A new study by Shanghai AI Laboratory shows that with the test-time scaling (TTS) techniques, an SLM with 1 billion parameters can outperform a 405B LLM on the complex MATH and AIME benchmarks.

Test-time scaling (TTS) is the process of giving LLMs extra compute cylces during inference to improve their performance on various tasks. Leading reasoning models, such as OpenAI o1 and DeepSeek-R1, use “internal TTS,” which means they are trained to “think” slowly by generating a long string of chain-of-thought (CoT) tokens. — Read More