Dmitry Vaintrob Not you?

Iliad Intensive

October 2026

The most important things

Before
Join the Iliad Discord and check you can see #intensive-october. That is where you post any material you need to share with participants. If you can't see it, tell Leonard (@leonardbereska on Discord).
During
At 17:10, make sure everyone fills out the feedback form.
After
Fill out the end-of-day log: rate the day and tell us anything that went wrong.
Fill out Who stood out: name anyone who made a positive impression.
  • Questions about pay, travel, logistics or materials: see the FAQ.

66 project proposals are open for review. Stars, a sentence, or both. Read them.

Your days

[B.3]Singular Learning Theory Thu 15 Oct, seconding 35 responses · energy 3.8/5
Overall +0.34 better than expected n = 32
Content +0.28 better than expected n = 32
Clarity -0.38 below expected n = 32
Motivation -0.25 below expected n = 32
Exercises -0.03 as expected n = 30
Readings +0.16 as expected n = 19
Other activities -0.05 as expected n = 19
Pace +0.44 too fast n = 32
Difficulty +0.69 too hard n = 32
Novelty +0.75 mostly new n = 32
Read what they wrote40
Worked well11
  • I think walking through concrete examples applying SLT was helpful in getting the concepts to connect
  • This working lectures were clear, and quote advanced
  • I really enjoyed Lin’s talk, I think it was the most mathematical-audience oriented talk so far and catered well to my knowledge and familiarity. Lin gave some of the best explanations of grokking, double descent, and the staircase I have seen so far. He also had very good suggestions for questions to pursue (in fact I wrote up one of them for my project proposal!)
  • The slides were good, once I really understood a bit of algebraic geometry etc.
  • Shaowei's talk.
  • i really appreciated the first speaker's presentation. it was more mathematically involved than i could follow at times, but i really appreciated the clarity and structure, and how we went from simple statistical models to algebraic geometry and back
  • the exercises were excellent. the content was awesome. the discussions with colleagues and the lecturers were great.
  • lectures were thorough, clear, and interesting.
  • The lecture slides were great, perfect balance between math and visual/organized info.
  • lecture quality and clarity
  • The exercises were good
Would improve21
  • I think today would have been a bit easier to follow had the broad motivations been presented earlier and upfront. Instead I think it followed a route of introducing things as they were developed which was difficult given so many levels of abstraction and new concepts.
  • I thought both lecturers were great, and the subject matter was very interesting. This was one of my favourite days so far. I was very impressed and I think I learned a lot from them.
  • I really really enjoyed today! The one comment I would leave which is very specific to today is perhaps having a brief interlude where the connection between the morning talk by Lin and afternoon talk by Dmitry is explained! They were both using the word singular models but meant actually very different things by it. In fact in the morning Lin said most models are singular but by afternoon Dmitry said most models are regular!
  • As a non-math person, I found the contents a bit heavy and difficult to parse and used Chatgpt extensively to understand. I would really exactly one thing of the lectures - If we could have the slides beforehand, that would be idea. The people not familiar with the background can look up and try to make sense of the pre-requisites.
  • The Slides for the second Part we're Not ideal, maybe a more rigorous writeup of everything (both First and second Part) i.e. that goes into more depth and is less handwavey (or pointers to papers that are rigorous)
  • Catalogize materials in terms of prerequisites maybe? I don't think my path to impact lies in becomes a physicist and then applying phisics to ML. But then I struggle to motivate myself to work through this material and I don't know when it's worth paying more attention. (I've already wasted years studying math I never used and forgot.)
  • The post lunch lectures could be a bit clearer.
  • Better slides in the second half.
  • I think today's lecture might have been improved by breaking them all in half and having mini-exercise sessions in between all of them. There was a lot of theory here and it was easy to get lost; having breaks to catch up and do exercises more regularly might have helped.
  • The lectures are good. Would be nicer if we can learn a bit more about the recent literature on singularity theory and the open questions that people are interested in.
  • Prepare the slides better (Dmitry). First speaker was excellent.
  • i think having two speakers, one presenting big picture and one working things out in more detail is a great idea, but the second speaker's lecture was really hard to follow in the sense it was not clear to me what was assumed and what was shown and in general where we were going overall, i think it would have been helpful to spend some time motivating why we should care about SLT
  • Maybe the second half can be better structured
  • both lecturers had great insights to share. however, Dimitri could have improved the preparation, and polished his slides more, for a more efficient impact on me.
  • The first part of the day was engaging. But the second half didn't really connect with me. Also, both sessions were lecture heavy. Would be better if there is a balance with exercises and coding.
  • nothing.
  • The last lecture was hard to follow from the slides.
  • Not much - I really enjoyed this one.
  • both talks were good!
  • Maybe some more preparation of the presentation for the second half.
  • The second part of the day, while it was very very interesting, was a little bit hard to follow. Maybe, for the second part of the day, a possible improvement would be to concentrate on one specific result and focus a bit more on the implications for safety.
Changed my mind7
  • Loss isn't always the best measure of how a network is developing and generalization is an important thing to measure for capability development.
  • I think today convinced me that my particular skillset (the kind of math I’m familiar with and do) can be used to understand key questions in ml theory (grokking for instance!). I’ve walked away from this talk seeing good directions in AI Safety I can apply my knowledge to!
  • Mostly new, so no.
  • SLT seems less impactful, after today.
  • got more motivation into research of the mixture in both theory and practice for AI safety and MechInterp.
  • I was told that SLT was substantially algebro-geometric, but such seems like an overstatement. Nevertheless, there seem to be some cool problems to solve related to the content from today.
  • I've since become very motivated to study SLT, and am considering researching it in the context of math grad school or upskilling in the research engineering side, or both.
Other thoughts1
  • I was a bit low energy, so I might not have the usual level of energy to follow and understand.

The day

10:00 Lecture starts
12:30 Lunch at Mox
13:30 Afternoon session
17:10 Feedback form
17:30 Day ends. Dinner

Resources

Elsewhere