视觉推理强化学习一直缺乏有效的信用分配机制，VEPO解决了这个痛点——做多模态RL的团队可以直接参考这个框架，在视觉-语义交叉场景中提升模型表现。
研究发现，在视觉推理任务中，仅依赖令牌级熵进行强化学习（RLVR）会失效，因为视觉敏感但熵低的令牌被忽略。现有多模态RL方法要么缺乏系统视觉度量，要么忽视熵主要驱动语义探索。为此，研究者提出VEPO框架，通过视觉敏感性与令牌熵的乘法耦合，将梯度信用分配给同时具备视觉基础和高信息量的令牌。实验表明，VEPO在7B和3B规模上分别比熵基线提升2.28和3.15个百分点，消融实验验证了方法的有效性。
Entropy Is Not Enough: Unlocking Effective Reinforcement Learning for Visual Reasoning via Vision-Anchored Token Selection
While token-level entropy is commonly recognized as effective for credit assignment in text-only reinforcement learning with verifiable rewards (RLVR), it remains unclear whether this mechanism still holds in visual reasoning. Our controlled study shows that this mechanism collapses in visual reasoning due to the omission of vision-sensitive tokens with naturally low entropy. Although existing multimodal RL methods increasingly acknowledge the importance of visual perception, they struggle to satisfy the inherent demand for interleaving precise perceptual grounding with semantic reasoning, either lacking systematic visual measurements or overlooking that token entropy primarily drives semantic exploration. To address this, we introduce VEPO (Vision-Entropy token-selection for Policy Optimization), an effective RL framework explicitly integrating visual sensitivity with token entropy via a principled multiplicative coupling, where VEPO redirects gradient credit toward tokens which are simultaneously visually grounded and highly informative. Extensive experiments demonstrate VEPO's leading performance, significantly outperforming the entropy-only baseline by 2.28 points at 7B-scale and 3.15 points at 3B-scale. Ablations further substantiate the soundness of our method.