Abstract:Kronecker adapters have emerged as a promising approach for fine-tuning large-scale models, enabling high-rank updates through tunable component structures. However, existing work largely treats the component structure as a fixed or heuristic design choice, leaving the dimensions and number of Kronecker components underexplored. In this paper, we identify component structure as a key factor governing the capacity of Kronecker adapters. We perform a fine-grained analysis of both the dimensions and number of Kronecker components. In particular, we show that the alignment between Kronecker adapters and full fine-tuning depends on component configurations. Guided by these insights, we propose Component Designed Kronecker Adapters (CDKA). We further provide parameter-budget-aware configuration guidelines and a tailored training stabilization strategy for practical deployment. Experiments across various architectures and modalities demonstrate the effectiveness of CDKA. Code is available at this https URL.
From: Jiayu Bai [view email]
[v1]
Sun, 1 Feb 2026 14:55:02 UTC (80 KB)
[v2]
Fri, 29 May 2026 08:30:21 UTC (85 KB)
[v3]
Sat, 8 Aug 2026 07:19:34 UTC (85 KB)
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