Method Drift›Mixture-of-experts routing
MoLA
Mixture-of-experts routing
superseded — cited as a baseline and beaten by newer methods
1 papers critique it · 3 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites MoLA as a baseline.
“While effective, this approach introduces three inefficiencies: (i) parameter explosion—with E experts, methods like MoLA or LoRAMoE replicate adapters, causing parameters to grow with E.”
— LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
Beaten on benchmarks
Head-to-head results where a newer method reports beating MoLA. Values are copied from the source paper's tables — verify against the cited paper.
- A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$Δ$ Integration into Upcycled MoE
DeltaMoE beats MoLA · Avg. [Expanded Languages (hu, sr, bn)]
74.97 vs 73.21
- A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$Δ$ Integration into Upcycled MoE
DeltaMoE beats MoLA · Avg. [Original Languages (en, zh, es, fr)]
81.17 vs 79.62
- GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism
GraphMoE(MixLoRA) beats MoLA · AVG [LoRA+MoE baseline methods]
84.9 vs 82.9
- GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism
GraphMoE(MoLA) beats MoLA · AVG [MoLA variant]
85.0 vs 82.9
- GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
GMoE beats MoLA · Accuracy Average [Llama3]
80.52 vs 78.24
- GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
GMoE beats MoLA · Stability Average (Std) [Llama3]
0.45 vs 0.79
- GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
GMoE beats MoLA · Accuracy Average [Qwen2]
83.29 vs 82.05
- GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
GMoE beats MoLA · Stability Average (Std) [Qwen2]
0.48 vs 1.18
- GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
GMoE beats MoLA · Accuracy Average [Yi-1.5]
83.28 vs 82.01
- GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
GMoE beats MoLA · Stability Average (Std) [Yi-1.5]
0.38 vs 0.54
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.
- PARAMΔ Integration into Upcycled MoEA Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM$Δ$ Integration into Upcycled MoEMay 18, 2026
- MEMIT-like framework for MoEScalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured UpdatesMay 15, 2026
- May 11, 2026
- May 8, 2026
- Apr 28, 2026
- CoGR-MoECoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question AnsweringApr 18, 2026
- Apr 2, 2026
- On Token's DilemmaOn Token's Dilemma: Dynamic MoE with Drift-Aware Token Assignment for Continual Learning of Large Vision Language ModelsMar 29, 2026
- Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for MLIPsScaling Machine Learning Interatomic Potentials with Mixtures of ExpertsMar 9, 2026
- Mar 5, 2026
- Feb 13, 2026
- Multiscale Interaction Mixture of Experts (MI-MoE)Topology-Aware Multiscale Mixture of Experts for Efficient Molecular Property PredictionJan 19, 2026