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Improving Multimodal Reasoning via Worst Dimension Optimization

Haocheng Lv, Huaping Zhang, Qiuchi Li, Lei Li, Chunxiao Gao

arXiv:2606.07801Published June 5, 20260 citations
  • cs.AI

Abstract

Multimodal reasoning requires a path that retains integrity over a wide range of constraints, from visual grounding to logic consistency. However, the current Process Reward Models focus on heuristically defined rewards that equally weigh these factors, which may lead to the concealment of individual dimension failures by the dominating factors, without guaranteeing the validity of the reasoning process in general.

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