I guess we mean here ”usefull reasoning” instead of the idiot-savant. I mean it’s a fair ask since these are marketed as _tools_ you can use to implement _industrial processes_ and even replace you human workers.
In that I guess the model does not need to be the most reasonable intepreter of vague and poorly formulated user inputs but I think to improve a bit at least, to become usefull general appliances and not just test-scoring-automatons.
The key differentiator here is that tests generally _are made to be unambiguously scoreable_. Real world problems are often more vague from the point of view of optimal outcome.
^^This is a great view and it seems generally widely understood by the file and rank techies. I feel pitty for the general public retail investors which are about to be left holding the bag for the VCs, after a certain major <ahem> champion goes into IPO soon.
Thanks. So, people are extending "reasoning" to include making good decisions, rather than just solving logic problems. That makes sense to me that if people use that definition, LLMs are pretty bad at "reasoning".
Although, I would argue that this is not reasoning at all, but rather "common sense" or the ability to have a broader perspective or think of the future. These are tasks that come with experience. That is why these do not seem like reasoning tasks to me, but rather soft skills that LLMs lack. In my mind these are pretty separate concerns to whether LLMs can logically step through problems or apply algorithms, which is what I would call reasoning.
Ah yes then, let me then unchain my LLM on those nasty unsolved math and logic problems I've absolutely not be struggling with in the course of my career.
They say LLM are PhD-level. Despite billion dollars, PhD-LLMs sure are not contributing a lot solving known problems. Except of course few limited marketing stunts.
You can give a human PhD an _unsolved problem_ in field adjacent to their expertise and expect some reasonable resolution. LLM PhD:s solve only known problems.
That said humans can also be really bad problem solvers.
If you don't care about solving the problem and only want to create paperwork for bureaucracy I guess you don't care either way ("My team's on it!") but companies that don't go out of business generally recognize pretty soon lack of outcomes where it matters.
I wish our press was not effectively muted or bought by the money, so none of the journos has cojones to call out the specific people who were blabbing about PhD-levels, AGI etc. They should be god damn calling them out every single day, essentially doing their job, but they are now too timid for that.
I've "unchained" my LLM on a lot of problems that I probably could solve, but that would take me time I don't have, and that it has solved in many case faster than I could. It may not be good enough to solve problems that are beyond us for most of us, but it certainly can solve a lot of problems for a lot of us that have gone unsolved for lack of resources.
Unless you can show us concrete metrics and problems solved, I am inclined not to believe your statement (source: own intensive experience with the LLMs).
Exactly my experience too. Whoever says they're able to solve "very complex" problems with LLMs, is clearly not working on objectively complex problems.
I'm not usually micro-managing it, that's the point.
I sometimes do on problems where I have particular insight, but I mostly find it is far more effective to give it test cases and give it instructions on how to approach a task, and then let it iterate with little to no oversight.
I'm letting Claude Code run for longer and longer with --dangerously-skip-permissions, to the point I'm pondering rigging up something to just keep feeding it "continue" and run it in parallel on multiple problems.
Because at least when you have a good way of measuring success, it works.
A lot of maths students would also struggle to contribute to frontier math problems, but we would still say they are reasoning. Their skill at reasoning might not be as good as professional mathematicians, but that does not stop us from recognising that they can solve logic problems without memorisation, which is a form of reasoning.
I am just saying that LLMs have demonstrated they can reason, at least a little bit. Whereas it seems other people are saying that LLM reasoning is flawed, which does not negate the fact that they can reason, at least some of the time.
Maybe generalisation is one area where LLM's reasoning is weakest though. They can be near-elite performance at nicely boxed up competition math problems, but their performance dramatically drops on real-world problems where things aren't so neat. We see similar problems in programming as well. I'd argue the progress on this has been promising, but other people would probably vehemently disagree with that. Time will tell.
A lot of people appear to be - often not consciously or intentionally - setting the bar for "reasoning" at a level many or most people would not meet.
Sometimes that is just a reaction to wanting an LLM that is producing result that is good for their own level. Sometimes it reveals a view of fellow humans that would be quite elitist if stated outright. Sometimes it's a kneejerk attempt at setting the bar at a point that would justify a claim that LLMs aren't reasoning.
Whatever the reason, it's a massive pet peeve of mine that it is rarely made explicit in these conversations, and it makes a lot of these conversations pointless because people keep talking past each other.
For my part a lot of these models often clearly reason by my standard, even if poorly. People also often reason poorly, even when they demonstrably attempt to reason step by step. Either because they have motivations to skip over uncomfortable steps, or because they don't know how to do it right. But we still would rarely claim they are not capable of reasoning.
I wish more evaluations of LLMs would establish a human baseline to test them against for much this reason. It would be illuminating in terms of actually telling us more about how LLMs match up to humans in different areas.
In that I guess the model does not need to be the most reasonable intepreter of vague and poorly formulated user inputs but I think to improve a bit at least, to become usefull general appliances and not just test-scoring-automatons.
The key differentiator here is that tests generally _are made to be unambiguously scoreable_. Real world problems are often more vague from the point of view of optimal outcome.