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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

#31

Post by mohsen1 »

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

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

#32

Post by logicchains »

bpfrh wrote: post #18
logicchains wrote: post #13

Earlier quoted context omitted.

As in there's an objective truth that can be determined by a computer. E.g. whether code compiles, whether a unit test passes, whether the answer given to a mathematical question like 3+5 is correct. Many other fields have no objective truth (like art or creative writing), or objective truth requires measurement of the physical world (although if the world can be simulated accurately enough for the problem class at h…

Isn't "code compiles" an insufficient criteria? e.g you would need to prove that for all inputs the code produces the correct output which would in turn make the problem way more complex

It's not a sufficient criteria by itself, but where no better criteria is possible it would still produce better results in reinforcement learning than if the model has no reward for producing correctly compiling code vs code that failed to compile.

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

#33

Post by ClumsyPilot »

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…

> The most promising idea is to use reasoning models to generate data, and then train our non-reasoning models with the reasoning-embedded data.

Why is it promising, aren’t you potentially amplifying AI biases and errors?

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

#34

Post by pants2 »

vidarh wrote: post #26
talldayo wrote: post #14

Earlier quoted context omitted.

Nvidia can actually charge larger margins if inference compute goes down. It would enable them to manufacture more units of smaller GPUs using inferior and cheaper silicon, all of which would increase the profits per unit sold as well as the number of units they can manufacture. The industry has to find a way to separate itself from Nvidia's GPGPU technology if they want to stop being gouged. The issue is that nobody…

For inference Nvidia has more significant competition than for training. See Groq, Google's TPU's etc.

People talk about Groq and Cerberus as competitors but it seems to me their manufacturing process makes the availability of those chips extremely limited. You can call up Nvidia and order $10B worth of GPUs and have them delivered the next week. Can't say the same for these specialty competitors.

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

#35

Post by jumploops »

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…

The future is bespoke software.

In some sense, this is how computers were always supposed to work!

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

#36

Post by philipkiely »

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

They're using Llama.cpp which is an amazing tool for local inference but doesn't match fast inference frameworks like TensorRT-LLM/SGLang for production speeds and throughputs on Hopper GPUs.

The Unsloth quantizations are really cool, but if you want to experiment with the R1 models in a smaller form factor the R1 Distills like Llama 70B are great and should run a lot faster as they take advantage of existing optimizations around inferencing llama-architecture models.

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

#37

Post by ClumsyPilot »

logicchains wrote: post #19
dboreham wrote: post #9

Earlier quoted context omitted.

It doesn't need to write tests: it can just use the application and figure out if it works.

That's going to be much slower and more expensive than writing tests because image/video processing is slower and more expensive than writing tests. And because of lag in using the UI (and re-building the whole application from scratch after every change to test again).

But it’s actually correct from a usability perspective

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

#38

Post by prmph »

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…

This will almost certainly never materialize, and the reasons are not just technical

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

#39

Post by jeffbee »

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

If such labels are collected and used to retrain the model then yes. But these models are not learning online.

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

#40

Post by aprilthird2021 »

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

So if I just pay OpenAI $200/mo, and randomly tell the AI, no that's wrong.

I can stop the AI takeover?

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