The scaling studies reveal that despite the common belief that Transformers outperform recurrent models, careful tuning shows that recurrent models can scale efficiently and perform comparably in various downstream tasks.
Our evaluations demonstrate that the Griffin model, which combines recurrent blocks with local attention, successfully mitigates many challenges posed by traditional Transformers and achieves competitive performance.
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