Computer Science > Computation and Language
arXiv:2505.14679 (cs)
[Submitted on 20 May 2025 (v1), last revised 10 Mar 2026 (this version, v3)]
Title:UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models
View a PDF of the paper titled UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models, by Xiaojie Gu and 3 other authors
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Abstract:Lifelong learning enables large language models (LLMs) to adapt to evolving information by continually updating their internal knowledge. An ideal system should support efficient, wide-ranging updates while preserving existing capabilities and ensuring reliable deployment. Model editing stands out as a promising solution for this goal, offering a focused and efficient way to revise a model's internal knowledge. Although recent paradigms have made notable progress, they often struggle to meet the demands of practical lifelong adaptation at scale. To bridge this gap, we propose UltraEdit, a training-, subject-, and memory-free approach that is well-suited for ultra-scalable, real-world lifelong model editing. UltraEdit fundamentally differs from traditional paradigms by computing parameter shifts in one step using only a hidden state and its gradient, making the approach simple yet efficient. To improve scalability in lifelong settings, UltraEdit employs a lifelong normalization strategy that continuously updates feature statistics across turns, allowing it to adapt to distributional shifts and maintain consistency over time. UltraEdit achieves editing speeds more than $7\times$ faster than the previous state-of-the-art method, while requiring $4\times$ less VRAM. This makes it the only method currently capable of editing a 7B LLM on a 24GB consumer-grade GPU. Furthermore, we construct UltraEditBench, the largest dataset in the field to date with over 2M editing pairs, and demonstrate that our method supports up to 2M edits while maintaining high accuracy. Comprehensive experiments on five datasets and six models show that UltraEdit consistently achieves superior performance across diverse model editing scenarios, taking a further step towards safe and scalable lifelong learning. Our code is available at this https URL.
| Comments: | |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2505.14679 [cs.CL] |
| (or arXiv:2505.14679v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2505.14679
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Submission history
From: Xiaojie Gu [view email][v1] Tue, 20 May 2025 17:59:04 UTC (630 KB)
[v2] Fri, 26 Sep 2025 04:37:01 UTC (632 KB)
[v3] Tue, 10 Mar 2026 07:07:15 UTC (617 KB)
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View a PDF of the paper titled UltraEdit: Training-, Subject-, and Memory-Free Lifelong Editing in Language Models, by Xiaojie Gu and 3 other authors
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