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Apple研究人员在ICLR 2026上发布并行RNN训练和增强SSM

作者 PulseAugur 编辑部 · [1 个来源] ·

Apple研究人员正在ICLR 2026上展示新成果，重点关注循环神经网络（RNN）和状态空间模型（SSM）的进步。他们的论文“ParaRNN”介绍了一个并行化训练框架，使大规模RNN能够实现与Transformer相媲美的性能，并已将代码库开源。另一篇论文“To Infinity and Beyond”表明，虽然SSM提供了效率，但由于内存有限，其在长文本生成任务上的性能会下降，而通过访问外部工具可以克服这一限制。 AI

影响 ParaRNN的开源发布可能会加速对高效序列建模和LLM开发的研究，尤其是在资源受限的环境中。

排序理由 Apple研究人员将在ICLR 2026会议上展示新论文和开源代码。

在 Apple Machine Learning Research 阅读 →

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Apple研究人员在ICLR 2026上发布并行RNN训练和增强SSM

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Apple研究人员将在ICLR 2026会议上展示新论文和开源代码。
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报道来源 [1]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    Apple Machine Learning Research at ICLR 2026

    Apple is advancing AI and ML with fundamental research, much of which is shared through publications and engagement at conferences in order to accelerate progress in this important field and support the broader community. This week, the Fourteenth International Conference on Lear…

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