Bridging Visual Representation and Reinforcement Learning from Verifiable Rewards in Large Vision-Language Models

Reinforcement Learning from Verifiable Rewards (RLVR)has substantially enhanced the reasoning capabilities of large languagemodels in abstract reasoning tasks. However, its application to LargeVision-Language Models (LVLMs) remains constrained by a structuralrepresentational bottleneck. Existing approaches generally lack explicitmodeling and effective utilization of visual information, preventing vi-sual representations from being tightly coupled with the reinforcementlearning optimization process and thereby limiting further improvementsin multimodal reasoning performance. To address this limitation, wepropose KAWHI (Key-Region Aligned Weighted Harmonic Incentive),a plug-and-play reward reweighting mechanism that explicitly incorpo-rates structured visual information into uniform reward policy optimiza-tion methods (e.g., GRPO and GSPO). The method adaptively localizessemantically salient regions through hierarchical geometric aggregation,identifies vision-critical attention heads via structured attribution, andperforms paragraph-level credit reallocation to align spatial visual ev-idence with semantically decisive reasoning steps. Extensive empiricalevaluations on diverse reasoning benchmarks substantiate KAWHI as ageneral-purpose enhancement module, consistently improving the perfor-mance of various uniform reward optimization methods. Code is availableat https://github.com/kawhiiiileo/KAWHI.