Most post-training tasks are based on real open source projects. A lot of time on real issues posted on issue trackers.

Besides that, the capabilities of a model are heavily dependent on the unsupervised learning phase, that gobbles all kind of other people's IP without giving a fuck. All the underpaid work behind the masses of third world programmers creating those post-training datasets would be completely uselless without it.

Also, it is kind of funny that labs resort to the "Research, time, money and expertise" argumet, when it is basically the same argument from publishers and other IP creator that the labs spent millions of dollars of lawyering money to resist. Besides, US law rejects in: Effort and cost by themselves not necessarely generate protectable interests.

About encryption, I think we're all contaminated by the bad ideology behind DMCA. While encryption established the intent, it doesn't follow that they have a legal claim of exclusivity just because of it.

Technically, you're overstating the value of so called "reasoning traces". You can't infer the verifier design, the reward shaping,or the data pipeline from them. Also, what you can extract are not the traces themselves, but the written summary of it, and you can't even guarantee that this summary reflects the exactly reasoning trace, models have show to have lied about it. Besides, distillation works when the student model already has strong priors, you can't turn a weak model in a SOTA with it. Don't believe Amodei's outrageous lies about it, he is just trying to exercise some regulatory capture.