Existing machine unlearning (MU) approaches exhibit significant sensitivity to hyperparameters, requiring meticulous tuning that limits practical deployment. In this work, we first empirically demonstrate the instability and suboptimal performance of existing popular MU methods when deployed in different scenarios. To address this issue, we propose Dual Optimizer (DualOptim), which incorporates adaptive learning rate and decoupled momentum factors. Empirical and theoretical evidence demonstrates that DualOptim contributes to effective and stable unlearning. Through extensive experiments, we show that DualOptim can significantly boost MU efficacy and stability across diverse tasks, including image classification, image generation, and large language models, making it a versatile approach to empower existing MU algorithms.

翻译：现有的机器遗忘（MU）方法对超参数表现出显著的敏感性，需要精细调优，这限制了其实际部署。在本研究中，我们首先通过实证展示了现有主流MU方法在不同场景下部署时的不稳定性和次优性能。为解决这一问题，我们提出了双优化器（DualOptim），该算法融合了自适应学习率与解耦动量因子。实证与理论证据表明，DualOptim有助于实现高效且稳定的遗忘过程。通过大量实验，我们证明DualOptim能在图像分类、图像生成及大语言模型等多种任务中显著提升MU的效能与稳定性，使其成为增强现有MU算法的通用解决方案。