When AI reasoning goes wrong: Microsoft Research shows more tokens can mean more problems

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Large language models (LLMs) are increasingly capable of complex reasoning through “inference-time scaling,” a set of techniques that allocate more computational resources during inference to generate answers. However, a new study from Microsoft Research reveals that the effectiveness of these scaling methods isn’t universal. Performance boosts vary significantly across different models, tasks and problem complexities. The core finding is that simply throwing more compute at a problem during inference doesn’t guarantee better or more efficient results. The findings can help enterprises better understand cost volatility and model reliability as they look to integrate advanced AI reasoning into their applications. Putting scaling methods to the test The Microsoft Research team conducted an extensive empirical analysis across nine state-of-the-art foundation m ... Read full article.