Beyond Impair–Repair: Tuning-Efficient Machine Unlearning with Anti-Overrepair

Machine unlearning aims to remove the influence of selected training data without retraining a model from scratch while preserving performance on the remaining data. However, many approximate unlearning methods jointly optimize competing forgetting and retention objectives, introducing unlearning-specific hyperparameters that can be sensitive to the forgetting ratio, model architecture, and dataset. Repeated scenario-specific recalibration therefore adds nontrivial tuning and computational overhead, potentially reducing the practical efficiency advantage over retraining. We propose TriTune, a three-stage framework that decomposes this coupled optimization into a sequence of stage-wise, single-objective optimization problems. By avoiding directly competing objectives within each stage, TriTune simplifies hyperparameter selection and reduces sensitivity to hyperparameter choices. TriTune further introduces an adaptive anti-overrepair mechanism that prevents utility repair from restoring forgotten behavior, together with lightweight self-calibration for learning-rate initialization and stage termination. Experiments across diverse datasets, architectures, forgetting ratios, and learning tasks demonstrate effective forgetting, strong utility preservation, and reduced dependence on manual hyperparameter tuning.