13 ms·

Everyone is responding to the intellectual property issue, but isn't that the less interesting point? If Deepseek trained off OpenAI, then it wasn't trained fr

by blast 2y ago

Everyone is responding to the intellectual property issue, but isn't that the less interesting point? If Deepseek trained off OpenAI, then it wasn't trained from scratch for "pennies on the dollar" and isn't the Sputnik-like technical breakthrough that we've been hearing so much about. That's the news here. Or rather, the potential news, since we don't know if it's true yet.

jondwillis 2y ago
But it does mean moat is even less defensible for companies whose fortunes are tied to their foundation models having some performance edge, and a shift in the kinds of hardware used for inference (smaller, closer to the edge.)
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?
fumeux_fume 2y ago
This has been in the back of my head since the news broke. Has anyone built their own R1 from scratch and validated it?
- RevEng 2y ago
  In the last few days? No, that would be impossible; no one has the resources to train a base model that quickly. But there are definitely a lot of people working on it.
- buyucu 2y ago
  not the whole model obviously since it just came out. but people have been successful in replicating the core RL principle behind it.
philistine 2y ago
There’s a question of scale here: was it trained on 1000 outputs or 5 million?
deleted 2y ago
[deleted]
bangaladore 2y ago
That's only true if you assume that O1 synthetic data sets are much better than any other (comparably sized) opensource model. It's not apparently obvious to me that that is the case. Ie. do you need a SOTA model to produce a new SOTA model?
joe_the_user 2y ago
If Deepseek trained off OpenAI, then it wasn't trained from scratch for "pennies on the dollar" If OpenAI trained on the intellectual property of others, maybe it wasn't the creativity breakthrough people claim? Oppositely If you say ChatGPT was trained on "whatever data was available", and you say Deepseek was trained "whatever data was available", then they sound pretty equivalent. All the rough consensus language output of humanity is now roughly on the Internet. The various LLMs have roughly distilled that and the results are naturally going to be tighter and tighter. It's not surprising that companies are going to get better and better at solving the same problem. The situation of DeepSeek isn't so much that promises future achievements but that it shows that OpenAI's string of announcements are incremental progress that aren't going to be reaching the AGI that Altman now often harps on.
- el_cujo 2y ago
  I'm not an OpenAI apologist and don't like what they've done with other people's intellectual property but I think that's kind of a false equivalency. OpenAI's GPT 3.5/4 was a big leap forward in the technology in terms of functionality. DeepSeek-r1 isn't really a huge step forward in output, it's mostly comparable to existing models, one thing that is really cool about it is it being able to be trained from scratch quickly and cheaply. This is completely undercut if it was trained off of OpenAI's data. I don't care about adjudicating which one is a bigger thief, but it's notable if one of the biggest breakthroughs about DeepSeek-r1 is pretty much a lie. And it's still really cool that it's open source and can be run locally, it'll have that over OpenAI whether or not the training claims are a lie/misleading
  - pertymcpert 2y ago
    Not just the training cost, the inference cost is a fraction of o1.
  - buzzerbetrayed 2y ago
    How is it a “lie” for DeepSeek to train their data from ChatGPT but not if they train their data from all of Twitter and Reddit? Either way the training is 100x cheaper.
alecco 2y ago
Even if all that about training is true, the bigger cost is inference and Deepseek is 100x cheaper. That destroys OpenAI/Anthropic's value proposition of having a unique secret sauce so users are quickly fleeing to cheaper alternatives. Google Deepmind's recent Gemini 2.0 Flash Thinking is also priced at the new Deepseek level. It's pretty good (unlike previous Gemini models). [0] https://x.com/deedydas/status/1883355957838897409 https://x.com/deedydas/status/1883355957838897409 [1] https://x.com/raveeshbhalla/status/1883380722645512275 https://x.com/raveeshbhalla/status/1883380722645512275
- blast 2y ago
  > the bigger cost is inference I didn't know that. Is this always the case?
  - fcantournet 2y ago
    Well in the first years of AI no, it wasn't because nobody was using it. But at some point if you want to make money you have to provide a service to users, ideally hundreds of millions of users. So you can think of training as CI+TEST_ENV and inference as the cost of running your PROD deployments. Generally in traditional IT infra PROD >> CI+TEST_ENV (10-100 to 1) The ratio might be quite different for LLM, but still any SUCCESSFUL model will have inference > training at some point in time.
    - sfilmeyer 2y ago
      >The ratio might be quite different for LLM, but still any SUCCESSFUL model will have inference > training at some point in time. I think you're making assumptions here that don't necessarily have to be universally true for all successful models. Even without getting into particularly pathological cases, some models can be successful and profitable while only having a few customers. If you build a model that is very valuable to investment banks, to professional basketball teams, or some other much more limited group than consumers writ large, you might get paid handsomely for a limited amount of inference but still spend a lot on training.
FooBarWidget 2y ago
Have people on HN never heard of public ChatGPT conversations data sets? They've been mentioned multiple times in past HN conversations and I thought it'd be common knowledge here by now. Pretty much all open source models have been training on them for the past 2 years, it's common practice by now. And haven't people been having conversations about "synthetic data" for a pretty long time by now? Why is all of this suddenly an issue in the context of DeepSeek? Nobody made a fuss about this before. And just because a model trains on some ChatGPT data, doesn't mean that that data is the majority. It's just another dataset.
ohhhhhhhhhk 2y ago
Funny how the first principles people now want to claim the opposite of what they’ve been crowing about for decades since techbros climbed their way out of their billion dollar one hit wonders. Boo fucking hoo.
jjallen 2y ago
That may be true. But an even more interesting point may be that you don’t have to train a huge model ever again? Or at least not to train a new slightly improved model because now we have open weights of an excellent large model and a way to train smaller ones.
paul_e_warner 2y ago
I feel like which one you care about depends on whether you're an AI researcher or an investor.

by blast 2y ago

jondwillis 2y ago

But it does mean moat is even less defensible for companies whose fortunes are tied to their foundation models having some performance edge, and a shift in the kinds of hardware used for inference (smaller, closer to the edge.)

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?

fumeux_fume 2y ago

This has been in the back of my head since the news broke. Has anyone built their own R1 from scratch and validated it?

RevEng 2y ago
In the last few days? No, that would be impossible; no one has the resources to train a base model that quickly. But there are definitely a lot of people working on it.
buyucu 2y ago
not the whole model obviously since it just came out. but people have been successful in replicating the core RL principle behind it.

philistine 2y ago

There’s a question of scale here: was it trained on 1000 outputs or 5 million?

deleted 2y ago

[deleted]

bangaladore 2y ago

That's only true if you assume that O1 synthetic data sets are much better than any other (comparably sized) opensource model. It's not apparently obvious to me that that is the case. Ie. do you need a SOTA model to produce a new SOTA model?

joe_the_user 2y ago

If Deepseek trained off OpenAI, then it wasn't trained from scratch for "pennies on the dollar" If OpenAI trained on the intellectual property of others, maybe it wasn't the creativity breakthrough people claim? Oppositely If you say ChatGPT was trained on "whatever data was available", and you say Deepseek was trained "whatever data was available", then they sound pretty equivalent. All the rough consensus language output of humanity is now roughly on the Internet. The various LLMs have roughly distilled that and the results are naturally going to be tighter and tighter. It's not surprising that companies are going to get better and better at solving the same problem. The situation of DeepSeek isn't so much that promises future achievements but that it shows that OpenAI's string of announcements are incremental progress that aren't going to be reaching the AGI that Altman now often harps on.

el_cujo 2y ago
I'm not an OpenAI apologist and don't like what they've done with other people's intellectual property but I think that's kind of a false equivalency. OpenAI's GPT 3.5/4 was a big leap forward in the technology in terms of functionality. DeepSeek-r1 isn't really a huge step forward in output, it's mostly comparable to existing models, one thing that is really cool about it is it being able to be trained from scratch quickly and cheaply. This is completely undercut if it was trained off of OpenAI's data. I don't care about adjudicating which one is a bigger thief, but it's notable if one of the biggest breakthroughs about DeepSeek-r1 is pretty much a lie. And it's still really cool that it's open source and can be run locally, it'll have that over OpenAI whether or not the training claims are a lie/misleading
- pertymcpert 2y ago
  Not just the training cost, the inference cost is a fraction of o1.
- buzzerbetrayed 2y ago
  How is it a “lie” for DeepSeek to train their data from ChatGPT but not if they train their data from all of Twitter and Reddit? Either way the training is 100x cheaper.

alecco 2y ago

Even if all that about training is true, the bigger cost is inference and Deepseek is 100x cheaper. That destroys OpenAI/Anthropic's value proposition of having a unique secret sauce so users are quickly fleeing to cheaper alternatives. Google Deepmind's recent Gemini 2.0 Flash Thinking is also priced at the new Deepseek level. It's pretty good (unlike previous Gemini models). [0] https://x.com/deedydas/status/1883355957838897409 https://x.com/deedydas/status/1883355957838897409 [1] https://x.com/raveeshbhalla/status/1883380722645512275 https://x.com/raveeshbhalla/status/1883380722645512275

blast 2y ago
> the bigger cost is inference I didn't know that. Is this always the case?
- fcantournet 2y ago
  Well in the first years of AI no, it wasn't because nobody was using it. But at some point if you want to make money you have to provide a service to users, ideally hundreds of millions of users. So you can think of training as CI+TEST_ENV and inference as the cost of running your PROD deployments. Generally in traditional IT infra PROD >> CI+TEST_ENV (10-100 to 1) The ratio might be quite different for LLM, but still any SUCCESSFUL model will have inference > training at some point in time.
  - sfilmeyer 2y ago
    >The ratio might be quite different for LLM, but still any SUCCESSFUL model will have inference > training at some point in time. I think you're making assumptions here that don't necessarily have to be universally true for all successful models. Even without getting into particularly pathological cases, some models can be successful and profitable while only having a few customers. If you build a model that is very valuable to investment banks, to professional basketball teams, or some other much more limited group than consumers writ large, you might get paid handsomely for a limited amount of inference but still spend a lot on training.

FooBarWidget 2y ago

Have people on HN never heard of public ChatGPT conversations data sets? They've been mentioned multiple times in past HN conversations and I thought it'd be common knowledge here by now. Pretty much all open source models have been training on them for the past 2 years, it's common practice by now. And haven't people been having conversations about "synthetic data" for a pretty long time by now? Why is all of this suddenly an issue in the context of DeepSeek? Nobody made a fuss about this before. And just because a model trains on some ChatGPT data, doesn't mean that that data is the majority. It's just another dataset.

ohhhhhhhhhk 2y ago

Funny how the first principles people now want to claim the opposite of what they’ve been crowing about for decades since techbros climbed their way out of their billion dollar one hit wonders. Boo fucking hoo.

jjallen 2y ago

That may be true. But an even more interesting point may be that you don’t have to train a huge model ever again? Or at least not to train a new slightly improved model because now we have open weights of an excellent large model and a way to train smaller ones.

paul_e_warner 2y ago

I feel like which one you care about depends on whether you're an AI researcher or an investor.