MIT团队发布LRNBA框架，用共享神经基压缩网络，参数相同下网络更深更宽，训练更稳定。
Linear Reusable Neural Bases Architecture (LRNBA)是一种新型网络压缩框架，通过共享神经基表示网络块。该模型在相同参数预算下可构建更宽更深的网络，实验显示其收敛速度与经典架构相当或更快，损失更低。LRNBA受循环神经网络设计启发，能实现高网络压缩率并保持稳定训练。
A Mathematical Theory of Reusable Neural Bases for Network Compression
As large AI models become increasingly prevalent across a wide range of applications, memory cost has become a critical bottleneck in both training and inference. To mitigate this issue, we introduce the Linear Reusable Neural Bases Architecture (LRNBA), a novel framework aimed at improving parameter efficiency and reducing memory cost. Inspired by recurrent neural network (RNN) designs, the core idea of our approach is to represent each network block as a linear combination of a shared set of neural bases, thereby enjoying highly network compression rate while maintaining stable training. The proposed architecture allows for the construction of significantly wider and deeper networks under the same parameter budget. Extensive experiments demonstrate that our model achieves comparable or even faster convergence and lower loss than classical architectures, while maintaining stable training dynamics.