English(EN) Scaling Laws for Looped Mixture of Experts

新的循环缩放定律联合建模MoE模型中的递归和稀疏性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员引入了“循环缩放定律”，这是一个新颖的框架，可联合建模神经网络中的递归和稀疏性，特别是针对循环专家混合（MoE）架构。与单独分析递归或稀疏性的现有方法相比，这些定律能更准确地预测模型性能。该框架表明，对于推理任务，稀疏性可提供三倍的激活参数效率提升，而递归可提供两倍的总参数效率提升，联合缩放可进一步提升性能。 AI

影响通过联合优化递归和稀疏性，为设计更高效的大型语言模型引入了新的理论框架。

排序理由这是一篇介绍神经网络架构新缩放定律的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yanbei Chen, Anirudh Goyal, Raghuraman Krishnamoorthi · 2026-10-01 04:00

Looped Mixture of Experts 的规模法则

arXiv:2609.40316v1 Announce Type: cross Abstract: Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed parameters, while MoE sparsity expands total capacity at fixed active compute. Yet…

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报道来源 [1]

Looped Mixture of Experts 的规模法则

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