Intensive Teaching · 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.

You have reviewed 5 of them.

Your days

[B.2]Mysteries of Deep Learning Tue 13 Oct, leading 37 responses · energy 3.8/5

Overall -0.66 below expected n = 35

Content -0.17 as expected n = 35

Clarity -0.40 below expected n = 35

Motivation -0.20 as expected n = 35

Exercises -0.05 as expected n = 22

Readings +0.06 as expected n = 34

Other activities -0.15 as expected n = 13

Pace -0.14 about right n = 35

Difficulty +0.17 about right n = 35

Novelty +0.51 mostly new n = 35

Read what they wrote44

Worked well11

the reading material seems really good and structured and i am very grateful for it
Serendipitously, I had wonderful discussions with other participants that helped develop my inituitions for how I fit into AI safety.
I greatly appreciated open discussion with Charles.
There was a lot of material, but it was well organized.
Having time to read and work in groups to digest the material was nice.
The readings and resources on the website were very well organized
The Github page is nicely made and provides a good backbone for individual learning (although, as mentioned, I would have preferred a little less individuality).
The assortment of the readings on the various mysteries of DL.
Nothing really honestly, it was just read stuff by your self I could have done it at home
I like being allowed time to read the material thoroughly myself
For the whole day I read one of the suggested papers and reproduced one of the results with the help of conding agents. That worked out.

Would improve25

while the resources made available are extremely valuable, i really do not think that this format of "go and read whatever you want from this way too big corpus" is a good use of our time. i would have preferred some of the material be presented. Mysteries of Deep Learning, Training Dynamics, and Singular Learning theory are the days i was most looking forward too and I was very disappointed to hear that today was going to be this format and VERY disappointed thinking the other days are going to be like this
i think there needed to be more direction for mostly unfamiliar material
In the beginning of class, there was a brief discussion of what each section was about: I would expand on this a bit and give everyone a bit more of a sense of the major papers in each section.
I appreciate the collection of resources provided. Lecture format would have been preferred during the day.
The morning session was a bit unorganized. We basically had to explore several topics ourselves. Would be nice to have organized lectures around the most important topics so that we could at least get the high level ideas.
More structure given the fact that the topic I very broad. I don’t mind reading but I prefer it if it is aimed at something specific.
Though I appreciate the thought in a self-guided philosophy, I would strongly prefer a greater structure and greater input from the instructors. I can read curated papers and ask LLMs for advice on further curation in my own time. The only benefit of attending today was the presence of other participants. Note that I had trouble answering questions about pace, clarity, and difficulty given the absence of pacing, explanation, or challenges.
The format of the day could be improved, in my opinion. Specifically, I would have appreciated a more selective list of articles, with some structured discussion groups among the other participants (and Charles and Brianna). Ultimately, I find it much more useful to engage in discussion than quietly read articles, to the extent that the discussion doesn't dwell in too much bullshit.
It was all self reading today, so a heads up on Discord would have helped prepare us better for that.
I am a self-directed learning aficionado and I appreciate the github repo, but this is not what I traveled to SF, and commuted to Mox, for. I paid someone 1k today to do my job at 5 just to have the flexibility to stay here, and I am not sure for what. At least you could let us know in advance that's the format. The insane amount of material was also discouraging. It's the responsibility of a teacher help students make the most of the time when the amount of material is too high. It is one of the very unique few things an instructor can provide.
A bit more structure to the day. It would have been nice to have 15-min check-ins with a brief intro or overview of the modules during the day. The readings were also a lot; the check-ins would have helped highlight what was important to get and their relevance to later sections.
I would appreciate a high level overview of each of the 3-4 mysteries before diving into the readings. I think it remains important to distill some intuition/opinions to overall get a sense of direction or engagement with the research questions before diving into it ourselves.
I am not a big fan of the 'just read some stuff' approach. I personally need a more concrete approach and a stimulus to engage in the material with fellow students. The current approach made me drown in the freedom somewhat, which is a shame, because the material is genuinely interesting.
Might try a little more structure to the day. Perhaps two 20 minute talks to anchor the rest of the day's readings.
I understand the motivation behind giving us a repo for self-study, but what I value most in a lecture is a clear throughline connecting the most important ideas, even if that means being selective and leaving some material out. I was looking forward to these lectures as a chance to hear how researchers in the field think about the topic and which ideas they consider most useful. Discussion and questions are valuable too, but I think a prepared lecture gives the speaker more room to develop their perspective in depth than responding to questions on the spot.
Unfortunately, the lecture did not really take place. I believe many students, myself included, were genuinely looking forward to it
I think a general structure was missing and a better outcome could be achieved with more structure.
Make it a class
not much maybe a few lectures but i like self reading
Maybe a Bit more teaching in the classical Sense, content was good
Give an overview of the ideas before delegating the reading
Unstructured reading time does not feel productive, that is something we could have done remotely (outside of the intensive).
Required more structure: free-form from the get-go left a lot of people confused. Perhaps spend the time before lunch going over some basics and giving us some intuitions before setting us free.
It would be great if we get a general overview of the topic during the initial hours: The materials were good and the session gave an opportunity to dive deep into the topic of interest. Charles and Brianna were around all day to answer all questions. But a s a person who was new to this field, reading 'alone' was not really helpful to actually uderstand the topic well. I would recommend strongly to include bit more lecturing hours - say 1 hour in the morning and maybe 30 mins in the afternoon. This would help us steer into the reading sessions.
The formation of the lecture. I would have liked much better if the lecturer provides substantial materials explained.

Changed my mind6

I think I am convinced that research into aspects of deep learning or interpretability for transformers are not for me. These directions do not address fundamental concerns I have (total loss of control, gradual disempowerment, bad actors), while being less aesthetically pleasing than other research opportunities.
I'm doubtful Singular Learning Theory is a promising approach to native LLM ontology.
I was better able to understand why even measuring generalization is a difficult problem and how counterintuitive deep learning can be on that front.
I became a bit more aware of the magnitude of stuff for me to read/learn and it increased my drive to dive into things.
Virtually everything was new, so I did not change my mind.
No

Other thoughts2

I think the self directed learning approach was ineffective. There were too many readings to chose from and I didn’t know what to prioritize.
Please try to implement the suggestions about lecturing hours pleeeeease!!!!!

[B.4]Training Dynamics Fri 16 Oct, leading 35 responses · energy 3.9/5

Overall +0.91 better than expected n = 34

Content +0.91 better than expected n = 34

Clarity +0.88 better than expected n = 34

Motivation +0.82 better than expected n = 34

Exercises +0.29 better than expected n = 28

Readings +0.25 better than expected n = 24

Other activities +0.61 better than expected n = 18

Pace +0.09 about right n = 34

Difficulty +0.18 about right n = 34

Novelty +0.47 mostly new n = 34

Read what they wrote40

Worked well13

the lecture by Daniel Kunin was totally amazing: clear and inspiring and presenting a compelling argument for why training dynamics matters for safety. the part on the whiteboard was especially great!! and i wish there had been more of this during the intensive. it forces the speaker to progress at “the speed of thought” and we can all follow along
The second lecturer
The morning exercises were a great preparation for the afternoon's lecture.
The level of mathematical formalism was was tuned just right (for my background/taste) so it it did not obfuscate the larger point.
The first morning session could have been more lecture structured. Otherwise, the second half was great.
Guest lecture by Daniel was fantastic. Very clear and interesting.
Daniels lecture was one of the best talks ive heard
Discussion and drawing on whiteboards.
Both Brianna and Daniel were excellent in their delivery of the information. The exercises, as well, we excellent.
I thought working at the board was good.
The lectures were well prepared.
the white board discussion and motivation via toy models was perfect, I would encore more speakers to attempt this format, it lands well with mathematicians and physicists
Nice to start off with the basics. Doesn’t hurt to repeat some stuff from time to time.

Would improve17

It was great today!
Teaching was excellent (in the beginning it was very high level, but a good overview)
I found a lot of this stuff hard to motivate from a safety perspective. I think some more motivations would help me stay focused.
it would have been helpful to know that the morning lecture was presenting things that were going to be used in the afternoon lecture. Instead, I got the impression that the content of the morning lecture was irrelevant in a very broad sense. I remain a puzzled by Charles’ and Brianna’s approach to teaching in the intensive. the votes below are strongly influenced by Daniel’s amazing afternoon lecture
Very engaging. The split between lecture time and exercise were ideal.
Maybe more talks today would be helpful! There was a chunk of time where I wasn’t quite sure what I should dedicate to.
First lecture with more info and more precise
nothing much, the white board presentation was extremely nice
For today, a bit more of the big picture and big results upfront to motivate what is going on later and guide exercises. I think also the afternoon's derivations could have been sped up had it been made explicitly clear during the morning that exercises were crucial.
Not much
Almost nothing to improved. Perhaps some embedding GIFs displaying function space changing through training epochs
the content was kinda light in the morning, and kinda compressed in the afternoon. that could have been distributed more evenly. also, some guidance or coordination for doing the exercises could be helpful, so there are groups to collaborate with.
I think one of the lectures went quite long and a break could have been good, but it seems like people were excited and interested.
Both lectures today were amazing.
The second half was a bit dense and didn't have any breaks. There was about an hour left afterwards so the time could have been better utilized.
Not much, Daniel was a great lecturer.
Tiers for difficulty (easy/hard)

Changed my mind10

My main takeaway from Daniel’s talk was that initialization matters, I’m still trying to parse through exactly how initialization affects the output in transformer based networks as opposed to the linear networks he gave the talk on.
I feel more that there's a bag of 5 observations that a lot of people are coming up with increasingly divergent theories to explain. I genuinely enjoyed learning the material today but it makes me think there are higher priority areas for research when it comes to AI safety
Mysteries of Deep Learning and Training Dynamics have emerged as the central scientific interests for me.
Learning mechanics seems like a very promising theory for Alignment from first principles.
Lazy vs rich learning. Studying these with higher dimensional parameter spaces.
I finally understand what's up with Lazy / Rich regimes.
I wasn't too interested in linear networks before today but I think I will look into them a bit more.
Daniel's lecture on rich learning completely changed my mind about how generalization happens during optimization. This was a very fun lecture to watch.
Daniel mentioned that the black box nature of LLMs comes from the data and not the algorithms, and that makes a lot of sense in hindsights.
It was mostly new.

[B.5]Data Attribution Thu 22 Oct, leading 24 responses · energy 3.5/5

Overall -0.50 below expected n = 22

Content -0.27 below expected n = 22

Clarity -0.32 below expected n = 22

Motivation -0.14 as expected n = 22

Exercises -0.40 below expected n = 15

Readings +0.15 as expected n = 13

Other activities 0.00 as expected n = 5

Pace +0.09 about right n = 22

Difficulty -0.05 about right n = 22

Novelty +0.59 mostly new n = 22

Read what they wrote21

Worked well4

improvement on last lectures he has given as he gave somewhat of an overview before reading, bu could have given a more structured lecture
Nothing, really. Maybe the videos Charles spent 10 million tokens on are insightful, but he did not really present them in a way that raised our expectations much.
I think Charles is really interesting and always appreciate his takes.
I liked having lots of breakout time to walk through the derivations myself

Would improve13

Not much, maybe some dedicated slides?
Slides
The notes are well organized, but the lecture was not. Charles believe is self-directed learning too much for it to have worked.
I liked the initial lectures on the topic, but near the end some more guidance wouldve nice.
The lectures are a bit unstructured and unorganized. Improving the presentation and organization would definitely helps a lot for understanding the materials better.
Today felt like a wasted day. The other day that felt like this was the first day Charles was "teaching". I was optimistic to hear that he was going to alternate between lectures and exercises today, but unfortunately my optimism faded away within one hour, after Charles refused to provide informative answers to questions (rather than quips), failed to reproduce derivations on the board, and more generally had clearly not prepared a coherent presentation of the material. Most importantly, he kept undermining the material he was teaching as he was teaching it. The Intensive is described on the website as "A four-week, full-time taught course". I don't see why you would entrust so much of the course to someone who seems completely unwilling to provide good pedagogy. The course page for today also has pretty bad notation (β used both as the datapoint weights and as the inverse temperature).
I would add a bit more of structure. I like a lot the idea of learning by reading relevant literature, this was very cool! However, I would have appreciate a clearer program to follow, for instance to coordinate 30 min of reading and 15 min of discussion, or something on these lines.
Prepare a lecture, not reading a doc
I think data attribution can be really cool, and Charles is a fun and intriguing guy who clearly knows his stuff cold, but I did not learn much because of two reasons. First, I felt like we did not get enough time yesterday to really work through our project, and I found myself wanting to go back to refining it further, and more generally I felt like _doing_ something instead of another day of _learning_. Second, Charles seems like he does not enjoy teaching very much, and does not appreciate how important context is before moving straight to the math in a way that suggests that it's mostly trivial stuff that we would learn better on our own. And I'm sure that's the case for him, but that's just not the way that most people learn or get passionate about a new subject. So what would I improve: allow for more time for practical work (which many people chose to take spontaneously now), and get teachers to be passionate about teaching.
I think it is interesting in and of itself, but it seemed like data attribution is not generally used by frontier labs to improve models, which I thought was unfortunate. It wasn't entirely clear what place it has in larger machine learning research.
Thank you for taking into account the previous suggestion to include lectures. They helped me a lot to get the overview of topic. The topic itself was bit far from my expertise. Despite that I got an impression of why this is done. I would include few coding exercise too.
I think Charles did a good job showing us where the topic fits into the bigger picture
I would have spent a little more time on notation in the beginning. The lecture was pretty rapid on math and it made it hard to follow without grasping the notation.

Changed my mind3

I did not really know about this topic beforehand.
I think model susceptibility under data perturbation is a fascinating idea. Wish I had more time to look into it.
This really got me to think more rigorously about how to do attribution at different levels/scales within models and systems. However, I think it was a bit general and would have appreciated seeing how it gets used in practice to make the connection to AI safety clearer.

Other thoughts1

Let me be the person who raises some uncomfortable criticism. I believe the lecturer is knowledgeable and competent, but I would like to share a few concerns for the sake of improving future programs, even at the risk of becoming a less favorable fellowship candidate. 1. In a professional academic setting, I found the frequent use of F-words and certain jokes inappropriate. They made me uncomfortable and affected my trust in the learning environment. I spoke with a few others who shared similar concerns to varying degrees. 2. While no doubt that the lecturer clearly understood the physics, I struggled to see how the theoretical material connected to AI applications. This may partly reflect my own limited background in physics, but more concrete examples, motivation, and explicit connections to AI research would help participants from different disciplines stay engaged. 3. Some responses to participants' questions felt dismissive. Rather than carefully understanding and addressing the questions, the lecturer sometimes responded with jokes or answers that did not sufficiently address the point. I believe respectful engagement with questions is especially important in an interdisciplinary program. To be fair, I attended only the morning session and spent the afternoon working on our own project. My observations are therefore limited, and later sessions may have been better. I take this program seriously and want to make the most of every minute here. I have no personal disrespect for the lecturer, and my criticism is not about his expertise but about the learning experience. I am sharing this because I genuinely value the program and hope the feedback can help improve it.

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