Simple AI Tweak Beats OpenAI’s Model—Using Just a Simple “Wait” Command

Researchers from Stanford University and the University of Washington have found a surprisingly simple way to make AI models smarter—without expensive retraining. Their method, called test-time scaling, allows AI to improve its reasoning just by controlling how long it thinks before giving an answer. Unlike OpenAI’s o1 model, which relies on massive datasets and costly reinforcement learning, this new approach requires only a tiny dataset and a clever trick.

Think of it like solving a tough math problem. If you rush, you might get it wrong, but if you take a moment to double-check, you’re more likely to be right. The researchers discovered they could force AI to “pause and think” by adding a simple command—like telling it to “Wait”—before it finalizes an answer. This makes the AI review its reasoning, often catching and fixing its own mistakes.

Using this method, the team trained a model called s1-32B with just 1,000 carefully selected questions. Despite this small dataset, it outperformed OpenAI’s o1-preview model in solving advanced math problems—boosting accuracy by up to 27%.

This breakthrough challenges the idea that AI needs huge amounts of data and expensive training to improve. Instead, with a simple tweak to how AI thinks at test time, researchers have found a faster and cheaper way to make AI models more accurate. By sharing their work as open-source, they hope to inspire others to develop smarter AI without the high costs of traditional methods.

Source: arXiv, https://arxiv.org/pdf/2501.19393