aiqbench analysis
This paper introduces LadderEdit, a memory-efficient technique for lifelong editing of large language models that compresses LoRA adapters into low-rank sketches before promoting them only when necessary to maintain performance. By dynamically allocating rank along a "ladder," the method achieves a 5.2x reduction in storage while sustaining effectiveness across thousands of sequential edits on standard benchmarks like ZsRE and CounterFact. The approach directly addresses the scalability bottleneck of storing individual adapters for every fact update, making continuous model maintenance more viable.
LadderEdit: Edit-Level Residual Compression for Memory-Efficient Lifelong Editing of LLMs
arXiv:2610.11160v1 Announce Type: new Abstract: Lifelong editing of LLMs requires storing thousands of edits after acquisition. A widely used family of approaches attaches one LoRA adapter per edit, which preserves behavior but grows linearly in storage. To address this challenge, we propose LadderEdit, a method that compresses each LoRA adapter after it is acquired. Each edit is first stored at low rank as a cheap sketch. We then check whether this sketch still satisfies the rewrite, generalization, and locality contract on probe prompts. Edits that pass keep the sketch; those that fail are promoted to a higher rank along a ladder until the contract is met. Because every edit retains some representation, coverage is maintained, and only hard edits consume more rank. Across ZsRE, CounterFact, and WikiBigEdit benchmarks on LLaMA-3-8B, Mistral-7B, and Qwen2.5-7B, LadderEdit tracks exact LoRA storage at 5.2x less memory and remains effective at 50,000 sequential edits.
Abstract:Lifelong editing of LLMs requires storing thousands of edits after acquisition. A widely used family of approaches attaches one LoRA adapter per edit, which preserves behavior but grows linearly in storage. To address this challenge, we propose LadderEdit, a method that compresses each LoRA adapter after it is acquired. Each edit is first stored at low rank as a cheap sketch. We then check whether this sketch still satisfies the rewrite, generalization, and locality contract on probe prompts. Edits that pass keep the sketch; those that fail are promoted to a higher rank along a ladder until the contract is met. Because every edit retains some representation, coverage is maintained, and only hard edits consume more rank. Across ZsRE, CounterFact, and WikiBigEdit benchmarks on LLaMA-3-8B, Mistral-7B, and Qwen2.5-7B, LadderEdit tracks exact LoRA storage at 5.2x less memory and remains effective at 50,000 sequential edits.
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multimedia (cs.MM)
arXiv:2610.11160 [cs.CL]
(or arXiv:2610.11160v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.11160 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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