DeepVision-103K dataset trains AI to solve math problems with 103K visual examples
Researchers built a massive 103,000-item dataset covering K12 math topics to train multimodal AI models.
A research team led by Haoxiang Sun introduces DeepVision-103K, a comprehensive dataset for training Large Multimodal Models (LMMs) on mathematical reasoning. It contains 103,000 visually diverse examples covering K12 topics, designed for Reinforcement Learning with Verifiable Rewards (RLVR). Models trained on this data show enhanced visual perception and reasoning, achieving strong performance on math benchmarks and generalizing to other multimodal tasks. The dataset is publicly available to advance AI's problem-solving capabilities.
Why It Matters
Better training data leads to AI that can reliably interpret charts, diagrams, and solve real-world visual problems.