面向视觉语言模型的无对象幻觉强化反学习
Object Hallucination-Free Reinforcement Unlearning for Vision-Language Models
AI总结：
本文提出HFRU框架，通过视觉编码器深度语义删除，结合对齐破坏与GRPO优化，实现高效反学习，减少对象幻觉，实验显示在目标识别和人脸识别任务中性能优异。
摘要：
视觉语言模型（VLMs）因隐私、版权和偏见问题引发了对机器反学习的需求。然而现有方法仅微调语言解码器，导致表面遗忘无法消除底层视觉表示且常引入对象幻觉。本文提出HFRU，一种基于视觉编码器的强化反学习框架，采用两阶段方法结合对齐破坏与GRPO优化，使用复合奖励包括抽象奖励以鼓励语义有效替代并缓解幻觉。在目标识别和人脸识别任务中，HFRU实现了超过98%的遗忘和保留性能，同时引入极小的对象幻觉，显著优于先前方法。代码和实现细节可在https://github.com/XMUDeepLIT/HFRU获取。
英文摘要：
Vision-language models (VLMs) raise growing concerns about privacy, copyright, and bias, motivating machine unlearning to remove sensitive knowledge. However, existing methods primarily fine-tune the language decoder, leading to superficial forgetting that fails to erase underlying visual representations and often introduces object hallucination. We propose HFRU, a reinforcement unlearning framework that operates on the vision encoder for deep semantic removal. Our two-stage approach combines alignment disruption with GRPO-based optimization using a composite reward, including an abstraction reward that encourages semantically valid substitutions and mitigates hallucinations. Experiments on object recognition and face identity tasks show that HFRU achieves over 98% forgetting and retention performance, while introducing negligible object hallucination, significantly outperforming prior methods.Our code and implementation details are available at https://github.com/XMUDeepLIT/HFRU.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
