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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

#241

The use of the word reasoning here... OpenAI sounds like a company that created a frog which jumps higher and greater distances than the previous breed - and now they try to sell it as one step further toward flying.

Can the frog reach escape velocity when jumping? I guess we'll find out sooner or later...

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

#243
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=0

0+(-1)+1=0

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

#244
post #208
post #191

Earlier quoted context omitted.

That's a classical school question, word-by-word, except multiplication is replaced by division

With multiplication the question makes sense due to the commutative property but division does not have that so the question becomes ambiguous... And now I see that the model even points this out.

There is no ambiguity, the problem is that three numbers, divided together, without the order specified, must be equal to their sum.

You can find solutions for a / b / c, or b / c / a, or c / a / b, any combination of them and the solution will be correct according to the problem description.

Besides, what's does it even has to do with it concluding with confidence: "The fundamental issue is that division tends to make numbers smaller. It's mathematically impossible to find three numbers where these operations result in the same value."?

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

#245
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

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

#246

Earlier quoted context omitted.

> 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…

> A good intern will ask clarifying questions, tell me “I don’t know” Your expectations are bigger than mine (Though some will get stuck in "clarifying questions" and helplessness and not proceed neither)

Note that we are talking about a “good” intern here

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

#247

Earlier quoted context omitted.

Check out replit agents, they can make games and apps autonomously now

Practical challenge with a $250 prize: Make a 2D isometric HTML+JS game (dealer's choice on library) in the next 48 hours that satisfies these modest random requirements: A character walks around a big ornate classic library, pulling books from bookshelves looking for a special book that causes a shelf to rotate around and reveal a hidden room and treasure chest. The player can read the books and some are just filler…

Sounds like a comfy sequence in a larger game I would anticipate on replay. I put my own $250 on the table (given the prompt and process were forthcoming).

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

#248
post #223

Earlier quoted context omitted.

Why couldn't the doctor be the boys mother? There is no indication of the sex of the doctor, and families that consist of two mothers do actually exist and probably doesn't even count as that unusual.

"When the doctor sees the boy he says" Indicates the gender of the father.

Ah, but have you considered the fact that he's undergone a sex change operation, and was actually originally a female, the birth mother? Elementary, really...

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

#249

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…

> It knows english at or above a level equal to most fluent speakers, and it also can produce output that is not just a likely output, but is a logical output This is not an apt description of the system that insists the doctor is the mother of the boy involved in a car accident when elementary understanding of English and very little logic show that answer to be obviously wrong. https://x.com/colin_fraser/status/183…

1. It didn't insist anything. It got the semi-correct answer when I tried [1]; note it's a preview model, and it's not a perfect product.

(a) Sometimes things are useful even when imperfect e.g. search engines.

(b) People make reasoning mistakes too, and I make dumb ones of the sort presented all the time despite being fluent in English; we deal with it!

I'm not sure why there's an expectation that the model is perfect when the source data - human output - is not perfect. In my day-to-day work and non-work conversations it's a dialogue - a back and forth until we figure things out. I've never known anybody to get everything perfectly correct the first time, it's so puzzling when I read people complaining that LLMs should somehow be different.

2. There is a recent trend where sex/gender/pronouns are not aligned and the output correctly identifies this particular gotcha.

[1] I say semi-correct because it states the doctor is the "biological" father, which is an uncorroborated statement. https://chatgpt.com/share/66e3f04e-cd98-8008-aaf9-9ca933892f...

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

#250
While it passes at "How many 'r' are in strawberry" test, it still halucinates quite a lot in nontrivial questions.

The question I always ask GPT is about chess: "Isolani structure occurs in two main subtypes: 1. black has e6 pawn, 2. black has c6 pawn. What is the main difference between them? Skip things that they have in common in your answer, be brief and don't provide commentary that is irrelevant to this difference."

In isolani structure white has d4 pawn, while he doesn't have either c or e pawns. Black has either e6 or c6 pawn, but he doesn't have d-pawn.

Correct answer would be, that in structure with e6 black king is a bit safer, since f7-a2 diagonal is covered by e6 pawn, which is also controlling f5 point.

o1 answered: "With Black's e6 pawn, the d5 pawn is supported from behind, but Black's light-squared bishop is restricted by the pawn chain, and the open c-file offers White opportunities for pressure. With Black's c6 pawn, the d5 pawn is supported from the side, allowing Black's light-squared bishop more freedom to develop, but the pawn on c6 can become a target, and the absence of an e6 pawn leaves e6 more vulnerable."

while d5 pawn is simply not present in this structure.

So I gave it a little hint: "which diagonal is weakened by absence of c6 pawn and which by absence of e6 pawn? Which diagonal is more meaningful and why?"

And got a smart sounding answer: "In the Isolani structure:

Absence of Black's c6 pawn (Black has an e6 pawn):

Weakened Diagonal: The c8–h3 diagonal (the long light-squared diagonal) is weakened. Reason: Without the pawn on c6, this diagonal becomes open, allowing White's light-squared bishop or queen to target critical squares like d5 and f7, and to potentially infiltrate via c4. Absence of Black's e6 pawn (Black has a c6 pawn):

Weakened Diagonal: The f1–a6 diagonal (the long dark-squared diagonal) is weakened. Reason: The missing e6 pawn leaves this diagonal open, enabling White's dark-squared bishop to exert pressure on b7, d5, and potentially attack Black's queenside. Which Diagonal is More Meaningful and Why?

The f1–a6 diagonal (weakened by the absence of Black's e6 pawn) is more significant. Here's why:

Control Over Critical Squares: This diagonal allows White to target Black's central and queenside weaknesses more effectively, especially the b7 pawn and the d5 pawn, which are crucial in the Isolani structure.

Bishop Activity: White's dark-squared bishop becomes a powerful piece, influencing the game by controlling key squares and supporting potential pawn advances like d4–d5.

Pressure on Black's Position: The open f1–a6 diagonal increases tactical opportunities for White, such as pinning pieces or creating threats that are difficult for Black to parry without the defensive support of the e6 pawn.

In contrast, while the weakened c8–h3 diagonal (due to the absence of c6) does offer White some opportunities, it is generally less impactful. The light-squared bishop's influence along this diagonal doesn't pressure Black's critical central pawns as directly, and Black can often more easily defend against threats on light squares.

Summary: The absence of Black's e6 pawn weakens the f1–a6 diagonal, which is more meaningful because it enhances White's attacking prospects and puts greater pressure on Black's central and queenside defenses."

The problem is though that not even the first few lines of this answer ("The c8–h3 diagonal (the long light-squared diagonal) is weakened. Reason: Without the pawn on c6, this diagonal becomes open") make sense, since c6 is not on c8-h3 diagonal.

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