Ready2Unlearn: A Learning-Time Approach for Preparing Models with Future Unlearning Readiness

TL;DR

Ready2Unlearn tackles the inefficiency and unreliability of post hoc unlearning by embedding unlearning-readiness into training via a MAML-inspired framework. It partitions data into high-risk forget data and low-risk retain data, and optimizes for fast forget signaling, retention of retained information, and resistance to recovery, while preserving current task performance. Experiments on MNIST, PathMNIST, and LLaMA-3.2-1B demonstrate faster, more robust unlearning with less loss of utility and stronger resistance to re-learning forgotten data. This proactive approach offers a practical strategy for privacy, security, and governance in evolving data landscapes.

Abstract

This paper introduces Ready2Unlearn, a learning-time optimization approach designed to facilitate future unlearning processes. Unlike the majority of existing unlearning efforts that focus on designing unlearning algorithms, which are typically implemented reactively when an unlearning request is made during the model deployment phase, Ready2Unlearn shifts the focus to the training phase, adopting a "forward-looking" perspective. Building upon well-established meta-learning principles, Ready2Unlearn proactively trains machine learning models with unlearning readiness, such that they are well prepared and can handle future unlearning requests in a more efficient and principled manner. Ready2Unlearn is model-agnostic and compatible with any gradient ascent-based machine unlearning algorithms. We evaluate the method on both vision and language tasks under various unlearning settings, including class-wise unlearning and random data unlearning. Experimental results show that by incorporating such preparedness at training time, Ready2Unlearn produces an unlearning-ready model state, which offers several key advantages when future unlearning is required, including reduced unlearning time, improved retention of overall model capability, and enhanced resistance to the inadvertent recovery of forgotten data. We hope this work could inspire future efforts to explore more proactive strategies for equipping machine learning models with built-in readiness towards more reliable and principled machine unlearning.

Figures (9)

• Figure 1: Comparison of learning with (top) and without (bottom) unlearning preparation.
• Figure 2: An illustration of the forward-looking nature of Ready2Unlearn using conceptual 1D loss landscapes. Panel A: The model state obtained with unlearning preparedness (gray circle) lies adjacent to a steep ascent in the loss landscape with respect to the forget data, such that even a single gradient ascent step can trigger a substantial loss increase, enabling fast and effective unlearning. In contrast, the unprepared model (white circle) lies in a flatter region far from any steep increase, requiring many steps to achieve comparable unlearning. Panel B: The model state optimized by Ready2Unlearn resides in a region where the retain-data loss remains low and stable despite gradient ascent updates on forget data, thereby preserving the model’s utility. The unprepared model resides in a region where unlearning actions (i.e., gradient ascent on forget data) adversely impact performance on retain data, as indicated by a sharp increase in retain loss. Panel C: Around the unprepared model state, the loss landscapes of forget data and recovery data exhibit high similarity. As a result, fine-tuning on recovery data inadvertently lowers the forget loss as well, undoing the unlearning. In contrast, Ready2Unlearn prepares the model in a region where the recovery loss is low and exhibits a distinct pattern from the forget loss, thereby making the model less likely to re-acquire forgotten information during further fine-tuning on recovery data.
• Figure 3: Comparison of unlearning efficiency for MNIST (left) and PathMNIST (right). Each line represents the average forget-data accuracy across all class-wise unlearning settings, where each class is treated as the forget class in turn. All methods are evaluated with the same unlearning rate of $1 \times 10^{-5}$ for a fair comparison. The vertical dashed line marks the moment when unlearning begins.
• Figure 4: Performance retention for MNIST (left) and PathMNIST (right). Each axis of the radar chart corresponds to a class treated as the forget class. The value on each axis shows the model's retain accuracy when its forget accuracy reaches random guessing.
• Figure 5: Comparison of unlearning efficiency for MUSE-Books (left) and MUSE-News (right) using Llama-3.2-1B as the target model. Each line represents the cross-entropy loss on the forget data for each method. All methods are evaluated with the same unlearning rate of $1 \times 10^{-6}$ for a fair comparison. The vertical dashed line marks the moment when unlearning begins.