# Lifelong Knowledge Editing requires Better Regularization

arXiv: https://arxiv.org/abs/2502.01636 · HTML: https://mlsanity.com/paper/2502.01636/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Transformer Feed-Forward Layers Are Key-Value Memories](https://mlsanity.com/paper/2012.14913/) (2020)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [Fast Model Editing at Scale](https://mlsanity.com/paper/2110.11309/) (2021)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions](https://mlsanity.com/paper/2305.14795/) (2023)
- [Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors](https://mlsanity.com/paper/2211.11031/) (2022)
- [Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models](https://mlsanity.com/paper/2301.04213/) (2023)
- [AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models](https://mlsanity.com/paper/2410.02355/) (2024)
- [Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue](https://mlsanity.com/paper/2401.04700/) (2024)
- [Model Editing at Scale leads to Gradual and Catastrophic Forgetting](https://mlsanity.com/paper/2401.07453/) (2024)
- [A Unified Framework for Model Editing](https://mlsanity.com/paper/2403.14236/) (2024)
- [The Butterfly Effect of Model Editing: Few Edits Can Trigger Large Language Models Collapse](https://mlsanity.com/paper/2402.09656/) (2024)
- [Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing](https://mlsanity.com/paper/2403.07175/) (2024)
- [Uncovering Overfitting in Large Language Model Editing](https://mlsanity.com/paper/2410.07819/) (2024)
- [On the Expressivity Role of LayerNorm in Transformers' Attention](https://mlsanity.com/paper/2305.02582/) (2023)
- [Perturbation-Restrained Sequential Model Editing](https://mlsanity.com/paper/2405.16821/) (2024)
- [Is Bigger Edit Batch Size Always Better? -- An Empirical Study on Model Editing with Llama-3](https://mlsanity.com/paper/2405.00664/) (2024)

## Built on by (most cited first, 10 of 10)

- [Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse](https://mlsanity.com/paper/2601.11042/) (2026)
- [Efficient Knowledge Editing via Minimal Precomputation](https://mlsanity.com/paper/2506.04226/) (2025)
- [Norm Growth and Stability Challenges in Localized Sequential Knowledge Editing](https://mlsanity.com/paper/2502.19416/) (2025)
- [Norm Anchors Make Model Edits Last](https://mlsanity.com/paper/2602.02543/) (2026)
- [MetaKE: Meta-Learning for Knowledge Editing Toward a Better Accuracy-Editability Trade-off](https://mlsanity.com/paper/2603.12677/) (2026)
- [Output Vector Editing for Memorization Mitigation in Large Language Models](https://mlsanity.com/paper/2606.18767/) (2026)
- [Can a Language Model Learn Facts Continually in Its Weights?](https://mlsanity.com/paper/2607.11020/) (2026)
- [The Tokens Remember: When Tokenization Bypasses Knowledge Editing and Unlearning](https://mlsanity.com/paper/2609.29045/) (2026)
- [ManiEdit: Sequential Unstructured Knowledge Editing for Language Models from a Manifold Perspective](https://mlsanity.com/paper/2609.33534/) (2026)
- [Specialists Learn Faster: Knowledge Organization in Pre-training Shapes Knowledge Update Efficiency](https://mlsanity.com/paper/2610.13835/) (2026)

## Abstract (arXiv)

Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. In this paper, we formalize the popular locate-then-edit methods as a two-step fine-tuning process, allowing us to precisely identify the root cause of this degradation. We show that model degradation occurs due to (1) over-optimization of internal activations and (2) continuous norm-growth of edited matrices. To mitigate these issues, we introduce two regularization techniques: (1) Most-Probable Early Stopping (MPES) and (2) explicit Frobenius norm-constraint. We demonstrate that applying these simple yet effective regularization techniques at key points in the editing process can substantially mitigate model degradation. Combining these regularization methods enables scaling locate-then-edit methods to 10,000 edits while reducing editing time by 42-61%. These results show that targeted regularization is essential for lifelong knowledge editing.