Hey folks, OOP/original author and 20-year HN lurker here — a friend just told me about this and thought I'd chime in.
Reading through the comments, I think there's one key point that might be getting lost: this isn't really about whether scaling is "dead" (it's not), but rather how we continue to scale for language models at the current LM frontier — 4-8h METR tasks.
Someone commented below about verifiable rewards and IMO that's exactly it: if you can find a way to produce verifiable rewards about a target world, you can essentially produce unlimited amounts of data and (likely) scale past the current bottleneck. Then the question becomes, working backwards from the set of interesting 4-8h METR tasks, what worlds can we make verifiable rewards for and how do we scalably make them? [1]
Which is to say, it's not about more data in general, it's about the specific kind of data (or architecture) we need to break a specific bottleneck. For instance, real-world data is indeed verifiable and will be amazing for robotics, etc. but that frontier is further behind: there are some cool labs building foundational robotics models, but they're maybe ~5 years behind LMs today.
[1] There's another path with better design, e.g. CLIP that improves both architecture and data, but let's leave that aside for now.
Reading through the comments, I think there's one key point that might be getting lost: this isn't really about whether scaling is "dead" (it's not), but rather how we continue to scale for language models at the current LM frontier — 4-8h METR tasks.
Someone commented below about verifiable rewards and IMO that's exactly it: if you can find a way to produce verifiable rewards about a target world, you can essentially produce unlimited amounts of data and (likely) scale past the current bottleneck. Then the question becomes, working backwards from the set of interesting 4-8h METR tasks, what worlds can we make verifiable rewards for and how do we scalably make them? [1]
Which is to say, it's not about more data in general, it's about the specific kind of data (or architecture) we need to break a specific bottleneck. For instance, real-world data is indeed verifiable and will be amazing for robotics, etc. but that frontier is further behind: there are some cool labs building foundational robotics models, but they're maybe ~5 years behind LMs today.
[1] There's another path with better design, e.g. CLIP that improves both architecture and data, but let's leave that aside for now.