Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints

• URL: http://arxiv.org/abs/2605.30825v1
• Date: Fri, 29 May 2026 04:25:45 GMT
• Title: Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
• Abstract summary: We propose a principled constrained optimization framework that formulates unlearning as minimizing the deviation from a pretrained model.<n>We show that our KL-constrained approach achieves superior retention-based baselines for concept and data unlearning, and that our likelihood-based approach matches unlearning effectiveness.
• Score: 80.85967228615388
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Unlearning in diffusion models aims to remove undesirable data or concepts while preserving the utility of pretrained models -- two fundamentally conflicting objectives. We propose a principled constrained optimization framework that formulates unlearning as minimizing the deviation from a pretrained model, subject to explicit separation constraints from the unlearning distributions. Specifically, we formulate three constrained optimization problems based on reverse and forward KL divergences, and likelihood constraints. The first two generalize existing approaches for concept and data unlearning, while the third offers a novel and natural formulation for unlearning. Despite the nonconvexity of the KL constraints, we establish strong duality for all three problems, enabling us to explicitly characterize their optimal solutions as unlearning targets and develop primal-dual algorithms for each formulation. Experimental results demonstrate that our KL-constrained approach achieves superior retention-unlearning tradeoffs compared to weight-based baselines for concept and data unlearning, and that our likelihood-based approach matches unlearning effectiveness while better preserving retained concepts compared to baselines.

Related papers

• INSPIRE: An Internalize-Then-Improve Approach for Example-Driven Mathematical Reasoning [61.60016491769943]
  In human mathematics education, example-based reasoning such as constructing counterexamples to test theorem boundaries reflects deep conceptual understanding.<n>We propose INSPIRE, an Internalize-Then-Improve approach combining Reference-Guided Student Internalization (RGSI) with a stage-wise preference training strategy.<n>Experiments across multiple model scales and families demonstrate consistent improvements, even surpassing larger open-source models.
  arXiv  Detail & Related papers  (2026-08-27T03:24:03Z)
• Rethinking Concept Bottleneck Models: From Pitfalls to Solutions [53.84388497227224]
  Concept Bottleneck Models (CBMs) ground predictions in human-understandable concepts.<n>CBM-Suite is a methodological framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-05T19:37:49Z)
• Alignment as Distribution Learning: Your Preference Model is Explicitly a Language Model [12.063078727764045]
  We argue that alignment via reinforcement learning from human feedback lacks theoretical justification and incentivizes deterministic solutions.<n>We propose three principled learning objectives: preference maximum likelihood estimation, preference distillation, and reverse KL minimization.<n>We empirically demonstrate that our distribution learning framework, especially preference distillation, consistently outperforms or matches the performances of RLHF and DPO.
  arXiv  Detail & Related papers  (2025-06-02T10:36:31Z)
• Learning Constrained Optimization with Deep Augmented Lagrangian Methods [54.22290715244502]
  A machine learning (ML) model is trained to emulate a constrained optimization solver. This paper proposes an alternative approach, in which the ML model is trained to predict dual solution estimates directly. It enables an end-to-end training scheme is which the dual objective is as a loss function, and solution estimates toward primal feasibility, emulating a Dual Ascent method.
  arXiv  Detail & Related papers  (2024-03-06T04:43:22Z)
• MAST: Model-Agnostic Sparsified Training [4.962431253126472]
  We introduce a novel optimization problem formulation that departs from the conventional way of minimizing machine learning model loss as a black-box function. Unlike traditional formulations, the proposed approach explicitly incorporates an initially pre-trained model and random sketch operators. We present several variants of the Gradient Descent (SGD) method adapted to the new problem formulation.
  arXiv  Detail & Related papers  (2023-11-27T18:56:03Z)
• Class-Incremental Mixture of Gaussians for Deep Continual Learning [15.49323098362628]
  We propose end-to-end incorporation of the mixture of Gaussians model into the continual learning framework. We show that our model can effectively learn in memory-free scenarios with fixed extractors.
  arXiv  Detail & Related papers  (2023-07-09T04:33:19Z)
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints [51.12047280149546]
  A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints. We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
  arXiv  Detail & Related papers  (2022-12-23T22:29:08Z)
• Self-Supervised Primal-Dual Learning for Constrained Optimization [19.965556179096385]
  This paper studies how to train machine-learning models that directly approximate the optimal solutions of constrained optimization problems. It proposes the idea of Primal-Dual Learning (PDL), a self-supervised training method that does not require a set of pre-solved instances or an optimization solver for training and inference.
  arXiv  Detail & Related papers  (2022-08-18T20:07:10Z)
• Learning Diverse Representations for Fast Adaptation to Distribution Shift [78.83747601814669]
  We present a method for learning multiple models, incorporating an objective that pressures each to learn a distinct way to solve the task. We demonstrate our framework's ability to facilitate rapid adaptation to distribution shift.
  arXiv  Detail & Related papers  (2020-06-12T12:23:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.