I worked on geometric/equivariant deep learning a few years ago (with some success, leading to two ICLR papers and a patent, see my google scholar: https://scholar.google.com/citations?user=E3ae_sMAAAAJ&hl=en).

The type of research I did was very reasoning-heavy. It’s architecture research in which you think hard about how to mathematically guarantee that your network obeys some symmetry constraints appropriate for a domain and data source. 

As a researcher in that area, you have a very strong incentive to claim that a special sauce is necessary for intelligence, since providing special sauces is all you do. As such, my prior is to believe that these researchers don’t have any interesting objection to continued scaling and “normal” algorithmic improvements to lead to AGI and then superintelligence.

It might still be interesting to engage when the opportunity arises, but I wouldn’t put extra effort into making such a discussion happen.

I found this fun to read, even years later. There is one case where Rohin updated you to accept a conclusion, where I'm not sure I agree:

As long as there exists an exponentially large tree explaining the concept, debate should find a linear path through it. 

I think here and elsewhere, there seems to be a bit of conflation between "debate", "explanation", and "concept".

My impression is that debate relies on the assumption that there are exponentially large explanations for true statements. If I'm a debater, I can make such an explanation by saying "A and B", where A and B each have a whole tree of depth minus 1 below them. Then the debate picks out a linear path through that tree since the other debater tries to refute either A or B, after which I can answer by expanding the explanation of the one of them under attack. I agree with that argument.

However, I think this essentially relies on all concepts that I use in my arguments to already be understood by the judge. If I say "A", and the judge doesn't even understand what A means, then we could be in trouble, the reason being that I'm not sure the concepts in A can necessarily be explained efficiently, and possibly the concept is necessary to be understood for appreciating refutations of arguments of the second debater. For example, mathematics is full of concepts like "schemes" that just inherently take a long time to explain when the prerequisite concepts are not yet understood.

My hope would be that such complex abstractions usually have "interfaces" that make it easy to work with them. I.e., maybe the concept is complex, but it's not necessary to explain the entire concept to the judge -- maybe it's enough to say "This scheme has the following property: [...]", and maybe the property can be appreciated without understanding what a scheme is.

The two sides of AI Safety (AI risk as anarchy vs. concentration of power) pattern-match a bit to this abstract I found today in my inbox:

The trajectory of intelligence evolution is often framed around the emergence of artificial general intelligence (AGI) and its alignment with human values. This paper challenges that framing by introducing the concept of intelligence sequencing: the idea that the order in which AGI and decentralized collective intelligence (DCI) emerge determines the long-term attractor basin of intelligence. Using insights from dynamical systems, evolutionary game theory, and network models, it argues that intelligence follows a path-dependent, irreversible trajectory. Once development enters a centralized (AGI-first) or decentralized (DCI-first) regime, transitions become structurally infeasible due to feedback loops and resource lock-in. Intelligence attractors are modeled in functional state space as the co-navigation of conceptual and adaptive fitness spaces. Early-phase structuring constrains later dynamics, much like renormalization in physics. This has major implications for AI safety: traditional alignment assumes AGI will emerge and must be controlled after the fact, but this paper argues that intelligence sequencing is more foundational. If AGI-first architectures dominate before DCI reaches critical mass, hierarchical monopolization and existential risk become locked in. If DCI-first emerges, intelligence stabilizes around decentralized cooperative equilibrium. The paper further explores whether intelligence structurally biases itself toward an attractor based on its self-modeling method -- externally imposed axioms (favoring AGI) vs. recursive internal visualization (favoring DCI). Finally, it proposes methods to test this theory via simulations, historical lock-in case studies, and intelligence network analysis. The findings suggest that intelligence sequencing is a civilizational tipping point: determining whether the future is shaped by unbounded competition or unbounded cooperation.

Do you think the x-axis being a release date is more mysterious than the same fact regarding Moore's law?

(Tbc., I think this doesn't make it less mysterious: For Moore's law this also seems like a mystery to me. But this analogy makes it more plausible that there is a mysterious but true reason driving such trends, instead of the graph from METR simply being a weird coincidence. )

Maryna Viazovska, the Ukrainian Fields medalist, did her PhD in Germany under Don Zagier. 
--- 

I once saw at least one French TV talkshow where famous mathematicians were invited (I don't find the links anymore). Something like that would be pretty much unthinkable in Germany. So I wonder if math has generally more prestige in France than e.g. in Germany. 

I’m confused by the order Lesswrong shows posts to me: I’d expect to see them in chronological order if I select them by “Latest”.

But as you see, they were posted 1d, 4d, 21h, etc ago.

How can I see them chronologically?

Thanks for this post!

The deadline possibly requires clarification:

We will keep the application form open until at least 11:59pm AoE on Thursday, February 27.

In the job posting, you write:

Application deadline: 12pm PST Friday 28th February 2025

There are a few sentences in Anthropic's "conversation with our cofounders" regarding RLHF that I found quite striking:

Dario (2:57): "The whole reason for scaling these models up was that [...] the models weren't smart enough to do RLHF on top of. [...]"

Chris: "I think there was also an element of, like, the scaling work was done as part of the safety team that Dario started at OpenAI because we thought that forecasting AI trends was important to be able to have us taken seriously and take safety seriously as a problem."

Dario: "Correct."

That LLMs were scaled up partially in order to do RLHF on top of them is something I had previously heard from an OpenAI employee, but I wasn't sure it's true. This conversation seems to confirm it. 

Hi! Thanks a lot for your comments and very good points. I apologize for my late answer, caused by NeurIPS and all the end-of-year breakdown of routines :)

On 1: Yes, the formalism I'm currently working on also allows to talk about the case that the human "understands less" than the AI.

On 2: 

Have you considered the connection between partial observability and state aliasing/function approximation?

I am not entirely sure if I understand! Though if it's just what you express in the following sentences, here's my answers:

Maybe you could apply your theory to weak-to-strong generalization by considering a weak model as operating under partial observability.

Very good observation! :) I'm thinking about it slightly differently, but the link is there: Imagine a scenario where we have a pretrained foundation model, and we train a linear probe attached to the internal representations, which is supposed to learn the correct reward for full state sequences, based on feedback from a human on partial observations. Then if we show this model (including attached probe) during training just the partial observations, it's receiving the correct data and is supposed to generalize from feedback on "easy situations" (i.e., situations where the partial observations of the human provide enough information to make a correct judgment) to "hard situations" (full state sequences that the human couldn't oversee, and where possibly the partial observations miss crucial details).

So I think this setting is an instance of weak-to-strong generalization.

Alternatively, by introducing structure to the observations, the function approximation lens might open up new angles of attack on the problem.

Yes that's actually also part of what I'm exploring, if I understand your idea correctly. In particular, I'm considering the case that we may have "knowledge" of some form about the space in which the correct reward function lives. This may come from symmetries in the state space, for example: maybe we want to restrict to localized reward functions that are translation-invariant. All of that can easily be formalized in one framework. 

Pretrained foudation models on which we attach a "reward probe" can be viewed as another instance of considering symmetries in the state space: In this case, we're presuming that state sequences have the same reward if they give rise to the same "learned abstractions" in the form of the internal representations of the neural network.

On 3: Agreed. (Though I am not explicitly considering this case at this point. )

On 4:

I think you're exactly right to consider abstractions of trajectories, but I'm not convinced this needs to be complicated. What if you considered the case where the problem definition includes features of state trajectories on which (known) human utilities are defined, but these features themselves are not always observed? (This is something I'm currently thinking about, as a generalization of the work mentioned in the postscript.

This actually sounds very much like what I'm working on right now!! We should probably talk :) 

On 5:

Am I correct in my understanding that the role Boltzmann rationality plays in your setup is just to get a reward function out of preference data?

If I understand correctly, yes. In a sense, we just "invert" the sigmoid function to recover the return function on observation sequences from human preference data. If this return function on observation sequences was already known, we'd still be doomed, as you correctly point out.

Thanks also for the notes on gradient routing! I will read your post and will try to understand the connection.

This is a link to a big list of LLM safety papers based on a new big survey.