Alignment-Enhanced Integration of Connectivity and Spectral Sparse in Dynamic Sparse Training of LLM

With the rapid development of large language models (LLMs), identifying efficient strategies for training such large-scale systems has become increasingly critical. Although LLMs have achieved remarkable success across diverse applications, the necessity of maintaining full dense matrices during pre-training has been questioned, giving rise to parameter-efficient sparse pre-training methods which retains parameter-efficiency in both training and inference. These methods can be further divided into connectivity sparse training and spectral sparse training, with dynamic connectivity sparse training and low-rank factorization emerging as representative approaches for the two branches. However, a unified framework that effectively combines the strengths of both has yet to be established. In this work, we observe that the cancellation effect between the sparse and low-rank branches may limit the expressivity of the model, manifesting as output conflicts when the two components are combined. To address this issue, we propose a novel scheme that integrates dynamic sparse training with low-rank training, introducing a simple yet effective $\textbf{alignment loss}$ to mitigate the disagreement between the two branches and promote better collaboration. We validate this scheme by combining a representative dynamic sparse training method, CHTs, with low-rank training, resulting in a new parameter-efficient training approach termed $\textbf{CHTsL}$. The method is evaluated on LLaMA60M and LLaMA130M using the OpenWebText and C4 datasets, where only 10\%, 20\%, and 30\% of the parameters are preserved compared to dense training. Experimental results demonstrate that our proposed scheme effectively alleviates the cancellation effect and improves training stability and performance compared to the naive combination of sparse and low-rank components. Also, the new scheme enables CHTsL to consistently outperform other parameter-efficient sparse training methods under the same parameter budget, achieving performance most close to dense training.