Google DeepMind, in collaboration with UC Berkeley, has introduced a new approach in optimizing large language models (LLMs) by focusing on test-time compute rather than scaling model parameters. This method, detailed in a recent paper, suggests that by optimally allocating computational resources during test-time, smaller models can outperform models that are 14 times larger in a FLOPs matched evaluation. The study highlights the potential for LLMs to improve their outputs significantly by using more test-time computation, drawing parallels to how humans can improve decision-making by taking more time to think. This concept of 'compute-optimal' scaling is expected to gain more attention as it presents a critical step towards building generally self-improving AI systems.