Cross-modal inconsistencies in multimodal models aren't just failures to hide—they're valuable training signals that, when enforced through cycle consistency, improve reasoning accuracy by up to 7.6 points and reduce systematic biases.
This paper introduces RC2, a reinforcement learning approach that improves multimodal AI models by enforcing consistency between visual and textual understanding. Instead of ignoring when a model gives contradictory answers for the same concept in different modalities, the method uses these conflicts as training signals.