Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization
通过门控感知-推理优化解决大视觉-语言模型中的过度思考
机构 * Dartmouth College(达特茅斯学院) ; University of Notre Dame(诺丁汉大学) ; Cornell University(康奈尔大学) ; Northwestern University(西北大学)
AI总结 本文提出GPRO方法,通过动态路由计算路径提升大视觉-语言模型的推理效率与准确性,减少过度思考带来的冗余响应。
Comments Accepted to Annual Meeting of the Association for Computational Linguistics (ACL 2026)