Jongsu Liam Kim @sky0bserver
The CFD enthusiast has become a ML researcher. Senior Researcher at LG CNS AI Lab. Opinions are solely my own and do not express the opinions of my employer. liam.kim Seoul Joined November 2010-
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These blogs are so awesome, I feel like I should stop writing because I am not good enough.
These blogs are so awesome, I feel like I should stop writing because I am not good enough. https://t.co/U1yzUmoOSN
I do not think a better "short note" exists on the topic. This was extremely to the point and knowledge dense. Love this style.
It's really interesting comparing the Ultra-Scale playbook (huggingface.co/spaces/nanotro…) and How To Scale Your Model, aka the JAX book (jax-ml.github.io/scaling-book/) side-by-side. 🧵
Pretraining with cross entropy = learning the best compressor for all texts ever written Why? Minimize CE = Min. KL between two distributions, eg HUMANS and LLM = the expected cost in bits when compressing samples drawn from HUMANS, while using LLM as your best approximation
Some insights from training self-evolving LLM (R-Zero): 1. Larger size models -> stronger self-evolution ability 2. Challenger & Solver should NOT share parameters 3. Pseudo-label quality degrades over time (a key drawback to tackle) In Sec 5.4 and 5.5: arxiv.org/abs/2508.05004
“they built an image editing model. It could follow simple instructions well. When the ability to follow simple instructions is used and connected as needed for a task (CoT), visual reasoning problems will start to be solved.”
“they built an image editing model. It could follow simple instructions well. When the ability to follow simple instructions is used and connected as needed for a task (CoT), visual reasoning problems will start to be solved.”
This is my summary of a podcast interviewing Xiangyu Zhang (youtube.com/watch?v=vWrYHv…). I found this very insightful. As I have used whisper to transcribe and gemini to translate this, it could contain errors. Though, considering the overall flow of the content, I think it would be…
“In three words: RL finally works. More precisely: RL finally generalizes.”
“In three words: RL finally works. More precisely: RL finally generalizes.”
1. why RL didn't work before? 2. why it works now?
Well sorry to insist, but the catch is that the market is still small — that's less than chewing gum industry. Too small for a bubble.
One of Pi0's novel architecture bits is the use of a Flow Matching action head -- previous to this, modern VLAs like OpenVLA leveraged diffusion diffusion heads What is a flow matching head? What makes it easier to use versus other denoising heads? A short thread!🧵 (1/7)
VLAs offer an avenue for generalist robot policies; however, naively following the action predictions leads to brittle or unsafe behaviours. We introduce VLAPS, which integrates model-based search with pre-trained VLA policies to improve performance without additional training.
Most “robustness” work (adversarial, shift, etc.) is just training on reweighted samples (augmented, model-generated, or mined). OOD generalization then comes from: (1) inductive bias (2) similarity to train data (3) luck The 3rd one is the most important of the three.
Today we're putting out an update to the JAX TPU book, this time on GPUs. How do GPUs work, especially compared to TPUs? How are they networked? And how does this affect LLM training? 1/n
State of torch.compile, August 2025.
The best AI researchers zoom at three abstraction levels: - High: paper-level ideas & math - Mid: code-level implementation - Low: GPU/TPU reality (kernels/memory) Low exposes bottlenecks. High accelerates exploration. Mid makes it real. The job is to translate between them!
I wrote a short post on learning the fundamentals of distributed systems, with a few suggested resources to read and a few suggested projects to try.
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🪥Brush teeth🦷 @cleaner_than_u
294 Followers 6K Following RT not endorsement 언제나 그렇듯 말조심 최애 포켓몬: 겐가(팬텀), 파이리
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40 Followers 179 Following Master's Student, Digital Humanities. How AI Could Save (Not Destroy) Education.
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185 Followers 3K Following
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1K Followers 1K Following 말랑말랑 멜랑꼴리 세하마녀/현재 insight에서 outsight를 담당/과거 염세주의자였던 몽상가/아이의 보살핌을 받으며 느리게 길을 만드는 중/가장 오래한 일은 콘텐츠 기획
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946 Followers 600 Following Researching Efficient ML/AI ☘️ | Intern @NVIDIA | PhD in Data Science with @s_scardapane | Visiting @EdinburghNLP | https://t.co/wcDDNFdyW9 |
Shawn Lewis @shawnup
3K Followers 770 Following Founder & CTO @weights_biases. Building tools for AI. Building even more @CoreWeave.
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2K Followers 679 Following enjoying the late pre-agi; making llms go brrr @Aleph__Alpha; yapping about economics of AI systems at https://t.co/tbsybxOMHz
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781 Followers 319 Following my machine is not learning | discord @ rank.dim | email @ req
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3K Followers 341 Following I’m a software engineer building high-performance kernels and compilers at Anthropic! Previously at Facebook/Meta (PyTorch, HHVM, ReDex)
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