TaRA: Training-Aware Low-Rank Adaptation Initialization
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Computer Science > Computation and Language
arXiv:2609.02639 (cs)
[Submitted on 2 Sep 2026]
Title:TaRA: Training-Aware Low-Rank Adaptation Initialization
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Abstract:Low-Rank Adaptation (LoRA) has become a de facto standard for parameter-efficient fine-tuning (PEFT), yet its performance is highly sensitive to initialization due to the information bottleneck imposed by low-rank decomposition. Existing approaches attempt to construct high-quality LoRA initializations by exploiting principal components of pretrained weights, activations, or gradients. However, these methods do not directly account for the training dynamics of the full-rank model. In this paper, we propose Training-aware Low-Rank Adaptation Initialization (TaRA), a method that initializes LoRA such that the gradients induced by the low-rank factors closely approximate the gradient of the corresponding full-rank weight matrix. Derived from a mathematical formulation, TaRA improves gradient fidelity at the start of training while introducing negligible computational overhead. Across diverse and challenging fine-tuning tasks, TaRA consistently outperforms prior state-of-the-art methods, establishing a simple, robust, and scalable solution for effective LoRA initialization.
| Comments: | Accepted to the EMNLP 2026 Main Conference |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.02639 [cs.CL] |
| (or arXiv:2609.02639v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.02639
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled TaRA: Training-Aware Low-Rank Adaptation Initialization, by Taehyeon Kim and 1 other authors
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