Recent multimodal large language models achieve promising results on visual reasoning tasks, approaching human-level performance on existing STEM benchmarks. However, these capabilities degrade on complex real-world scenes. We identify two intertwined causes: chain-of-thought reasoning remains largely confined to the linguistic space, and training data rarely requires each step to be anchored in visual evidence. In this work, we introduce Visual Augmentation Primitives, which leverages high-quality instruction-guided generation models to synthesize diverse photorealistic training samples and construct coherent multimodal reasoning chains throughout the generation process, requiring models to unify visual perception with logical reasoning at every step. We further introduce multimodal chain-of-thought rubric-based reinforcement learning, which extends rubric-based reward design from the text-only domain to visual reasoning, scoring intermediate reasoning steps for logical validity, completeness, and consistency, and construct the UNREAL benchmark to systematically evaluate visual reasoning on hyperrealistic scenes across controllable difficulty levels. Experiments show that a 4B model built on this framework surpasses 8B baselines, with the approach scaling consistently to 32B across visual reasoning and VQA tasks. All models, data, and code will be publicly released.