Summary
R-C2 is a reinforcement learning framework that improves the robustness of multimodal reasoning by enforcing cross-modal cycle consistency. The method addresses internal conflicts in a model's perception and inference across sensory modalities by requiring the model to reason backwards, switch modalities, and reliably reconstruct the original answer through forward reasoning. This cycle produces dense, label-free rewards that train the model without additional annotations. The cyclic constraint encourages the model to autonomously align its internal representations across modalities, leading to gains of up to 7.6 percentage points in reasoning accuracy. The paper (arXiv:2603.25720) is authored by Zirui Zhang, Haoyu Dong, Kexin Pei, and Chengzhi Mao, and was posted in March 2026.
Paper Overview
Research Area: Computer Vision (CV)
Authors: Zirui Zhang, Haoyu Dong, Kexin Pei, Chengzhi Mao
Published: 2026-03-26
arXiv: 2603.25720
Abstract
Robust perception and reasoning require consistency across sensory modalities. This paper introduces R-C2, a reinforcement learning framework that resolves internal conflicts by enforcing cross-modal cycle consistency.
The approach works as follows:
- The model is required to perform backwards reasoning starting from an answer.
- It must switch modalities during the reasoning process.
- It must then reliably reconstruct the original answer via forward reasoning.
This cycle yields
dense, label-free rewards, meaning the model can be trained without additional human annotations.
The cyclic constraint encourages the model to autonomously align its internal representations across modalities, which improves reasoning accuracy by up to 7.6 percentage points.
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*Auto-collected on 2026-03-29*
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