end reinforcement learning paradigm thatgrounds the evidence acquisition process. ViRL ensures action faithful-ness and efficiency through: (1) process supervision with ground-truthvisual rationales, (2) rationale fidelity rewards that incentivize evidence-grounded zoom-ins, and (3) fine-grained credit assignment to penalizeredundant or erroneous steps. Experimental results show that ViRLachieves state-of-the-art performance across benchmarks spanning per-ception, hallucination, and perception-grounded reasoning. We also dis-sect the illusion of thinking with images and position visual rationales asa verifiable foundation for building trustworthy vision-language models.