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Reasoning models reason well, until they don't

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Re: Reasoning models reason well, until they don't

#171

Post by leptons »

rightbyte wrote: post #121
20k wrote: post #90

Earlier quoted context omitted.

Its worth noting that the idea that great apes have learnt sign language is largely a fabrication by a single person, and nobody has ever been able to replicate this. All the communication has to be interpreted through that individual, and anyone else (including people that speak sign language) have confirmed that they're just making random hand motions in exchange for food They don't have the dexterity to really sig…

I mean dogs can learn a simple sign language?

Can the dogs sign back? Even dogs that learn to press buttons are mostly just pressing them to get treats. They don't ask questions, and it's not really a conversation.

Re: Reasoning models reason well, until they don't

#172

Post by wavemode »

kordlessagain wrote: post #109

What specific reasoning capabilities matter for what real-world applications? Nobody knows. Moreover, nobody talks about that because it's boring and non-polarizing. Instead, supposedly smart people post stupid comments that prevent anyone from understanding this paper is worthless. The paper is worthless because it has a click-bait title. Blog posts get voted down for that, why not this? The implicit claim is worthl…

> The implicit claim is worthless. Failure to navigate a synthetic graph == failure to solve real world problems. False.

This statement is the dictionary definition of attacking a strawman.

Every new model that is sold to us, is sold on the basis that it performs better than the old model on synthetic benchmarks. This paper presents a different benchmark that those same LLMs perform much worse on.

You can certainly criticize the methodology if the authors have erred in some way, but I'm not sure why it's hard to understand the relevance of the topic itself. If benchmarks are so worthless then go tell that to the LLM companies.

Re: Reasoning models reason well, until they don't

#173

Post by BriggyDwiggs42 »

alyxya wrote: post #23

The key point the paper seems to make is that existing benchmarks have relatively low complexity on reasoning complexity, so they made a new dataset DeepRD with arbitrarily large reasoning complexity and demonstrated that existing models fail at a complex enough problem. Complexity is defined from the complexity of a graph created by modeling the problem as a graph and determining the traversals needed to go from som…

The issue is that no matter how much you train them they don’t generalize to arbitrary sized problems. Sure you can push out the horizon, but you won’t make something that can solve the problem always (assuming resources permit, and that isn’t the issue here).

Re: Reasoning models reason well, until they don't

#174

Post by riku_iki »

sothatsit wrote: post #6
iLoveOncall wrote: post #2

> [...] recent studies show that transformers and LLMs fail catastrophically once reasoning problems exceed modest complexity. We revisit these findings through the lens of large reasoning models (LRMs) -- LLMs fine-tuned with incentives for step-by-step argumentation and self-verification This was the obvious outcome of the study (don't get me wrong, obvious outcomes are still worth having research on). "LRMs" *are*…

What do you mean by reasoning? If you mean solving logic problems, then reasoning LLMs seem to pass that bar as they do very well programming and maths competitions. Reasoning LLMs can also complete problems like multiplying large numbers, which requires applying some sort of algorithm where the results cannot just be memorised. They also do this much better than standard pre-trained LLMs with no RL. So, that makes m…

> then reasoning LLMs seem to pass that bar as they do very well programming and maths competitions.

it could be this is just result of good stochastic parroting and not reasoning. Both of those niches are narrow with high amount of training data (e.g. corps buying solutions from leetcode and training LLMs on them).

From another hand we see that LLMs fail in more complex environment: e.g. ask to build some new feature in postgres database.

Re: Reasoning models reason well, until they don't

#175

Post by krackers »

anal_reactor wrote: post #39

I'm yet to see a task that AI fails at that bottom 10% of population wouldn't also fail at.

ARC-AGI v3 is a pretty good benchmark, and it's notably different from the other ARC-AGI in that it has a "truer" human baseline (you can go play it right now and add your datapoint), and captures the act of in-context learning better as you start an unfamiliar game then master it over time.

Also bottom 10% feels like a bad comparison, median human would be better. And unlike "specialized" things like programming, game playing is something almost all of us have done.

Re: Reasoning models reason well, until they don't

#176

Post by measurablefunc »

tomlockwood wrote: post #137
code_martial wrote: post #125

Earlier quoted context omitted.

No, that’s not what I said.

Why are we sending the trillion?

It must be deposited into OpenAI's bank account so that they can then deposit it into NVIDIA's account who can then in turn make a deal w/ OpenAI to deposit it back into OpenAI's account for some stock options. I think you can see how it works from here but if not then maybe one of the scaled up "reasoning" AIs will figure it out for you.

Re: Reasoning models reason well, until they don't

#177

Post by galaxyLogic »

alyxya wrote: post #23

The key point the paper seems to make is that existing benchmarks have relatively low complexity on reasoning complexity, so they made a new dataset DeepRD with arbitrarily large reasoning complexity and demonstrated that existing models fail at a complex enough problem. Complexity is defined from the complexity of a graph created by modeling the problem as a graph and determining the traversals needed to go from som…

> complexity of a graph created by modeling the problem as a graph and determining the traversals needed to go from some source node to a target node

Sounds interesting: Formalizing a problem once you know the solution. Seems like LLMs can't do that, or if they could they would evaluate where their problem solving is inadequate?

Re: Reasoning models reason well, until they don't

#178

Post by measurablefunc »

brap wrote: post #18

I wonder if we can get models to reason in a structured and verifiable way, like we have formal logic in math.

It's doing so already. All code executed on a computer, especially neural networks w/o any loops are simply doing boolean arithmetic. In fact, the computer can't do anything else other than boolean arithmetic.

Re: Reasoning models reason well, until they don't

#179

Post by galaxyLogic »

alyxya wrote: post #161
usrbinbash wrote: post #149

Earlier quoted context omitted.

> I don't think there's evidence that this issue would persist after continuing to scale models to be larger and doing more RL And how much larger do we need to make the models? 2x? 3x? 10x? 100x? How large do they need to get before scaling-up somehow solves everything? Because: 2x larger, means 2x more memory and compute required. Double the cost or half the capacity. Would people still pay for this tech if it doub…

Scaling applies to multiple dimensions simultaneously over time. A frontier model today could be replicated a year later with a model half the size, with a quarter of the FLOPS, etc. I don’t know the real numbers for optimization scaling, but you could check out NanoGPT speedrun [1] as an example. The best solution in the meantime is giving the LLM a harness that allows tool use like what coding agents have. I suspec…

Some problems are just too complex and the effort to solve them increases exponentially. No LLM can keep up with exponenentially increasing effort unless you run them for adequatte number of years.

Re: Reasoning models reason well, until they don't

#180

Post by galaxyLogic »

acdha wrote: post #110
pegasus wrote: post #101

Earlier quoted context omitted.

You only need a citation for the idea that apes aren't able to speak sign language?

They claimed fraud by a single person, with zero replication. That’s both testable so they should be able to support it. At the very least, more than one researcher was involved and more than one ape was alleged to have learned ASL. There is a better discussion about what our threshold is for speech, along with our threshold for saying that research is fraud vs. mistaken, but we don’t fix sloppiness by engaging in mo…

SO why wasn't the research continued further if results were good? My assumption is it was because of the - Fear of the Planet of Apes!
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