
In an era where the size of language models has often been equated with their performance, Hugging Face researchers have turned this notion on its head by showcasing a groundbreaking development known as test-time scaling. This novel approach demonstrates how small language models (SLMs) can outperform their larger counterparts by leveraging additional compute cycles during the inference phase. This development