6 ms·

That's not correct. First of all, training off of data generated by another AI is generally a bad idea because you'll end up with a strictly less accurate model

by tensor 2y ago

That's not correct. First of all, training off of data generated by another AI is generally a bad idea because you'll end up with a strictly less accurate model (usually). But secondly, and more to your point, even if you were to use training data from another model, YOU STILL NEED TO DO ALL THE TRAINING. Using data from another model won't save you any training time.

fumeux_fume 2y ago
I think the point is that if R1 isn't possible without access to OpenAI (at low, subsidized costs) then this isn't really a breakthrough as much as a hack to clone an existing model.
- tensor 2y ago
  The training techniques are a breakthrough no matter what data is used. It's not up for debate, it's an empirical question with a concrete answer. They can and did train orders of magnitude faster.
  - blast 2y ago
    Not arguing with your point about training efficiency, but the degree to which R1 is a technical breakthrough changes if they were calling an outside API to get the answers, doesn't it? It seems like the difference between someone doing a better writeup of (say) Wiles's proof vs. proving Fermat's Last Theorem independently.
    - pests 2y ago
      That outside API used to be humans, doing the work manually. Now we have ways to speed that up.
- bbor 2y ago
  R1 is--as far as we know from good ol' ClosedAI--far more efficient. Even if it were a "clone", A) that would be a terribly impressive achievement on its own that Anthropic and Google would be mighty jealous of, and B) it's at the very least a distillation of O1's reasoning capabilities into a more svelte form.
bbor 2y ago
I think you're missing the point being made here, IMHO: using an advanced model to build high quality training data (whatever that means for a given training paradigm) absolutely would increase the efficiency of the process. Remember that they're not fighting over sounding human, they're fighting over deliberative reasoning capabilities, something that's relatively rare in online discourse. Re: "generally a bad idea", I'd just highlight "generally" ;) Clearly it worked in this case!
- tensor 2y ago
  It's trivial to build synthetic reasoning datasets, likely even in natural languages. This is a well established technique that works (e.g. see Microsoft Phi, among others). I said generally because there are things like adversarial training that use a ruleset to help generate correct datasets that work well. Outside of techniques like that it's not just a rule of thumb, it's always true that training on the output of another model will result in a worse model. https://www.scientificamerican.com/article/ai-generated-data-can-poison-future-ai-models/ https://www.scientificamerican.com/article/ai-generated-data...
  - numba888 2y ago
    > it's always true that training on the output of another model will result in a worse model. Not convincing. You can imagine model doing some primitive thinking and coming to conclusion. Then you can train another model on summaries. If everything goes well it will be coming to conclusions quicker. That's at least. Or it may be able solve more complex problems with the same amount of 'thinking'. It will be self-propelled evolution. Another option is to use one model to produce 'thinking' part from known outputs. Then train another to reproduce thinking to get the right output, unknown to it initially. Using humans to create such dataset would be slow and very expensive. PS: if it was impossible humans would be still living on the trees.
    - tensor 2y ago
      Humans don't improve by "thinking." They improve my natural selection against a fitness function. If that fitness function is "doing better at math" then over a long time perhaps humans will get better at math. These models don't evolve like they, there is not a random process of architectural evolution. Nor is there a fitness function anything like "get better at math." A system like AlphaZero works because it has a rules to use as an oracle: the game rules. The game rules provide the new training information needed drive the process. Each game played produces new correct training data. These LLMs have no such oracle. Their fitness function is and remains: predict the next word, followed by: produce text that makes a human happy. Note that it's not "produce text that makes ChatGPT happy."
smitelli 2y ago
> training off of data generated by another AI is generally a bad idea Ah. So if I understand this... once the internet becomes completely overrun with AI-generated articles of no particular substance or importance, we should not bulk-scrape that internet again to train the subsequent generation of models. I look forward to that day.
- bangaladore 2y ago
  That's already happened. Its well established now that the internet is tainted. After essentially ChatGPT's public release, a non-insignificant amount of internet content is not written by humans.
- tensor 2y ago
  Yes, this is a real and serious concern that AI researchers have.
athrowaway3z 2y ago
Thats not right either. It proofs we _can_ optimize our training data. Just like humans have been genetically stable for a long time, the quality & structure of information available to a child today vs that of 2000 years ago makes them more skilled at certain tasks. Math being a good example.
deleted 2y ago
[deleted]
dragonwriter 2y ago
> training off of data generated by another AI is generally a bad idea It's...not, and its repeatedly been proven in practice that this is an invalid generalization because it is missing necessary qualifications, and its funny that this myth keeps persisting. It's probably a bad idea to use uncurated output from another AI to train a model if you are trying to make a better model rather than a distillation of the first model, and its definitely (and, ISTR, the actual research result from which the false generalization has developed) a bad idea to iteratively fine-tune a model on its own unfiltered output, but there has been lots of success using AI models to generate data which is curated and used to train other models, which can be much more efficient that trying to create new material without AI once you've gotten to the point where you've already hoovered up all the readily-accessible low hanging fruit of premade content relevant to your training goal.
- LPisGood 2y ago
  It is, of course not going to produce a “child” model that more accurately predicts the underlying true distribution that the “parent” model was trying to. That is, it will not add anything new. This is immediately obvious if you look at it through a statistical learning lens and not the mysticism crystal ball that many view NN’s through.
  - FridgeSeal 2y ago
    No no no you don’t understand, the models will magically overcome issues and somehow become 100x and do real AGI! Any day now! It’ll work because LLM’s are basically magic! Also, can I have some money to build more data centres pls?
  - kybernetikos 2y ago
    Fine tuning an llm on the output of another llm is exactly how deepseek made its progress. The way they got around the problem you describe is by doing this in a domain that can be relatively easily checked for correctness, so suggested training data for fine tuning could be automatically filtered out if it was wrong.
  - mattnewton 2y ago
sailingparrot 2y ago
> First of all, training off of data generated by another AI is generally a bad idea because you'll end up with a strictly less accurate model (usually). That is not true at all. We have known how to solve this for at least 2 years now. All the latest state of the art models depend heavily on training on synthetic data.
- bjourne 2y ago
  https://www.nature.com/articles/s41586-024-07566-y https://www.nature.com/articles/s41586-024-07566-y
  - sailingparrot 2y ago
    Key point from your linked paper: > We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models No one is training on indiscriminate synthetic data. It's very much discriminated, but still synthetic.
jjallen 2y ago
The DS R1 Model is slightly better though. So how does your statement square with that?

by tensor 2y ago

fumeux_fume 2y ago

I think the point is that if R1 isn't possible without access to OpenAI (at low, subsidized costs) then this isn't really a breakthrough as much as a hack to clone an existing model.

tensor 2y ago
The training techniques are a breakthrough no matter what data is used. It's not up for debate, it's an empirical question with a concrete answer. They can and did train orders of magnitude faster.
- blast 2y ago
  Not arguing with your point about training efficiency, but the degree to which R1 is a technical breakthrough changes if they were calling an outside API to get the answers, doesn't it? It seems like the difference between someone doing a better writeup of (say) Wiles's proof vs. proving Fermat's Last Theorem independently.
  - pests 2y ago
    That outside API used to be humans, doing the work manually. Now we have ways to speed that up.
bbor 2y ago
R1 is--as far as we know from good ol' ClosedAI--far more efficient. Even if it were a "clone", A) that would be a terribly impressive achievement on its own that Anthropic and Google would be mighty jealous of, and B) it's at the very least a distillation of O1's reasoning capabilities into a more svelte form.

bbor 2y ago

I think you're missing the point being made here, IMHO: using an advanced model to build high quality training data (whatever that means for a given training paradigm) absolutely would increase the efficiency of the process. Remember that they're not fighting over sounding human, they're fighting over deliberative reasoning capabilities, something that's relatively rare in online discourse. Re: "generally a bad idea", I'd just highlight "generally" ;) Clearly it worked in this case!

tensor 2y ago
It's trivial to build synthetic reasoning datasets, likely even in natural languages. This is a well established technique that works (e.g. see Microsoft Phi, among others). I said generally because there are things like adversarial training that use a ruleset to help generate correct datasets that work well. Outside of techniques like that it's not just a rule of thumb, it's always true that training on the output of another model will result in a worse model. https://www.scientificamerican.com/article/ai-generated-data-can-poison-future-ai-models/ https://www.scientificamerican.com/article/ai-generated-data...
- numba888 2y ago
  > it's always true that training on the output of another model will result in a worse model. Not convincing. You can imagine model doing some primitive thinking and coming to conclusion. Then you can train another model on summaries. If everything goes well it will be coming to conclusions quicker. That's at least. Or it may be able solve more complex problems with the same amount of 'thinking'. It will be self-propelled evolution. Another option is to use one model to produce 'thinking' part from known outputs. Then train another to reproduce thinking to get the right output, unknown to it initially. Using humans to create such dataset would be slow and very expensive. PS: if it was impossible humans would be still living on the trees.
  - tensor 2y ago
    Humans don't improve by "thinking." They improve my natural selection against a fitness function. If that fitness function is "doing better at math" then over a long time perhaps humans will get better at math. These models don't evolve like they, there is not a random process of architectural evolution. Nor is there a fitness function anything like "get better at math." A system like AlphaZero works because it has a rules to use as an oracle: the game rules. The game rules provide the new training information needed drive the process. Each game played produces new correct training data. These LLMs have no such oracle. Their fitness function is and remains: predict the next word, followed by: produce text that makes a human happy. Note that it's not "produce text that makes ChatGPT happy."

smitelli 2y ago

> training off of data generated by another AI is generally a bad idea Ah. So if I understand this... once the internet becomes completely overrun with AI-generated articles of no particular substance or importance, we should not bulk-scrape that internet again to train the subsequent generation of models. I look forward to that day.

bangaladore 2y ago
That's already happened. Its well established now that the internet is tainted. After essentially ChatGPT's public release, a non-insignificant amount of internet content is not written by humans.
tensor 2y ago
Yes, this is a real and serious concern that AI researchers have.

athrowaway3z 2y ago

Thats not right either. It proofs we _can_ optimize our training data. Just like humans have been genetically stable for a long time, the quality & structure of information available to a child today vs that of 2000 years ago makes them more skilled at certain tasks. Math being a good example.

deleted 2y ago

[deleted]

dragonwriter 2y ago

> training off of data generated by another AI is generally a bad idea It's...not, and its repeatedly been proven in practice that this is an invalid generalization because it is missing necessary qualifications, and its funny that this myth keeps persisting. It's probably a bad idea to use uncurated output from another AI to train a model if you are trying to make a better model rather than a distillation of the first model, and its definitely (and, ISTR, the actual research result from which the false generalization has developed) a bad idea to iteratively fine-tune a model on its own unfiltered output, but there has been lots of success using AI models to generate data which is curated and used to train other models, which can be much more efficient that trying to create new material without AI once you've gotten to the point where you've already hoovered up all the readily-accessible low hanging fruit of premade content relevant to your training goal.

LPisGood 2y ago
It is, of course not going to produce a “child” model that more accurately predicts the underlying true distribution that the “parent” model was trying to. That is, it will not add anything new. This is immediately obvious if you look at it through a statistical learning lens and not the mysticism crystal ball that many view NN’s through.
- FridgeSeal 2y ago
  No no no you don’t understand, the models will magically overcome issues and somehow become 100x and do real AGI! Any day now! It’ll work because LLM’s are basically magic! Also, can I have some money to build more data centres pls?
- kybernetikos 2y ago
  Fine tuning an llm on the output of another llm is exactly how deepseek made its progress. The way they got around the problem you describe is by doing this in a domain that can be relatively easily checked for correctness, so suggested training data for fine tuning could be automatically filtered out if it was wrong.
- mattnewton 2y ago

sailingparrot 2y ago

> First of all, training off of data generated by another AI is generally a bad idea because you'll end up with a strictly less accurate model (usually). That is not true at all. We have known how to solve this for at least 2 years now. All the latest state of the art models depend heavily on training on synthetic data.

bjourne 2y ago
https://www.nature.com/articles/s41586-024-07566-y https://www.nature.com/articles/s41586-024-07566-y
- sailingparrot 2y ago
  Key point from your linked paper: > We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models No one is training on indiscriminate synthetic data. It's very much discriminated, but still synthetic.

jjallen 2y ago

The DS R1 Model is slightly better though. So how does your statement square with that?