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arXiv · 2605.08031

Object Hallucination-Free Reinforcement Unlearning for Vision-Language Models

Kaidi Jia·Yujie Lin·Chengyi Yang·Jiayao Ma·Jinsong Su
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Abstract

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.

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Kaidi Jia, Yujie Lin, Chengyi Yang, Jiayao Ma, Jinsong Su. 2026-05-08. Object Hallucination-Free Reinforcement Unlearning for Vision-Language Models. https://arxiv.org/abs/2605.08031

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