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Learning to Reason with LLMs

openai.com

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Re: Learning to Reason with LLMs

#231

Earlier quoted context omitted.

This one [o1/Strawberry] is available. I have it, though it's limited to 30 messages/week in ChatGPT Plus.

How do you get access? I don’t have it and am a ChatGPT plus subscriber.

it will roll out to everyone over the next few hours

Re: Learning to Reason with LLMs

#232

Very interesting. I guess this is the strawberry model that was rumoured. I am a bit surprised that this does not beat GPT-4o for personal writing tasks. My expectations would be that a model that is better at one thing is better across the board. But I suppose writing is not a task that generally requires "reasoning steps", and may also be difficult to evaluate objectively.

The solution of the cipher example problem also strongly hints at this: "there are three r's in strawberry"

Re: Learning to Reason with LLMs

#233
post #184

Earlier quoted context omitted.

People have been celebrating the fact that tokens got 100x cheaper and now here's a new system that will use 100x more tokens.

Also you now have to pay for tokens you can't see, and just have to trust that OpenAI is using them economically.

Token count was always an approximation of value. This may help break that silly idea.

Re: Learning to Reason with LLMs

#234
I'm confused. Is this the "GPT-5" that was coming in summer, just with a different name? Or is this more like a parallel development doing chain-of-thought type prompt engineering on GPT-4o? Is there still a big new foundational model coming, or is this it?

Re: Learning to Reason with LLMs

#235

Earlier quoted context omitted.

Why not? Isn't that basically what humans do? Sit there and think for a while before answering, going down different branches/chains of thought?

This new approach is showing: 1) The "bitter lesson" may not be true, and there is a fundamental limit to transformer intelligence. 2) The "bitter lesson" is true, and there just isn't enough data/compute/energy to train AGI. All the cognition should be happening inside the transformer. Attention is all you need. The possible cognition and reasoning occurring "inside" in high dimensions is much more advanced than any…

I think it's too soon to tell. Training the next generation of models means building out entire datacenters. So while they wait they have engineers build these sidequests/hacks.

Re: Learning to Reason with LLMs

#236

I'm confused. Is this the "GPT-5" that was coming in summer, just with a different name? Or is this more like a parallel development doing chain-of-thought type prompt engineering on GPT-4o? Is there still a big new foundational model coming, or is this it?

It looks like parallel development, it's unclear to me what is going on with GPT-5, don't think it has ever had a predicted release date, and it's not even clear that this would be the name.

Re: Learning to Reason with LLMs

#237
> However, o1-preview is not preferred on some natural language tasks, suggesting that it is not well-suited for all use cases.

Fascinating... Personal writing was not preferred vs gpt4, but for math calculations it was... Maybe we're at the point where its getting too smart? There is a depressing related thought here about how we're too stupid to vote for actually smart politicians ;)

Re: Learning to Reason with LLMs

#238

One thing that makes me skeptical is the lack of specific labels on the first two accuracy graphs. They just say it's a "log scale", without giving even a ballpark on the amount of time it took. Did the 80% accuracy test results take 10 seconds of compute? 10 minutes? 10 hours? 10 days? It's impossible to say with the data they've given us. The coding section indicates "ten hours to solve six challenging algorithmic…

I don't think it's worth any debate. You can simply find out how it does for you, now(-ish, rolling out).

In contrast: Gemini Ultra, the best, non-existent Google Model for the past few month now, that people nonetheless are happy to extrapolate excitement over.

Re: Learning to Reason with LLMs

#240

Just did some preliminary testing on decrypting some ROT cyphertext which would have been viable for a human on paper. The output was pretty disappointing: lots of "workish" steps creating letter counts, identifying common words, etc, but many steps were incorrect or not followed up on. In the end, it claimed to check its work and deliver an incorrect solution that did not satisfy the previous steps. I'm not one to j…

Out of curiousity can you try the same thing with Claude. Because when I tried Claude with any sort of ROT, it had amazing performance, compared to GPT.
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