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SSM Limitations in Parameter-Constrained Training

SSMs face structural disadvantages in parameter-constrained training regimes, particularly with large models and limited resources. This leads to inefficiencies in compressed parameter budgets and architectural performance instability. These limitations hinder SSMs' competitiveness with transformers in certain scenarios.

Computing & Technology, Computer Science, Machine Learning