Why Are Linear RNNs More Parallelizable?
Researchers have explored linear RNNs (LRNNs) as language models, noting their expressivity and parallelizability. A new paper connects LRNNs to arithmetic circuits, explaining their parallel nature by showing they are similar to log-depth circuits, unlike nonlinear RNNs which can solve more complex problems. This theoretical work identifies expressivity differences between LRNN variants and provides a foundation for designing LLM architectures that balance expressivity and parallelism. AI
IMPACT Provides theoretical grounding for designing LLM architectures that balance expressivity and parallelism.