Core Viewpoint - The article introduces CoTo, a progressive training strategy designed to enhance the robustness and effectiveness of Low-Rank Adaptation (LoRA) models, addressing issues such as training instability and performance drop after pruning [1][4][23]. Summary by Sections Conventional LoRA Training Issues - LoRA faces challenges including "lazy training," where optimization gets stuck near suboptimal solutions, limiting generalization [7] - There is a hierarchical imbalance in training, with gradient updates concentrated on top layers, leading to undertraining of lower layers [7] - These issues complicate downstream operations like model fusion and pruning, often resulting in unsatisfactory outcomes [7] CoTo Strategy - CoTo employs a simple yet effective progressive activation strategy, initially deactivating a portion of LoRA adapters to encourage uniform gradient flow across all layers [5][8] - The activation probability of adapters is gradually increased during training, returning to standard fine-tuning mode in later stages [8] Experimental Results - CoTo significantly improves the fusion and pruning capabilities of LoRA models, enhancing single-task generalization performance and training efficiency [12][23] - In linear interpolation tasks, CoTo models maintain smooth performance transitions, unlike standard LoRA, which experiences sharp declines [13] - CoTo outperforms standard LoRA in both structured and unstructured pruning scenarios, demonstrating enhanced fault tolerance [17] Performance and Efficiency Improvements - CoTo consistently boosts performance across various benchmarks, including visual and language tasks, and achieves over 24% training acceleration when applied to HiRA [24][23] Ablation Studies - Rigorous ablation studies validate the design choices of CoTo and provide insights into effective regularization of LoRA [21] Conclusion - CoTo effectively resolves hierarchical imbalance and lazy optimization issues in LoRA training, enhancing model robustness and simplifying downstream operations like fusion and pruning [23]