VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
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.
683e380cb028cae60270adcf
Yichen Feng
claimed_verified
2025-06-03T08:47:01.523Z
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/avatars/2e65e83e8d13ca129f6382deb6e8bdfc.svg
Yichen Feng
user
EthanSta
683e380cb028cae60270add0
Zhangchen Xu
claimed_verified
2025-06-03T08:46:58.968Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/653df1323479e9ebbe3eb6cc/K_g-r1iMRNKj99LXPuYF3.jpeg
Zhangchen Xu
user
zhangchenxu
683e380cb028cae60270add1
Fengqing Jiang
683e380cb028cae60270add2
Yuetai Li
683e380cb028cae60270add3
Bhaskar Ramasubramanian
683e380cb028cae60270add4
Luyao Niu
claimed_verified
2025-07-15T19:12:07.756Z
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/avatars/fa0e0dbc203a21e58dda8fdb4cbc67ad.svg
Luyao Niu
user
LNIU
683e380cb028cae60270add5
Bill Yuchen Lin
683e380cb028cae60270add6
Radha Poovendran
VisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
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.
VisualSphinx provides a large-scale synthetic dataset to improve multimodal reasoning in vision language models, enhancing performance on various logical reasoning tasks.
vision language models multimodal reasoning logical reasoning large-scale synthetic visual logical reasoning image synthesis rule-to-image synthesis GRPO