Why LLMs Overthink Easy Puzzles but Give Up on Hard Ones

The Puzzling Minds of AI: Why Smart Machines Sometimes Outthink Themselves

It’s easy to be dazzled by the rapid progress of artificial intelligence. In just a few years, sleek systems like GPT-3, BERT, and their more strategically minded successors—Large Reasoning Models—have gained the power to write stories, translate languages, and respond to your questions with uncanny fluency. But look closely, and you’ll see a strange quirk: the smarter these AIs get, the more they sometimes trip over their own thinking, overcomplicating simple questions while freezing up on harder ones.

A new study out of Apple takes a hard look at this weirdness, stripping away the showy benchmarks and instead dropping popular AIs into classic puzzles: try moving discs in the Tower of Hanoi, leapfrogging checkers, or guiding voyagers across tricky rivers. As the challenges ramped up, researchers watched how both standard language models and specialized reasoning models handled the heat.

The results were as fascinating as they were revealing. For easy puzzles, the usual suspects—language models trained on oceans of internet text—were straightforward and to the point. But their “reasoning” cousins, tuned to explain their every step, actually overcomplicated things: they’d spill out more steps than needed, making the simple hard. It’s as if a chess master insisted on narrating every obvious pawn move as a philosophical treatise.

Curiously, when puzzles got a bit trickier, those same reasoning AIs shined. They could break problems into steps, staying organized and rarely getting lost. But crank up the complexity further—and suddenly, all that careful thinking didn’t help. The AIs lost their grip, sometimes giving up entirely. It’s almost human: easy things become needlessly elaborate, hard things spark a flight response.

What’s going on here? Much of it comes down to how these models learn. AI reasoning models soak up patterns from millions of examples, but they often fail to “generalize” when the question doesn’t look like what they’ve seen before. Instead of grasping deep logic, they string together familiar moves. So when the math gets wild or the logic twists, the pattern falls apart—and so does the AI’s reasoning.

The Apple team’s work hasn’t gone unnoticed. The findings have sparked lively debate in the AI community. Some critics argue that while today’s AI doesn’t “think” like a person, it still solves many useful problems efficiently. Others say it’s time to rethink our benchmarks and what we really mean by “reasoning” in machines. On forums and at conferences, folks are quick to point out the gap between impressive language tricks and true cognitive adaptability.

Despite all this, one thing is clear: we’re nowhere near an AI that reasons quite like a human mind. The next frontier? Designing systems that know when to keep it simple and when to go deep—call it “dynamic reasoning.” As AI slinks further into our daily routines, from customer service to science labs, building that flexibility will be crucial for its continued progress.

The details—and all the puzzles—are in Apple’s original study. To dive deeper, see the source news here: https://www.unite.ai/why-llms-overthink-easy-puzzles-but-give-up-on-hard-ones/