This paper presents SpatialReward, a novel verifiable reward model designed to evaluate fine-grained spatial relationships in text-to-image generation. The approach employs a multi-stage pipeline consisting of a Prompt Decomposer for extracting spatial metadata, expert detectors for visual grounding of object positions, and a vision-language model with chain-of-thought reasoning to assess complex spatial relations. By focusing on spatial layout evaluation rather than just semantic alignment, this work addresses a critical gap in current reward modeling approaches for generative AI systems.