BTX (Mixture-of-experts routing): superseded — cited as a baseline and beaten by newer methods. 2 paper(s) critique it, 5 beat it on benchmarks — #44 of 1370 most-superseded. Sub-problem: cluster led by Upcycling. Newer alternatives in the same sub-problem include MetaMoE, BAR, BERT-MoE Framework, null experts within token-choice MoE, MixtureKit.

Mixture-of-experts routing

BTX

Superseded baseline#44 of 1,370 most-superseded

Superseded — cited as a baseline and beaten by newer methods

2 papers critique it · 5 beat it on benchmarks

What papers say

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

However, unlike our method, both approaches only upcycle the FFN part of the dense (seed or specialized) models.

— BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts

“This could constitute a limitation in certain settings where such finetuning is unfeasible, for instance, because it requires to aggregate domain data into a single centralized node to train the final MoE model, which could raise concerns about privacy, or simply because of computational costs.”

— Training-Free Dynamic Upcycling of Expert Language Models

Beaten on benchmarks

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

DU (r=0.5) beats BTX
19.7 vs 18.5
Avg · [Dense 152M → MoE 8×152M]
Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization

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Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.