Apple's ParaRNN Framework Trains Large Nonlinear RNNs Up to 665x Faster

Apple researchers have unveiled ParaRNN, a novel framework that enables parallel training of large-scale nonlinear recurrent neural networks (RNNs). Traditional RNNs, while efficient for inference, have struggled with large-scale training due to their inherently sequential computation. ParaRNN overcomes this by leveraging the Newton method to parallelize training, achieving speedups of up to 665 times compared to conventional sequential approaches. This breakthrough positions RNNs as a viable alternative to Transformer-based architectures for sequence modeling, highlighting the potential for more efficient large language models. The work is set to be presented at ICLR 2026.