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ArrrZero: Why DeepSeek R1 is less important than R1-Zero. While everyone's talking about DeepSeek R1, the real game-changer is R1-Zero. In this video I cover how this model went straight from base model to reasoning chatbot in one giant leap.

60,031 次观看 • 1 年前 •via X (Twitter)

11 条评论

Ishan Anand 的头像
Ishan Anand1 年前

Youtube version:

CodeRabbit 的头像
CodeRabbit1 年前

AI-first pull request reviewer with context-aware feedback, line-by-line code suggestions, and real-time chat.

will brown 的头像
will brown1 年前

nice, very cool!! went back and forth about base vs instruct as the default, it works pretty well with Qwen2.5-1.5B (base) but mem requirements are higher, base Llama-1B struggled more in my tests unless you add in the one-shot prompt seeing just the format-learning from an instruct model is the fastest + cheapest demonstration of GRPO "working" i could think of, and you do see the context length growth and some sporadic aha moments if you let it go for long enough

Ishan Anand 的头像
Ishan Anand1 年前

Yeah when I first saw it my gut reaction was that it was less “pure” to start with an instruct model but quickly realized it was actually better, especially for teaching, for the reasons you outlined.

daberryman 的头像
daberryman1 年前

Amazing content, you explained karpathys slides much clearer than he did although I’m not sure if that’s because I absorbed more knowledge of LLM training passively since that state video was posted or because of your explanation Regardless it was very easy to follow through 👍

Ishan Anand 的头像
Ishan Anand1 年前

thanks! I'm a fan of his work and to be fair he was targeting a different audience and had a much shorter space of time to present it in.

Yogesh Kumar 的头像
Yogesh Kumar1 年前

Great video! Got to learn a lot of other helpful stuff too 🙌 Quick notes: Couldn't really understand the reasoning vs base model difference, and how would a base vs instruct model differ when reasoning could've be a good add! All in all, amazing video ❤️ and really helpful

Ishan Anand 的头像
Ishan Anand1 年前

thanks! to answer your question Base model = can't chat. when given a prompt it guesses what kind of web page would contain that prompt and writes out the likely rest of the page (like "autocomplete a whole web page" from this prompt)

lee 的头像
lee1 年前

well said!! strong follow!

Yong Li 的头像
Yong Li1 年前

This was super helpful thank you! You have a great communication style for clearly conveying these concepts.

Em Pe 的头像
Em Pe1 年前

great video! thanks

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