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This paper introduces ReasonMatch-Bench, a benchmark for wide-baseline matching in multimodal LLMs, and proposes Dynamic Correspondence Reinforcement Learning (DCRL) to improve spatial reasoning. Experiments show significant gains on the benchmark while maintaining general performance.