FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models
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arXiv
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| Format: | Preprint |
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2026
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| author | Sun, Yi Zhang, Zhiqi Zhong, Xinhao Zhou, Yimin Sun, Shuoyang Chen, Bin Xia, Shu-Tao Xu, Ke |
| author_facet | Sun, Yi Zhang, Zhiqi Zhong, Xinhao Zhou, Yimin Sun, Shuoyang Chen, Bin Xia, Shu-Tao Xu, Ke |
| contents | Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the generation of harmful or undesirable content. Existing concept erasure methods are either inference-time interventions with limited effectiveness or rely on supervised fine-tuning (SFT), which requires precisely aligned data and struggles with scalability and multi-concept settings. In this paper, we propose \emph{FlowErase-RL}, the first GRPO-based framework for concept erasure in flow matching models. We reformulate concept erasure as a reward optimization problem and introduce a \textbf{dynamic dual-path reward mechanism} that jointly optimizes (i) a Concept Erasure (CE) reward to suppress target concepts and (ii) a Non-target Space (NS) reward to preserve generative fidelity. The two reward paths are adaptively balanced during training via a performance-driven switching strategy, enabling stable optimization without explicit supervision. Extensive experiments on nudity, object, and artistic style erasure demonstrate that our method achieves state-of-the-art erasure performance while maintaining strong image quality and semantic alignment. Moreover, it exhibits robust resistance to adversarial attacks and scales effectively to multi-concept scenarios. Our results establish a new paradigm for safe and controllable generation in flow matching models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19739 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models Sun, Yi Zhang, Zhiqi Zhong, Xinhao Zhou, Yimin Sun, Shuoyang Chen, Bin Xia, Shu-Tao Xu, Ke Computer Vision and Pattern Recognition Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the generation of harmful or undesirable content. Existing concept erasure methods are either inference-time interventions with limited effectiveness or rely on supervised fine-tuning (SFT), which requires precisely aligned data and struggles with scalability and multi-concept settings. In this paper, we propose \emph{FlowErase-RL}, the first GRPO-based framework for concept erasure in flow matching models. We reformulate concept erasure as a reward optimization problem and introduce a \textbf{dynamic dual-path reward mechanism} that jointly optimizes (i) a Concept Erasure (CE) reward to suppress target concepts and (ii) a Non-target Space (NS) reward to preserve generative fidelity. The two reward paths are adaptively balanced during training via a performance-driven switching strategy, enabling stable optimization without explicit supervision. Extensive experiments on nudity, object, and artistic style erasure demonstrate that our method achieves state-of-the-art erasure performance while maintaining strong image quality and semantic alignment. Moreover, it exhibits robust resistance to adversarial attacks and scales effectively to multi-concept scenarios. Our results establish a new paradigm for safe and controllable generation in flow matching models. |
| title | FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.19739 |