Stanford Researcher Explains Why Larger Models Retain Rare Skills: Capacity Competition
SinclairWang1 · x · 2026-08-14
Jing Huang (Stanford NLP) explains why larger models retain rare skills that smaller ones tend to lose.
The core reason lies in interference between tasks during training. Models have limited capacity, and tasks compete for it. In smaller models, rare tail tasks get crowded out, whereas larger models have enough parameter space to accommodate these edge capabilities.
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