弥合差距：面向标准化考试题目的视觉语言模型数据驱动微调
Closing the Gap: Data-Centric Fine-Tuning of Vision Language Models for the Standardized Exam Questions
AI总结：
本研究通过高质量数据和优化语法提升视觉语言模型在标准化考试题目的多模态推理性能，达到接近SOTA的水平。
摘要：
多模态推理已成为现代人工智能研究的基石。标准化考试题目为这种推理提供了独特的严格测试环境，提供结构化的视觉上下文和可验证的答案。尽管最近的进展主要集中在算法进步上，如强化学习（例如GRPO、DPO），但视觉语言推理的数据驱动基础仍较少被探索。我们证明，使用高质量数据的监督微调（SFT）可以与专有方法相媲美。为此，我们编译了一个包含教材问题-解答对、课程对齐的图表和上下文材料的16140万个标记多模态数据集，并使用优化的推理语法（QMSA）对Qwen-2.5VL-32B进行微调。所得到的模型在我们新发布的基准YKSUniform上实现了78.6%的准确率，仅比Gemini 2.0 Flash低1.0%，该基准标准化了1854个多模态考试题目，涵盖309个课程主题。我们的结果表明，数据组成和表征语法在多模态推理中起决定性作用。这项工作建立了一个以数据为中心的框架，推动开放式权重的视觉语言模型发展，证明了精心编排且基于课程的多模态数据可以将监督微调提升到接近最先进的性能。
英文摘要：
Multimodal reasoning has become a cornerstone of modern AI research. Standardized exam questions offer a uniquely rigorous testbed for such reasoning, providing structured visual contexts and verifiable answers. While recent progress has largely focused on algorithmic advances such as reinforcement learning (e.g., GRPO, DPO), the data centric foundations of vision language reasoning remain less explored. We show that supervised fine-tuning (SFT) with high-quality data can rival proprietary approaches. To this end, we compile a 161.4 million token multimodal dataset combining textbook question-solution pairs, curriculum aligned diagrams, and contextual materials, and fine-tune Qwen-2.5VL-32B using an optimized reasoning syntax (QMSA). The resulting model achieves 78.6% accuracy, only 1.0% below Gemini 2.0 Flash, on our newly released benchmark YKSUniform, which standardizes 1,854 multimodal exam questions across 309 curriculum topics. Our results reveal that data composition and representational syntax play a decisive role in multimodal reasoning. This work establishes a data centric framework for advancing open weight vision language models, demonstrating that carefully curated and curriculum-grounded multimodal data can elevate supervised fine-tuning to near state-of-the-art performance.
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