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Mixture of Experts
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2026
05-16
EN
An Interpretable Latency Model for Speculative Decoding in LLM Serving — Technical Review
05-16
中
用 Little 定律解释推测解码在真实服务中的提速曲线 —— 阅读笔记
05-14
EN
DisagMoE: Disaggregating Attention and FFN to Beat the MoE All-to-All Bottleneck
05-14
中
DisagMoE:用解耦 Attention 和 FFN 打通 MoE 训练的 all-to-all 瓶颈
05-07
EN
Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism
04-29
EN
FEPLB Technical Review: Nearly Free MoE Load Balancing with the NVLink Copy Engine
04-24
EN
FEPLB: Zero-Cost MoE Load Balancing via NVLink Copy Engine
04-14
EN
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts — Deep Technical Review
04-14
中
ArmoRM:用“多目标奖励建模 + 混合专家门控”做可解释偏好学习——深度阅读笔记
04-04
EN
Switch Transformers: Scaling to Trillion-Parameter Sparse Models — In-Depth Technical Review
04-04
中
Switch Transformers:用简单高效的稀疏性扩展到万亿参数模型 — 深度阅读笔记
02-18
EN
DeepSeek-V2: Multi-head Latent Attention and DeepSeekMoE — Technical Review