title: VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
summary: 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.
paper_authors._id: 683e380cb028cae60270adcf
paper_authors.name: Yichen Feng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:47:01.523Z
paper_authors._id: 6702d9e2db3b7a57f9420e8d
paper_authors.avatarUrl: /avatars/2e65e83e8d13ca129f6382deb6e8bdfc.svg
paper_authors.fullname: Yichen Feng
paper_authors.type: user
paper_authors.user: EthanSta
paper_authors._id: 683e380cb028cae60270add0
paper_authors.name: Zhangchen Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:46:58.968Z
paper_authors._id: 653df1323479e9ebbe3eb6cc
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/653df1323479e9ebbe3eb6cc/K_g-r1iMRNKj99LXPuYF3.jpeg
paper_authors.fullname: Zhangchen Xu
paper_authors.type: user
paper_authors.user: zhangchenxu
paper_authors._id: 683e380cb028cae60270add1
paper_authors.name: Fengqing Jiang
paper_authors._id: 683e380cb028cae60270add2
paper_authors.name: Yuetai Li
paper_authors._id: 683e380cb028cae60270add3
paper_authors.name: Bhaskar Ramasubramanian
paper_authors._id: 683e380cb028cae60270add4
paper_authors.name: Luyao Niu
paper_authors._id: 683e380cb028cae60270add5
paper_authors.name: Bill Yuchen Lin
paper_authors._id: 683e380cb028cae60270add6
paper_authors.name: Radha Poovendran
paper_title: VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
paper_summary: 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.
paper_ai_summary: VisualSphinx provides a large-scale synthetic dataset to improve multimodal reasoning in vision language models, enhancing performance on various logical reasoning tasks.
paper_ai_keywords: vision language models multimodal reasoning logical reasoning large-scale synthetic visual logical reasoning image synthesis rule-to-image synthesis GRPO