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Learning to Reason with LLMs

openai.com

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Re: Learning to Reason with LLMs

#21

Post by tomduncalf »

tomtom1337 wrote: post #15
valine wrote: post #8

The model performance is driven by chain of thought, but they will not be providing chain of thought responses to the user for various reasons including competitive advantage. After the release of GPT4 it became very common to fine-tune non-OpenAI models on GPT4 output. I’d say OpenAI is rightly concerned that fine-tuning on chain of thought responses from this model would allow for quicker reproduction of their resu…

Can you explain what you mean by this?

I think they mean that you won’t be able to see the “thinking”/“reasoning” part of the model’s output, even though you pay for it. If you could see that, you might be able to infer better how these models reason and replicate it as a competitor

Re: Learning to Reason with LLMs

#22

Post by RandomThoughts3 »

> “Therefore, after weighing multiple factors including user experience, competitive advantage, and the option to pursue the chain of thought monitoring, we have decided not to show the raw chains of thought to users.”

Trust us, we have your best intention in mind. I’m still impressed by how astonishingly impossible to like and root for OpenAI is for a company with such an innovative product.

Re: Learning to Reason with LLMs

#23

Post by ffreire »

tomtom1337 wrote: post #15
valine wrote: post #8

The model performance is driven by chain of thought, but they will not be providing chain of thought responses to the user for various reasons including competitive advantage. After the release of GPT4 it became very common to fine-tune non-OpenAI models on GPT4 output. I’d say OpenAI is rightly concerned that fine-tuning on chain of thought responses from this model would allow for quicker reproduction of their resu…

Can you explain what you mean by this?

You can see an example of the Chain of Thought in the post, it's quite extensive. Presumably they don't want to release this so that it is raw and unfiltered and can better monitor for cases of manipulation or deviation from training. What GP is also referring to is explicitly stated in the post: they also aren't release the CoT for competitive reasons, so that presumably competitors like Anthropic are unable to use the CoT to train their own frontier models.

Re: Learning to Reason with LLMs

#24

Post by teaearlgraycold »

tomtom1337 wrote: post #15
valine wrote: post #8

The model performance is driven by chain of thought, but they will not be providing chain of thought responses to the user for various reasons including competitive advantage. After the release of GPT4 it became very common to fine-tune non-OpenAI models on GPT4 output. I’d say OpenAI is rightly concerned that fine-tuning on chain of thought responses from this model would allow for quicker reproduction of their resu…

Can you explain what you mean by this?

Including the chain of thought would provide competitors with training data.

Re: Learning to Reason with LLMs

#25

Post by [deleted] »

deisteve wrote: post #18

yeah this is kinda cool i guess but 808 elo is still pretty bad for a model that can supposedly code like a human, i mean 11th percentile is like barely scraping by, and what even is the point of simulating codeforces if youre just gonna make a model that can barely compete with a decent amateur, and btw what kind of contest allows 10 submissions, thats not how codeforces works, and what about the time limits and mem…

[deleted]

Re: Learning to Reason with LLMs

#26

Post by TheAceOfHearts »

Kinda disappointed that they're hiding the thought process. Hopefully the open source community will figure out how to effectively match and replicate what OpenAI is doing.

I wonder how far we are from having a model that can correctly solve a word soup search problem directly from just a prompt and input image. It seems like the crossword example is close. For a word search it would require turning the image into an internal grid representation, prepare the list of words, and do a search. I'd be interested in seeing if this model can already solve the word grid search problem if you give it the correct representation as an input.

Re: Learning to Reason with LLMs

#27

Post by deisteve »

farresito wrote: post #3

Damn, that looks like a big jump.

so o1 seems like it has real measurable edge, crushing it in every single metric, i mean 1673 elo is insane, and 89th percentile is like a whole different league, and it looks like it's not just a one off either, it's consistently performing way better than gpt-4o across all the datasets, even in the ones where gpt-4o was already doing pretty well, like math and mmlu, o1 is just taking it to the next level, and the fact that it's not even showing up in some of the metrics, like mmmu and mathvista, just makes it look even more impressive, i mean what's going on with gpt-4o, is it just a total dud or what, and btw what's the deal with the preview model, is that like a beta version or something, and how does it compare to o1, is it like a stepping stone to o1 or something, and btw has anyone tried to dig into the actual performance of o1, like what's it doing differently, is it just a matter of more training data or is there something more going on, and btw what's the plan for o1, is it going to be released to the public or is it just going to be some internal tool or something

Re: Learning to Reason with LLMs

#29

Post by Lalabadie »

immortal3 wrote: post #16

Honestly, it doesn't matter for the end user if there are more tokens generated between the AI reply and human message. This is like getting rid of AI wrappers for specific tasks. If the jump in accuracy is actual, then for all practical purposes, we have a sufficiently capable AI which has the potential to boost productivity at the largest scale in human history.

It starts to matter if the compute time is 10-100 fold, as the provider needs to bill for it.

Of course, that's assuming it's not priced for market acquisition funded by a huge operational deficit, which is a rarely safe to conclude with AI right now.

Re: Learning to Reason with LLMs

#30

Post by zaptrem »

p1esk wrote: post #13

after weighing multiple factors including user experience, competitive advantage, and the option to pursue the chain of thought monitoring, we have decided not to show the raw chains of thought to users.

This also makes them less useful because I can’t just click stop generation when they make a logical error re: coding.
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