Researcher tackles embedding collapse in small language models

Chen Liu identified dispersion loss as a solution to embedding condensation—a problem where small LMs pack all semantic information into a tiny region of their embedding space, degrading performance. The finding could help indie developers and researchers squeeze better accuracy from lightweight models without massive compute budgets.