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An analysis of DeepSeek's R1-Zero and R1

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Re: An analysis of DeepSeek's R1-Zero and R1

#91

Post by genewitch »

echelon wrote: post #85
mohsen1 wrote: post #31

Earlier quoted context omitted.

every time you respond to an AI model "no, you got that wrong, do it this way" you provide a very valuable piece of data to train on. With reasoning tokens there is just a lot more of that data to train on now

> you provide a very valuable piece of data to train on We've been saying this "we get valuable data" thing since the 2010s [1]. When will our collective Netflix thumbs ups give us artificial super-intelligence? [1] Especially to investors. They love that line.

our collective netflix thumbs up indicators gave investors and netflix the confidence to deploy a series of adam sandler movies that cost 60 to 80 million US dollars to "make". So depending on who you are, the system might be working great.

Re: An analysis of DeepSeek's R1-Zero and R1

#92

Post by baq »

optimalsolver wrote: post #25
dagelf wrote: post #2

Well o3 scored 75% on AGI-1, R1 and o1 only 25%.... watch this space though....

But can o3 write a symphony? Seriously though, I'd like to hear suggestions on how to automatically evaluate an AI model's creativity, no humans in the loop.

LLMs have read everything humans made so just ask one if there’s anything truly new in that freshly confabulated slop-phony.

Re: An analysis of DeepSeek's R1-Zero and R1

#93

Post by deegles »

mohsen1 wrote: post #31
spyckie2 wrote: post #22

> But now with reasoning systems and verifiers, we can create brand new legitimate data to train on. This can either be done offline where the developer pays to create the data or at inference time where the end user pays! > This is a fascinating shift in economics and suggests there could be a runaway power concentrating moment for AI system developers who have the largest number of paying customers. Those customers…

every time you respond to an AI model "no, you got that wrong, do it this way" you provide a very valuable piece of data to train on. With reasoning tokens there is just a lot more of that data to train on now

not being snarky, but what is the point of using the model if you already know enough to correct it into giving the right answer?

an example that just occurred to me - if you asked it to generate an image of a mushroom that is safe to eat in your area, how would you tell it it was wrong? "oh, they never got back to me, I'll generate this image for others as well!"

Re: An analysis of DeepSeek's R1-Zero and R1

#94

Post by 7thpower »

Stevvo wrote: post #20

"The o3 system demonstrates the first practical, general implementation of a computer adapting to novel unseen problems" Yet, they said when it was announced: "OpenAI shared they trained the o3 we tested on 75% of the Public Training set. They have not shared more details. We have not yet tested the ARC-untrained model to understand how much of the performance is due to ARC-AGI data." These two statements are complet…

They are testing with a different dataset. The authors saying that they have not tested on the version of o3 that has not seen the training set.

Re: An analysis of DeepSeek's R1-Zero and R1

#95

Post by beAbU »

genewitch wrote: post #91
echelon wrote: post #85

Earlier quoted context omitted.

> you provide a very valuable piece of data to train on We've been saying this "we get valuable data" thing since the 2010s [1]. When will our collective Netflix thumbs ups give us artificial super-intelligence? [1] Especially to investors. They love that line.

our collective netflix thumbs up indicators gave investors and netflix the confidence to deploy a series of adam sandler movies that cost 60 to 80 million US dollars to "make". So depending on who you are, the system might be working great.

Through analytics Netflix should know exactly when people stop watching a series, or even when in a movie they exit out. They no doubt know this by user.

They know exactly what makes you stay, and what makes you leave.

I would not be surprised if in the near future movies and series are modifed on the fly to ensure users stay glued to their screens.

In the distant future this might be done on a per user level.

Re: An analysis of DeepSeek's R1-Zero and R1

#96

Post by vincentperes »

deegles wrote: post #93
mohsen1 wrote: post #31

Earlier quoted context omitted.

every time you respond to an AI model "no, you got that wrong, do it this way" you provide a very valuable piece of data to train on. With reasoning tokens there is just a lot more of that data to train on now

not being snarky, but what is the point of using the model if you already know enough to correct it into giving the right answer? an example that just occurred to me - if you asked it to generate an image of a mushroom that is safe to eat in your area, how would you tell it it was wrong? "oh, they never got back to me, I'll generate this image for others as well!"

You constantly have to correct an AI when using it because it either didn't get the question right or you guide him towards a more narrowed answer. There is only more to learn.

Re: An analysis of DeepSeek's R1-Zero and R1

#97

Post by coder543 »

littlestymaar wrote: post #30
mikejulietbravo wrote: post #16

Mike from Baseten here We're super proud to support this work. If you're thinking of running deepseek in production, give us a shout!

Earlier today I read a reddit comment[1] about a guy who tried running the quantized version from unsloth[2] on 4xH100 and the results was underwhelming (it ended up costing $137 per 1 million tokens). Any idea of what they're doing wrong? [1]: https://www.reddit.com/r/LocalLLaMA/comments/1icphqa/how_to_... [2]: https://unsloth.ai/blog/deepseekr1-dynamic

I’m not an expert on at-scale inference, but they surely can’t have been running at a batch size of more than 1 if they were getting performance that bad on 4xH100… and I’m not even sure how they were getting performance that low even at batch size 1. Batching is essential to serving large token volumes at scale.

As the comments on reddit said, those numbers don’t make sense.

Re: An analysis of DeepSeek's R1-Zero and R1

#98

Post by calebkaiser »

Onavo wrote: post #5
anothermathbozo wrote: post #3

The claim is that this removes the human bottleneck (aka SFT or supervised fine tuning) on domains with a verifiable reward. Critically, this verifiable reward is extremely hard to pin down in nearly all domains besides mathematics and computer science.

By verifiable do they mean it in the complexity theory P/NP sense of the word?

In the case of DeepSeek-R1, they used a series of heuristic reward functions that were built for different data types. The paper mentions the use of sandboxed environments to execute generated code against a suite of tests, for example, to evaluate it for correctness. The reward functions also evaluated syntax and formatting.

In general, the use of externally verifiable sources of truth (like simulators) is referred to as "grounding" and there has been quite a bit of research around it over the years, if you're interested in digging deeper. I've always found it super compelling as a research direction.

Re: An analysis of DeepSeek's R1-Zero and R1

#99

Post by dematz »

deegles wrote: post #93
mohsen1 wrote: post #31

Earlier quoted context omitted.

every time you respond to an AI model "no, you got that wrong, do it this way" you provide a very valuable piece of data to train on. With reasoning tokens there is just a lot more of that data to train on now

not being snarky, but what is the point of using the model if you already know enough to correct it into giving the right answer? an example that just occurred to me - if you asked it to generate an image of a mushroom that is safe to eat in your area, how would you tell it it was wrong? "oh, they never got back to me, I'll generate this image for others as well!"

A common use of these models is asking for code, and maybe you don't know the answer or would take a while to figure it out. For example, here's some html, make it blue and centered. You could give the model feedback on if its answer worked or not, without knowing the correct answer yourself ahead of time.

Re: An analysis of DeepSeek's R1-Zero and R1

#100

Post by travoc »

kristjansson wrote: post #53
polishdude20 wrote: post #6

I predict that the future of LLM's when it comes to coding and software creation is in "custom individually tailored apps". Imagine telling an AI agent what app you want, the requirements and all that and it just builds everything needed from backend to frontend, asks for your input on how things should work, clarifying questions etc. It tests the software by compiling and running it reading errors and failed tests a…

Most people really do not know what they want at any level of detail.

It's ok, they'll know it when they see it. Keep trying.
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