AI RESEARCH
Why Are Linear RNNs More Parallelizable?
arXiv CS.LG
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ArXi:2603.03612v3 Announce Type: replace The community is increasingly exploring linear RNNs (LRNNs) as language models, motivated by their expressive power and parallelizability. While prior work establishes the expressivity benefits of LRNNs over transformers, it is unclear what makes LRNNs -- but not traditional, nonlinear RNNs -- as easy to parallelize in practice as transformers. We answer this question by providing a tight connection between types of RNNs and standard complexity classes.