Not All Tokens Should Be Treated Equally: Context Credits Reassignment

Multimodal Reinforcement learning with verifiable rewards (RLVR) algorithms assign the same advantage to every token in a generated sequence, providing no differential signal for tokens with varying degrees of dependence on visual evidence. A simple experiment confirms this issue: \textbf{scaling token advantages by uninformative random noise already outperforms uniform credit}, suggesting that uniform credit is empirically suboptimal. Beyond that, we observe RLVR tuning under uniform credit weakens visual grounding along three complementary axes: attention, gradient attribution, and functional dependence on image embeddings, \textbf{collectively indicating insufficient utilization of visual evidence}. To understand this degradation, we provide a theoretical analysis showing that, under language-prior dominance and regularity conditions, uniform credit can amplify a pretrained text-favored gradient imbalance, contributing to weaker visual attention. In response, we propose Hierarchical Context Credit Reassignment (\textbf{HiCCR}), which reassigns credit at two levels: a token-level weight amplifies the advantage for visually grounded tokens, and a trajectory-level weight upweights rollouts with stronger visual engagement. The entire mechanism \textbf{adds less than 3\% wall-clock overhead} and applies to mainstream RLVR algorithms without auxiliary models or additional data. On ten benchmarks spanning mathematical and general multimodal reasoning, HiCCR consistently improves upon its corresponding base algorithm and achieves state-of-the-art results among open-source models of comparable scale, while alleviating the weakening of visual grounding observed under RLVR training.