Don’t Overthink It: Why Shorter AI Reasoning Works Better

A new study from Meta and Hebrew University reveals something surprising: large language models like ChatGPT often perform better when they generate less — not more.

The researchers introduced a method called short-m@k.

Instead of letting a model generate one long reasoning chain, this method runs several short ones in parallel and picks the best. The result? Up to 34% more accurate answers, using less time and fewer tokens.

This challenges a common assumption in AI: that more reasoning equals better results. In fact, longer chains often lead to more errors, not fewer. Shorter, focused reasoning tends to be faster and more accurate.

If you’re using AI to review designs, run diagnostics, or crunch numbers, this approach helps you get faster, clearer results — without burning extra compute. You’ll run simulations quicker, catch issues earlier, and generate clean documentation with less effort.

And here’s the practical part: you don’t need long, complex prompts.

Break tasks into smaller steps. Run them in parallel or link them together in a simple automation workflow. It’s easier to manage, quicker to test, and often gets better results.

Bottom line? Don’t overthink it. Neither should your AI. Link to the Study: https://arxiv.org/pdf/2505.17813