The post training step in technology development, like RLHF, is crucial for enhancing the performance of the base model.
Creating a product that aligns with user needs, ensuring scalability for multiple users, and making it user-friendly are key challenges in technology development.
Significant achievements and scaling efforts are necessary for successful product development in the technology industry. (Time 0:00:00)
Importance of Post Training Step in Technology Development
Summary:
The post training step in technology development, like RLHF, is crucial as it involves additional processes on top of the base model to enhance its performance.
Apart from inventing the technology, the challenge lies in creating a product that aligns with user needs, ensuring scalability for multiple users, and making it user-friendly. This entails significant achievements and scaling efforts, distinguishing it as a critical aspect of product development in the technology industry.
Transcript:
Speaker 1
I mean, they're both super important, but the RLHF, the post training step, the little wrapper of things that, from a compute perspective, little wrapper of things that we do on top Of the base model, even though it's a huge monowork, that's really important to say nothing of the product that we build around it. You know, in some sense, like we did have to do two things. We had to invent the underlying technology, and then we had to figure out how to make it into a product people would love, which is not just about the actual product work itself, but this Whole other step of how you align and make it useful.
Speaker 2
And how you make the scale work where a lot of people can use it at the same time, all that kind of stuff.
Speaker 1
And that. But, you know, that was like a known, difficult thing. Like we knew we were going to have to scale it up. We had to go do two things that had like never been done before that were both like, I would say quite significant achievements. And then a lot of things like scaling it up that other companies have had to do before.
Speaker 2
How does the context window, I'll go from 8k to 128k tokens, compare from the, from GPT forward to GPT forward to about people like long. (Time 0:55:41)
