They have maxed out the base data with the given model architecture and scale "intelligence" through inference-based RL.

Notably, in order to achieve a specific quality, the model has to "reason" for every prompt proportional to the training inference.

This is arguably the case of diminishing returns being reached, as answer token prices saturate the willingness of customers to pay.

They've run into a wall.

▲

sebzim4500 an hour ago | parent | next [-]

There's a whole frontier of cost vs performance and this model pushes it out in a bunch of places, so its not just a matter of making a different tradeoff as you make it sound.

▲

Loquebantur an hour ago | parent [-]

What you're looking at there is the cost/performance in inference. The "frontier".

An economically successful step would be to improve training cost/frontier advance.

They essentially failed at that, assuming they kept their training costs for the model step at least constant.

	▲	rsalus 18 minutes ago \| parent [-]
		isn't that what Opus is for? this is the "strongest" model, not the most economical

▲

mattnewton 36 minutes ago | parent | prev | next [-]

Maybe a dumb question but once you have a model that spends a lot of tokens for high performance why can’t you distill it into a model that thinks less? Sure it wouldn’t generalize well to new tasks but plenty of tasks are important and recurrent enough to justify this

	▲	calebkaiser 4 minutes ago \| parent \| next [-]
		There's a lot of research along this particular line, if you're interested! This is a dataset basically constructed by taking a bunch of DeepSeek R1 trajectories and filtering out the incredibly long rambling ones: https://huggingface.co/datasets/open-thoughts/OpenThoughts-1... There's also a bunch of RL research around strategies to coax the student model towards more concise trajectories. In general, a lot of people in industry are finding cost savings these days by tuning a smaller model over longer agent trajectories (not exactly the same as distilling the reasoning traces, since those aren't exposed by many frontier labs anymore) collected from their production agents. Typically works best when you have a pretty workflow-y agent whose tasks all share a similar shape.
	▲	jaggirs 8 minutes ago \| parent \| prev \| next [-]
		They do this already. Have you noticed the stronger model tends to get released first and the weaker models soon after?
	▲	buckle8017 33 minutes ago \| parent \| prev [-]
		The reasoning/thinking is basically trying many many different branches. To do that you need a larger model to have many many branches to try.

▲

mirekrusin an hour ago | parent | prev | next [-]

There are tasks that people will easily pay hundreds or thousands of dollars per pop.

And maybe that's how all of it is going to evolve in the future – so you pay per solution, not tokens?

	▲	Loquebantur 43 minutes ago \| parent [-]
		Diminishing returns would catch you there as well. Perhaps, it's a bit like being a super smart person in contemporary society: at some point, being smarter doesn't really reliably translate into a better life. When you learn from available human data, those text books max out at some point. You cannot learn what isn't there.

▲

rowanG077 23 minutes ago | parent | prev | next [-]

How do you explain Opus 5.5 or GPT 6.1? I could maybe agree if this is a general trend. But one "supermodel" existing that is really expensive does not signal diminishing returns.

▲

lnenad an hour ago | parent | prev | next [-]

That would be my layman's assumption as well. A theory is they're just releasing this as a stepping stone towards something bigger before the IPO. Either way it's the same way chinese models get higher scores on benchmarks. Drop enough of wait actuallys and you'll be top the charts, but time per task and tokens per task go up by a mile (as is the case with this release).

▲

an hour ago | parent | prev | next [-]

[deleted]

▲

suddenlybananas an hour ago | parent | prev [-]

This would be great for my sanity.