DyCo-RL: Dynamic Cross-Modal Coordination for Visual Reasoning

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a leading paradigm for enhancing visual reasoning in Multimodal Large Language Models (MLLMs). However, existing RLVR methods optimize primarily for the reasoning outcome and overlook how that outcome is produced: as a chain-of-thought unfolds, some tokens must extract evidence from the image while others must anchor to the preceding textual context, yet a single sequence-level reward provides no signal about whether each token draws on the modality its role demands. Token-level analyses and controlled interventions reveal that tokens frequently under-attend to their designated modality, and that this coordination breakdown may contribute to reasoning failures. Motivated by these findings, we propose DyCo-RL, which integrates dynamic cross-modal coordination into RLVR optimization. Specifically, DyCo-RL uses the Fisher-Rao geodesic distance to measure within-modality attention shifts, assigning tokens to either visually-oriented or text-oriented functional roles. It then evaluates the alignment between a token's actual attention allocation and its assigned role, leveraging this score for alignment-guided advantage reweighting during policy optimization. Extensive experiments using 11K examples for RL training demonstrate that the algorithm-agnostic DyCo-RL, when applied to Qwen2.5-VL-3B/7B and Qwen3.5-27B, improves the average performance of all four evaluated RLVR algorithms across seven benchmarks spanning visual-centric and mathematical reasoning.