google/path-foundation
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efe PRO
efecelik
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NLP, LLM
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Recent Updates on ScalingOpt | Your Stars are Appreciated
We are pleased to announce several key updates to the ScalingOpt project:
Pyramid Visualization Structure
Following a suggestion from Yufei, we have introduced a pyramid-based visualization framework to systematically outline the layered architecture of Foundation Models—from foundational principles to infrastructure-level details. This addition is designed to assist teams in organizing and presenting related materials more clearly.
Integration of Optimizer Summaries by Yifeng
We extend a warm welcome to Yifeng (author of MARS), who has joined the project. He has contributed a comprehensive summary of over 100 optimizers, now available in ScalingOpt. This resource can be accessed via the “Optimization Summary Sheet” on the homepage or under the Optimizers page, featuring a reader-friendly interface that supports easy viewing, downloading, and citation.
Growing Community of Members
We continue to update and expand the list of active members. Researchers interested in Optimization & Efficient AI are encouraged to join and participate in discussions. Feedback and suggestions are also highly welcomed and will be reviewed and incorporated on an ongoing basis.
Tutorials in Progress
The tutorial development is actively underway. Currently, we have prepared over 300 slides and are refining and expanding the content in collaboration with contributors.
This community is driven purely by passion and a commitment to open knowledge sharing. Your support through starring the repository is greatly appreciated!
We are pleased to announce several key updates to the ScalingOpt project:
Pyramid Visualization Structure
Following a suggestion from Yufei, we have introduced a pyramid-based visualization framework to systematically outline the layered architecture of Foundation Models—from foundational principles to infrastructure-level details. This addition is designed to assist teams in organizing and presenting related materials more clearly.
Integration of Optimizer Summaries by Yifeng
We extend a warm welcome to Yifeng (author of MARS), who has joined the project. He has contributed a comprehensive summary of over 100 optimizers, now available in ScalingOpt. This resource can be accessed via the “Optimization Summary Sheet” on the homepage or under the Optimizers page, featuring a reader-friendly interface that supports easy viewing, downloading, and citation.
Growing Community of Members
We continue to update and expand the list of active members. Researchers interested in Optimization & Efficient AI are encouraged to join and participate in discussions. Feedback and suggestions are also highly welcomed and will be reviewed and incorporated on an ongoing basis.
Tutorials in Progress
The tutorial development is actively underway. Currently, we have prepared over 300 slides and are refining and expanding the content in collaboration with contributors.
This community is driven purely by passion and a commitment to open knowledge sharing. Your support through starring the repository is greatly appreciated!
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1291
🎮 Introducing: Paper Popularity Game
Think you know which AI papers go viral? Test your instincts!
I built a little game where you try to guess the popularity of AI research papers from the Hugging Face Daily Papers feed.
How it works:
You'll see two papers side by side—read the titles, check the abstracts, and pick which one you think got more upvotes from the HF community.
It's a great way to discover trending AI research while having fun.
Tests your intuition about what the ML community finds interesting.
Try it out:
efecelik/paper-popularity-game
Would love to hear your high scores and feedback!
Think you know which AI papers go viral? Test your instincts!
I built a little game where you try to guess the popularity of AI research papers from the Hugging Face Daily Papers feed.
How it works:
You'll see two papers side by side—read the titles, check the abstracts, and pick which one you think got more upvotes from the HF community.
It's a great way to discover trending AI research while having fun.
Tests your intuition about what the ML community finds interesting.
Try it out:
efecelik/paper-popularity-game
Would love to hear your high scores and feedback!
updated
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Space
3 days ago
reacted to
Ujjwal-Tyagi's
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4 days ago
Post
2733
So, Koreans are also doing great progress behind Chinese,
Their two open source ai models that are actually good in coding. upstage/Solar-Open-100B skt/A.X-K1
Their two open source ai models that are actually good in coding. upstage/Solar-Open-100B skt/A.X-K1
- 1 reply
published
a
Space
4 days ago
posted
an
update
4 days ago
Post
1605
Interesting paper: PhysRVG
The core idea: instead of treating physics as a soft condition the model can work around during optimization, enforce it strictly via reinforcement learning. The paper focuses on rigid body dynamics - collisions, pendulums, free fall, rolling.
PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models (2601.11087)
The core idea: instead of treating physics as a soft condition the model can work around during optimization, enforce it strictly via reinforcement learning. The paper focuses on rigid body dynamics - collisions, pendulums, free fall, rolling.
PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models (2601.11087)
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upvoted
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4 days ago
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4 days ago
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4 days ago
updated
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4 days ago
published
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Space
5 days ago
replied to
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5 days ago
Yeah I think you're right. Deep expertise used to be the thing, but now AI kinda levels that playing field. Feels like the real advantage is being able to pull from different areas and connect stuff others don't see.