DeepSeek-R1, every mention
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every year anyone Shawn Wang 11Will Brown 5Nathan Lambert 4Swyx (Marcos Swix) 2Stephanie Palazzolo 2Rishabh Agarwal 2Marc Andreessen 2Ethan Sutin 2Aymeric (Emmerich) 2Alessio Fanelli 2
Verbatim, from the transcripts: the passages where DeepSeek-R1 comes up
Next 100x in AI: Inference, Networking, & Self-Optimizing Models — Philip Kiely & Ali Taha, Baseten
- ▶ 1:13:35 Philip Kiely And so, for example, when DeepSeq R-One came out, it was, you know, it was six hundred seventy one billion parameters, which at the time was really huge, and I think did a lot to push us to really quickly adopt Blackwell and get good at…
Marc Andreessen introspects on Death of the Browser, Pi + OpenClaw, and Why "This Time Is Different"
- ▶ 10:46 Marc Andreessen It was O-one and then R-one that basically answered that question and basically said, oh, no, we're going to be able to actually turn this into something that's going to work in the real world.
- ▶ 29:56 Marc Andreessen And then our one comes out and it's just like, there's the code and there's the paper.
Goodfire AI’s Bet: Interpretability as the Next Frontier of Model Design — Myra Deng & Mark Bissell
- ▶ 10:00 Mark Bissell Like you look at Quen or, um, R-one and they have sort of like this CCP bias in them and-
Artificial Analysis: The Independent LLM Analysis House — with George Cameron and Micah Hill-Smith
- ▶ 26:23 Micah Hill-Smith Um, the world really noticed when they followed that up with the RL working on top of E three and R one succeeding like a few weeks later.
[State of AI Papers 2025] Fixing Research with Social Signals, OCR & Implementation — Team AlphaXiv
How Zyphra went all-in on AMD + Why Devs feel faster with AI but are slower — with Quentin Anthony
- ▶ 7:08 unnamed speaker I think when Deep Sea Card One came out, everybody was, like, going crazy about, oh, they went, like, so deep in, like, the actual GPU code to, like, improve things.
- ▶ 54:06 unnamed speaker So between understanding, I don't know, R-one and understanding a Luther train model that is like fully open, what, what's the delta between the two and the amount of work that you can do? 3 times in the scene
Long Live Context Engineering - with Jeff Huber of Chroma
- ▶ 36:12 Jeff Huber I think it was called, unfortunately, Ragar one, where they like teach, uh, deep cigar one, you know, kind of give it the tool of how to retrieve.
The AI Agenda: GPT5 leaks and the business of AI News — Steph Palazzolo, The Information
- ▶ 14:12 Stephanie Palazzolo Or, I even, I know even whenever, like, the DeepSeek R-One model first came out, one of the major, like, startups in the space was, like, yeah, 75% of the usage that we see for R-One is people trying to distill it into, like, 2 times in the scene
The RLVR Revolution — with Nathan Lambert (AI2, Interconnects.ai)
- ▶ 18:52 Nathan Lambert And then deep seek R one is still the canonical recipe on a like reasoning only model.
- ▶ 23:05 Nathan Lambert It's like DeepSeq R-one to the new R-one, it goes down.
- ▶ 38:37 Nathan Lambert What I would say that we have already done with O-one and R-one, which is you do a lot of RL, you show the inference time scaling works and you get really high benchmark numbers. 2 times in the scene
- ▶ 46:57 Shawn Wang There's one case where, with O-one and sort of the, the sort of Q-star ideas, there was one case where it was sort of overhyped in some sense, but now it's coming back with O-one Pro and DeepThink. 2 times in the scene
⚡️Using RFT to Build Clinical Superintelligence
- ▶ 5:59 Brendan Fortuna And it's the same technique that's used to train these state-of-the-art reasoning models, like O-three, you know, R-one, uh, Claude-four.
Information Theory for Language Models: Jack Morris
- ▶ 1:05:34 Shawn Wang Decently often for the open model labs, like even Deep Seek R R one like has released an update.
⚡️Launching AI Diplomacy: the hardest LLM Game Benchmark yet - Alex Duffy
- ▶ 13:20 Alex Duffy And so what you're looking at, just for context, you're playing as Deep Seek reasoner, which you can see in the bottom right.
⚡️Multi-Turn RL for Multi-Hour Agents — with Will Brown, Prime Intellect
- ▶ 27:04 Will Brown I think my last, I don't know, this isn't like, I wasn't the first person to do this, but like, it was pretty clear to me, like after one and before R one, that like RL was going to work and that that was going to intersect with agents…
- ▶ 30:03 Will Brown A model like R one, like a hundred percent of the time it is going to use its think tokens.
- ▶ 34:16 Will Brown If you're doing like an R-one and people are like, oh, math is easy to verify.
⚡️Open Questions in Agentic RL — Will Brown (Prime Intellect)
- ▶ 0:48 Will Brown We have things like R-one, which are great at kind of the single-term math and code reasoning problems.
- ▶ 2:55 Will Brown Single-turn RLVR world, the models like O-one and R-one, and we want to, like, have these things become more agentic, and it seems like the path is to incorporate reinforcement learning into this process.
What is an RL environment? w/ Nous Research's Roger Jin
Sleep-Time Compute — Letta AI (Charles Packer, Charlie Snell, Kevin Lin)
- ▶ 26:45 Charles Packer And then to, I guess, enable this sort of like to send, to tell like R one on the API, whether or not to like go to 10 K tokens versus like two K tokens, um, also is like a, you know, different mechanism.
The #1 SWE-Bench Verified Agent
- ▶ 27:24 Guy Gur-Ari So I think the DeepSeq paper R one was very good understanding DPO and the variants like GRPO is very popular now.
The Magic of LLM Distillation — Rishabh Agarwal, Google DeepMind
- ▶ 13:50 Rishabh Agarwal So, uh, if you saw that paper, they trained the DeepSeq reasoner model, and then they distilled the, the, basically that model to other models. 2 times in the scene
S1: the $6 DeepSeek R1 Competitor (ft. Entropix)
- ▶ 0:25 unnamed speaker Uh, and we wind up looking for a guest and Tim has been blogging about R one, S one, um, and, and all the other stuff. 3 times in the scene
- ▶ 2:34 unnamed speaker Um, R one, uh, I was posting on it about, about it on blue sky
- ▶ 2:56 unnamed speaker Um, should we double click more on R-One, or are we, should we go into S-One? 2 times in the scene
- ▶ 13:07 unnamed speaker I mean, R one, R one did both are there. 2 times in the scene
Bee AI: The Wearable Ambient Agent
- ▶ 55:07 Ethan Sutin You do need a sonnet level model to, like, execute things like the agent, and, um, we will be having a subscription for, like, features like that, because it's, you know, although now with the R-one, like, we'll see, uh, we haven't… 2 times in the scene
smol agents are all you need
- ▶ 14:25 Aymeric (Emmerich) I tried R one, but R one is a bit under, uh, O one, uh, with small agents. 2 times in the scene
- ▶ 21:46 Swyx (Marcos Swix) All the R-one derivatives, I think, are happening as well. 2 times in the scene
Why every AI Engineer needs an AI Gateway (ft Portkey.ai CEO)
- ▶ 19:16 Rohit Agarwal So you could always say, okay, if it comes to node A, always hit GPT-FORO, and node B is DeepSeek R-ONE,
Beating OpenAI and Anthropic by Looking At Data: the new #1 on SWE-Bench w/ W&B CTO Shawn Lewis
- ▶ 15:52 unnamed speaker Well, uh, the, uh, the, the sort of apparent metagame from Paul Gauffier on Ader is that you use R-One as an architect and Sonnet as a code model, and that's apparently the best combination that beats O-One for him.
- ▶ 34:15 Shawn Lewis The next question is, does our one do as well?
The Unreasonable Effectiveness of Reasoning Distillation: using DeepSeek R1 to beat OpenAI o1
- ▶ 2:07 unnamed speaker So on Monday, when Monday morning, DeepSeek one was released, we were like thinking, okay, maybe we can use Curator. 4 times in the scene
- ▶ 7:10 unnamed speaker So with R one, it's the first time we actually get the reasoning traces in, in open source. 2 times in the scene
- ▶ 10:28 Shawn Wang Mostly I agree on, on this discussion, especially from the R-one paper. 2 times in the scene
- ▶ 11:58 Shawn Wang It seems like at least R-one is generalizing very well. 4 times in the scene
- ▶ 15:21 unnamed speaker The, the, the, the simple thing that we changed is changing out QWQ for DeepSeq R-One. 7 times in the scene
- ▶ 23:18 unnamed speaker I mean, it's amazing to see how since this R-one model came out every three hours or something.
2024 Year in Review: The Big Scaling Debate, the Four Wars of AI, Top Themes and the Rise of Agents
- ▶ 37:29 Shawn Wang How much of a moat is there in this like proprietary sort of training data that they've, uh, presumably accomplished because like even deep seek, it was able to do it and they had, you know, two months notice to do this, to do R one.
- ▶ 1:20:43 Shawn Wang Flash thinking is not on here, as well as all the other QWQs, R-ones, and, and all the other sort of thinking models.
- ▶ 1:36:51 Alessio Fanelli R-one, 2 times in the scene