#Mixture of Experts

2026

• 08-17 中

  0 train-infer mismatch：给开源 MoE RL 训练栈做一次字节级对齐

• 08-06 EN

  Fine-Grained Compute-Communication Overlap for MoE: Hiding All-to-All Behind GEMM with Tile-Level Signaling

• 08-06 中

  MoE 计算-通信细粒度重叠：用 Tile 级信号把 All-to-All 藏在 GEMM 背后

• 08-02 EN

  FEPLB: Exploiting Copy Engines for Nearly Free MoE Load Balancing in Distributed Training

• 08-02 中

  FEPLB 阅读笔记：用 Copy Engine 几乎零成本地做 MoE 负载均衡

• 06-28 EN

  Moebius: Seamless Runtime Parallelism Switching for MoE LLM Serving

• 06-28 中

  Moebius：为 MoE 大模型推理服务实现无缝运行时并行策略切换

• 06-13 EN

  ForeMoE: Micro-step-level MoE Load Balancing for RL Post-training via Routing Foresight

• 06-13 中

  ForeMoE：利用路由预见性实现 RL 后训练中 MoE 微步级负载均衡

• 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