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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Jul 24·5 min read
MIRROR: Learning from the Other View for Multi-Modal Reasoning
TL;DR
MIRROR introduces a novel approach to enhance visual reasoning in vision-language models (VLMs) by leveraging multiple problem views. This method significantly improves reasoning accuracy and consistency across modalities, outperforming standard reinforcement learning techniques.
✦ Why It Matters
Implement MIRROR to enhance your VLMs' reasoning capabilities in multimodal tasks today.
Key Takeaways
- Utilize ODA-Data for training VLMs on multimodal geometry problems.
- Apply MIRROR to improve reasoning consistency across text and visual inputs.
- Evaluate model performance across different views to identify strengths.
- Leverage reinforcement learning techniques for self-supervised training.
How It Works
MIRROR operates by evaluating a model's performance on geometry problems across multiple views: text, image, and combined. It identifies the best-performing view and uses it as a teacher to guide the training of other views.
This is achieved through a reverse-KL divergence objective, which encourages the model to align its reasoning across modalities, effectively learning from its own mistakes.