FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models

• URL: http://arxiv.org/abs/2605.19739v2
• Date: Mon, 25 May 2026 09:07:55 GMT
• Title: FlowErase-RL: Rethinking Concept Erasure as Reward Optimization in Flow Matching Models
• Abstract summary: Flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks.<n>We propose emphFlowErase-RL, the first GRPO-based framework for concept erasure in flow matching models.<n>We show that our method achieves state-of-the-art erasure performance while maintaining strong image quality and semantic alignment.
• Score: 47.80885954469943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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