How transformers, RNNs and SSMs are more alike than you think

Despite the rise of models like Mamba and other linear RNNs and state space models (SSMs), transformer architectures maintain dominance in large language models (LLMs), though promising hybrid architectures such as Jamba, Samba, and Griffin are emerging due to their efficiency in time and memory. Deep connections between architectures like transformers, RNNs, SSMs, and matrix mixers have been established, facilitating the transfer of ideas across them. Notably, transformers can sometimes be reinterpreted as RNNs, with state space models potentially integrated into the self-attention mechanism. Linearized attention, an alternative to traditional attention, offers computational advantages by altering the matrix multiplication order, though it faces stability challenges during training. The concept of state space duality links semiseparable matrices in state space models with masked attention, showing potential for efficient transformer models and highlighting ongoing research into non-transformer models like MLP-Mixer and FNet.

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