Breaking Information Islands in Sparse Tuning via Small-World Connectivity

Sparse tuning is widely used to adapt large language models due to its parameter efficiency. However, its effectiveness depends not only on the number of trainable parameters, but also on the feature-interaction topology induced by the sparse update. Limited or uneven cross-dimensional interactions may isolate subsets of feature dimensions, forming information islands that hinder task-specific information mixing. To address this issue, we propose **S**mall-**W**orld **I**nduced **F**ine-**T**uning (**SWIFT**), a sparse tuning framework that induces small-world connectivity in parameter space. SWIFT combines block-diagonal sparse updates for local feature interactions with a fixed sufficiently scattering permutation that routes these updates to distant feature groups, creating sparse long-range shortcuts without additional trainable parameters. We further show theoretically that random or block-diagonal sparse tuning can suffer from information islands, whereas SWIFT restores positive expansion through permutation-induced routing. Extensive experiments on commonsense reasoning, natural language generation, and image classification demonstrate that SWIFT consistently outperforms competitive PEFT baselines and can match or surpass full fine-tuning.