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Mixture-of-experts routing

FlexMoE

FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement

Superseded baseline#33 of 1,370 most-superseded · first seen Apr 8, 2023

Superseded — cited as a baseline and beaten by newer methods

2 papers critique it · 1 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites FlexMoE as a baseline.

each re-balancing introduces significant overhead due to copying optimizer state, limiting the frequency that rebalancing can be performed and thus the efficacy of its adaptive replication.
— SYMI: Efficient Mixture-of-Experts Training via Model and Optimizer State Decoupling
“still cannot resolve the additional communication of expert parameters”
— LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training

Beaten on benchmarks

Head-to-head results where a newer method reports beating FlexMoE. Values are copied from the source paper's tables — verify against the cited paper.

What to use instead

Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.