SD-MAR: Multi-image Analytical Reasoning via Synthetic Data and Reinforcement Learning
Researchers present SD-MAR, a framework for training and evaluating vision-language models on multi-image analytical reasoning tasks, such as change detection and quantitative comparison. By leveraging synthetic data and a reinforcement learning method called GRPO-lite with Backward Discounted Allocation, they report up to 36.95% accuracy improvement on in-domain benchmarks. Notably, Qwen2.5-VL-7B outperforms GPT-4.1 on the SD-MAR benchmark, and out-of-domain generalization is maintained or improved on several standard benchmarks.
Why it matters: This work advances multimodal AI by enabling models to reason analytically across multiple images, a capability important for real-world tasks involving visual comparison and inference.
Full story at: arXiv Computer Vision ↗