[danielmarkbruce]

It depends what you mean by "engineering these solutions into the model". Using better data leads to better models given the same architecture and training. Nothing wrong with it, it's hard work, it might be with as specific goal in mind. LLM "breakthroughs" aren't really a thing at this point. It's just one little thing after another.

[nurettin]

Sure, I specifically pre-agreed to it not being ill will. What I mean is keeping tabs on the latest demand (newer benchmarks) and making sure their model delivers in some fashion. But it is mundane and they don't say that. And when a major number increases, people don't assume they just added more specific training data.

[danielmarkbruce]

Yup, it's a fair point. We very quickly got down to the nitty gritty with these things. Hopefully, like semiconductors nitty gritty results in a lot of big performance gains for decades.