Government-mandated training techniques are a dead end: any simple rule dictating techniques would freeze in place what will be obsolete in two years, be impossible to enforce, and ban-specific rules just push labs toward worse workaround methods, so outcomes should be incentivized rather than techniques prescribed.
Zvi argues that trying to legislate which training techniques (like RLVR vs constitutional) labs use is unwieldy at best and counterproductive, because the government moves too slowly, techniques will change in two years, enforcement is impossible, and banning a specific technique just produces worse workarounds. Instead he proposes setting incentives via strict liability for real-world harms. ✦ AI generated
Zvi Mowshowitz · The Cognitive Revolution · 2026-08-05 · original ↗
starts at this moment · 115:32
“Can we come up with simple rules that people could agree to where we could ... take most of the risk that we are currently asking for off the board?”
one of the lessons that we've had over the course of years is that there is tremendous resistance to anything but the most simple interventions and the most simple rules. ... like this idea of like locking into requiring certain training techniques like I think there are legitimate complaints ... the government moves so slowly and like you can't like undo those kind of requirements. But like you definitely can't do that. ... if you ban a specific technique, what they come up with or like find a way around the rule is just going to be worse in some sense. Because like at least with the current techniques, we've had some years to figure out the worst possible ways to do them.
verbatim transcript · starts at 115:32
115:13you know, again, like we might want to have some sort of limit to how much work you can go into doing that, but like it's probably mostly fine. And again, like we would want diffusion. We'd want lower prices. We want like more compute used for mundane purposes. That's great. But like there's a reason why we're trying to use very blunt instruments when we talk about these things. like
115:32you you were talking about like what we were talking about training techniques earlier in the podcast and I was like well you can't really do that but you can encourage it you can try to explain to them why this is not proven you can create incentives but you can't you can't use that as your regulation as your hammer so like the reason we keep falling back on okay how much compute
115:50you know how many chips you know can we use for this purpose because like training is the distinct thing that like it's very easy to identify and for other purposes we kind of can't limit you that much necessarily. You could also potentially limit I'm spitballing how much compute you could get from unreleased models. similarly to try and contain that because like if you have to go through
116:16the release process then like the way the AI goes truly ballistic would be like you would you know you would use n to train n plus one to train n plus two to train plus three to train n four and if you were doing this like the cycle became a month and it became a week then that's when suddenly like holy but like if you had a rule that like you
116:34have to release these models or you only use so much compute for inference on those models Then, you know, you have to go through the process of submitting this thing and releasing it and then exposing it to the public and giving us an idea of what it is and allowing us to react to that information and that then slows you down from going truly like ballistic. But
116:56this has, you know, obviously a very limited effect on the amount of diffusion and the amount of other progress that you can make. And so maybe that's a good trade. But again, that's the like I thought about this for 30 seconds right now. I haven't been focusing much on the exact limitations, but like I do think that if you pace the frontier, you would you would be doing
117:14it by placing restrictions on methods of training and what what you could do to develop and use internal unreleased frontier style like models that like actively recurse these methods. You wouldn't prevent like mundane utility cycle. >> Yeah, I've had some similar ideas about just the relationship between unreleased models and released models. I do think there's something there that would be really helpful for preventing runaway internal and largely invisible
117:46processes. I think your point on not wanting the government to come in and say what training techniques can and can't be used, but I do wonder if there are ways and again they could be short-term, but what if the two companies got together and said it probably wouldn't be a good idea for us to train models with an RLVR on like how much money they make in the