ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning
ReCoLoRA addresses catastrophic forgetting in continual fine-tuning by recursively consolidating low-rank adapters. It outperforms LoRA, PiSSA, AdaLoRA, and DoRA on three out of four 7-8B backbones on a six-task GLUE sequence while using fewer parameters.
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[Submitted on 4 Jul 2026]
Title:ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning
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Abstract:Parameter-efficient fine-tuning adapts a large language model to one task cheaply, but across a task sequence LoRA-style methods keep stacking low-rank updates on the same frozen weight, so each new task tends to overwrite the previous ones. We present ReCoLoRA (Recursive Consolidation of Low-Rank Adapters), a spectrum-aware framework for continual fine-tuning: adapters are initialized from a randomized SVD of the pretrained weight, per-layer effective ranks are selected by an elbow criterion, and the principal subspace is adapted before residual capacity is opened. Before each new task, ReCoLoRA re-decomposes the current effective weight, rather than the original one, into a frozen residual, a slowly updated principal component, and a fresh adapter (recursive consolidation), so every task starts from the model that has already absorbed its predecessors. On a six-task continual GLUE sequence over four 7-8B backbones, ReCoLoRA attains the best final average score on three of the four backbones against rank-swept LoRA, PiSSA, AdaLoRA, and DoRA baselines while training fewer parameters; an oracle-routed task-bank variant serves as an upper bound under full task isolation. Code: this https URL.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.07719 [cs.LG]
(or arXiv:2607.07719v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.07719
arXiv-issued DOI via DataCite
Submission history
From: Wentao Lu [view email] [v1] Sat, 4 Jul 2026 12:58:26 UTC (72 KB)
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