DareU unlearns LLMs by PPO-optimizing attribution rewards so outputs are no longer attributable to forget owners, outperforming loss-based methods on forget-utility trade-offs.
Rule: Reinforcement unlearning achieves forget-retain pareto optimality.arXiv preprint arXiv:2506.07171
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ASRU combines activation redirection and reward-optimized fine-tuning to unlearn cross-modal sensitive knowledge in MLLMs, reporting +24.6% better unlearning effectiveness and 5.8x higher generation quality on Qwen3-VL while preserving utility with limited retained data.
OFMU is a penalty-based bi-level optimizer for machine unlearning that alternates between a gradient-ascent forgetting step and a gradient-descent utility-restoration step, with a similarity penalty between forget and retain gradients.
citing papers explorer
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De-attribute to Forget for LLM Unlearning
DareU unlearns LLMs by PPO-optimizing attribution rewards so outputs are no longer attributable to forget owners, outperforming loss-based methods on forget-utility trade-offs.
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ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models
ASRU combines activation redirection and reward-optimized fine-tuning to unlearn cross-modal sensitive knowledge in MLLMs, reporting +24.6% better unlearning effectiveness and 5.8x higher generation quality on Qwen3-VL while preserving utility with limited retained data.
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OFMU: Optimization-Driven Framework for Machine Unlearning
OFMU is a penalty-based bi-level optimizer for machine unlearning that alternates between a gradient-ascent forgetting step and a gradient-descent utility-restoration step, with a similarity penalty between forget and retain gradients.