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DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

arxiv.org

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Re: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

#121

Post by buyucu »

leetharris wrote: post #21
siliconc0w wrote: post #3

The US Economy is pretty vulnerable here. If it turns out that you, in fact, don't need a gazillion GPUs to build SOTA models it destroys a lot of perceived value. I wonder if this was a deliberate move by PRC or really our own fault in falling for the fallacy that more is always better.

CEO of Scale said Deepseek is lying and actually has a 50k GPU cluster. He said they lied in the paper because technically they aren't supposed to have them due to export laws. I feel like this is very likely. They obvious did some great breakthroughs, but I doubt they were able to train on so much less hardware.

Why would Deepseek lie? They are in China, American export laws can't touch them.

Re: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

#123

Post by buyucu »

vlaaad wrote: post #35

Reddit's /r/chatgpt subreddit is currently heavily brigaded by bots/shills praising r1, I'd be very suspicious of any claims about it.

I'm running the 7b distillation on my laptop this very moment. It's an insanely good model. You don't need reddit to judge how good a model is.

Re: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

#124

Post by rambojohnson »

faitswulff wrote: post #47
rikafurude21 wrote: post #24

Earlier quoted context omitted.

Why do americans think china is like a hivemind controlled by an omnisicient Xi, making strategic moves to undermine them? Is it really that unlikely that a lab of genius engineers found a way to improve efficiency 10x?

China is actually just one person (Xi) acting in perfect unison and its purpose is not to benefit its own people, but solely to undermine the West.

that's the McCarthy era red scare nonsense still polluting the minds of (mostly boomers / older gen-x) americans. it's so juvenile and overly simplistic.

Re: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

#126

Post by fspeech »

breadwinner wrote: post #89
cjbgkagh wrote: post #67

I've always been leery about outrageous GPU investments, at some point I'll dig through and find my prior comments where I've said as much to that effect. The CEOs, upper management, and governments derive their importance on how much money they can spend - AI gave them the opportunity for them to confidently say that if you give me $X I can deliver Y and they turn around and give that money to NVidia. The problem wa…

Latest GPUs and efficiency are not mutually exclusive, right? If you combine them both presumably you can build even more powerful models.

Not necessarily if you are pushing against a data wall. One could ask: after adjusting for DS efficiency gains how much more compute has OpenAI spent? Is their model correspondingly better? Or even DS could easily afford more than $6 million in compute but why didn't they just push the scaling？

Re: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

#127

Post by spacemanspiff01 »

dboreham wrote: post #103
cjbgkagh wrote: post #67

I've always been leery about outrageous GPU investments, at some point I'll dig through and find my prior comments where I've said as much to that effect. The CEOs, upper management, and governments derive their importance on how much money they can spend - AI gave them the opportunity for them to confidently say that if you give me $X I can deliver Y and they turn around and give that money to NVidia. The problem wa…

Agree. The "need to build new buildings, new power plants, buy huge numbers of today's chips from one vendor" never made any sense considering we don't know what would be done in those buildings in 5 years when they're ready.

The other side of this is that if this is over investment (likely)

Then in 5 years time resources will be much cheaper and spur alot of exploration developments. There are many people with many ideas, and a lot of them are just lacking compute to attempt them.

My back of mind thought is that worst case it will be like how the US overbuilt fiber in the 90s, which led the way for cloud, network and such in 2000s.

Re: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

#128

Post by mackyspace »

faitswulff wrote: post #47
rikafurude21 wrote: post #24

Earlier quoted context omitted.

Why do americans think china is like a hivemind controlled by an omnisicient Xi, making strategic moves to undermine them? Is it really that unlikely that a lab of genius engineers found a way to improve efficiency 10x?

China is actually just one person (Xi) acting in perfect unison and its purpose is not to benefit its own people, but solely to undermine the West.

China is doing what it's always done and its culture far predates "the west".

Re: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

#129

Post by [deleted] »

rikafurude21 wrote: post #24
siliconc0w wrote: post #3

The US Economy is pretty vulnerable here. If it turns out that you, in fact, don't need a gazillion GPUs to build SOTA models it destroys a lot of perceived value. I wonder if this was a deliberate move by PRC or really our own fault in falling for the fallacy that more is always better.

Why do americans think china is like a hivemind controlled by an omnisicient Xi, making strategic moves to undermine them? Is it really that unlikely that a lab of genius engineers found a way to improve efficiency 10x?

[deleted]

Re: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

#130

Post by coder543 »

swyx wrote: post #12
GaggiX wrote: post #4

I wonder if the decision to make o3-mini available for free user in the near (hopefully) future is a response to this really good, cheap and open reasoning model.

almost certainly (see chart) https://www.latent.space/p/reasoning-price-war (disclaimer i made it)

I understand you were trying to make “up and to the right” = “best”, but the inverted x-axis really confused me at first. Not a huge fan.

Also, I wonder how you’re calculating costs, because while a 3:1 ratio kind of sort of makes sense for traditional LLMs… it doesn’t really work for “reasoning” models that implicitly use several hundred to several thousand additional output tokens for their reasoning step. It’s almost like a “fixed” overhead, regardless of the input or output size around that reasoning step. (Fixed is in quotes, because some reasoning chains are longer than others.)

I would also argue that token-heavy use cases are dominated by large input/output ratios of like 100:1 or 1000:1 tokens. Token-light use cases are your typical chatbot where the user and model are exchanging roughly equal numbers of tokens… and probably not that many per message.

It’s hard to come up with an optimal formula… one would almost need to offer a dynamic chart where the user can enter their own ratio of input:output, and choose a number for the reasoning token overhead. (Or, select from several predefined options like “chatbot”, “summarization”, “coding assistant”, where those would pre-select some reasonable defaults.)

Anyways, an interesting chart nonetheless.

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