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Rethinking Routing Exploration in Modern Mixture-of-Experts

Paper recorded by Signals 4 on 2026-10-15 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-15 on arXiv · recorded by Signals 4 on 2026-10-16

Category: cs.AI · 人工智能 · first seen 2026-10-16

Abstract

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. 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 e

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#285 most recent of 960 cs.AI papers we have recorded · ↑ newer: CoDA: Co-Evolution of Data Engines and Action Policies · ↓ older: FDE-Gym: The Last Mile of AI Delivery
Cite this page: Rethinking Routing Exploration in Modern Mixture-of-Experts: the #285 most recent of 960 cs.AI papers we have recorded (as of 2026-10-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/rethinking-routing-exploration-in-modern-mixture-of-experts.html
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