The Bitter Lesson

“One thing that I like to say is kind of like complexity and being bitter lesson pill, they're like fundamentally at odds. For example, I think like outfold three, I might get this number wrong, but I think it was like 23 sub modules. And at that point, that's …”

“I think it is a mistake to throw out those learnings and say, you know, bitter lesson, throw out those learnings. We're just going to have the agents at runtime develop an entirely new way to do a tax return that is, you know, because bitter lesson, yada, yada…”

“A violation of Richard Sutton's bitter lesson is for sure the biggest risk to this trade. To all of AI.”

“And I can tell you throwing more compute at the problem is probably having super exponential gains right now per iteration. So it depends on which domain you're talking about, which modality. There's no saturation in superconductor discovery, for example, at a…”

“I do feel like sometimes people confuse like, oh, like we're taking an, a method with abstraction. That means they don't believe in bitter lesson. Like that's just false, right? Like we are believers in bitter lesson, but then I feel like the question that we …”

“I think at this point there's a lot of acceptance that learning is a really important part of robotics, but I don't think there's still universal acceptance that end-to-end learning is the right way to go. Basically, I don't think there's universal acceptance …”

“The same bitter lessons or lessons that you can draw in LLM space are eventually going to be true in this space as well, I think.”

“The bitter lesson gets used too much in, like, too conveniently used around, but actually there's also a little bit of a, not a bit, there's also a sweet lesson where it's like, ideas matter.”

“You want to build a scaffolding around the model, a wooden scaffolding that if the model gets better or you have to switch it out, the scaffolding falls away. You know, like the bitter lesson, like embrace that a lot of stuff might fall into the model as soon …”

“We should be adding structure necessary to get things to work today, but keeping an eye on improving models and keep, but keeping a close eye on models, improving rapidly and removing structure in order to un-bottleneck ourselves.”

“Conceptual, you know, the blog post is right, but that's only true when you have enough data, and depending on the use case, the level of data you need may not be possible for years, decades, and you may need to hack your way there through human interactions.”

“I think that a lot of the approaches that have been taken have been very heuristic and haven't really been following like the bitter lesson approach to scaling and research.”

“But I also agree that, you know, maybe in a month or two months or three months, you won't need this anymore because, you know, the bitter lesson always wins.”

“I think people fail to truly, and me included, they fail to truly internalize the bitter lesson. So for the listeners out there who don't know about it, it's basically like you just scale the model, like GPUs go brrr, it's all that matters. I think it also hol…”