How test-time scaling unlocks hidden reasoning abilities in small language models (and allows them to outperform LLMs)
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Very small language models (SLMs) can outperform leading large language models (LLMs) in reasoning tasks, according to a new study by Shanghai AI Laboratory. The authors show that with the right tools and test-time scaling techniques, an SLM with 1 billion parameters can outperform a 405B LLM on complicated math benchmarks. The ability to deploy SLMs in complex reasoni