VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
Authors: name: Yichen Feng, name: Zhangchen Xu, name: Fengqing Jiang, name: Yuetai Li, name: Bhaskar Ramasubramanian, name: Luyao Niu, name: Bill Yuchen Lin, name: Radha Poovendran

Vision language models (VLMs) are expected to perform effective multimodal
reasoning and make logically coherent decisions, which is critical to tasks
such as diagram understanding and spatial problem solving. However, current VLM
reasoning lacks large-scale and well-structured training datasets. To bridge
this gap, we propose VisualSphinx, a first-of-its-kind large-scale synthetic
visual logical reasoning training data. To tackle the challenge of image
synthesis with grounding answers, we propose a rule-to-image synthesis
pipeline, which extracts and expands puzzle rules from seed questions and
generates the code of grounding synthesis image synthesis for puzzle sample
assembly. Experiments demonstrate that VLM trained using GRPO on VisualSphinx
benefit from logical coherence and readability of our dataset and exhibit
improved performance on logical reasoning tasks. The enhanced reasoning
capabilities developed from VisualSphinx also benefit other reasoning tasks
such as algebraic reasoning, arithmetic reasoning and geometry reasoning.