Recurrent Neural Networks (RNNs) are naturally suited to efficient inference, requiring far less memory and compute than attention-based architectures, but the sequential nature of their computation has historically made it impractical to scale up RNNs to billions of parameters. A new advancement from Apple researchers makes RNN training dramatically more efficient - enabling large-scale training for the first time and widening the set of architecture choices available to practitioners in designing LLMs, particularly for resource-constrained deployment.
In ParaRNN: Unlocking Parallel Training...
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