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        <title>TaRA: Training-Aware Low-Rank Adaptation Initialization</title>
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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.</abstract>
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%0 Conference Proceedings
%T TaRA: Training-Aware Low-Rank Adaptation Initialization
%A Kim, Taehyeon
%A Park, Eunhyeok
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F kim-park-2026-tara
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2014/
%P 45885-45901

Markdown (Informal)

[TaRA: Training-Aware Low-Rank Adaptation Initialization](https://aclanthology.org/2026.emnlp-main.2014/) (Kim & Park, EMNLP 2026)

• TaRA: Training-Aware Low-Rank Adaptation Initialization (Kim & Park, EMNLP 2026)

ACL

• Taehyeon Kim and Eunhyeok Park. 2026. TaRA: Training-Aware Low-Rank Adaptation Initialization. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 45885–45901, Budapest, Hungary. Association for Computational Linguistics.