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arXiv · 2605.21539

DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models

Xuyang Zhong·Qizhang Li·Yiwen Guo·Chen Liu
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Abstract

We propose DualOptim+, a novel optimization framework for improving machine unlearning in large language models. It introduces a base state to capture common representations shared by forgetting and retaining objectives and delta states to preserve objective-specific residuals. This architecture allows the optimizer to adaptively bridge shared and decoupled states based on the directional conflict between forgetting and retaining gradients. We further introduce DualOptim+ 8bit, a quantized variant that reduces memory overhead without compromising performance. Extensive experiments across fictitious and real-world unlearning, safety alignment, and multi-task learning tasks demonstrate that DualOptim+ consistently achieves a superior trade-off between different objectives. Codes are available at https://github.com/CityU-MLO/DualOptimPlus.

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Xuyang Zhong, Qizhang Li, Yiwen Guo, Chen Liu. 2026-05-20. DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models. https://arxiv.org/abs/2605.21539

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