MIRROR: Learning from the Other View for Multi-Modal Reasoning
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TL;DR - MIRROR is a self-supervised reinforcement-learning method that transfers reasoning behavior between textual, visual, and combined views of geometry problems. It improves both accuracy and cross-modal consistency compared with standard RL.
- Introduces ODA-Data, a paired geometry dataset containing equivalent text-dominant, image-dominant, and combined views.
- Identifies complementary, modality-dependent failures in vision-language models.
- Selects the best-performing view as a teacher and aligns other views toward it using a reverse-KL objective.
- Reports gains across geometry reasoning benchmarks without specifying numerical results in the provided abstract.
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MIRROR: Learning from the Other View for Multi-Modal Reasoning
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TL;DR - MIRROR is a self-supervised reinforcement-learning method that transfers reasoning behavior between textual, visual, and combined views of geometry problems. It improves both accuracy and cross-modal consistency compared with standard RL.
- Introduces ODA-Data, a paired geometry dataset containing equivalent text-dominant, image-dominant, and combined views.
- Identifies complementary, modality-dependent failures in vision-language models.
- Selects the best-performing view as a teacher and aligns other views toward it using a reverse-KL objective.
- Reports gains across geometry reasoning benchmarks without specifying numerical results in the provided abstract.