Dynamic Model Merging Made Slim
DiDi-Merging is a slim dynamic model merging framework that balances shared and task-specific parameters using differentiable rank allocation. It optimizes parameter budgets through rank selection and refines performance with a data-free step, achieving near-original accuracy at 1.24x the storage of one backbone across vision, language, and multimodal tasks.