Critic-Guided Reinforcement Unlearning in Text-to-Image Diffusion

• URL: http://arxiv.org/abs/2601.03213v1
• Date: Tue, 06 Jan 2026 17:52:02 GMT
• Title: Critic-Guided Reinforcement Unlearning in Text-to-Image Diffusion
• Authors: Mykola Vysotskyi, Zahar Kohut, Mariia Shpir, Taras Rumezhak, Volodymyr Karpiv,
• Abstract summary: Machine unlearning in text-to-image diffusion models aims to remove targeted concepts while preserving overall utility.<n>We present a general RL framework for diffusion unlearning that treats denoising as a sequential decision process.<n>Our algorithm is simple to implement, supports off-policy reuse, and plugs into standard text-to-image backbones.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning in text-to-image diffusion models aims to remove targeted concepts while preserving overall utility. Prior diffusion unlearning methods typically rely on supervised weight edits or global penalties; reinforcement-learning (RL) approaches, while flexible, often optimize sparse end-of-trajectory rewards, yielding high-variance updates and weak credit assignment. We present a general RL framework for diffusion unlearning that treats denoising as a sequential decision process and introduces a timestep-aware critic with noisy-step rewards. Concretely, we train a CLIP-based reward predictor on noisy latents and use its per-step signal to compute advantage estimates for policy-gradient updates of the reverse diffusion kernel. Our algorithm is simple to implement, supports off-policy reuse, and plugs into standard text-to-image backbones. Across multiple concepts, the method achieves better or comparable forgetting to strong baselines while maintaining image quality and benign prompt fidelity; ablations show that (i) per-step critics and (ii) noisy-conditioned rewards are key to stability and effectiveness. We release code and evaluation scripts to facilitate reproducibility and future research on RL-based diffusion unlearning.

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