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Learning to Reason with LLMs

openai.com

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Re: Learning to Reason with LLMs

#671

Post by tagawa »

forgotthepasswd wrote: post #655

I had trouble in the past to make any model give me accurate unix epochs for specific dates. I just went to GPT-4o (via DDG) and asked three questions: 1. Please give me the unix epoch for September 1, 2020 at 1:00 GMT. > 1598913600 2. Please give me the unix epoch for September 1, 2020 at 1:00 GMT. Before reaching the conclusion of the answer, please output the entire chain of thought, your reasoning, and the maths…

Quick link for checking the result: https://duckduckgo.com/?q=timestamp+1598922000&ia=answer

Re: Learning to Reason with LLMs

#672

Post by [deleted] »

spaceman_2020 wrote: post #602
lumost wrote: post #526

Earlier quoted context omitted.

LLMs perform well on small tasks that are well defined. This definition matches almost every task that a student will work on in school leading to an overestimation of LLM capabiity. LLMs cannot decide what to work on, or manage large bodies of work/code easily. They do not understand the risk of making a change and deploying it to production, or play nicely in autonomous settings. There is going to be a massive amou…

Careers are 30 years long Can you confidently say that an LLM won’t be better than an average 22 year old coder within these 30 years?

[deleted]

Re: Learning to Reason with LLMs

#673

Post by staticman2 »

zozbot234 wrote: post #49
TheAceOfHearts wrote: post #26

Kinda disappointed that they're hiding the thought process. Hopefully the open source community will figure out how to effectively match and replicate what OpenAI is doing. I wonder how far we are from having a model that can correctly solve a word soup search problem directly from just a prompt and input image. It seems like the crossword example is close. For a word search it would require turning the image into an…

> Hopefully the open source community will figure out how to effectively match and replicate what OpenAI is doing. No need for that, there is a Reflection 70B model that does the exact same thing - with chains of thought being separated from the "final answer" via custom 'tag' tokens.

That reflection model is in no way comparable to whatever OpenAI is doing.

Re: Learning to Reason with LLMs

#674

Post by airstrike »

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…

I think it's clear their strategy has changed. The whole landscape has changed. The size of models, amount of dollars, numbers of competitors and how much compute this whole exercise takes in the long term have all changed, so it's fair for them to adapt.

It just so happens that they're keeping their old name.

I think people focus too much on the "open" part of the name. I read "OpenAI" sort of like I read "Blackberry" or "Apple". I don't really think of fruits, I think of companies and their products.

Re: Learning to Reason with LLMs

#675

Post by threatofrain »

dvt wrote: post #639
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…

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 models.

Re: Learning to Reason with LLMs

#676

Post by phillipcarter »

dvt wrote: post #639
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…

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.

Re: Learning to Reason with LLMs

#677

Post by sashank_1509 »

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…

I have access to this and there is no way I spend more than 50$ on OpenAI api. I have ChatGPT + since day q though (240$ probably in total)

Re: Learning to Reason with LLMs

#678

Post by Buttons840 »

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…

I am a Plus user and pay $20 per month. I have access to the o1 models.

Re: Learning to Reason with LLMs

#679

Post by liamwire »

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…

Unless this is specifically relating to API access, I don’t think it’s correct. I’ve been paying for ChatGPT via the App Store IAP for around a year or less, and I’ve already got both o1-preview and o1-mini available in-app.

Re: Learning to Reason with LLMs

#680

Post by MVissers »

dragonwriter wrote: post #200
msp26 wrote: post #127

Earlier quoted context omitted.

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.

I’d say depends. If the model iterates 100x I’d just say give me the output.

Same with problem solving in my brain: Sure, sometimes it helps to think out loud. But taking a break and let my unconcious do the work is helpful as well. For complex problems that’s actually nice.

I think eventually we don’t care as long as it works or we can easily debug it.

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