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arXiv AI By 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

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

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PEAR is an adaptive advantage reshaping method designed to improve perception and exploration in large vision‑language models. It builds on visual dependency as a base signal and adds an entropy‑based exploration gain only at high‑entropy positions, giving larger gains where visual dependency is lower. Experiments on Qwen2.5‑VL‑3B/7B demonstrate that PEAR outperforms baseline methods across multimodal reasoning benchmarks and achieves leading results on textual reasoning and general multimodal tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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