Kron-LoRA: Hybrid Kronecker-LoRA Adapters for Scalable, Sustainable Fine-tuning

• URL: http://arxiv.org/abs/2508.01961v2
• Date: Wed, 24 Sep 2025 10:03:27 GMT
• Title: Kron-LoRA: Hybrid Kronecker-LoRA Adapters for Scalable, Sustainable Fine-tuning
• Authors: Yixin Shen,
• Abstract summary: We introduce textbfKron-LoRA, a hybrid adapter that combines Kronecker-structured factorization with low-rank LoRA compression.<n>Experiments on DistilBERT, Mistral-7B, LLaMA-2-7B, and LLaMA-3-8B show that Kron-LoRA matches or exceeds LoRA baselines with modest memory savings and only a 5-8% speed overhead.
• Score: 0.8761302078860441
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fine-tuning massive pre-trained language models across many tasks demands adapters that are both parameter-efficient and expressive. We introduce \textbf{Kron-LoRA}, a hybrid adapter that combines Kronecker-structured factorization with low-rank LoRA compression-an integration that, to our knowledge, has not been explored in parameter-efficient fine-tuning or in matrix approximation literature. Kron-LoRA achieves up to 4$\times$ fewer parameters than standard LoRA while retaining similar expressivity. Experiments on DistilBERT, Mistral-7B, LLaMA-2-7B, and LLaMA-3-8B across eight benchmarks show that Kron-LoRA matches or exceeds LoRA baselines with modest memory savings and only a 5-8\% speed overhead. In sequential fine-tuning, it also delivers competitive cross-task transfer despite using only one-quarter of the adapter parameters. Kron-LoRA thus offers a scalable, sustainable solution for multi-task adaptation of large language models.

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