Comment by famouswaffles
21 hours ago
>Then 4.5 flopped, while o1 demonstrated that gains could continue by reasoning (scaling up compute at inference time). o1 is now the ancestor of all their flagship models from GPT-5 on.
That's not quite right. They are still scaling model size and have had several new base pre-trains, just nothing so big as 4.5 (as far as we're aware). o1/4o has not been the base for some time now.
Data is obviously a bottleneck for some regimes and LLMs will have to get their hands dirty experimenting but it doesn't look like an insurmountable wall either.
> "get their hands dirty" > "insurmountable wall"
This is gibberish, you may as well tell me you've found a load-bearing seam.
Okay?
There's no reason the reinforcement learning that is getting them better at computer use can't be applied to other domains, like biology, chemistry etc. It's just expensive, because the environment often becomes the physical world, it requires creating labs like anthropic are doing here, and gathering a lot of data, it requires llms attempting their own experiments(that's what 'getting their hands dirty' means).
Getting the data and setup will be expensive, but not impossible, and labs are clearly gearing up to do just that. If you can't understand that then that seems like a you problem.