ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Rank Adaptation (LoRA) adapters. ReLoRA is a knowledge-reusing re-adaptation framework that efficiently restores service-ready LoRA adapters for evolving LLM services while preserving or improving task performance. It consists of adaptive LoRA initialization using Bayesian optimization and fine-tuning with scheduled regularization. Experiments show ReLoRA reduces time-to-readiness by up to 8.9× and improves accuracy by up to 4.6%.
[2606.02606] ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
[Submitted on 23 May 2026]
Title:ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
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Abstract:Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Rank Adaptation (LoRA) adapters. For service providers managing numerous downstream model services, retraining each LoRA adapter from scratch for every updated base model is computationally prohibitive and delays service rollout. Meanwhile, the simpler alternative, i.e., naively applying the original LoRA adapter to the updated base model, often leads to degraded service quality due to adapter-backbone incompatibility. To address this problem, we propose ReLoRA, a knowledge-reusing re-adaptation framework that efficiently restores service-ready LoRA adapters for evolving LLM services while preserving or improving task performance. Specifically, ReLoRA comprises two key optimization steps: 1) Adaptive LoRA initialization leverages Bayesian optimization to construct a compatibility-aware starting point by fusing information from both the previously deployed task adapter and the base model's evolution; 2) Fine-tuning with scheduled regularization first rapidly steers the adapter to a high-quality region via strong regularization, followed by relaxed regularization for task-specific refinement. This design enables rapid service-quality recovery with reduced re-adaptation overhead. Extensive experiments demonstrate that ReLoRA reduces time-to-readiness by up to 8.9$\times$ and improves accuracy by up to 4.6\% compared to baselines.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.02606 [cs.LG]
(or arXiv:2606.02606v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.02606
arXiv-issued DOI via DataCite
Submission history
From: Zihuai Xu [view email] [v1] Sat, 23 May 2026 15:56:16 UTC (4,843 KB)
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