Mixture-of-experts routing
MixLoRA
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
Superseded baseline#28 of 1,370 most-superseded · first seen Apr 22, 2024
Superseded — cited as a baseline and beaten by newer methods
0 papers critique it · 3 beat it on benchmarks
Beaten on benchmarks
Head-to-head results where a newer method reports beating MixLoRA. Values are copied from the source paper's tables — verify against the cited paper.
GMoE beats MixLoRA
0.48 vs 1.04
Stability Average (Std) · [Qwen2]
GMoE: Empowering LLMs Fine-Tuning via MoE Graph CollaborationMoORE (L=8) beats MixLoRA
85.11 vs 82.55
Overall · [CSR-MTL multi-task adaptation]
MoORE: SVD-based Model MoE-ization for Conflict- and Oblivion-Resistant Multi-Task AdaptationGraphMoE(MixLoRA) beats MixLoRA
84.9 vs 82.6
AVG · [LoRA+MoE baseline methods]
GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism
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Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.
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- CoGR-MoECoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question AnsweringApr 18, 2026
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- 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
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- Feb 13, 2026
- PASs-MoEPASs-MoE: Mitigating Misaligned Co-drift among Router and Experts via Pathway Activation Subspaces for Continual LearningJan 19, 2026