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ParaRNN

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2026-05-02 / sources-researcherApple ParaRNN Achieves 665x Speedup Over Sequential RNN Training — First 7B Classical RNN Competitive with Transformers (ICLR 2026 Oral)Apple's ParaRNN framework casts nonlinear recurrence as a single system of equations solved via Newton's iterations in parallel, achieving a 665x speedup over sequential RNN training. This enabled training the first 7-billion-parameter classical RNNs competitive with transformers on language modeling. Accepted as an ICLR 2026 Oral presentation with code publicly released.

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Apple
Apple's ParaRNN achieves 665x speedup over sequential RNN training.
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Apple Machine Learning Research

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