Ethan He
AI Researcher & Engineer. On 1 show, 2 appearances. The Shows tab opens the full record on each.
scientistengineeracademic@EthanHe_42 ↗LinkedIn ↗yihui-he.github.io ↗
Ethan He previously worked on NVIDIA's Cosmos world model platform and Megatron-LM MoE architectures before joining xAI to help build Grok Imagine. He has authored research on model compression and currently focuses on LLM context architecture and video agents.
1shows
2appearances
56statements
8resolved
7supported
1contradicted
88%fully supported
2said about them ↓ Everything Ethan He said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Ethan He: Pipeline bug fixes drive more model gains than new algorithms
“And often I find that this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the model training pipeline. Those gave the biggest boost to the model …”
Ethan He: Falling inference costs will enable generative UIs for everything
“So I think as a inference cost come down, we are going to have generative UI for everything.”
Ethan He: Training video models costs roughly the same as medium-scale LLMs
“So surprisingly video models is like the cost is very, is comparable to language models. And obviously the largest scale is language model. Maybe like a medium scale language models.”
Ethan He: Visual intelligence in video generation models stems primarily from language models
“The visual intelligence are actually mostly coming from language. Like, these video models, especially from now, since the diffusion model technology is more mature, the, like, every time you see there, there's some improvement on these models, I would say mos…”
Ethan He: LLM Video Agents Will Orchestrate Diffusion Models and Editing Tools
“Video agents, mostly language models, they'll call these generative model, either it's a separate model or a diffusion head or whatever as tool. So this model can iteratively Refine the results or even like you generate longer content through a very long trend…”
Ethan He: Video Agents Will Reach Production-Grade Quality by Year-End
“I guess by the end of this year is this is going to be a big hit. So the inflection point will be there and the videos generated by video agents can get to like production great quality. So it can be presented and it can be distributed in, in ads.”
Ethan He: Peak pre-training learning rate works best for MoE upcycling
“We found that the best is to use the original highest peak learning rate from pre-training, which works the best.”
Ethan He: Small xAI Team Built Grok Imagine in Three Months
“There were no, no infra, no data, and no model. And it just a few engineers, we built it in three months and released the first model, Grok Imagine,”
Ethan He: Video models require image foundations and 100% synthetic caption pairs
“Building a video model. You actually need to build a image model first and building, building these two models. The data you need is a hundred percent synthetic pair of language and image or language to video because on the internet, actually the videos Don't …”
Ethan He: Neural OS models can synthesize novel user interfaces
“So if you train your neural OS or neural computer on the standard screen recordings on the entire internet, the model can imagine completely new interface to interact with the computer.”
Ethan He: Storing and moving video datasets costs millions per month
“So, so it's like just storing, storing the network, those costs, it's just I guess it would be a few millions per month to just storing everything, not to mention the GPU costs.”
Ethan He: Grok Imagine Video Extension Tracks Full Historical Context
“So the Glock Imagine video extension, it has historical context of all of the previous generated videos. It can it has a context of who is speaking and what objects have appeared and everything having that to generate the next video.”
Ethan He: Long context management in video models leads LLM context work
“I feel this is actually, this part of long contacts is a little bit ahead of the LLM part.”
Ethan He: Powerful video AI will naturally learn to control physical robots
“Once these models can use computers and understand the future state of computer extremely well, the robots might be Might be one of the tools a very powerful AI can use. So the powerful AI might just be able to control the physical embodiment naturally.”
He: Upcycling dense models to MoE beats continuing dense training per FLOP
“By training these upcycled models, you can achieve better accuracy than simply training the dense model further for the same number of flops.”
He: Mixtral's top-k before softmax routing hurts MoE upcycling performance
“We actually found the mix-throughs approach didn't work as well as expected, because the original model, the original switch transformer from Google uses a softmax and topk for a reason. And because of upcycling, if you switch to topk, then softmax, it actuall…”
Ethan He: Video Foundation Models Follow Scaling Laws Like LLMs
“There, once I built the Cosmos one, I realized as this thing also has a scaling law similar to language model.”
Ethan He: Daily iteration speed is the top factor in model training
“When I look at like training models, I don't so actually the top important thing is like how many how many iterations can you do like per, per day? And the more iteration can you do, you can train the model much faster. So if you have a very strong infra and y…”
Ethan He: Coding Models Shift Research Bottlenecks Back to Compute
“Compute might become a bottleneck again, because previously, like if you want to train a new model, say you want to generate new synthetic data and then, or write a new algorithm, it might take a few weeks. And during that period of time, you don't, you might …”
Ethan He: Training generative video models on unlabeled data aids generalization
“For the generative model training, there's also really like a small percentage of unlabeled data. So, so the model is instructed to generate a video without any text instruction. That, that can also help the model generalize.”
Ethan He: Training transformers directly on raw image pixels is impossible
“If you're trying, if you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's a lot of tokens. So like one image, like it's a thousand by a thousand is like one million tokens, one million pixels. It's im…”
Ethan He: Diffusion transformer training closely mirrors LLM training architecture
“So now the training, training of the diffusion transformer, you already generated models use diffusion transformers. It is actually quite standard. It's very similar to how you train a language transformer models. It's not that much difference. It's just the t…”
Ethan He: Video Models Must Bootstrap From Image Diffusion Models for Semantic Understanding
“After you train such model, such image model, the reason it's a foundation for video models is that image, image models are Cheaper to train and they have much denser connection between language and text. So, sorry, language and images. For example, you train …”
Ethan He: Frame-by-frame video compression enables real-time interactivity, temporal compression adds lag
“That being said, the benefit of the frame per frame compression, we might come back to this later, is real timeliness and interactivity. Because if you strain the output of the model frame by frame, you can As a model can respond to any user request immediatel…”
The other half of the tape: Ethan He's own voice is left out of every number here. Other people bring the name up 2 times in 1 episode across the shows. every mention, with the transcript →
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One line per show, most statements first. The link opens Ethan's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| LATENT SPACELEDGER | AI Researcher & Engineer | 2 | 56 | 88% 7/8 | full record on Latent Space → |