The chain of thought would be the final path through the tree. Interactively showing the thought tokens would give the game away, which is why they don’t show that.

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).

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.

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.

https://x.com/polynoamial/status/1834641202215297487

I've gotten mini to think harder by asking it to, but it didn't make a better answer. Though now I've run out of usage limits for both of them so can't try any more…

nobody except for recent deepmind research has shown test time scaling like o1

https://arxiv.org/pdf/2403.09629

> In the Self-Taught Reasoner (STaR, Zelikman et al. 2022), useful thinking is learned by inferring rationales from few-shot examples in question-answering and learning from those that lead to a correct answer. This is a highly constrained setting – ideally, a language model could instead learn to infer unstated rationales in arbitrary text. We present Quiet-STaR, a generalization of STaR in which LMs learn to generate rationales at each token to explain future text, improving their predictions.

>[...]

>We generate thoughts, in parallel, following all tokens in the text (think). The model produces a mixture of its next-token predictions with and without a thought (talk). We apply REINFORCE, as in STaR, to increase the likelihood of thoughts that help the model predict future text while discarding thoughts that make the future text less likely (learn).

e: answer to my own question https://x.com/_xjdr/status/1835352391648158189

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

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

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 limitations (any more than humans do). I expect this will create false refusals, as the model becomes overcautious.

Can it not be trained to do so? From my anecdotal observations, the knowledge cutoff is one thing that LLMs are really well trained to know about. Those are limitations that LLMs are currently well trained to handle. Why can it not be trained to know that it is quite frequently bad at math, it may produce sometimes inaccurate code etc.

For humans also, some people know some things are just not their cup of tea. Sure there are times people may have half baked knowledge about things but one can tell if they are good at XYZ things, and not so much at other things.

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 also not just call it a 'trick'. This looks simple, weird or whatnot but i do believe that this is part of AI thinking process research.

Its a good question though what did they train? New Architecture? More parameters? Is this training a mix of experiments they did? Some auto optimization mechanism?

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 proofs when we develop. An AI could actually doing this. But at the end its more of a question who does it better, faster and cheaper eh?

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.

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.

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.

alignment is a tough problem and aligning long reasoning sequences to correct answer is also a tough problem. collecting high quality CoT from experts is another tough problem. they started this project in october, more than plausible it could take this time

Or it's a unique advantage because this stuff doesn't happen without good researches who may want:

1) Their name in scientific papers

2) They might actually care about the openess of AI

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 to openness.

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 it because they didn't realise what they had.

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 a "sentient" model up their sleeve after all. Then the updated, rebranded model had a bunch of image generation embarrassments of its own.

Admittedly, they have recovered somewhat since then; they're second on some performance leaderboards, which is respectable.

But there was a real tortoise-and-hare situation where they thought they were so far ahead they had time for a nap, until they got overtaken. Any lead they had from inventing transformers and being the only people with TPUs has been squandered.