Method Drift›Parameter-efficient fine-tuning (LoRA family)
Superseded baseline#243 of 1,113 most-superseded
AdaMoLE
AdaMoLE: Fine-Tuning Large Language Models with Adaptive Mixture of Low-Rank Adaptation ExpertsParameter-efficient fine-tuning (LoRA family) · first seen May 1, 2024
superseded — cited as a baseline and beaten by newer methods
0 papers critique it · 1 beat it on benchmarks
Beaten on benchmarks
Head-to-head results where a newer method reports beating AdaMoLE. Values are copied from the source paper's tables — verify against the cited paper.
- VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
VLA-GSE beats AdaMoLE · Total success rate [Comparable parameter budget (~2.5%)]
81.2 vs 75.5
- VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
VLA-GSE beats AdaMoLE · Average success rate [LIBERO standard evaluation]
98.4 vs 95.4
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.