Game2Real-Bench: Do Toy Games Reveal and Transfer to Real-World Visual Reasoning?

Visual reasoning is an important capability for vision-language models (VLMs) to understand visual evidence, spatial structure, constraints, and state changes. Existing evaluations mainly rely on either real-world data or procedurally generated tasks. Real-world data preserve the complexity of natural scenes, while procedural games provide controllable generation and verifiable solutions. However, it remains unclear whether the abilities measured in toy games correspond to those required in real-world visual reasoning, and whether game examples can provide useful signals for solving real-world tasks. We introduce Game2Real-Bench, a benchmark for studying these connections across four capability groups, including fine-grained perception, spatial reasoning, constraint reasoning, and sequential planning. On the game side, we construct 1,000 independently generated samples from executable task specifications and verify their answers programmatically. On the real-world side, we curate and construct 400 samples from diverse real-world sources. We connect the two settings according to shared reasoning requirements, including visual evidence, spatial operations, constraints, and state transitions, rather than visual similarity. We evaluate 25 representative VLMs across both settings. Performance on game and real-world tasks shows consistent cross-setting trends, with a Pearson correlation of 0.935 and a Spearman correlation of 0.935. We further examine game-to-real transfer through one-shot in-context learning on four models. The effect varies across capabilities and models. Constraint reasoning shows positive gains for several models, while fine-grained perception consistently declines with game demonstrations. These results show that toy games can reflect some reasoning patterns observed in real-world tasks and can provide useful demonstrations in selected settings. To further support model training, we construct Game2Real-Dataset, containing nearly 24K verified game samples across 18 tasks and 65 QA families. The samples are generated from executable task specifications and verified programmatically, providing scalable training data for visual reasoning. We will publicly release the benchmark, dataset, and code.