Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

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

github.com

Post reply on HN
Print view View original

141–150 of 158 posts

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

#141

Post by ttul »

arresin wrote: post #41
ttul wrote: post #25

Earlier quoted context omitted.

Pasting from my Perplexity page on the topic: The core innovation [1] of o1 lies in its ability to generate and refine internal chains of thought before producing a final output [2]. Unlike traditional LLMs that primarily focus on next-token prediction, o1 learns to: 1. Recognize and correct mistakes 2. Break down complex steps into simpler ones 3. Try alternative approaches when initial strategies fail This process…

That answers nothing the commenter asked.

Thanks for the critique. Here is how I would answer their question myself:

o1 is far more than just CoT mechanics. It relies on a specialized model or collection of models that offer new capabilities to make CoT work far better than it works with a stock LLM.

For instance, o1 can recognize and correct its own mistakes and it seems to know how to dig deeper when needed. That's not something that stock LLMs do very well.

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

#142

Post by [deleted] »

ttul wrote: post #141
arresin wrote: post #41

Earlier quoted context omitted.

That answers nothing the commenter asked.

Thanks for the critique. Here is how I would answer their question myself: o1 is far more than just CoT mechanics. It relies on a specialized model or collection of models that offer new capabilities to make CoT work far better than it works with a stock LLM. For instance, o1 can recognize and correct its own mistakes and it seems to know how to dig deeper when needed. That's not something that stock LLMs do very wel…

[deleted]

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

#143

Post by stevenhuang »

Hugsun wrote: post #115
Meganet wrote: post #96

Earlier quoted context omitted.

You actually don't know that. A LLM has a huge amount of data ingested. It can create character profiles, audience, personas etc. Why wouldn't it have potentially even learned to 'understand' what 'being aware of your limitations' means? Right now for me 'change of reasoning' feels a little bit of quering the existing meta space through the reasoning process to adjust weights. Basically priming the model. I would als…

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 of the question LLMs won't eventually be able to more reliably understand its limitations.

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

#144

Post by imtringued »

zby wrote: post #116

I am always looking for definitions of "reasoning". My theory is that if we find a good definition - then it will turn out that we can build systems that would combine fuzzy llm thinking with classical algorithms to solve "reasoning". All the problems with llm not reasoning (like planning, counting letters or deductive inference) are easy for classical algos. There needs to be a way to split the thinking process into…

Solving a decidable problem is a large subset of reasoning tasks. Counting is also a critical reasoning task, since it requires you to both understand natural numbers and the concept of distinct instances of objects belonging to a general category.

Two centuries ago there were no computers, everything had to be done by humans. Get to that level first before you whip out code.

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

#145

Post by whimsicalism »

quantadev wrote: post #139
whimsicalism wrote: post #138

Earlier quoted context omitted.

can you link to one speculating about multiple inferences for their CoT? i am curious e: answer to my own question https://x.com/_xjdr/status/1835352391648158189

So far it's been unanimous. Everyone I've heard talk about it believes Strawberry is mainly just CoT. I'm not saying they didn't fine tune a model too, I'm just saying I agree with most people that clever CoT is where most of the leap in capability seems to have come from.

> believes Strawberry is mainly just CoT. I'm not saying they didn't fine tune a model too

You don't see the scaling with respect to token length with non-FT'd CoT like this, in my opinion.

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

#146

Post by quantadev »

whimsicalism wrote: post #145
quantadev wrote: post #139

Earlier quoted context omitted.

So far it's been unanimous. Everyone I've heard talk about it believes Strawberry is mainly just CoT. I'm not saying they didn't fine tune a model too, I'm just saying I agree with most people that clever CoT is where most of the leap in capability seems to have come from.

> believes Strawberry is mainly just CoT. I'm not saying they didn't fine tune a model too You don't see the scaling with respect to token length with non-FT'd CoT like this, in my opinion.

I haven't even added Strawberry support to my app yet, and so haven't checked what it's context length is, but you're right that additional context length is a scaling factor that's totally independent of whether CoT is used or not.

I'm just saying whatever they did in their [new] model, I think they also added CoT on top of it, as the outer layer of the onion so to speak.

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

#147

Post by janalsncm »

whimsicalism wrote: post #137
ricardobeat wrote: post #127

Earlier quoted context omitted.

Care to elaborate? Your comment would be a lot more useful if it included a little why . Otherwise it’s just teasing readers and at the same time smearing the author without anything to back it up.

reinforcement learning with ppo doesn’t involve mcts and has been the bread and butter of aligning LLMs since 2020. nothing about saying they use rl implies mcts

> nothing about saying they use rl implies they use mcts

We can say the same thing about RL implying PPO, however there’s pretty big hints, namely Noam Brown being involved. Many of the things Noam Brown has worked on involve RL in tree search contexts.

He has also been consistently advocating the use of additional test-time compute to solve search problems. This is also consistent with the messaging regarding the reasoning tokens. There is likely some learned tree search algorithm, such as a learned policy/value function as in AlphaGo.

It’s all speculation until we have an actual paper. So we can’t categorically say MCTS/learned tree search isn’t involved.

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

#148

Post by wkat4242 »

NavinF wrote: post #126
wkat4242 wrote: post #78

Earlier quoted context omitted.

I just don't think Apple would release something like this. They're the company that laser engraves their screws because of their attention to detail.

Which apple screws are laser engraved?

The ones on the MacBook Pro used to be. At least were when I still used Apple until 2015 or so.

The butterfly keyboards were unusable to me and also the OS got too locked down so I left the platform.

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

#149

Post by cubefox »

michaelt wrote: post #135
zozbot234 wrote: post #113

Earlier quoted context omitted.

Didn't Google have the LaMDA model pretty early, which was even described as "sentient" at some point? That doesn't look "fumbled" to me.

What Google did was sit on their ass, not deigning to release anything. In the meantime, OpenAI became a $150 billion company. And Anthropic came out with Claude, and Facebook with Llama, and Mistral with their models. Only then did Google realise there might be something to this LLM stuff - so they responded with Bard, a product so poorly received they later had to completely rebrand it. Looks like they didn't have…

I have the impression they regarded generative AI as too dangerous. Before the success of ChatGPT, they never considered making PaLM or LaMDA or Chinchilla or Imagen publicly available until they saw themselves in a competitive disadvantage.

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

#150

Post by hjaveed »

thorum wrote: post #32

o1’s innovation is not Chain-of-Thought. It’s teaching the model to do CoT well (from massive amounts of human feedback) instead of just pretending to. You’ll never get o1 performance just from prompt engineering.

can you share any resource that mentions about teaching the model to do COT.. their release blog does not document much
Post reply on HN