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g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

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Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#151

Post by famouswaffles »

orbital-decay wrote: post #132
whimsicalism wrote: post #124

Earlier quoted context omitted.

nobody has shown CoT scaling like this except deepmind, it is very obviously a result of their alignment pipeline not just prompting.

Scaling like what? Are there any comparisons with and without CoT, or with other models with their CoT? As far as I'm aware, their CoT part is secret. I'm sure the finetuning does some lifting, but I'm also sure the difference in a fair comparison won't be remotely as significant as it's being hyped currently. This is still clearly CoT, with all its limitations and caveats as expected. That's an improvement, sure, bu…

CoT performance of any other SOTA quickly plateaus as tokens grow. It does not have anywhere near the same graph as o1's test time compute plot.

Saying it's just CoT is kind of meaningless. Even just looking at the examples on Open AI's blog and you quickly see no other model today can generate or utilize CoT to anywhere near that quality through prompting or naive fine-tuning.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#152

Post by cubefox »

killerstorm wrote: post #82
narrator wrote: post #71

Earlier quoted context omitted.

If the training data for these LLMs is from humanity in general, and it is trying to imitate humanity, wouldn't its IQ tend to be the average of all of humanity? Perhaps the only people who talk about STEM topics are people of higher IQ generally, including a lot of poor students asking homework questions. Thus, the way to get to higher IQ output is to critique the lower IQ answers, which may be more numerous by reje…

No. Does Midjourney output look like an average human drawing? Obviously, OpenAI knows how to train a classifier...

> Does Midjourney output look like an average human drawing?

No, perhaps because it's heavily trained on photos.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#153

Post by pseudotensor »

This is related: https://www.reddit.com/r/LocalLLaMA/comments/1fiw84a/open_st...

The idea is not silly in my view, I did something similar here: https://github.com/pseudotensor/open-strawberry

The idea is that data generation is required first, to make the reasoning traces. ToT etc. are not required.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#154

Post by astrange »

anonzzzies wrote: post #76
astrange wrote: post #67

Earlier quoted context omitted.

That second prompt is interesting. Not magic though. I tried it with every other model I know and they're still basically unable to do: * give me three sentences that end in "is" * tell me the line of Star Spangled Banner that comes before "gave proof through the night" But they did some good thinking before failing at it…

> Not magic though It's just a pile on of trial and error instructions (maybe learned from previous 'projects', but). There is no magic or skill to prompt 'engineering' anywhere.

Skill is just learning from trial and error.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#155

Post by orbital-decay »

famouswaffles wrote: post #151
orbital-decay wrote: post #132

Earlier quoted context omitted.

Scaling like what? Are there any comparisons with and without CoT, or with other models with their CoT? As far as I'm aware, their CoT part is secret. I'm sure the finetuning does some lifting, but I'm also sure the difference in a fair comparison won't be remotely as significant as it's being hyped currently. This is still clearly CoT, with all its limitations and caveats as expected. That's an improvement, sure, bu…

CoT performance of any other SOTA quickly plateaus as tokens grow. It does not have anywhere near the same graph as o1's test time compute plot. Saying it's just CoT is kind of meaningless. Even just looking at the examples on Open AI's blog and you quickly see no other model today can generate or utilize CoT to anywhere near that quality through prompting or naive fine-tuning.

I don't know, I've played with o1 and it seems obvious that it has the same issues as any other CoT - it has to be custom tailored to the task to work effectively, which quickly turns into whack-a-mole (even CoT generators like Self-Discover still have the same limitation).

Starting from the strawberry example: it counts 3 "r"s in "strawbery", because the training makes it ignore grammatical errors if they're not relevant to the conversation (which makes sense in an instruction-tuned model) and their CoT doesn't catch it because it's not specialized enough. Will this scale with more compute thrown at it? I'm not sure I believe their scaling numbers. The proof should be in the pudding.

I've also had mixed results with coding in Python, it's barely better than Sonnet in my experience, but wastes a lot more tokens, which are a lot more expensive.

They might have improved things and made a SotA CoT that works in tandem with their training method, but that is definitely not what they were originally hyping (some architecture-level mechanism, or at least something qualitatively different). It also pretty obviously still has limited compute time per token and has to fit into the context (which is also still suffering from the lost-in-the-middle issue by the way). This puts the hard limit on the expressivity and task complexity.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#156

Post by Hugsun »

Meganet wrote: post #140
Hugsun wrote: post #115

Earlier quoted context omitted.

It might understand the concept of it having limitations, but it can't AFAIK reliably recognize when it does or doesn't know something, or has encountered a limitation.

Its the same thing as with humans, thats right. It doesn't do Logical reasoning but even the best humans stop at some level. But if you read all the knowledge of humans, were does your reasoning start? Probably at a very high level of it. If you look at human brains, we conduct experiments right? As a software developer, we write tests. ChatGPT can already run python code and it can write unit tests. We do not use pr…

There is an important difference between humans and LLMs in this context.

Humans do in most cases have some knowledge about why they know the things they know. They can recall the topics they learned at school, and can deduce that they probably heard a given story from a friend who likes to discuss similar topics, etc.

LLMs have no access to the information they were trained on. They could know that everything they know was learned during the training, but they have no way of determining what they learned about and what they didn't.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#157

Post by Hugsun »

stevenhuang wrote: post #143
Hugsun wrote: post #115

Earlier quoted context omitted.

It might understand the concept of it having limitations, but it can't AFAIK reliably recognize when it does or doesn't know something, or has encountered a limitation.

If you think about it, those criticisms extend to human thinking too. We aren't infallible in all situations either. It's only when we can interact with the environment to test our hypothesis that we then refine what we know and update our priors appropriately. If we let LLMs do that as well, by allowing it to run code and interact with documentation/the internet and double-check things its not sure of, it's not out…

As they are currently constructed, I would say that it is out of the question.

Humans usually know (at least roughly) the source of anything they know, as there will be a memory or a known event associated with that knowledge.

LLMs have no analogous way to determine the source of their knowledge. They might know that all their knowledge comes from their training, but it has no way of knowing what was included in the training and what wasn't.

This could maybe be achieved with some more fancy RAG systems, or online training abilities. I think an essential piece is the ability to know the source of information. When LLMs reliably do, and apply that knowledge, they'll be much more useful. Hopefully somebody can achieve this.

Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains

#158

Post by TrapLord_Rhodo »

COAGULOPATH wrote: post #54
segmondy wrote: post #30

This is not even remotely close and very silly. A ChainOfThought in a loop. TreeOfThoughts is a more sophisticated method, see - https://arxiv.org/pdf/2305.10601 The clue we all had with OpenAI for a long time that this was a search through a tree, they hired Noam Brown, and his past work all hinted towards that. Q , is obviously a search on a tree like A . So take something like CoT, build out a tree, search for the…

Came here hoping to find this. You will not unlock "o1-like" reasoning by making a model think step by step. This is an old trick that people were using on GPT3 in 2020. If it were that simple, it wouldn't have taken OpenAI so long to release it. Additionally, some of the prompt seems counterproductive: >Be aware of your limitations as an llm and what you can and cannot do. The LLM doesn't have a good idea of its lim…

overcautious when trimming branches on the tree seems like a feature, not a bug.
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