In this work, we demonstrate that recurrent models can scale as efficiently as Transformers, providing an effective alternative in scenarios where training and inference efficiency is critical.
Through various experiments, we provide evidence that recurrent models outperform state space models in several downstream tasks, which asserts their relevance in modern AI architectures.
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