By fine-tuning models with carefully curated techniques, TTT improved accuracy sixfold in some cases and set a new state-of-the-art for purely neural approaches, achieving 53% accuracy with an 8-billion-parameter model. When combined with program synthesis methods, the models reached 61.9% accuracy, matching average human performance. The findings suggest that symbolic reasoning isn’t essential for solving complex problems, emphasizing the power of dynamic, computation-focused approaches during inference. → Read the full paper here.