Jerry Zhi-Yang He

LLM research @ Bytedance Seed. prev. PhD at @berkeley_ai with @ancadianadragan, @facebookai, @StanfordSVL and @StanfordHRI.

[Jerry Zhi-Yang He @_herobotics_]

Is your robot 🤖 safe around humans🚸? Or will it fail catastrophically🤕?

Excited to introduce "Natural Adversarial Frontier", a framework for probing the robustness of Human-Robot Interactions.

To appear at #CoRL2023 🧵(1/8)
Joint work with @ancadianadragan @daniel_s_brown @ZackoryErickson
https://t.co/JbAc2tdKVz
https://t.co/EQGCRuXvaL

[Jerry Zhi-Yang He @_herobotics_]

@GuanyaShi Congrats!

[Jerry Zhi-Yang He @_herobotics_]

@dwarkesh_sp comedy

[Sasha Rush @srush_nlp]

Talk: Training Composer
https://t.co/diSjKhP4g5

Overview of the methods that we use at Cursor to build our model.

[Po-Shen Loh @PoShenLoh]

Whoa. This breakthrough is going to fundamentally affect the structure of how universities select and retain professors. And more generally the structure of work and creativity. AI has managed to discover a significant result in research mathematics in a one-shot query (!!!), disproving a conjecture that a huge number of people have tried working on (including myself, although I am not anywhere as good as the other experts who have thought about it).

@wtgowers (Fields Medalist who has been thinking a lot about this space of AI and math) wrote: "if a human had written the paper and submitted it to the Annals of Mathematics and I had been asked for a quick opinion, I would have recommended acceptance without any hesitation. No previous AI-generated proof has come close to that." [The Annals of Mathematics is perhaps the most prestigious math journal in the world.]

OpenAI's announcement: https://t.co/faCjJFkY43

Mathematicians' discussion: https://t.co/RF0PzhRonM

In college classes, we already have a major issue where take-home assignments are susceptible to students using AI. Even with in-person exams, we're having issues where students use AI in the bathroom during the exam. Now, how will universities decide which professors to hire and promote? Universities used to judge on the basis of whether you got papers published in highly prestigious journals. Should the person who can use AI to generate massive quantities of publishable results be picked, if it can be done with one-shot prompts?

More generally, everyone (not just universities) needs to rethink their objectives for hiring and promotion. I am also an entrepreneur, and have been refining my hiring process in this age of AI. Before, I used to be particularly impressed by academic competition performance. Today, I search for people who hold 2 certain principles very strongly: they enjoy (1) delighting other people, and (2) achieving understanding through their own thought. I find these people are good at figuring out what makes customers/partners tick (hence able to identify good directions to run without micromanagement), and also curious enough to learn forever. They also tend to already be pretty strong skills-wise, because those two principles inherently drive them to build skills.

I actually think the whole world would be better off with more Thought Full people: https://t.co/cPdqAhQRz3. We'll need people like that to figure out how to help humanity survive. If you'd like to collaborate on ways to make a future, feel free to reach out. That's all I work on nowadays.

[Sam Sheffer @samsheffer]

i don’t think you understand how insane omni is

[Dwarkesh Patel @dwarkesh_sp]

New blackboard lecture w @ericjang11

He walks through how to build AlphaGo from scratch, but with modern AI tools.

Sometimes you understand the future better by stepping backward. AlphaGo is still the cleanest worked example of the primitives of intelligence: search, learning from experience, and self-play. You have to go back to 2017 to get insight into how the more general AIs of the future might learn.

Once he explained how AlphaGo works, it gave us the context to have a discussion about how RL works in LLMs and how it could work better – naive policy gradient RL has to figure out which of the 100k+ tokens in your trajectory actually got you the right answer, while AlphaGo’s MCTS suggests a strictly better action every single move, giving you a training target that sidesteps the credit assignment problem. The way humans learn is surely closer to the second.

Eric also kickstarted an Autoresearch loop on his project. And it was very interesting to discuss which parts of AI research LLMs can already automate pretty well (implementing and running experiments, optimizing hyperparameters) and which they still struggle with (choosing the right question to investigate next, escaping research dead ends). Informative to all the recent discussion about when we should expect an intelligence explosion, and what it would look like from the inside.

Timestamps:

0:00:00 – Basics of Go
0:08:06 – Monte Carlo Tree Search
0:31:53 – What the neural network does
1:00:22 – Self-play
1:25:27 – Alternative RL approaches
1:45:36 – Why doesn’t MCTS work for LLMs
2:00:58 – Off-policy training
2:11:51 – RL is even more information inefficient than you thought
2:22:05 – Automated AI researchers

[Amir Zamir @zamir_ar]

Test-time scaling, reasoning, and generally search-like processes clearly drive significant gains in LLMs. Largely owed to the structure of language. One would think the same could apply to non-linguistic domains, like image generation, but that obviously depends on whether the structure of the domain's representation lends itself to search.

1D ordered tokens (e.g., image FlexTok, video FlexTok) seem like a natural fit since they enable a step-by-step coarse-to-fine generation. We investigated that and found they indeed enable search and scale far better with test-time compute than 2D grids. See the visuals on the webpage. Appearing in @icmlconf 2026.

🔗 https://t.co/yOFqeIJrEz
📄 https://t.co/WFZCihp1m4,

[Jerry Zhi-Yang He @_herobotics_]

@MillionInt maybe if LLM is analogous to the “brain”, then HS are the “spine”.

spine is fast, high-freq around perception. It can stabilize, dodge, react faster than an LLM loop, but it’s also reflex-based, local, and hacky. Evolution discovered the spine early on because it won the game

[Dwarkesh Patel @dwarkesh_sp]

Did a very different format with @reinerpope – a blackboard lecture where he walks through how frontier LLMs are trained and served.

It's shocking how much you can deduce about what the labs are doing from a handful of equations, public API prices, and some chalk.

It’s a bit technical, but I encourage you to hang in there - it’s really worth it.

There are less than a handful of people who understand the full stack of AI, from chip design to model architecture, as well as Reiner. It was a real delight to learn from him.

Recommend watching this one on YouTube so you can see the chalkboard.

0:00:00 – How batch size affects token cost and speed
0:31:59 – How MoE models are laid out across GPU racks
0:47:02 – How pipeline parallelism spreads model layers across racks
1:03:27 – Why Ilya said, “As we now know, pipelining is not wise.”
1:18:49 – Because of RL, models may be 100x over-trained beyond Chinchilla-optimal
1:32:52 – Deducing long context memory costs from API pricing
2:03:52 – Convergent evolution between neural nets and cryptography

[Pulkit Agrawal @pulkitology]

Until now, robotics stopped where the human hand begins.

Strong, but not delicate.
Precise, but not adaptive.
Repetitive, but not creative.

The human hand wasn’t a benchmark — it was a boundary.

We are crossing it.
@EkaRobotics.
Coming soon.

[Michelle Lee @michellearning]

Welcome to the scientific revolution. 100s of robots. Zero coffee breaks. America’s largest autonomous lab, open today.

[Ziqian Zhong @fjzzq2002]

https://t.co/9YS942pplc

[Jerry Zhi-Yang He @_herobotics_]

@FakePsyho lol

[Tenobrus @tenobrus]

Donald Knuth is vibemathing now. real tough day for the stochastic-parrot crew.

[Frank Yan @FrankYan2]

As promised, here's the short film Jia Zhangke produced using Seedance 2.0 for Chinese New Year and his take on AI filmmaking

[Eric Jang @ericjang11]

As Rocks May Think: an interactive essay on thinking models, automated research, and where I think they are headed. Enjoy!
https://t.co/dcFYrXUQYg

[Jerry Zhi-Yang He @_herobotics_]

@DorsaSadigh Congrats Dorsa! Really happy for you!

[Dwarkesh Patel @dwarkesh_sp]

The @ilyasut episode

0:00:00 – Explaining model jaggedness
0:09:39 - Emotions and value functions
0:18:49 – What are we scaling?
0:25:13 – Why humans generalize better than models
0:35:45 – Straight-shotting superintelligence
0:46:47 – SSI’s model will learn from deployment
0:55:07 – Alignment
1:18:13 – “We are squarely an age of research company”
1:29:23 – Self-play and multi-agent
1:32:42 – Research taste

Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!

[Jerry Zhi-Yang He @_herobotics_]

@WinstonGu_ very cool!

[Jason Peng @xbpeng4]

I have always been surprised by how few positive samples adversarial imitation learning needs to be effective. With ADD we take this to the extreme! A differential discriminator trained with a SINGLE positive sample can still be effective for a wide range of tasks.