alt Hacker NewsThis seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression.
If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence.
I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm
“The seeker after truth is not one who studies the writings of the ancients and, following his natural disposition, puts his trust in them, but rather the one who suspects his faith in them and questions what he gathers from them, the one who submits to argument and demonstration and not the sayings of human beings whose nature is fraught with all kinds of imperfection and deficiency. Thus the duty of the man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads, and, applying his mind to the core and margins of of its content, attack it from every side. he should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency.” - ibn al-Haytham
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>This seems to be the end of the road for LLM's
This is an amazingly ignorant thing to say given the current pace of progress.
I am wondering whether the reason he needed to say it was because he was arguing with those who did out their trust in the writings of the ancients.
Idk about end of the road, I’m sure they can squeeze out some more performance by curating even more data and doing even more RL.
But I would bet that pretty much all of the improvement we’ve seen over the last year with coding has come from RL, not from the models becoming particularly stronger. And this makes sense, if models grow sublinearly with compute. And it seems like they do.
It's also important not to put too much faith into ancient sayings and aphorisms.
As a civilization, we are currently brushing up against the physics of efficiency. In many areas we have achieved close to what is theoretically possible, based on physics.
Such was not the case for the majority of human existence.
The body of research a.k.a. "writings of the ancients" is now insurmountably higher than it would have been during the time of ibn al-Haytham, when any kind of writing at all was scarce and literacy was low.
>This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression.
It's just inadequate benchmarks. Anyone who has used Fable for anything particularly difficult will have seen that it's miles ahead of Opus 5.0, yet the majority of benchmarks are completely unable to capture this.
The seeker of truth must also hold his breath.
Do you draw that conclusion from the fact that AI surprisingly quickly reaches the end of each ruler we try to measure it with?
Weird moment for this take. We're seeing some of the fastest and most impressive progress ever right now.
Frontier labs have categorically different & better set ups for evaluation, they're fine. It's work but it's not a crisis.
I really don’t think we know enough about what „intelligence“ is or how LLMs actually work to confidently say that this is the end of the road for LLM.