ParaRNN
Summary
ParaRNN is Apple’s framework for training nonlinear recurrent neural networks in parallel by solving the hidden-state trajectory as a nonlinear system with Newton iterations and parallel reduction.
Role In The Wiki
ParaRNN anchors the nonlinear branch of efficient recurrent sequence models. Where Mamba-style SSMs preserve parallel training by keeping hidden-state updates linear, ParaRNN shows that adapted GRU and LSTM cells can be trained at billion-parameter language-model scale with parallelized nonlinear state updates.