The Role of Consistency and Sharing in Efficient Fine-Tuning

This ablation study on AdaMix highlights the factors driving its efficiency in parameter-efficient fine-tuning. Results show that adaptation merging consistently outperforms random or fixed routing, while consistency regularization proves essential to maintaining strong performance. Module sharing is particularly effective in low-resource tasks, boosting convergence speed and lowering training loss compared to models without sharing. Experiments with adaptation module count and bottleneck dimension reveal diminishing returns, stressing the importance of balance over brute force scaling. Overall, AdaMix demonstrates how thoughtful design choices yield superior results to full model tuning.