Mixture-of-experts routing
FlexOlmo
FlexOlmo: Open Language Models for Flexible Data Use
Superseded baseline#47 of 1,370 most-superseded · first seen Jul 9, 2025
Superseded — cited as a baseline and beaten by newer methods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 FlexOlmo as a baseline.
freezing shared (non-FFN) parameters during expert training (as done in shi2025flexolmoopenlanguagemodels) significantly degrades performance in our setting
“reliance on similarity-based proxy selection often produces redundant and narrowly concentrated proxies, limiting coverage of domain-relevant modes and weakening router supervision”
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Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
- MetaMoEMetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts UnificationMay 14, 2026
- Apr 20, 2026
- BERT-MoE FrameworkAspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User ReviewsFeb 13, 2026
- null experts within token-choice MoEImproving MoE Compute Efficiency by Composing Weight and Data SparsityJan 21, 2026
- MixtureKitMixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts ModelsDec 13, 2025
- ERMoEERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable SpecializationNov 14, 2025
- Dirichlet-Prior Shaping Loss (DPSL)Dirichlet-Prior Shaping: Guiding Expert Specialization in Upcycled MoEsOct 1, 2025
- Symphony-MoESymphony-MoE: Harmonizing Disparate Pre-trained Models into a Coherent Mixture-of-ExpertsSep 23, 2025