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Learning to Reason with LLMs

openai.com

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Re: Learning to Reason with LLMs

#681

Post by resters »

I tested o1-preview on some coding stuff I've been using gpt-4o for. I am not impressed. The new, more intentional chain of thought logic is apparently not something it can meaningfully apply to a non-trivial codebase.

Sadly I think this OpenAI announcement is hot air. I am now (unfortunately) much less enthusiastic about upcoming OpenAI announcements. This is the first one that has been extremely underwhelming (though the big announcement about structured responses (months after it had already been supported nearly identically via JSONSchema) was in hindsight also hot air.

I think OpenAI is making the same mistake Google made with the search interface. Rather than considering it a command line to be mastered, Google optimized to generate better results for someone who had no mastery of how to type a search phrase.

Similarly, OpenAI is optimizing for someone who doesn't know how to interact with a context-limited LLM. Sure it helps the low end, but based on my initial testing this is not going to be helpful to anyone who had already come to understand how to create good prompts.

What is needed is the ability for the LLM to create a useful, ongoing meta-context for the conversation so that it doesn't make stupid mistakes and omissions. I was really hoping OpenAI would have something like this ready for use.

Re: Learning to Reason with LLMs

#682

Post by astrange »

adamrezich wrote: post #527
ComputerGuru wrote: post #347

The "safety" example in the "chain-of-thought" widget/preview in the middle of the article is absolutely ridiculous. Take a step back and look at what OpenAI is saying here "an LLM giving detailed instructions on the synthesis of strychnine is unacceptable, here is what was previously generated vs our preferred, neutered content " What's this obsession with "safety" when it comes to LLMs? "This knowledge is perfectly…

It doesn't matter how many people regularly die in automobile accidents each year—a single wrongful death caused by a self-driving car is disastrous for the company that makes it. This does not make the state of things any less ridiculous, however.

The one caused by Uber required three different safety systems to fail (the AI system, the safety driver, and the base car's radar), and it looked bad for them because the radar had been explicitly disabled and the driver wasn't paying attention or being tracked.

I think the real issue was that Uber's self driving was not a good business for them and was just to impress investors, so they wanted to get rid of it anyway.

(Also, the real problem is that American roads are designed for speed, which means they're designed to kill people.)

Re: Learning to Reason with LLMs

#683

Post by dvt »

phillipcarter wrote: post #676
dvt wrote: post #639

Earlier quoted context omitted.

It's clear to me that OpenAI is quickly realizing they have no moat. Even this obfuscation of the chain-of-thought isn't really a moat. On top of CoT being pretty easy to implement and tweak, there's a serious push to on-device inference (which imo is the future), so the question is: will GPT-5 and beyond be really that much better than what we can run locally?

I don't understand the idea that they have no moat. Their moat is not technological. It's sociological. Most AI through APIs uses their models. Most consumer use of AI involves their models, or ChatGPT directly. They're clearly not in the "train your own model on your data in your environment" game, as that's a market for someone else. But make no mistake, they have a moat and it is strong.

> But make no mistake, they have a moat and it is strong.

Given that Mistral, Llama, Claude, and even Gemini are competitive with (if not better than) OpenAI's flagships, I don't really think this is true.

Re: Learning to Reason with LLMs

#684

Post by beambot »

kmeisthax wrote: post #622

>We believe that a hidden chain of thought presents a unique opportunity for monitoring models. Assuming it is faithful and legible, the hidden chain of thought allows us to "read the mind" of the model and understand its thought process. For example, in the future we may wish to monitor the chain of thought for signs of manipulating the user. However, for this to work the model must have freedom to express its thoug…

Very anti-open and getting less and less with each release. Rooting for Meta in this regard, at least.

Re: Learning to Reason with LLMs

#685

Post by [deleted] »

OkGoDoIt wrote: post #522

Some practical notes from digging around in their documentation: In order to get access to this, you need to be on their tier 5 level, which requires $1,000 total paid and 30+ days since first successful payment. Pricing is $15.00 / 1M input tokens and $60.00 / 1M output tokens. Context window is 128k token, max output is 32,768 tokens. There is also a mini version with double the maximum output tokens (65,536 tokens…

[deleted]

Re: Learning to Reason with LLMs

#686

Post by sturza »

It seems like it's just a lot of prompting the same old models in the background, no "reasoning" there. My age old test is "draw a hand in ascii" - i've had no success with any model yet.

Re: Learning to Reason with LLMs

#687

Post by shawndrost »

w4 wrote: post #439
ComputerGuru wrote: post #347

The "safety" example in the "chain-of-thought" widget/preview in the middle of the article is absolutely ridiculous. Take a step back and look at what OpenAI is saying here "an LLM giving detailed instructions on the synthesis of strychnine is unacceptable, here is what was previously generated vs our preferred, neutered content " What's this obsession with "safety" when it comes to LLMs? "This knowledge is perfectly…

There are two basic versions of “safety” which are related, but distinct: One version of “safety” is a pernicious censorship impulse shared by many modern intellectuals, some of whom are in tech. They believe that they alone are capable of safely engaging with the world of ideas to determine what is true, and thus feel strongly that information and speech ought to be censored to prevent the rabble from engaging in wr…

Imagine I am a PM for an AI product. I saw Tay get yanked in 24 hours because of a PR shitstorm. If I cause a PR shitstorm it means I am bad at my job, so I take steps to prevent this.

Are my choices bad? Should I resist them?

Re: Learning to Reason with LLMs

#688

Post by egorfine »

ComputerGuru wrote: post #347

The "safety" example in the "chain-of-thought" widget/preview in the middle of the article is absolutely ridiculous. Take a step back and look at what OpenAI is saying here "an LLM giving detailed instructions on the synthesis of strychnine is unacceptable, here is what was previously generated vs our preferred, neutered content " What's this obsession with "safety" when it comes to LLMs? "This knowledge is perfectly…

Interestingly I was able to successfully receive detailed information about intrinsic details of nuclear weapons design. Previous models absolutely refused to provide this very public information, but o1-preview did.

Re: Learning to Reason with LLMs

#689

Post by CuriouslyC »

cs391231 wrote: post #408

Student here. Can someone give me one reason why I should continue in software engineering that isn't denial and hopium?

It still has issues with crossing service boundaries, working in systems, stuff like that. That stuff will get better but the amount of context you need to load to get good results with a decently sized system will still be prohibitive. The software engineer skillset is being devalued but architecture and systems thinking is still going to be valuable for quite some time.

Re: Learning to Reason with LLMs

#690

Post by p1necone »

cs391231 wrote: post #408

Student here. Can someone give me one reason why I should continue in software engineering that isn't denial and hopium?

For basically all the existing data we have, efficiency improvements always result in more work, not less.

Humans never say "oh neat I can do thing with 10% of the effort now, guess I'll go watch tv for the rest of the week", they say "oh neat I can do thing with 10% of the effort now, I'm going to hire twice as many people and produce like 20x as much as I was before because there's so much less risk to scaling now."

I think there's enough unmet demand for software that efficiency increases from automation are going to be eaten up for a long time to come.

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