Nathan Lambert: evaluation

Transcript context

…ls. So there are some hybrids. But the main idea is how can we parallelize it. It’s an interesting avenue. I think right now there are mostly research models out there, like LaMDA and some other ones. I saw some by startups, some deployed models, but there is no big diffusion model at scale yet on the level of Gemini or ChatGPT. But there was an announcement by Google where they said they are launching Gemini Diffusion, and they put it into context of their Nano 2 model. They said for the same quality on most benchmarks, we can generate things much faster. I don’t think the text diffusion model is going to replace autoregressive LLMs, but it will be something for quick, cheap, at-scale tasks. Maybe the free tier in the future will be something like that. I think there are a couple of examples where it’s actually started to be used. To paint an example of why this is so much better: when a model like GPT-5 takes time to respond, it’s generating one token at a time. This diffusion idea is essentially generating all of those tokens in the completion in one batch, which is why it could be way faster. The startups I’m hearing are code startups where you have a codebase and somebody is effectively vibe coding. They say, “Make this change,” and a code diff is essentially a huge reply from the model. It doesn’t have to have that much external context, and you can get it really fast by using these diffusion models. They use text diffusion to generate really long diffs because doing it with an autoregressive model would take minutes, and that time causes a lot of churn for a user-facing product. Every second, you lose users. So I think that it’s going to be this thing where it’s going to- -grow and have some applications, but I actually thought that different types of models were going to be used for different things sooner than they have been. I think the tool use point is the one that’s stopping them from being most general purpose because, with something like Claude Code or ChatGPT with search, the autoregressive chain is interrupted with an external tool, and I don’t know how to do that with the diffusion setup. So what’s the future of tool use this year and in the coming years? Do you think there’s going to be a lot of developments there, and how that’s integrated into the entire stack?…

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