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.
MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）；Sydney AI Centre, The University of Sydney（悉尼人工智能中心，悉尼大学）；Department of Computer Science, Hong Kong Baptist University（计算机科学系，香港 Baptist 大学）
Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam.（信息科技学院，科学大学，胡志明市，越南）；Vietnam National University, Ho Chi Minh City, Vietnam.（越南国家大学，胡志明市，越南）
Distribution-Guided and Constrained Quantum Machine Unlearning
Department of Electrical Engineering, Lahore University of Management Sciences, Lahore 54792, Pakistan.（电气工程系，拉合尔管理科学大学，巴基斯坦拉合尔）；Department of Physics, Lahore University of Management Sciences, Lahore 54792, Pakistan.（物理系，拉合尔管理科学大学，巴基斯坦拉合尔）
Mechanism-Guided Selective Unlearning for RLVR-Induced Reasoning
School of Engineering, Institute of Science Tokyo, Japan（东京科学研究院工程学院）；College of Control Science and Engineering, Zhejiang University, China（浙江大学控制科学与工程学院）；Department of Electrical and Computer Engineering, National University of Singapore, Singapore（新加坡国立大学电子与计算机工程系）
Beyond Sharp Minima: Robust LLM Unlearning via Feedback-Guided Multi-Point Optimization
Department of Statistics and Data Science（统计与数据科学系）；Northwestern University（西北大学）；Department of Computer Science and Engineering（计算机科学与工程系）；University of Notre Dame（圣母大学）；Department of Computer Science（计算机科学系）；Illinois Institute of Technology（伊利诺伊理工学院）
CLUE: Conflict-guided Localization for LLM Unlearning Framework
Hang Chen School of Computer Science and Technology Xi’an Jiaotong University（陈hang 硕士生 计算机科学与技术学院 西安交通大学）；Jiaying Zhu School of Computer Science and Engineering The Chinese University of Hong Kong（朱纪莹 计算机科学与工程学院 香港中文大学）；Xinyu Yang School of Computer Science and Technology Xi’an Jiaotong University（杨新宇 硕士生 计算机科学与技术学院 西安交通大学）；Wenya Wang School of Computer Science and Engineering Nanyang Technological University（王文亚 计算机科学与工程学院 新加坡国立大学）
Image Can Bring Your Memory Back: A Novel Multi-Modal Guided Attack against Image Generation Model Unlearning
Institute of Data Science, National University of Singapore（数据科学研究所，新加坡国立大学）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
Roots Beneath the Cut: Uncovering the Risk of Concept Revival in Pruning-Based Unlearning for Diffusion Models
University of Georgia（佐治亚大学）；Carnegie Mellon University（卡内基梅隆大学）；Northeastern University（东北大学）；Stevens Institute of Technology（史蒂文斯理工学院）；University of Arizona（亚利桑那大学）
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Department of Neurosurgery, Qilu Hospital of Shandong University and Institute of Brain and Brain-Inspired Science（齐鲁医院山东大学神经外科和脑科学与脑启发科学研究院）；Department of Neurosurgery, The Affiliated Hospital of Qingdao University（青岛大学附属医院神经外科）；Department of Neurosurgery, Peking Union Medical College Hospital（北京地坛医院神经外科）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）；College of Artificial Intelligence, Dalian Maritime University（大连海事大学人工智能学院）；University of Colorado Boulder（科罗拉多大学波尔德分校）；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Georgia Institute of Technology（佐治亚理工学院）
