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S1: A $6 R1 competitor?

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Re: S1: A $6 R1 competitor?

#211

Post by LordDragonfang »

bloomingkales wrote: post #75

This thing that people are calling “reasoning” is more like rendering to me really, or multi pass rendering. We’re just refining the render, there’s no reasoning involved.

How are you defining "reasoning"?

Because I see these sorts of gnostic assertion about LLMs all the time about how they "definitely aren't doing " by gesturing at the technical things it's doing, with no attempts to actually justify the negative assertion.

It often comes across as privileged reason trying to justify that of course the machine isn't doing some ineffable thing only meat-brains do.

Re: S1: A $6 R1 competitor?

#212

Post by palmotea »

zoogeny wrote: post #192
cubefox wrote: post #137

Earlier quoted context omitted.

Now imagine where we are in 12 months from now. This article from February 5 2025 will feel quaint by then. The acceleration keeps increasing. It seems likely we will soon have recursive self-improving AI -- reasoning models which do AI research. This will accelerate the rate of acceleration itself. It sounds stupid to say it, but yes, the singularity is near. Vastly superhuman AI now seems to arrive within the next…

This is something I have been suppressing since I don't want to become chicken little. Anyone who isn't terrified by the last 3 months probably doesn't really understand what is happening. I went from accepting I wouldn't see a true AI in my lifetime, to thinking it is possible before I die, to thinking it is possible in in the next decade, to thinking it is probably in the next 3 years to wondering if we might see i…

> The intelligence that will be available to the average technically literate individual will be frightening.

That's not the scary part. The scary part is the intelligence at scale that could be available to the average employer. Lots of us like to LARP that we're capitalists, but very few of us are. There's zero ideological or cultural framework in place to prioritize the well being of the general population over the profits of some capitalists.

AI, especially accelerating AI, is bad news for anyone who needs to work for a living. It's not going to lead to a Star Trek fantasy. It means an eventual phase change for the economy that consigns us (and most consumer product companies) to wither and fade away.

Re: S1: A $6 R1 competitor?

#213

Post by nico »

layer8 wrote: post #206
nico wrote: post #171

> In s1, when the LLM tries to stop thinking with " ", they force it to keep going by replacing it with "Wait". It’ll then begin to second guess and double check its answer. They do this to trim or extend thinking time (trimming is just abruptly inserting " ") I know some are really opposed to anthropomorphizing here, but this feels eerily similar to the way humans work, ie. if you just dedicate more time to analyzin…

What’s missing in that analogy is that humans tend to have a good hunch about when they have to think more and when they are “done”. LLMs seem to be missing a mechanism for that kind of awareness.

Great observation. Maybe an additional “routing model” could be trained to predict when it’s better to think more vs just using the current result

Re: S1: A $6 R1 competitor?

#214

Post by zoogeny »

int_19h wrote: post #210
zoogeny wrote: post #202

Earlier quoted context omitted.

I can't believe this hasn't been done yet, perhaps it is a cost issue. My literal first thought about AI was wondering why we couldn't just put it in a loop. Heck, one update per day, or one update per hour would even be a start. You have a running "context", the output is the next context (or a set of transformations on a context that is a bit larger than the output window). Then ramp that up ... one loop per minute…

The hard part is coming up with a good way to grade results. Which you need to update the weights based on the outcome, otherwise the model will not actually learn anything.

For the "looping" I'm talking about you don't need to update the weights. It is simply, old context in, new context out, new context in, new-new context out, etc.

Of course, keeping that coherent over numerous loops isn't going to be easy. No doubt there is a chance it goes off the rails. So you might have a section of context that is constantly stable, a section of context that updates each loop, etc.

In the other response to my comment someone mentioned eventually updating the weights (e.g. daily) and you would in that case have to have some kind of loss function.

Re: S1: A $6 R1 competitor?

#215

Post by throwaway314155 »

pas wrote: post #70
jebarker wrote: post #66

S1 (and R1 tbh) has a bad smell to me or at least points towards an inefficiency. It's incredible that a tiny number of samples and some inserted tokens can have such a huge effect on model behavior. I bet that we'll see a way to have the network learn and "emerge" these capabilities during pre-training. We probably just need to look beyond the GPT objective.

can you please elaborate on the wait tokens? what's that? how do they work? is that also from the R1 paper?

There's a decent explanation in the article, just FYI.

Re: S1: A $6 R1 competitor?

#216

Post by int_19h »

amarcheschi wrote: post #16
yapyap wrote: post #8

> If you believe that AI development is a prime national security advantage, then you absolutely should want even more money poured into AI development, to make it go even faster. This, this is the problem for me with people deep in AI. They think it’s the end all be all for everything. They have the vision of the ‘AI’ they’ve seen in movies in mind, see the current ‘AI’ being used and to them it’s basically almost t…

I couldn't agree more. If we're not talking about cyber war exclusively, such as finding and exploiting vulnerabilities, for the time being national security will still be based on traditional army. Just a few weeks ago, italy announced a 16bln€ plan to buy >1000 rheinmetall ifv vehicles. That alone would make italy's army one of the most equipped in Europe. I can't imagine what would happen with a 500$bln investment…

It's not one or the other, though. AI-controlled drones are already a thing in Ukraine, today.

Re: S1: A $6 R1 competitor?

#217

Post by int_19h »

sidewndr46 wrote: post #34
yapyap wrote: post #8

> If you believe that AI development is a prime national security advantage, then you absolutely should want even more money poured into AI development, to make it go even faster. This, this is the problem for me with people deep in AI. They think it’s the end all be all for everything. They have the vision of the ‘AI’ they’ve seen in movies in mind, see the current ‘AI’ being used and to them it’s basically almost t…

What is even the possible usage of AI for national security? Generating pictures of kittens riding nuclear weapons to the very end like in Dr Strangelove?

Autonomous weapons.

Re: S1: A $6 R1 competitor?

#218

Post by int_19h »

swiftcoder wrote: post #23
spacebanana7 wrote: post #11

Earlier quoted context omitted.

> I think using any of this in a national security setting is stupid What about AI enabled drones and guided missiles/rockets? The case for their effectiveness is relatively simple in terms of jamming resistance.

drone and missile guidance system development has been using ML for decades at this point. That's just as much "AI" as anything currently coming out of the LLM craze.

It's not just target guidance at this point. There are prototypes of drone swarms, for example.

Re: S1: A $6 R1 competitor?

#219

Post by mitchelld »

menaerus wrote: post #172
mitchelld wrote: post #134

Earlier quoted context omitted.

This line of thinking doesn't really correspond to the reason Transformers were developed in the first place, which was to better utilize how GPUs do computation. RNNs were too slow to train at scale because you had to sequentially compute the time steps, Transformers (with masking) can run the input through in a single pass. It is worth noting that the first "LLM" you referring to was only 300M parameters, but even…

What line of thinking you're referring to? Transformers were aimed to solve the "context" problem and authors, being aware that RNNs don't scale at all neither do they solve that particular problem, had to come up with the algorithm that overcomes both of those issues. It turned out that the self-attention compute-scale was the crucial ingredient to solve the problem, something that RNNs were totally incapable of. Th…

> This is not "just" machine learning because we have never been able to do things which we are today and this is not only the result of better hardware. Better hardware is actually a byproduct. Why build a PFLOPS GPU when there is nothing that can utilize it?

This is the line of thinking I'm referring to.

The "context" problem had already been somewhat solved. The attention mechanism existed prior to Transformers and was specifically used on RNNs. They certainly improved it, but innovation of the architecture was making it computation efficient to train.

I'm not really following your argument. Clearly your acknowledging that it was first the case that with the hardware at the time, researchers demonstrated that simply scaling up training with more data yielded better models. The fact that hardware was then optimized for these for these architectures only reinforces this point.

All the papers discussing scaling laws point to the same thing, simply using more compute and data yields better results.

> this is not only the result of better hardware

Regarding this in particular. A majority of the improvement from GPT-2 and GPT-4 was simply training on a much larger scale. That was enabled by better hardware and lots of it.

Re: S1: A $6 R1 competitor?

#220

Post by cyanydeez »

mtrovo wrote: post #133

I found the discussion around inference scaling with the 'Wait' hack so surreal. The fact such an ingeniously simple method can impact performance makes me wonder how many low-hanging fruit we're still missing. So weird to think that improvements on a branch of computer science is boiling down to conjuring the right incantation words, how you even change your mindset to start thinking this way?

its fascinating how certain political movements avoid that Wait moment...
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