The chapter explores the trade-offs between training efficiency and inference efficiency in State Space Models (SSMs) and the potential benefits of using dense matrices over diagonal matrices. It delves into the concept of hybrid attention in SSMs, discussing the balance between attention layers and state-based models in neural networks. The discussion extends to the importance of optimizing the state in state-based models, moving away from the transformer regime to a stateful compression regime for revolutionizing various tasks and applications.

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