In figure 1 bottom-right they show how the correct answers are being found later as the complexity goes higher.
In the description they even state that in false responses the LRM often focusses on a wrong answer early and then runs out of tokens before being able to self-correct.
This seems obvious and indicates that it’s simply a matter of scaling (bigger token budget would lead better abilities for complexer tasks). Am I missing something?
The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
61–70 of 276 posts
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#62Earlier quoted context omitted.
I am not sure if you mean this to refute something in what I've written but to be clear I am not arguing for or against what the authors think. I'm trying to state why I think there is a disconnect between them and more optimistic groups that work on AI.
I think that commenter was disagreeing with this line: > because omniscient-yet-dim-witted models terminate at "superhumanly assistive" It might be that with dim wits + enough brute force (knowledge, parallelism, trial-and-error, specialisation, speed) models could still substitute for humans and transform the economy in short order.
Sorry, I can't edit it any more, but what I was trying to say is that if the authors are correct, that this distinction is philosophically meaningful, then that is the conclusion. If they are not correct, then all their papers on this subject are basically meaningless.
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#63I'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.
> show humans are capable of what you just defined as generalizable reasoning.
I would also add "and plot those capabilities on a curve". My intuition is that the SotA models are already past the median human abilities in a lot of areas.
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#64I have a somewhat similar point of view to the one voiced by other people, but I like to think about it slightly differently, so I'll chime in - here's my take (although, admittedly, I'm operating with a quite small reasoning budget (5 minutes tops)): Time and again, for centuries - with the pace picking up dramatically in recent decades - we thought we were special and we were wrong. Sun does not rotate around the e…
This analogy doesn’t really work, because the former examples are ones in which humanity discovered that it existed in a larger world.
The recent AI example is humanity building, or attempting to build, a tool complex enough to mimic a human being.
If anything, you could use recent AI developments as proof of humanity’s uniqueness - what other animal is creating things of such a scale and complexity?
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#65Man, 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.
Waymo's pretty good at unprotected lefts
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#66Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#67Earlier quoted context omitted.
> I think AI maximalists will continue to think that the models are in fact getting less dim-witted I'm bullish (and scared) about AI progress precisely because I think they've only gotten a little less dim-witted in the last few years, but their practical capabilities have improved a lot thanks to better knowledge, taste, context, tooling etc. What scares me is that I think there's a reasoning/agency capabilities ov…
I think you are right, and that the next step function can be achieved using the models we have, either by scaling the inference, or changing the way inference is done.
People are doing all manner of very sophisticated inferency stuff now - it just tends to be extremely expensive for now and... people are keeping it secret.
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#68[dead]
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#69This 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.
A reasoning model is an LLM that has had additional training phases that reward problem solving abilities. (But in a black box way - it’s not clear if the model is learning actual reasoning or better pattern matching, or memorization, or heuristics… maybe a bit of everything).
Re: The Illusion of Thinking: Strengths and limitations of reasoning models [pdf]
#70Earlier quoted context omitted.
I think we just around at 80% of progress the easy part is done but the hard part is so hard it takes years to progress
> the easy part is done but the hard part is so hard it takes years to progress There is also no guarantee of continued progress to a breakthrough. We have been through several "AI Winters" before where promising new technology was discovered and people in the field were convinced that the breakthrough was just around the corner and it never came. LLMs aren't quite the same situation as they do have some undeniable u…
> We have been through several "AI Winters" before
Yeah, remember when we spent 15 years (~2000 to ~2015) calling it “machine learning” because AI was a bad word?
We use so much AI in production every day but nobody notices because as soon as a technology becomes useful, we stop calling it AI. Then it’s suddenly “just face recognition” or “just product recommendations” or “just [plane] autopilot” or “just adaptive cruise control” etc
You know a technology isn’t practical yet because it’s still being called AI.