The study challenges the assumption that more “thinking” or longer reasoning traces necessarily lead to better problem-solving in LRMs
The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
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Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#22Okay Apple, you got my attention. But I'm a strong proponent of "something is better than nothing" philosophy—even if OpenAI/Google/etc. are building reasoning models with the limitations that you describe, they are still a huge progress compared to what we had not long ago. Meanwhile you're not even trying. It's so easy to criticize the works of others and not deliver anything. Apple—be Sam in Game of Thrones: "I'm…
there is enough hype already - with AGI being promised as imminent.
this work balances the hype and shows fundamental limitations so the AI hypesters are checked.
why be salty ?
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#23Earlier quoted context omitted.
"It's just statistics!", said the statistical-search survival-selected statistically-driven-learning biological Rube Goldberg contraption, between methane emissions. Imagining that a gradient learning algorithm, optimizing its statistical performance, is the same thing as only learning statistical relationships. Inconvertibly demonstrating a dramatic failure in its, and many of its kind's, ability to reason.
Reasoning exists on a spectrum, not as a binary property. I'm not claiming that LLMs reason identically to humans in all contexts. You act as if statistical processes can’t ever scale into reasoning, despite the fact that humans themselves are gradient-trained statistical learners over evolutionary and developmental timescales.
> cretins proclaiming that LLMs aren't truly capable of reasoning
> Reasoning is not difficult to define
> Reasoning exists on a spectrum
> statistical processes [can] scale into reasoning
It seems like quite a descent here, starting with the lofty heights of condemning skeptics as "cretins" and insisting the definition is easy... down to what sounds like the introduction to a flavor of panpsychism [0], where even water flowing downhill is a "statistical process" which at enough scale would be "reasoning".
I don't think that's a faithful match to what other people mean [1] when they argue LLMs don't "reason."
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#24Man, remember when everyone was like 'AGI just around the corner!' Funny how well the Gartner hype cycle captures these sorts of things
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#25This is easily explained by accepting that there is no such thing as LRMs. LRMs are just LLMs that iterate on its own answers more (or provides itself more context information of a certain type). The reasoning loop on an "LRM" will be equivalent to asking a regular LLM to "refine" its own response, or "consider" additional context of a certain type. There is no such thing as reasoning basically, as it was always a me…
Is that what "reasoning" means? That sounds pretty ridiculous.
I've thought before that AI is as "intelligent" as your smartphone is "smart," but I didn't think "reasoning" would be just another buzzword.
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#26This is easily explained by accepting that there is no such thing as LRMs. LRMs are just LLMs that iterate on its own answers more (or provides itself more context information of a certain type). The reasoning loop on an "LRM" will be equivalent to asking a regular LLM to "refine" its own response, or "consider" additional context of a certain type. There is no such thing as reasoning basically, as it was always a me…
Yep. This is exactly the conclusion I reached as an RLHF'er. Reasoning/LRM/SxS/CoT is "just" more context. There never was reasoning. But of course, more context can be good.
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#27Man, remember when everyone was like 'AGI just around the corner!' Funny how well the Gartner hype cycle captures these sorts of things
They're similar to self-driving vehicles. Both are around the corner, but neither can negotiate the turn.
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#28I've seen this too often, papers that ask questions they don't even bother to properly define. > Are these models capable of generalizable reasoning, or are they leveraging different forms of pattern matching? Define reasoning, define generalizable, define pattern matching. For additional credits after you have done so, show humans are capable of what you just defined as generalizable reasoning.
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Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#29The study challenges the assumption that more “thinking” or longer reasoning traces necessarily lead to better problem-solving in LRMs
As a test, I asked Gemini 2.5 Flash and Gemini 2.5 Pro to decode a single BASE64 string.
Flash answered correctly in ~2 seconds, at most. Pro answered very wrongly after thinking and elaborating for ~5 minutes.
Flash was also giving a wrong answer for the same string in the past, but it improved.
Prompt was the same: "Hey, can you decode $BASE64_string?"
I have no further comments.
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#30Man, remember when everyone was like 'AGI just around the corner!' Funny how well the Gartner hype cycle captures these sorts of things
We will be treating LLMs “like a junior developer” forever.