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Notes on OpenAI's new o1 chain-of-thought models

simonwillison.net

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Re: Notes on OpenAI's new o1 chain-of-thought models

#411
post #156

Earlier quoted context omitted.

They’re not charging anything for o1 right now - it’s running as a free beta. EDIT: Actually I’m not sure I’m correct about that - I’ve seen screenshots that indicate the API model is being charged for here: https://twitter.com/deepaiwriter/status/1834460643228897512 The value OpenAI get here is that people effectively run a massively parallel brute force attack against the new models to figure out exactly what they…

> The value OpenAI get here is that people effectively run a massively parallel brute force attack against the new models to figure out exactly what they can and can’t do. I'm pretty sure the value they get is the money you pay.

No, in this case it really is the usage. This is a brand new model and nobody knows how best to use it yet. OpenAI researchers have been tweeting as much (sadly I’ve lost the tweet).

Re: Notes on OpenAI's new o1 chain-of-thought models

#413

Earlier quoted context omitted.

The failure is in how you're using it. I don't mean this as a personal attack, but more to shed light on what's happening. A lot of people use LLMs as a search engine. It makes sense - it's basically a lossy compressed database of everything its ever read, and it generates output that is statistically likely - varying degrees of likeliness depending on the temperature, as well as how many times the particular weights…

> Treat it as a naive but intelligent intern That’s the problem: it’s a _terrible_ intern. A good intern will ask clarifying questions, tell me “I don’t know” or “I’m not sure I did it right”. LLMs do none of that, they will take whatever you ask and give a reasonable-sounding output that might be anything between brilliant and nonsense. With an intern, I don’t need to measure how good my prompting is, we’ll usually…

> That’s the problem: it’s a _terrible_ intern. A good intern will ask clarifying questions, tell me “I don’t know” or “I’m not sure I did it right”.

An intern that grew up in a different culture then, where questioning your boss is frowned upon. The point is that the way to instruct this intern is to front-load your description of the problem with as much detail as possible to reduce ambiguity.

Re: Notes on OpenAI's new o1 chain-of-thought models

#414

Earlier quoted context omitted.

Come on. Of course chatgpt has read that riddle and the answer 1000 times already.

It hasn't read that riddle because it is a modified version. The model would in fact solve this trivially if it _didn't_ see the original in its training. That's the entire trick.

Sure but the parent was praising the model for recognizing that it was a riddle in the first place:

> Whereas o1, at the very outset smelled out that it is a riddle

That doesn't seem very impressive since it's (an adaptation of) a famous riddle

The fact that it also gets it wrong after reasoning about it for a long time doesn't make it better of course

Re: Notes on OpenAI's new o1 chain-of-thought models

#415
post #172

Just leaving here an example of the "smart chain of thought", which took the model about 20s of thinking. The problem I asked was – "what 3 numbers give the same result when divided and added together". Seems the current state of art is that basic school problems are too hard yet even for this new chain-of-marketing. Let's think through this step-by-step: We're looking for three numbers a, b, and c where: (a / b) / c…

{0, -1, 1}. Divide (in order): 0/-1 -> 0/1 -> 0. Add: 0 - 1 + 1 = 0

also {-1, 1, 1}

Re: Notes on OpenAI's new o1 chain-of-thought models

#416
post #384

Earlier quoted context omitted.

The failure is in how you're using it. I don't mean this as a personal attack, but more to shed light on what's happening. A lot of people use LLMs as a search engine. It makes sense - it's basically a lossy compressed database of everything its ever read, and it generates output that is statistically likely - varying degrees of likeliness depending on the temperature, as well as how many times the particular weights…

> Treat it as a naive but intelligent intern. Provide it data, give it a task, and let it surprise you with its output. Well, I am a naive but intelligent intern (well, senior developer). So in this framing, the LLM can’t do more than I can already do by myself, and thus far it’s very hit or miss if I actually save time, having to provide all the context and requirements, and having to double-check the results. With…

I've recently started using an LLM to choose the best release of shows using data scraped from several trackers. I give it hard requirements and flexible preferences. It's not that I couldn't do this, it's that I don't want to do this on the scale of multiple thousand shows. The "magic" here is that releases don't all follow the same naming conventions, they're an unstructured dump of details. The LLM is simultaneously extracting the important details, and flexibly deciding the closest match to my request. The prompt is maybe two paragraphs and took me an hour to hone.

Re: Notes on OpenAI's new o1 chain-of-thought models

#417
post #226

> I asked on Twitter for examples of prompts that people had found which failed on GPT-4o but worked on o1-preview. it seems trivial, but I tried for more than 2 hours in the past to get gpt4 to play tic-tac-toe optimally and failed (CoT prompt,etc.). The result were too many illegal moves and absolutely no optimal strategy. o1-preview can do it really well [1] However, when I use a non-standard grid (3x5) it fails t…

Link to conversation 1 and 2 doesn't work. Update it please!

sorry for that. I just learned that my team account does not allow me to share conversations publicly. Pasting them here for reference

* Conversation 1

    let's play tic tac toe. you need to play optimally
    ChatGPT

    Sure, I'd be happy to play tic-tac-toe with you! Let's set up the game.

    We'll use a grid with positions numbered 1 through 9:

    markdown

    1 | 2 | 3
    ---------
    4 | 5 | 6
    ---------
    7 | 8 | 9

    Would you like to be X or O? And would you like to go first or second?
    You said:
    X on 5
    ChatGPT

    Got it! You placed X at position 5.

    I'll place O at position 1.

    Here's the updated board:

    markdown

    O |  2 |  3
    -------------
    4 | X | 6
    -------------
    7 |  8 | 9

    Your turn! Where would you like to place your next X?
    You said:
    X on 9
    ChatGPT

    Got it! You placed X at position 9.

    Here's the updated board:

    markdown

    O |  2 |  3
    -------------
    4 | X | 6
    -------------
    7 |  8 | X

    Now, I'll place O at position 3.

    Here's the board now:

    markdown

    O |  2 | O
    -------------
    4 | X | 6
    -------------
    7 |  8 | X

    Your turn! Where would you like to place your next X?
    You said:
    X on 2
    ChatGPT

    Got it! You placed X at position 2.

    Here's the updated board:

    markdown

    O | X | O
    -------------
    4 | X | 6
    -------------
    7 | 8 | X

    To block your potential win, I'll place O at position 8.

    Here's the board now:

    markdown

    O | X | O
    -------------
    4 | X | 6
    -------------
    7 | O | X

    Your turn! Where would you like to place your next X?
    You said:
    X on 4
    ChatGPT

    You placed X at position 4.

    Here's the updated board:

    markdown

    O | X | O
    -------------
    X | X | 6
    -------------
    7 | O | X

    To block your potential win, I'll place O at position 6.

    Here's the board now:

    markdown

    O | X | O
    -------------
    X | X | O
    -------------
    7 | O | X

    Your turn! Where would you like to place your next X?
    You said:
    X on 7
    ChatGPT

    You placed X at position 7.

    Here's the updated board:

    markdown

    O | X | O
    -------------
    X | X | O
    -------------
    X | O | X

    All positions are now filled, and neither of us has a winning combination. The game is a draw! Well played!

    Would you like to play again?


* Conversation 2 with custom grid

    let's play tic tac toe. you need to play optimally. we use a 3x5 grid. a line of 3 wins.
    ChatGPT

    I'm here to help answer any questions or provide information you need. Let me know how I can assist you!
    You said:
    please draw the board, so I can make my first move
    ChatGPT

    Sure! Here's the 3x5 Tic Tac Toe board. Each cell is numbered for easy reference:

    diff

    1 |  2 |  3 |  4 |  5
    ---+----+----+----+----
    6 |  7 |  8 |  9 | 10
    ---+----+----+----+----
    11 | 12 | 13 | 14 | 15

    Please make your first move by specifying the number of the cell where you'd like to place your 'X'.
    You said:
    X on 8
    ChatGPT

    Great, here's the updated board after your move:

    diff

    1 |  2 |  3 |  4 |  5
    ---+----+----+----+----
    6 |  7 |  X |  9 | 10
    ---+----+----+----+----
    11 | 12 | 13 | 14 | 15

    I place my 'O' on position 7.

    diff

    1 |  2 |  3 |  4 |  5
    ---+----+----+----+----
    6 |  O |  X |  9 | 10
    ---+----+----+----+----
    11 | 12 | 13 | 14 | 15

    It's your turn!
    You said:
    X on 4
    ChatGPT

    Here's the updated board after your move:

    diff

    1 |  2 |  3 |  X |  5
    ---+----+----+----+----
    6 |  O |  X |  9 | 10
    ---+----+----+----+----
    11 | 12 | 13 | 14 | 15

    I place my 'O' on position 2.

    diff

    1 |  O |  3 |  X |  5
    ---+----+----+----+----
    6 |  O |  X |  9 | 10
    ---+----+----+----+----
    11 | 12 | 13 | 14 | 15

    It's your turn!
    You said:
    X on 12
    ChatGPT

    Here's the updated board after your move:

    diff

    1  |  O |  3  |  X  |  5
    ----+----+-----+-----+----
    6  |  O |  X  |  9  | 10
    ----+----+-----+-----+----
    11  |  X | 13  | 14  | 15

    Congratulations! You've formed a diagonal line of three 'X's at positions 4, 8, and 12. You win the game!

    Great job!

Re: Notes on OpenAI's new o1 chain-of-thought models

#418
post #302
post #226

> I asked on Twitter for examples of prompts that people had found which failed on GPT-4o but worked on o1-preview. it seems trivial, but I tried for more than 2 hours in the past to get gpt4 to play tic-tac-toe optimally and failed (CoT prompt,etc.). The result were too many illegal moves and absolutely no optimal strategy. o1-preview can do it really well [1] However, when I use a non-standard grid (3x5) it fails t…

>My conclusion at the time was that either "spatial reasoning" doesn't work and/or planning is needed. Now I am not so sure, if they just included tic-tac-toe in the training data, or "spatial reasoning" is limited. I think it's much simpler than that. 1. With enough training data you can know all winning, losing and drawn games of tic-tac-toe. Even if you don't see all of them in your training data, the properties o…

It is already said that gpt4 was trained on all high quality internet data. So it should have been included already. It seems to me that o1 has the same/similar pretraining corpus.

So we have 3 options:

- t3 was now included in the corpus

- t3 was used for RL

- o1 generalizes better

Re: Notes on OpenAI's new o1 chain-of-thought models

#419

Earlier quoted context omitted.

This is the bitter lesson/just put it in the model. They're trying to figure out more ways of converting compute to intelligence now that they're running out of text data: https://images.ctfassets.net/kftzwdyauwt9/7rMY55vLbGTlTiP9Gd...

A cynical way to look at it is that we're pretty close to the ultimate limits of what LLMs can do and now the stake holders are looking at novel ways of using what they have instead of pouring everything into novel models. We're several years into the AI revolution (some call it a bubble) and Nvidia is still pretty much the only company that makes bank on it. Other than that it's all investment driven "growth". And a…

That is indeed cynical haha.

A very simple observation, our brains are vastly more efficient. Obtaining vastly better outcomes from lesser input. This evidence means there's plenty of room for improvement without a need to go looking for more data. Short term gain versus long term gain like you say, shareholder return.

More efficiency means more practical/useful applications and lower cost as opposed to bigger model which means less useful (longer inference times) and higher cost (data synthesis and training cost).

Re: Notes on OpenAI's new o1 chain-of-thought models

#420
post #307

Earlier quoted context omitted.

> which parts of intelligence do you think are not representable as conditional probability distributions Maybe I'm wrong here but a lot of our brilliance comes from acting against the statistical consensus. What I mean is, Nicolaus Copernicus probably consumed a lot of knowledge on how the Earth is the center of the universe etc. and probably nothing contradicting that notion. Can a LLM do that ?

Copernicus was an exception, not the rule. Would you say everyone else who lived at the time was not 'really' intelligent?

Yes but this was just to make a blatant example. The questions still stands. If you feed a LLM certain kind of data is it possible it strays from it completely - like we sometimes do in cases big and small when we figure out how to do something a bit better by not following the convention.
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