[2603.25720] R-C2: Cycle-Consistent Reinforcement Learning Improves Multimodal Reasoning

Computer Science > Artificial Intelligence arXiv:2603.25720 (cs) [Submitted on 26 Mar 2026] Title:R-C2: Cycle-Consistent Reinforcement Learning Improves Multimodal Reasoning Authors:Zirui Zhang, Haoyu Dong, Kexin Pei, Chengzhi Mao View a PDF of the paper titled R-C2: Cycle-Consistent Reinforcement Learning Improves Multimodal Reasoning, by Zirui Zhang and 3 other authors View PDF HTML (experimental) Abstract:Robust perception and reasoning require consistency across sensory modalities. Yet current multimodal models often violate this principle, yielding contradictory predictions for visual and textual representations of the same concept. Rather than masking these failures with standard voting mechanisms, which can amplify systematic biases, we show that cross-modal inconsistency provides a rich and natural signal for learning. We introduce RC2, a reinforcement learning framework that resolves internal conflicts by enforcing cross-modal cycle consistency. By requiring a model to perform backward inference, switch modalities, and reliably reconstruct the answer through forward inference, we obtain a dense, label-free reward. This cyclic constraint encourages the model to align its internal representations autonomously. Optimizing for this structure mitigates modality-specific errors and improves reasoning accuracy by up to 7.6 points. Our results suggest that advanced reasoning emerges not only from scaling data, but also from enforcing a structurally consistent understandin...