Mixture-of-experts routing
Mixtral
Superseded baseline#10 of 1,370 most-superseded · first seen Jan 8, 2024
Superseded — cited as a baseline and beaten by newer methods
1 papers critique it · 2 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites Mixtral as a baseline.
They show that the choice of experts seems to be influenced more by syntax than by domain, particularly in the first and last layers
Beaten on benchmarks
Head-to-head results where a newer method reports beating Mixtral. Values are copied from the source paper's tables — verify against the cited paper.
AdaMoE beats Mixtral
1.66 vs 2.00
Average Load · [Fine-tuned Mixtral-8x7B (top-2 routing)]
AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language ModelsWDMoE beats Mixtral
37.4 vs 35.2
MBPP · [all benchmarks]
WDMoE: Wireless Distributed Mixture of Experts for Large Language Models
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Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
- PADDPADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student LearningJun 9, 2026
- May 30, 2026
- May 29, 2026
- May 1, 2026
- Apr 30, 2026
- Feb 9, 2026
- SocialNav-MoESocialNav-MoE: A Mixture-of-Experts Vision Language Model for Socially Compliant Navigation with Reinforcement Fine-TuningDec 15, 2025
- OrdMoEOrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMsNov 24, 2025
- Mix- and MoE-DPOMix- and MoE-DPO: A Variational Inference Approach to Direct Preference OptimizationOct 9, 2025