Imagine a world where AI problem-solving isn't limited by the size of the model, but by how effectively it uses the resources it has. That's the world researchers are building, and it’s all about *compute-optimal inference*. Current AI scaling laws focus mainly on *training*—making bigger models with more data. This research flips the script, exploring how to get the most out of AI during *inference*—when it’s actually solving problems. The surprising finding? Smaller models can often outperform larger ones given the same compute budget if paired with the right inference strategy. Think of it like a small, nimble race car versus a large, powerful truck. The truck may have raw horsepower, but on a tight, winding track, the race car’s agility wins. Similarly, smaller AI models, paired with advanced inference techniques, can navigate complex reasoning tasks more efficiently than their larger counterparts. One such technique is *REBASE* (REward BAlanced SEarch), a new algorithm that cleverly uses a reward model to guide the AI’s exploration, balancing between exploring new possibilities and exploiting promising leads. The results? REBASE consistently outperforms traditional methods, achieving higher accuracy with significantly less compute. This means we can potentially deploy powerful AI on devices with limited resources, from smartphones to embedded systems. There are broader implications, too. As AI models grow ever larger, the cost of training and deployment becomes a barrier. Compute-optimal inference offers a path to sustainable AI development, maximizing performance without breaking the bank. The next frontier is to expand these findings beyond mathematical problem-solving to other domains, from natural language understanding to image recognition. If we can unlock the full potential of AI inference, we can build a future where intelligent systems are not just powerful, but also efficient and accessible to all.