Summary
R-C2 is a reinforcement learning framework designed to resolve internal conflicts in multimodal reasoning by enforcing cross-modal cycle consistency. The method requires a model to perform backward reasoning, switch between sensory modalities, and reliably reconstruct the original answer through forward reasoning. This cycle constraint produces dense, label-free rewards without requiring annotated data, encouraging the model to autonomously align its internal representations across modalities. According to the paper summary, the approach improves reasoning accuracy by up to 7.6 percentage points. The paper is in the computer vision (CV) research area, authored by Zirui Zhang, Haoyu Dong, Kexin Pei, and Chengzhi Mao, released on March 26, 2026, and available on arXiv as 2603.25720.
Paper Overview
Research Area: CV
Authors: Zirui Zhang, Haoyu Dong, Kexin Pei, Chengzhi Mao
Release Date: 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 method works by requiring the model to:
- Perform backward reasoning from an answer
- Switch modalities between sensory inputs
- Reliably reconstruct the answer via forward reasoning
This cycle constraint yields dense, label-free rewards, encouraging the model to autonomously align its internal representations across modalities. As a result, reasoning accuracy improves by up to 7.6 percentage points.
Links
- arXiv page: https://arxiv.org/abs/2603.25720
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