this post was submitted on 08 Jun 2025
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I see a lot of misunderstandings in the comments ๐ซค
This is a pretty important finding for researchers, and it's not obvious by any means. This finding is not showing a problem with LLMs' abilities in general. The issue they discovered is specifically for so-called "reasoning models" that iterate on their answer before replying. It might indicate that the training process is not sufficient for true reasoning.
Most reasoning models are not incentivized to think correctly, and are only rewarded based on their final answer. This research might indicate that's a flaw that needs to be corrected before models can actually reason.
What confuses me is that we seemingly keep pushing away what counts as reasoning. Not too long ago, some smart alghoritms or a bunch of instructions for software (if/then) was officially, by definition, software/computer reasoning. Logically, CPUs do it all the time. Suddenly, when AI is doing that with pattern recognition, memory and even more advanced alghoritms, it's no longer reasoning? I feel like at this point a more relevant question is "What exactly is reasoning?". Before you answer, understand that most humans seemingly live by pattern recognition, not reasoning.
https://en.wikipedia.org/wiki/Reasoning_system
If you want to boil down human reasoning to pattern recognition, the sheer amount of stimuli and associations built off of that input absolutely dwarfs anything an LLM will ever be able to handle. It's like comparing PhD reasoning to a dog's reasoning.
While a dog can learn some interesting tricks and the smartest dogs can solve simple novel problems, there are hard limits. They simply lack a strong metacognition and the ability to make simple logical inferences (eg: why they fail at the shell game).
Now we make that chasm even larger by cutting the stimuli to a fixed token limit. An LLM can do some clever tricks within that limit, but it's designed to do exactly those tricks and nothing more. To get anything resembling human ability you would have to design something to match human complexity, and we don't have the tech to make a synthetic human.