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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

#451

Post by soheil »

baq wrote: post #311
dtquad wrote: post #146

Larry Ellison is 80. Masayoshi Son is 67. Both have said that anti-aging and eternal life is one of their main goals with investing toward ASI. For them it's worth it to use their own wealth and rally the industry to invest $500 billion in GPUs if that means they will get to ASI 5 years faster and ask the ASI to give them eternal life.

Side note: I’ve read enough sci-fi to know that letting rich people live much longer than not rich is a recipe for a dystopian disaster. The world needs incompetent heirs to waste most of their inheritance, otherwise the civilization collapses to some kind of feudal nightmare.

the fi part is fiction

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

#452

Post by soheil »

dtquad wrote: post #146

Larry Ellison is 80. Masayoshi Son is 67. Both have said that anti-aging and eternal life is one of their main goals with investing toward ASI. For them it's worth it to use their own wealth and rally the industry to invest $500 billion in GPUs if that means they will get to ASI 5 years faster and ask the ASI to give them eternal life.

that's a bit of a stretch - why take the absolutely worst case scenario and not instead assume maybe they want their legacy to be the ones who helped humanity achieve in 5 years what took it 5 millennia?

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

#453

Post by soheil »

verdverm wrote: post #45

Over 100 authors on arxiv and published under the team name, that's how you recognize everyone and build comradery. I bet morale is high over there

It's actually exactly 200 if you include the first author someone named DeepSeek-AI.

For reference

  DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z.F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, Jiawei Wang, Jingchang Chen, Jingyang Yuan, Junjie Qiu, Junlong Li, J.L. Cai, Jiaqi Ni, Jian Liang, Jin Chen, Kai Dong, Kai Hu, Kaige Gao, Kang Guan, Kexin Huang, Kuai Yu, Lean Wang, Lecong Zhang, Liang Zhao, Litong Wang, Liyue Zhang, Lei Xu, Leyi Xia, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Meng Li, Miaojun Wang, Mingming Li, Ning Tian, Panpan Huang, Peng Zhang, Qiancheng Wang, Qinyu Chen, Qiushi Du, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, R.J. Chen, R.L. Jin, Ruyi Chen, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shengfeng Ye, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, S.S. Li , Shuang Zhou, Shaoqing Wu, Shengfeng Ye, Tao Yun, Tian Pei, Tianyu Sun, T. Wang, Wangding Zeng, Wanjia Zhao, Wen Liu, Wenfeng Liang, Wenjun Gao, Wenqin Yu, Wentao Zhang, W.L. Xiao, Wei An, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaotao Nie, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xinyu Yang, Xinyuan Li, Xuecheng Su, Xuheng Lin, X.Q. Li, Xiangyue Jin, Xiaojin Shen, Xiaosha Chen, Xiaowen Sun, Xiaoxiang Wang, Xinnan Song, Xinyi Zhou, Xianzu Wang, Xinxia Shan, Y.K. Li, Y.Q. Wang, Y.X. Wei, Yang Zhang, Yanhong Xu, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Yu, Yichao Zhang, Yifan Shi, Yiliang Xiong, Ying He, Yishi Piao, Yisong Wang, Yixuan Tan, Yiyang Ma, Yiyuan Liu, Yongqiang Guo, Yuan Ou, Yuduan Wang, Yue Gong, Yuheng Zou, Yujia He, Yunfan Xiong, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuyang Zhou, Y.X. Zhu, Yanhong Xu, Yanping Huang, Yaohui Li, Yi Zheng, Yuchen Zhu, Yunxian Ma, Ying Tang, Yukun Zha, Yuting Yan, Z.Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zhewen Hao, Zhicheng Ma, Zhigang Yan, Zhiyu Wu, Zihui Gu, Zijia Zhu, Zijun Liu, Zilin Li, Ziwei Xie, Ziyang Song, Zizheng Pan, Zhen Huang, Zhipeng Xu, Zhongyu Zhang, Zhen Zhang

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

#454

Post by rcpt »

iamgopal wrote: post #434
mrtksn wrote: post #182

Earlier quoted context omitted.

Correct me if I'm wrong but if Chinese can produce the same quality at %99 discount, then the supposed $500B investment is actually worth $5B. Isn't that the kind wrong investment that can break nations? Edit: Just to clarify, I don't imply that this is public money to be spent. It will commission $500B worth of human and material resources for 5 years that can be much more productive if used for something else - i.e…

500 billion can move whole country to renewable energy

Really? How? That's very interesting

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

#456

Post by wkat4242 »

swyx wrote: post #9

we've been tracking the deepseek threads extensively in LS. related reads: - i consider the deepseek v3 paper required preread https://github.com/deepseek-ai/DeepSeek-V3 - R1 + Sonnet > R1 or O1 or R1+R1 or O1+Sonnet or any other combo https://aider.chat/2025/01/24/r1-sonnet.html - independent repros: 1) https://hkust-nlp.notion.site/simplerl-reason 2) https://buttondown.com/ainews/archive/ainews-tinyzero-reprod... 3…

> R1 distillations are going to hit us every few days

I'm hoping someone will make a distillation of llama8b like they released, but with reinforcement learning included as well. The full DeepSeek model includes reinforcement learning and supervised fine-tuning but the distilled model only feature the latter. The developers said they would leave adding reinforcement learning as an exercise for others. Because their main point was that supervised fine-tuning is a viable method for a reasoning model. But with RL it could be even better.

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

#457

Post by jdietrich »

AYBABTME wrote: post #335
placardloop wrote: post #283

Earlier quoted context omitted.

Thinking of the $500B as only an aspirational number is wrong. It’s true that the specific Stargate investment isn’t fully invested yet, but that’s hardly the only money being spent on AI development. The existing hyperscalers have already sunk ungodly amounts of money into literally hundreds of new data centers, millions of GPUs to fill them, chip manufacturing facilities, and even power plants with the impression t…

If the hardware can be used more efficiently to do even more work, the value of the hardware will hold since demand will not reduce but actually increase much faster than supply. Efficiency going up tends to increase demand by much more than the efficiency-induced supply increase. Assuming that the world is hungry for as much AI as it can get. Which I think is true, we're nowhere near the peak of leveraging AI. We ba…

>Efficiency going up tends to increase demand by much more than the efficiency-induced supply increase.

https://en.wikipedia.org/wiki/Jevons_paradox

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

#458

Post by placardloop »

arnaudsm wrote: post #386
placardloop wrote: post #283

Earlier quoted context omitted.

Thinking of the $500B as only an aspirational number is wrong. It’s true that the specific Stargate investment isn’t fully invested yet, but that’s hardly the only money being spent on AI development. The existing hyperscalers have already sunk ungodly amounts of money into literally hundreds of new data centers, millions of GPUs to fill them, chip manufacturing facilities, and even power plants with the impression t…

I agree except on the "isn't easily repurposed" part. Nvidia's chips have CUDA and can be repurposed for many HPC projects once the AI bubble will be done. Meteorology, encoding, and especially any kind of high compute research.

None of those things are going to result in a monetary return of investment though, which is the problem. These big companies are betting a huge amount of their capital on the prospect of being able to make significant profit off of these investments, and meteorology etc isn’t going to do it.

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

#460

Post by 999900000999 »

tyfon wrote: post #265
HarHarVeryFunny wrote: post #152

DeepSeek-R1 has apparently caused quite a shock wave in SV ... https://venturebeat.com/ai/why-everyone-in-ai-is-freaking-ou...

The censorship described in the article must be in the front-end. I just tried both the 32b (based on qwen 2.5) and 70b (based on llama 3.3) running locally and asked "What happened at tianamen square". Both answered in detail about the event. The models themselves seem very good based on other questions / tests I've run.

It's also not a uniquely Chinese problem.

You had American models generating ethnically diverse founding fathers when asked to draw them.

China is doing America better than we are. Do we really think 300 million people, in a nation that's rapidly becoming anti science and for lack of a better term "pridefully stupid" can keep up.

When compared to over a billion people who are making significant progress every day.

America has no issues backing countries that commit all manners of human rights abuse, as long as they let us park a few tanks to watch.

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