ParaRNN: Parallel Training for Non-Linear RNNs (sLSTM, RWKV-7, CfC, Titans) via Triton Newton Scans [P]

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On September 7, 2026, the r/MachineLearning community announced the ParaRNN project. This project aims to provide parallel training support for non-linear recurrent neural networks such as sLSTM, RWKV-7, CfC, and Titans using the Triton Newton scanning method.

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2026-09-07 21:02The `GPT-6 Astra model was released, and OpenAI President Brockman confidently declared, “Welcome to the era of AGI.”
2026-09-08 19:21OpenAI released GPT-6 Astra, marking the arrival of the AGI era, with capabilities for autonomous computer operation and scientific research.
2026-09-08 22:40① Nvidia CEO Jensen Huang posted that OpenAI’s GPT-6 Astra, released last week, was trained using approximately 100,000 NV Link 72 clusters, and he believes that General Artificial Intelligence (AGI) has officially arrived; ② GPT-6 Astra can directly operate computers and software to perform complex tasks such as programming, reaching the most advanced level in multiple fields. OpenAI has announced the beginning of the AGI era.

Key entitiesKEY ENTITIES

ParaRNNPyTorchRWKV-7TritonsLSTM

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SignalsSIGNALS

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ParaRNN1
PyTorch1
Triton1
sLSTM1
RWKV-71

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R r/MachineLearning en 2026-09-07 18:31

开发者开源了名为 ParaRNN 的 PyTorch/Triton 库，该库利用牛顿 - 拉弗森迭代映射到关联前缀扫描，实现了非线性循环架构在时间维度上的并行训练。通过自定义融合 Triton 求解器，ParaRNN 将墙钟时间加速约 200 倍，在 RTX 3060 上处理 CfC（T=2048）耗时从 643ms 降至 2.79ms；经验验证表明其牛顿迭代预算在新线性度下保持平坦（K≤3），匹配顺序展开性能直至 131,072 个 token。该库支持 sLSTM、RWKV-7、CfC、Titans、Modern Hopfield 及 M²RNN 等单元，并提供 torch.compile 生产级钩子及 vLLM 插件，相关代码已托管于 GitHub。