Rethinking Routing Exploration in Modern Mixture-of-Experts
Mixture-of-experts (MoE) models have become an important approach for scaling model parameters.
ProofPaper ↗
Key points
- Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts.
- The former increases expressiveness by enabling more expert combinations at a fixed activation ratio, and the latter introduces experts that are active for every token.
- We identify an exploration dilemma between these designs: shared experts can make routing less exploratory by reducing the variability of token representations.
- This conflicts with the greater need for exploration induced by fine-grained expert segmentation due to the combinatorially enlarged routing space.
Sources (1)
- [1]Rethinking Routing Exploration in Modern Mixture-of-ExpertsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:58 PM
Mixture-of-experts (MoE) models have become an important approach for scaling model parameters.
Modern MoE architectures increasingly combine two design choices: fine-grained expert segmentation and shared experts.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 15, 2026ggml-org/llama.cpp v0.7.0
- Oct 15, 2026NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning
- Oct 15, 2026StepFun Releases Step 5 Preview Open Weights: 600B MoE, 27B Active, 1M Context
- Oct 15, 2026Local and Distributed Large LLM Inference with MLX and RDMA on Mac Studio
- Oct 15, 2026Explaining the Roles of Experts in Mixture-of-Experts LLMs for Personalization
- Oct 15, 2026MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering