You can reproduce both of those responses zero shot on 70B with "Let's verify step by step" appended at the end.
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
#112How does this benchmark against Reflection, which was fine-tuned to do the same thing-- provide a detailed Chain of Thought with self-corrections, then write out a final answer?
You still believe it was real? They had a model then they said it couldn’t reproduce those results lmao
They seem to have a fine-tune of Llama 3 70B that's available for download, so obviously "real" in that sense. That ought to be better behaved than a pure system prompt approach.
Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
#113Earlier quoted context omitted.
They feel behind because they didn't have the smart guy with a new idea a few years back, and HE decided to work at a place which started as open. Playing catch up and trying to attract talent from the hot-new-thing OpenAI requires incentives beyond lots of money. I contend actually being open helps. I'm sure that's one reason Facebook has an open source model, scientists can care about ethics and could be attracted…
> They feel behind because they didn't have the smart guy with a new idea a few years back, and HE decided to work at a place which started as open. The "Attention Is All You Need" guys all worked at Google. Google is where they are despite having the smart guys with a new idea a few years back. Of course, IMHO it wouldn't have have helped Google if they'd kept the transformer architecture secret. They'd have fumbled…
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.
Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
#114Earlier quoted context omitted.
I’ve had pretty good experience with it personally. It quite often just tells me it doesn’t know or isn’t sure instead of just making something up.
Playing around with local models, Gemma for example will usually comply when I tell it "Say you don't know if you don't know the answer". Others, like Phi-3, completely ignores that instruction and confabulates away.
Stop trying to make f̶e̶t̶c̶h̶ confabulate happen, it's not going to happen.
Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
#115Earlier quoted context omitted.
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…
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.
Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
#116I 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 two parts and then execute each part on the appropriate model.
Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
#117> This alone, without any training, is sufficient to achieve ~70% accuracy on the Strawberry problem (n=10, "How many Rs are in strawberry?"). Without prompting, Llama-3.1-70b had 0% accuracy and ChatGPT-4o had 30% accuracy.
I think this class of problem might be better solved by allowing the LLM to 'zoom in' and view the input differently. Rather like you might peer closer for more detail if someone asked you about the print quality of something you were reading.
'zoom in' could input the same text letter by letter, or even in image form (rasterize the text) to help answer questions like "How many letters in the word strawberry contain straight lines?"
Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
#118This 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…
Where in their blog post (which seemingly had complete examples of the model’s chain of thought) did they suggest they were using search or tree of thoughts?
nowhere lol
Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
#119Earlier quoted context omitted.
Where in their blog post (which seemingly had complete examples of the model’s chain of thought) did they suggest they were using search or tree of thoughts?
They mention reinforcement learning, so I guess they used some sort of Monte Carlo tree search (the same algorithm used for AlphaGo). In this case, the model would explore several chain of thoughts during training, but only output a single chain during inference (as the sibling comment suggests).
as someone who works in this field, this comment is obviously uninformed even about old public research trends
Re: g1: Using Llama-3.1 70B on Groq to create o1-like reasoning chains
#120Earlier quoted context omitted.
OAI revealed on Twitter that there is no "system" at inference time, this is just a model. Did they maybe expand to a tree during training to learn more robust reasoning? Maybe. But it still comes down to a regular transformer model at inference time.
I don't think you can claim you know what's happening internally when OpenAI processes a request. They are a competitive company and will lie for competitive reasons. Most people think Q-Star is doing multiple inferences to accomplish a single task, and that's what all the evidence suggests. Whatever Sam Altman says means absolutely nothing, but I don't think he's claimed they use only a single inference either.
what is “all the evidence”? please share