Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models
Complete-muE is a framework for transferring hyperparameters across dense FFN and Mixture-of-Experts (MoE) transformer architectures, addressing limitations of existing tools like μP and SDE that cannot handle simultaneous architecture and token-per-expert changes. It uses a two-bridge system: Bridge I maps dense FFN to Dense MoE via active-width μP with normalized router scale, and Bridge II maps Dense MoE to sparse MoE via activated-expert scaling with a first-order SDE correction. The practical outcome is a 'tune dense once, transfer to all' recipe that enables near-optimal hyperparameter reuse across MoE configurations without costly re-tuning. Experiments on language model and diffusion model pretraining confirm stable hyperparameter optima across architectures and parameter counts.
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MobileMoE: Scaling Mixture-of-Experts for Sub-Billion Parameter On-Device Deployment
MobileMoE introduces a family of on-device MoE language models with 0.3–0.9B active parameters and 1.3–5.3B total parameters, targeting mobile deployment under memory and compute constraints. The authors derive an on-device MoE scaling law identifying a sweet spot of moderate sparsity with fine-grained and shared experts, then train models through a four-stage recipe including quantization-aware training on open-source data. Across 14 benchmarks, MobileMoE matches or exceeds leading dense on-device LLMs with 2–4× fewer inference FLOPs, and delivers 1.8–3.8× faster prefill and 2.2–3.4× faster decode than dense baselines on commodity smartphones at comparable INT4 memory.
Mixture of Experts (MoEs) in Transformers
A Hugging Face blog post covering Mixture of Experts (MoE) architectures as applied to transformer models. The post likely explains the technical foundations, training considerations, and practical deployment aspects of MoE models. Given the timing in early 2026, it likely contextualizes recent MoE-based frontier models and tooling support within the Hugging Face ecosystem.
Expert Tying reduces MoE LLM memory footprint by ~2x with minimal quality loss
Researchers introduce Expert Tying, an architectural modification for Mixture-of-Experts LLMs that shares expert parameters across consecutive transformer layers while keeping routing and attention layer-independent. Evaluated on OLMoE, Qwen3, and DeepSeek-style MoE architectures, the method achieves nearly 2x memory reduction with negligible perplexity or downstream quality degradation. The approach exploits parameter redundancy in MoE pathways to improve the compute-to-memory trade-off for training and inference.
Mixture of Experts Explained
This Hugging Face blog post provides a technical overview of the Mixture of Experts (MoE) architecture, explaining how sparse gating mechanisms route tokens to subsets of expert feed-forward layers to achieve computational efficiency. The post covers training dynamics, inference considerations, and the tradeoffs between dense and sparse models. It serves as a reference document contextualizing MoE's growing relevance following high-profile model releases using the architecture.
Global-batch Load Balancing for MoE LLM Training from Qwen
Qwen Research introduces a global-batch load balancing technique for Mixture-of-Experts (MoE) LLM training, claiming it is nearly a 'free lunch' improvement. The method addresses expert load imbalance across training batches, a known efficiency and quality bottleneck in MoE architectures. The approach targets the router and expert activation dynamics in transformer-based MoE layers.
Quantifying Hyperparameter Transfer: Embedding Layer Learning Rate as Key Driver of μP Benefits
This paper develops a three-metric framework to quantify hyperparameter transfer quality across model scales, targeting the problem of extrapolating optimal hyperparameters from small to large LLMs. The central empirical finding is that the well-known advantage of Maximal Update Parameterization (μP) over standard parameterization (SP) with AdamW largely reduces to a single factor: the embedding layer learning rate. In SP, the embedding layer acts as a training bottleneck causing instabilities; scaling its learning rate by model width to match μP substantially stabilizes training and improves transfer. The paper also characterizes how weight decay affects scaling law fit quality versus extrapolation robustness in opposite directions.
ZEDA: Post-Trained MoE Models Can Skip Half Their Experts via Self-Distillation
This paper introduces Zero-Expert Self-Distillation Adaptation (ZEDA), a framework that converts static post-trained Mixture-of-Experts (MoE) language models into dynamic ones without pre-training from scratch. ZEDA injects parameter-free zero-output experts into each MoE layer and uses two-stage self-distillation with the original model as a frozen teacher. Applied to Qwen3-30B-A3B and GLM-4.7-Flash across 11 benchmarks, ZEDA eliminates over 50% of expert FLOPs with marginal accuracy loss and achieves approximately 1.20× end-to-end inference speedup, outperforming the strongest dynamic MoE baseline by 4–6 points.
EMO: Pretraining Mixture of Experts for Emergent Modularity
AllenAI introduces EMO, a pretraining approach for Mixture of Experts (MoE) models that aims to produce emergent modularity during training. The work explores how MoE architectures can develop specialized expert routing without explicit supervision. Published on the Hugging Face blog, this represents research-level work on improving MoE training dynamics and efficiency.