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About this paper
Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, Pavlo Molchanov
The paper argues that agents should route routine, narrow language tasks to small language models (SLMs), retaining larger models for open-ended conversation and broad reasoning. A cited comparison reports that 2.7B-parameter Phi-2 matches 30B models on commonsense reasoning and code generation while running about 15× faster. This is a model comparison, not an end-to-end agent result or universal cost guarantee.
Summary by alphaXiv.
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