Researchers at Hugging Face have demonstrated that small language models can outperform their larger counterparts using advanced test-time scaling methods. As reported by Ben Dickson for VentureBeat, a Llama 3 model with just 3 billion parameters matched the performance of its 70-billion-parameter version on complex mathematical tasks. The breakthrough relies on scaling “test-time compute,” which uses additional processing power during inference to verify different responses. The technique combines several approaches, including majority voting, reward models, and specialized search algorithms. Researchers implemented a “compute-optimal scaling strategy” that dynamically selects the best method based on problem difficulty. While the approach shows promise, it currently requires a separate verification model and works best for tasks with clearly evaluable answers like mathematics and coding. The findings offer organizations new options for balancing computational resources against model performance.