AIs that break down questions reason better

under science research AI chronicle AI chronicle   Read time: 3 min.

The key to the most powerful conversational AIs is to reason by breaking down a complex task into simpler subproblems. Why is this crucial, and how does it work?

The recent release of the conversational AI “DeepSeek R1” shook the financial markets because it showed a significant reduction in the costs of reasoning models. But what are these reasoning models?

To understand the challenges of reasoning in conversational AIs, we can ask them to solve riddles. I tried various logical riddles on different AIs, such as the puzzle where a man has to get a fox, a chicken, and a sack of corn across a river without one eating the other. The AI responds brilliantly. But how can we ensure that the AI is truly reasoning and not just reciting answers it has seen before? By replacing them with other equivalent animals (wolf, lamb, and hay), the AI does just as well. But it could have solved the problem by analogy with the previous classic one, rather than with reasonning. Indeed language models are very good at analogies. A conversational AI typically works by proposing a answer inspired by the flow of words (and corresponding concepts) in the texts on which it was trained.

If, instead of a riddle resembling a story, we try to play tic-tac-toe, the weaknesses appear. Most conversational AIs are very bad at tic-tac-toe, even going so far as to declare victory when faced with a defeat. Perhaps this is because analogy is not as useful. But activating the “reasoning” option makes them unbeatable. What is behind this option?

A third task helps to understand the reasoning mechanisms of a conversational AI: let’s ask it how many “L”s there are in “LOLLAPALOUZA”. There is a catch: ChatGPT was able to give me the correct for the number of Ls in “LOLLAPALOOZA”, a question often used in the past to show its limits. For “LOLLAPALOUZA”, it fails. Or rather, it needs help: if we tell it to spell out the word, then count the “L”s, it gives the correct answer. With the right intermediate steps, a problem is often much simpler. These decompositions into subproblems are called chains of thought in conversational AIs. The “reasoning” option of some AIs generates such chains.

DeepSeek R1 received much attention due to its excellence in breaking down problems to reason in such a way. To do this, it has been trained to generate reasoning patterns from questions, using reinforcement learning: through trial and error, on many problems generated with the associated answer, like math problems. Faced with a task, the AI still proceeds by analogy with the tasks it has seen during this learning phase, but it uses this analogy to sketch a battle plan, rather than a response. Each subproblem is then easier, and the AI can tackle it by analogy to problems already seen. By observing the chains of thought, we can even see the AI verifying its intermediate results. These chains of thought are not always visible, but we can guess them from the AI’s response time.

With these reasoning mechanisms, a conversational AI is as good as I am at tic-tac-toe. But using such a model to play tic-tac-toe is like using a sledgehammer to crush a fly: it is very inefficient in computational cost compared to a specialized program for tic-tac-toe, which we have known how to do for decades.

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