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arXiv · 2602.10217

Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance

Jacob L. Block·Mehryar Mohri·Aryan Mokhtari·Sanjay Shakkottai
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Abstract

We study machine unlearning in large generative models by framing the task as density ratio estimation to a target distribution rather than supervised fine-tuning. While classifier guidance is a standard approach for approximating this ratio and can succeed in general, we show it can fail to faithfully unlearn with finite samples when the forget set represents a sharp, concentrated data distribution. To address this, we introduce Temper-Then-Tilt Unlearning (T3-Unlearning), which freezes the base model and applies a two-step inference procedure: (i) tempering the base distribution to flatten high-confidence spikes, and (ii) tilting the tempered distribution using a lightweight classifier trained to distinguish retain from forget samples. Our theoretical analysis provides finite-sample guarantees linking the surrogate classifier's risk to unlearning error, proving that tempering is necessary to successfully unlearn for concentrated distributions. Empirical evaluations on the TOFU benchmark show that T3-Unlearning improves forget quality and generative utility over existing baselines, while training only a fraction of the parameters with a minimal runtime.

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Jacob L. Block, Mehryar Mohri, Aryan Mokhtari, Sanjay Shakkottai. 2026-06-19. Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance. https://arxiv.org/abs/2602.10217

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