Maintaining efficiency across larger and more complex vision models using linear attention layers requires careful consideration of model design and training strategies. To ensure stability when scaling up, it is essential to implement techniques such as relative positional bias, bounded exponential decay mechanisms, layer normalization adjustments, and additional layer scale regularization where needed. These modifications help prevent issues like vanishing gradients or overflow during training while enabling the model to handle increased complexities efficiently without sacrificing performance or scalability.