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Neural scaling law

Neural scaling law

Search complete. 9 mentions across 3 episodes found for "Neural scaling law".

Sep 30, 2026

Jack FarleyHOST
47:25
It won't end until the models stop getting better the more compute you invest in it.
Jack FarleyHOST
47:33
So basically, it won't stop until the scaling laws, which have held up this entire time, end.
Jack FarleyHOST
47:38
Do you think that is a reasonable statement?
Ben PouladianGUEST
47:40
Yes.
Ben PouladianGUEST
47:41
I think until now, the bigger the training run, the better the results, and the more capable the model has been.
Ben PouladianGUEST
47:50
And because scaling laws are holding, there's this insatiable demand for compute.
Ben PouladianGUEST
47:54
But at the same time, you could also use the corollary that there's an insatiable demand for intelligence, right? Like, when do people want to stop knowing things? Does it go on forever? I mean, possibly.
Ben PouladianGUEST
48:11
So if you have an insatiable demand for intelligence, theoretically these trees could grow to the sky, which I don't believe in.
Diogo AlmeidaGUEST
9:20
You know, like even my take here is practical and like, am I a scaling law fan? Um, Depends.
Diogo AlmeidaGUEST
9:29
Scaling laws tell you how much better you get at a thing for amount in.
Diogo AlmeidaGUEST
9:33
Scaling law does mean exponentially more resources for normally sublinear gains, which looks to be a bad investment unless those linear gains are really, really valuable.
Diogo AlmeidaGUEST
9:44
But to me, it's all about what can we do with what we have to make the biggest possible fucking difference.
Diogo AlmeidaGUEST
11:14
Twitter is just like Jeff, Jeff, Jeff, you know? It's like a party.
Diogo AlmeidaGUEST
11:18
It's inspiring
SwyxHOST
11:19
because it's so different than what we're used to, which is, I'm sorry you can't do this, but we do scaling laws, and only the big labs can do it, right? That
Diogo AlmeidaGUEST
11:28
actually, if I I'm a tangent, if that's okay.

Unknown podcast

Neural Scaling Laws: The Proof Comes From Rydberg Atoms

Sep 21 · 2 Mentions

speaker_0HOST
0:00
Good morning and welcome to Agora Intelligence.
speaker_0HOST
0:02
Today's subject, Neural Scaling Laws.
speaker_0HOST
0:05
The proof comes from Rydberg-Adams, an ablation study that takes apart the data, not the model.
speaker_0HOST
0:11
An ablation study on a language model usually takes apart the architecture, attention heads, layers, tokenizer.
speaker_0HOST
0:18
The work filed on ArcSciv on 17 September 2026, won by David S. Berman, Ying Jirkaio, Roger G. Melko, and Alexander G. Stapleton, takes apart the data instead, and the result concerns anyone who trains language models.
speaker_0HOST
0:33
The authors study the neural scaling laws of Rydberg, an autoregressive transformer trained on projective qubit measurement data collected from arrays of interacting Rydberg atoms.
speaker_0HOST
0:44
The quantum system shows a finite size remnant of a critical point as the laser detuning is varied.
speaker_0HOST
0:49
Ten pages, six figures, one tight thesis.

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