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Learning to Reason with LLMs

openai.com

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Re: Learning to Reason with LLMs

#191

Post by packetlost »

ARandumGuy wrote: post #137

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…

When one axis is on log scale and the other is linear with the plot points appearing linear-ish, doesn't it mean there's a roughly exponential relationship between the two axis?

Re: Learning to Reason with LLMs

#192

Post by rglullis »

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…

When are they going to change the name to reflect their complete change of direction?

Also, what is going to be their excuse to defend themselves against copyright lawsuits if they are going to "understandably" keep their models closed?

Re: Learning to Reason with LLMs

#193

Post by logicchains »

Infinity315 wrote: post #132
paxys wrote: post #96

2018 - gpt1 2019 - gpt2 2020 - gpt3 2022 - gpt3.5 2023 - gpt4 2023 - gpt4-turbo 2024 - gpt-4o 2024 - o1 Did OpenAI hire Google's product marketing team in recent years?

No, this is just how Microsoft names things.

We'll know the Microsoft takeover is complete when OpenAI release Ai.net.

Re: Learning to Reason with LLMs

#195

Post by wmf »

yunohn wrote: post #156
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…

Given the significant chain of thought tokens being generated, it also feels a bit odd to hide it from a cost fairness perspective. How do we believe they aren't inflating it for profit?

That sounds like the GPU labor theory of value that was debunked a century ago.

Re: Learning to Reason with LLMs

#196

Post by accrual »

logicchains wrote: post #139
islewis wrote: post #124

My first interpretation of this is that it's jazzed-up Chain-Of-Thought. The results look pretty promising, but i'm most interested in this: > 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. Mentioning competitive advantage here signals to me that OpenA…

>my gut reaction is this negatively impacts model usability, but i'm having a hard time putting my finger on why. If the model outputs an incorrect answer due to a single mistake/incorrect assumption in reasoning, the user has no way to correct it as it can't see the reasoning so can't see where the mistake was.

Maybe CriticGPT could be used here [0]. Have the CoT model produce a result, and either automatically or upon user request, ask CriticGPT to review the hidden CoT and feed the critique into the next response. This way the error can (hopefully) be spotted and corrected without revealing the whole process to the user.

[0] https://openai.com/index/finding-gpt4s-mistakes-with-gpt-4/

Day dreaming: imagine if this architecture takes off and the AI "thought process" becomes hidden and private much like human thoughts. I wonder then if a future robot's inner dialog could be subpoenaed in court, connected to some special debugger, and have their "thoughts" read out loud in court to determine why it acted in some way.

Re: Learning to Reason with LLMs

#197

Post by rvnx »

farresito wrote: post #43
deisteve wrote: post #27

Earlier quoted context omitted.

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 f…

> like what's it doing differently, is it just a matter of more training data or is there something more going on Well, the model doesn't start with "GPT", so maybe they have come up with something better.

It sounds like GPT-4o with a long CoT prompt no ?

Re: Learning to Reason with LLMs

#198

Post by [deleted] »

apsec112 wrote: post #152
cal85 wrote: post #54

Sounds great, but so does their "new flagship model that can reason across audio, vision, and text in real time" announced in May. [0] [0] https://openai.com/index/hello-gpt-4o/

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

[deleted]

Re: Learning to Reason with LLMs

#199

Post by mewpmewp2 »

not_pleased wrote: post #190

The progress in AI is incredibly depressing, at this point I don't think there's much to look forward to in life. It's sad that due to unearned hubris and a complete lack of second-order thinking we are automating ourselves out of existence. EDIT: I understand you guys might not agree with my comments. But don't you thinking that flagging them is going a bit too far?

It seems opposite to me. Imagine all the amazing technological advancements, etc. If there wasn't something like that what would you be looking forward to? Everything would be what it has already been for years. If this evolves it helps us open so many secrets of the universe.

Re: Learning to Reason with LLMs

#200

Post by dragonwriter »

msp26 wrote: post #127
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…

That's unfortunate. When an LLM makes a mistake it's very helpful to read the CoT and see what went wrong (input error/instruction error/random shit)

Yeah, exposed chain of thought is more useful as a user, as well as being useful for training purposes.
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