Emergency Pod: Reinforcement Learning Works! Reflecting on Chinese Reasoning Models DeepSeek-R1 and Kimi k1.5
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What motivated the release of DeepSeek‑R1 and Moonshot‑Kimi on Trump’s inauguration day?
Welcome to the cognitive revolution. Today I'm gonna do a walkthrough of everything that I am learning and understanding and taking away from the latest Chinese reasoning model releases that have come out this week. Perhaps not coincidentally, both R one from Deep Seek and the new Kimi reasoning model from a company called Moonshot AI were released on Trump's inauguration day and it was We now have two Chinese models. Deep Seek is out. The weights are open source. You can download them. The Kimmy paper from Moonshot came a little later in the day. Honestly shades of open AI and and Google kind of racing to preempt each other with their launches. I don't know if that's what's happening in China or not, but it certainly had the flavor of like Deep Seek put their paper out and then a few hours later, here comes the Kimmy paper.
Their model isn't quite yet available. They said it'll be available via API and presumably in their product soon, but it's not yet. So unclear what's going on in China. Did they intend to put these out on Trump's inauguration day or was that just an accident? Um it's kind of hard to believe that it is an accident. But at the same time, these folks are focused on unraveling the mysteries of AGI with curiosity. So maybe they don't care about um when Trump is getting inaugurated. Or maybe they're coordinating. I have a lot of uh questions about the dynamics that are going on um in China behind this. But what is clear is that at least Deep Seek with this R one model has joined the top tier of global AI developers.
Potentially Moonshot with their Kimmy model could be there as well, but it's obviously hard to say that kind of stuff purely from the benchmarks. So we we'll have to wait and get our hands on it before we can be too confident about that. What I want to do is walk through what stands out to me about this and and try to make some sense of it. I suspect this will be the first of several conversations about this because this R1 story touches on so many different aspects of AI at the same time. The research itself is really important. The Consequences for just practical utility are significant as well. Uh the gap between closed source and open source, also the gap between the West and China, I would say shrinking gaps at the moment.
Certainly the gap between the West and China seems to have shrunk significantly from where it was a couple of years ago. And You know, that's just the start, right? Then there's, of course, the strategic dynamics, like why is China open sourcing this? What are they getting out of that? How, you know, if at all, should the US respond? Does this challenge narratives that are increasingly dominant in the West about AI race? We've now seen uh no less than Alex Wang, the CEO of Scale AI, take out a full-page ad in the newspaper calling the current situation. an AI war, which I honestly totally hate and think is is wildly irresponsible. You don't have to be a China dove to recognize that an AI war does not exist and would be bad for everyone.
Uh I hated to see that. we need to reconsider some of those framings in light of what we're seeing here. Uh and certainly the strategy that we want to play, if if our strategy is predicated on preventing China from doing certain things so that we can have certain advantage, so that we can solve certain problems, so we can be the good guys, uh, I think that the window of opportunity that we have where we're gonna have this sort of, you know, unassailable AI lead looks quite short. I think we'll understand that better as we go through the research and and understand how simple a lot of the stuff is driving a lot of these significant advances in reasoning capability. But at the end of that, you know, what what sort of policy response, if any, makes sense to this?
Does it still make sense to think about pre-training as being the real measure of model power or the standards by which a model would qualify for, you know, some sort of special process, special government review, special government notification.
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Chapters
8 chapters
1
What motivated the release of DeepSeek‑R1 and Moonshot‑Kimi on Trump’s inauguration day?
0:05–14:21
2
How is the DeepSeek‑R1 model architected and why does it use a mixture‑of‑experts?
14:21–27:53
3
What reinforcement‑learning tricks let R1 learn reasoning without human preference data?
27:53–41:49
4
Why do chains of thought grow longer during training and what emergent behaviors appear?
41:49–54:54
5
How does DeepSeek‑R1 compare to Moonshot‑Kimi and to OpenAI’s GPT‑4/O1 models?
54:54–1:08:03
6
What steps were taken to productize R1 and make it usable via chat.deepseek.com?
1:08:03–1:19:51
7
What are the strategic, economic, and policy implications of open‑sourcing powerful reasoning models?
1:19:51–1:32:12
8
How can listeners get the most out of R1/R1‑Zero and contribute to future research?
1:32:12–1:42:33
Speakers
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