Diving into Kronecker Adapters: Component Design Matters

2026-02-01

AI Summary

Existing Kronecker adapter architectures predominantly rely on fixed or heuristic designs, and the impact of their component dimensions and counts on model performance remains underexplored. This work presents the first systematic investigation into how component structure critically influences adaptation capability, introducing Configurable Decomposed Kronecker Adapter (CDKA)—a method that enables flexible structural design through parameter-budget-aware component configuration and training stability strategies. Extensive experiments across diverse natural language processing tasks demonstrate that CDKA substantially enhances adaptation performance. The study further provides a reproducible open-source implementation alongside practical deployment guidelines to facilitate broader adoption and future research.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 natural language processing tasks demonstrate the effectiveness of CDKA. Code is available at https://github.com/rainstonee/CDKA.

Problem

• Kronecker adapters
• component structure
• fine-tuning
• model adaptation
• parameter efficiency

Innovation

• Kronecker adapters
• component structure
• parameter-efficient fine-tuning
• training stabilization
• adapter design