AI reasoning models can stop thinking 25% earlier and still get the right answer

Researchers found that when AI models solving complex problems generate long chains of reasoning, they can identify when they've already figured out the answer and stop computing instead of overthinking. A technique called CoDE-Stop watches the model's confidence levels during reasoning and cuts off computation early, reducing the computational cost of running these models by a quarter to half without losing accuracy.

Large reasoning models are expensive to run because they generate very long sequences of thought to solve problems. If you can tell when the model has actually solved the problem and cut off the rest of the computation, you lower the cost of using these models in production significantly. This matters because right now the cost curve of running reasoning models is a major constraint on deployment. Cheaper reasoning means more places can actually afford to use them.