PEAR: Adaptive Advantage Reshaping for Perception and Exploration in Large Vision-Language Models

Authors: Junxin Wang , Yingfeng Luo , Xiangchen Song , Weiqiao Shan , Runsong Zhao , Xinyu Liu , Shunjie Xing , Yifu Huo , Dingyang Lin , Qiuyu Gu , Yongyu Mu , Chenglong Wang , Changliang Li , Jingbo Zhu , Tong Xiao

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Abstract: Effective multimodal reasoning requires a model to reliably utilize visual information while sufficiently exploring diverse reasoning paths. To achieve this goal, prior work combines visual dependency and token entropy to reshape sequence-level advantages, but scaling the contribution of token entropy by visual dependency can leave high-entropy tokens with low visual dependency underweighted, despite their important role in reasoning exploration. To address this limitation, we propose PEAR, an adaptive advantage reshaping method for improving perception and exploration in multimodal reasoning. PEAR retains visual dependency as the base signal and adds an entropy-based exploration gain only at high-entropy positions, with lower visual dependency leading to a larger additional gain. The fused signal is then mapped to positive token-level weights that reshape the sequence-level advantage. Experiments on Qwen2.5-VL-3B/7B show that PEAR achieves the highest average performance among evaluated baselines across multimodal reasoning benchmarks, alongside leading results on textual reasoning and general multimodal benchmarks.

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Artificial Intelligence (cs.AI)

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arXiv:2610.32216 [cs.AI]

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arXiv:2610.32216v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.32216

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From: Junxin Wang [ view email ]

[v1]

Fri, 23 Oct 2026 06:14:15 UTC (2,902 KB)

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