Phi-4-reasoning-vision-15B Technical Report
We present Phi-4-reasoning-vision-15B, a compact open-weight multimodal reasoning model, and share the motivations, design choices, experiments, and learnings that informed its development. Our goal is to contribute practical insight to the research community on building smaller, efficient multimodal reasoning models and to share the result of these learnings as an open-weight model that is good at common vision and language tasks and excels at scientific and mathematical reasoning and understanding user interfaces. Our contributions include demonstrating that careful architecture choices and rigorous data curation enable smaller, open-weight multimodal models to achieve competitive performance with significantly less training and inference-time compute and tokens. The most substantial improvements come from systematic filtering, error correction, and synthetic augmentation -- reinforcing that data quality remains the primary lever for model performance. Systematic ablations show that high-resolution, dynamic-resolution encoders yield consistent improvements, as accurate perception is a prerequisite for high-quality reasoning. Finally, a hybrid mix of reasoning and non-reasoning data with explicit mode tokens allows a single model to deliver fast direct answers for simpler tasks and chain-of-thought reasoning for complex problems.
69a8ecc98b5b27c7ea9fa5d1
Jyoti Aneja
admin_assigned
2026-03-05T10:28:29.475Z
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Jyoti Aneja
jyotiA
user
jyotiA
69a8ecc98b5b27c7ea9fa5d2
Michael Harrison
69a8ecc98b5b27c7ea9fa5d3
Neel Joshi
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Tyler LaBonte
admin_assigned
2026-03-05T10:28:42.590Z
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Tyler LaBonte
tmlabonte
user
tmlabonte
69a8ecc98b5b27c7ea9fa5d5
John Langford
69a8ecc98b5b27c7ea9fa5d6
Eduardo Salinas
Phi-4-reasoning-vision-15B Technical Report
We present Phi-4-reasoning-vision-15B, a compact open-weight multimodal reasoning model, and share the motivations, design choices, experiments, and learnings that informed its development. Our goal is to contribute practical insight to the research community on building smaller, efficient multimodal reasoning models and to share the result of these learnings as an open-weight model that is good at common vision and language tasks and excels at scientific and mathematical reasoning and understanding user interfaces. Our contributions include demonstrating that careful architecture choices and rigorous data curation enable smaller, open-weight multimodal models to achieve competitive performance with significantly less training and inference-time compute and tokens. The most substantial improvements come from systematic filtering, error correction, and synthetic augmentation -- reinforcing that data quality remains the primary lever for model performance. Systematic ablations show that high-resolution, dynamic-resolution encoders yield consistent improvements, as accurate perception is a prerequisite for high-quality reasoning. Finally, a hybrid mix of reasoning and non-reasoning data with explicit mode tokens allows a single model to deliver fast direct answers for simpler tasks and chain-of-thought reasoning for complex problems.
A compact open-weight multimodal reasoning model is presented that achieves competitive performance through careful architecture design, high-quality data curation, and a hybrid approach combining direct answering with chain-of-thought reasoning.
multimodal reasoning model open-weight model vision-language tasks scientific reasoning mathematical reasoning user interface understanding architecture choices data curation synthetic augmentation high-resolution encoders dynamic-resolution encoders chain-of-thought reasoning mode tokens