We are missing the value function that allowed AlphaGo to go from mid range player trained on human moves to superhuman by playing itself. As we have only made progress on unsupervised learning, and RL is constrained as above, I don't see this getting better.
We went from 2 + 7 = 11 to "solved a frontier math problem" in 3 years, yet people don't think this will improve?
Step away from LLMs for a second and recognize that “Yesterday it was X, so today it must be X+1” is such a naive take and obviously something that humans so easily fall into a trap of believing (see: flying cars).
We have robust scaling laws that continue to hold at the largest scales. It is absolutely a very safe bet that more compute + more training + algorithmic improvements will certainly improve performance it's not like we're rolling a 1 trillion dollar die.
For RL, we are arriving at a similar point https://www.tobyord.com/writing/how-well-does-rl-scale
Next stop is inference scaling with longer context window and longer reasoning. But instead of it being a one-off training cost, it becomes a running cost.
In essence we are chasing ever smaller gains in exchange for exponentially increasing costs. This energy will run out. There needs to be something completely different than LLMs for meaningful further progress.
We are just meat-computers.
But at the same time, there is absolutely no indication or reason to believe that this wave of AI hype is the AGI one and that LLMs can be scaled further. We absolutely don't know almost anything about the nature of human intelligence, so we can't even really claim whether we are close or far.
This is disingenuous... I don't think people were impressed by GPT 3.5 because it was bad at math.
It's like saying: "We went from being unable to take off and the crew dying in a fire to a moon landing in 2 years, imagine how soon we'll have people on Mars"
The bitter lesson is that the best languages / tools are the ones for which the most quality training data exists, and that's pretty much necessarily the same languages / tools most commonly used by humans.
> Correct code not nice looking code
"Nice looking" is subjective, but simple, clear, readable code is just as important as ever for projects to be long-term successful. Arguably even more so. The aphorism about code being read much more often than it's written applies to LLMs "reading" code as well. They can go over the complexity cliff very fast. Just look at OpenClaw.
Is it though? I'm a long-time code purist, but I am beginning to wonder about the assumptions underlying our vocation.
That's literally the thing they suggested to move away from. That is just an issue when using tools designed for us.
Make them write in formal verification languages and we only have to understand the types.
To be clear, I don't think this is a good idea, at least not yet, but we do not have to always understand the code.
Let it write a black box no human understands. Give the means of production away.
I think they have a good optimization target with SWE-Bench-CI.
You are tested for continuous changes to a repository, spanning multiple years in the original repository. Cumulative edits needs to be kept maintainable and composable.
If there are something missing with the definition of "can be maintained for multiple years incorporating bugfixes and feature additions" for code quality, then more work is needed, but I think it's a good starting point.
Whether or not selling access to massive frontier models is a viable business model, or trillion-dollar valuations for AI companies can be justified... These questions are of a completely different scale, with near-term implications for the global economy.
Including code quality. Not because they are exceptionally good (you are right that they aren’t superhuman like AlphaGo) but because most humans are rather not that good at it anyway and also somehow « hallucinate » because of tiredness.
Even today’s models are far from being exploited at their full potential because we actually developed pretty much no tools around it except tooling to generate code.
I’m also a long time « doubter » but as a curious person I used the tool anyway with all its flaws in the latest 3 years. And I’m forced to admit that hallucinations are pretty rare nowadays. Errors still happen but they are very rare and it’s easier than ever to get it back in track.
I think I’m also a « believer » now and believe me, I really don’t want to because as much as I’m excited by this, I’m also pretty much frightened of all the bad things that this tech could to the world in the wrong hands and I don’t feel like it’s particularly in the right hands.
When doing math you only ever care about the proof, not the answer itself.
If your proof is machine checkable, that's even easier.
You'd need a completely different post-training and agent stack for that.
