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Learning to Reason with LLMs

openai.com

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Re: Learning to Reason with LLMs

#741

Post by nimchimpsky »

ponector wrote: post #499
al_borland wrote: post #473

Earlier quoted context omitted.

The calculator didn’t eliminate math majors. Excel and accounting software didn’t eliminate accountants and CPAs. These are all just tools. I spend very little of my overall time at work actually coding. It’s a nice treat when I get a day where that’s all I do. From my limited work with Copilot so far, the user still needs to know what they’re doing. I have 0 faith a product owner, without a coding background, can us…

But excel eliminated need in multiple accountants. One accountant with excel replaced ten with paper. Chatgpt already eliminated many entry-level jobs like writer or illustrator. Instead of hiring multiple teams of developers, there will be one team with few seniors and multiple AI coding tools. Guess how depressing to the IT salaries it will be?

[dead]

Re: Learning to Reason with LLMs

#742

Post by fragmede »

evilfred wrote: post #695

it still fails at logic puzzles https://x.com/colin_fraser/status/1834334418007457897

Weird, it works to say the father when I try it:

https://chatgpt.com/share/66e3601f-4bec-8009-ac0c-57bfa4f059...

And also works on this variation:

https://chatgpt.com/share/66e35f0e-6c98-8009-a128-e9ac677480...

Re: Learning to Reason with LLMs

#743

Post by quantisan »

Amazing! OpenAI figured out how to scale inference. https://arxiv.org/abs/2407.21787 show how using more compute during inference can outperform much larger models in tasks like math problems

I wonder how do they decide when to stop these Chain of Thought for each query? As anyone that played with agents can attest, LLMs can talk with themselves forever.

Re: Learning to Reason with LLMs

#744

Post by KoolKat23 »

al_borland wrote: post #480
LouisSayers wrote: post #431

Earlier quoted context omitted.

Don't do it, help us keep our high salaries :D Joking aside, even with AI generating code, someone has to know how to talk to it, how to understand the output, and know what to do with it. AI is also not great for novel concepts and may not fully get what's happening when a bug occurs. Remember, it's just a tool at the end of the day.

> may not fully get what's happening when a bug occurs. And may still not understand even when you explicitly tell it. It wrote some code for me last week and made an error with an index off by 1. It had set the index to 1, then later was assuming a 0 index. I specifically told it this and it was unable to fix it. It was in debug hell, adding print statements everywhere. I eventually fixed it myself after it was clea…

Ironically, just yesterday I asked sonnet to write a script in JavaScript, it went in a bit of a perpetual loop unable to provide an error free script (the reason for the errors were not immediately obvious). I then mentioned that it needs to be zero indexed, and it immediately provided an issue free version that worked.

Re: Learning to Reason with LLMs

#745

Post by [deleted] »

stale2002 wrote: post #594
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?

Sure. Software engineers are actually the best situated to take advantage of this new technology. Your concern would be like once C got invented, why should you bother being a software engineer? Because C is so much easier to use than assembly code! The answer, of course, is that software engineering will simply happen in even more powerful and abstract layers. But, you still might need to know how those lower layers…

[deleted]

Re: Learning to Reason with LLMs

#746

Post by xcv123 »

stale2002 wrote: post #594
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?

Sure. Software engineers are actually the best situated to take advantage of this new technology. Your concern would be like once C got invented, why should you bother being a software engineer? Because C is so much easier to use than assembly code! The answer, of course, is that software engineering will simply happen in even more powerful and abstract layers. But, you still might need to know how those lower layers…

C did not write itself.

We now have a tool that writes code and solves problems autonomously. It's not comparable.

Re: Learning to Reason with LLMs

#747

Post by commodoreboxer »

Doorknob8479 wrote: post #667

Why so much hate? They're doing their best. This is the state of progress in the field so far. The best minds are racing to innovate. The benchmarks are impressive nonetheless. Give them a break. At the end of the day, they built the chatbot who's saving your ass each day ever since.

I think you're overestimating LLM usage.

Re: Learning to Reason with LLMs

#748

Post by azeirah »

smusamashah wrote: post #738
bartman wrote: post #367

This is incredible. In April I used the standard GPT-4 model via ChatGPT to help me reverse engineer the binary bluetooth protocol used by my kitchen fan to integrate it into Home Assistant. It was helpful in a rubber duck way, but could not determine the pattern used to transmit the remaining runtime of the fan in a certain mode. Initial prompt here [0] I pasted the same prompt into o1-preview and o1-mini and both c…

What if you copy the whole reasoning process example provided by OpenAI, use it as a system prompt (to teach how to reason), use that system prompt in Claude, got4o etc?

It might work a little bit. It's like doing few shot prompting instead of training it to reason.

Re: Learning to Reason with LLMs

#749

Post by unethical_ban »

threatofrain wrote: post #675
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 see why on-device inference is the future. For consumers, only a small set of use cases cannot tolerate the increased latency. Corporate customers will be satisfied if the model can be hosted within their borders. Pooling compute is less wasteful overall as a collective strategy. This argument can really only meet its tipping point when massive models no longer offer a gotta-have-it difference vs smaller mode…

On-device inference will succeed the way Linux does: It is "free" in that it only requires the user to acquire a model to run vs. paying for processing. It protects privacy, and it doesn't require internet. It may not take over for all users, but it will be around.

This assumes that openly developed (or at least weight-available) models are available for free, and continue being improved.

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