DeepSeek-R1
also referred to as: deepseek r1
part of DeepSeek
5 statements across 2 episodes · 4 bullish · 0 bearish · 3 people on the record · first statement Feb 6, 2025 by Martin Casado · said 38 times in 5 episodes since 2025 · across every show →
Mentions by year
brought up most by Guido Appenzeller (19), Marco Mascorro (9), Martin Casado (5), Anjney Midha (3)
tap a year for its mentions
2025 38 mentions in 5 episodes 8 per episode
- DeepSeek, Reasoning Models, and the Future of LLMs
- What DeepSeek Means For The Future Of AI | Tech Veterans Weigh In
- The Current Reality of American AI Policy: From ‘Pause AI’ to ‘Build’
- Google DeepMind Lead Researchers on Genie 3 & the Future of World-Building
- Building the Next Generation of Conversational AI
- every mention in 2025, scene by scene →
Everything said about DeepSeek-R1, oldest first
Feb 6, 2025 bullish
Casado: DeepSeek Reasoning Traces Enable Model Distillation for Edge Devices
“It turns out that that chain of thought, if you have access to that, it allows you to train smaller models very quickly and very cheaply, and that's called distilling. So like the general, Term of distilling in LLM world means you have a teacher model, train a…”
Mar 5, 2025 positive
Mar 5, 2025 positive
Mascorro: Distillations from DeepSeek-R1 Outperformed Direct RL on Smaller Models
“So it turns out in their experiments, they took Lama's EV and some of these are QN models, and they basically apply RL straight the same way they did it with R one on these base models. And it turns out that it improved in some fields, but it was not a signifi…”
Mar 5, 2025 bullish
Mascorro: DeepSeek-R1 proved reinforcement learning improves models without human feedback
“And I think the big thing in, in R-one, or generally with these reasoning models is, We were doing before there was a human in the loop always, right? Like when we have this SFT training and these other techniques that we're doing after like RLHF and having R …”
Mar 5, 2025 neutral
Mascorro: DeepSeek-R1 post-training used two SFT and two RL phases
“So basically the way they did that, trying to fix R one zero, is it added a couple more phases in the post-training. That included two supervised fine tuning phases and two reinforcement learning phases. And these reinforcement learning phases, they were a lar…”