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gpm | on: Ripple-65B Preview: U.S. Open-Weight SOTA on Agentic Coding
Playing games like this also makes it very easy to accidentally say things that aren't true, like in the headline of the article...

> Introducing Ripple-65B Preview, a Frontier Open-Weight Model

It's not "frontier", it's not "frontier" even in the category of open-weight models (though usually that phrasing would mean frontier over all, and also open weight)... it's only "frontier" in the small sub-category of US made open-weight models.

GuB-42 | parent
It is technically frontier, if by "frontier" we mean the Pareto frontier.

Being at the Pareto frontier means that you can't get something better on one aspect without compromising on some other aspect. The trick is that it is easy to be at the Pareto frontier by having enough dimensions.

Here, we have 4: score, number of parameters, origin (US) and open-weight. You can get a better score or a lower number of parameters, but you have to compromise on at least one of the other aspects. That's a pretty weak frontier if you ask me.

derefr | root | parent
“Origin” isn’t part of the Pareto frontier because it’s not a quantitative dimension.

(You could say that “ability to be trusted by US government/military users” is quantifiable, but I would argue that by that metric, this model falls behind non-US-originated open-weight models that have more eyes on them!)

Routine8502 | root | parent
Discrete dimensions can exist in Pareto considerations. Not that I'm too fond of this game, but given that "Origin" is definitely part of the calculus of model deployment, it makes sense to consider it in a Pareto calculation.
bastawhiz | root | parent | next
It's just a different pareto frontier because you're filtering out items that don't fit your criteria. "Model was made by Anthropic" would be an equivalent dimension. You would never plot this, you're just reducing the subset of models you're comparing.

By that logic, any model is a frontier model. My GPT-2 clone is a frontier model when you compare it against models that were trained on my homelab.

kennywinker | root | parent
Which you're welcome to do and then people are welcome to disagree that is an important frontier to consider.
getcrunk | root | parent
Ahh I run into this issue all the time with ai when applying any sort of dimensional reasoning abstractly it always implicits continuous