English(EN) Aligning Thoughts with Answers: Probability Rewards to Tame Thinking Drift

新的Rita框架提高了视觉语言模型推理的一致性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员推出了一种新颖的强化学习框架Rita，旨在提高视觉语言模型推理过程与其最终答案之间的一致性。Rita解决了“思维漂移”问题，即模型可能在内部逻辑存在缺陷的情况下得出正确输出。该框架利用了两种新的奖励：思维奖励和一致性奖励，这两种奖励均源自参考答案的条件概率，并结合了难度感知的数据过滤策略。在EgoIntention和RefEgo-Int基准上的实验表明，Rita的性能优于现有的监督微调和标准RL方法。 AI

影响通过确保视觉语言模型的推理与其输出保持一致，提高了其可靠性，有望在复杂任务中提升性能。

排序理由该集群包含一篇详细介绍改进AI模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Pengzhan Sun, Shiu-hong Kao, Shijie Li, Yongyi Su, Junbin Xiao, Arjun Reddy Akula, Angela Yao · 2026-10-01 04:00

思想与答案对齐：概率奖励驯服思维漂移

arXiv:2609.39183v1 Announce Type: new Abstract: This paper studies \textbf{thinking--answer consistency} in vision-language models. We focus on Visual Intention Grounding, where a model infers a target object based on a human intention query and predicts a bounding box. We reveal…

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思想与答案对齐：概率奖励驯服思维漂移

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