Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
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reacted to Juanxi's post with 🔥😎🚀❤️🤝👍🤗🔥 3 months ago
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2026
ScalingOpt | Welcome to join and co-build the Optimization Community!
ScalingOpt is a professional platform focusing on optimization for large-scale deep learning, aiming to advocate for "Optimization at Scale," which means verifiable and scalable optimization algorithms.
This community platform is dedicated to gathering, discovering, comparing, and contributing various cutting-edge optimizers and optimization algorithms.
It's not just a simple Awesome List, it also includes:
Visualizations: Covers visualization scripts for the Rosenbrock Function and the Rastrigin Function for users to freely explore.
Benchmark: We recommend Algoperf as the primary source, along with other verifiable benchmarks and analysis articles, for users to reference the best optimizer.
Papers & Blogs Recommendation: The platform summarizes high-quality papers and blogs from recent years, and continuously adds the latest papers based on daily arXiv updates, currently totaling nearly a hundred articles.
Tutorials Sharing: The platform collects quality resources from the community and is independently producing a "From Classical to Modern Optimizers" Tutorial Series, which is expected to be updated by the end of the month.
We welcome everyone to use it and provide valuable feedback. We also welcome you to join us in co-building the community and focusing on the latest developments in optimization algorithms. The ScalingOpt community platform will be continuously updated.
We hope everyone can give ScalingOpt a star ⭐️. Thank you very much 🙏
Website: https://tianshijing.github.io/ScalingOpt
Github: https://github.com/tianshijing/ScalingOpt
ScalingOpt is a professional platform focusing on optimization for large-scale deep learning, aiming to advocate for "Optimization at Scale," which means verifiable and scalable optimization algorithms.
This community platform is dedicated to gathering, discovering, comparing, and contributing various cutting-edge optimizers and optimization algorithms.
It's not just a simple Awesome List, it also includes:
Visualizations: Covers visualization scripts for the Rosenbrock Function and the Rastrigin Function for users to freely explore.
Benchmark: We recommend Algoperf as the primary source, along with other verifiable benchmarks and analysis articles, for users to reference the best optimizer.
Papers & Blogs Recommendation: The platform summarizes high-quality papers and blogs from recent years, and continuously adds the latest papers based on daily arXiv updates, currently totaling nearly a hundred articles.
Tutorials Sharing: The platform collects quality resources from the community and is independently producing a "From Classical to Modern Optimizers" Tutorial Series, which is expected to be updated by the end of the month.
We welcome everyone to use it and provide valuable feedback. We also welcome you to join us in co-building the community and focusing on the latest developments in optimization algorithms. The ScalingOpt community platform will be continuously updated.
We hope everyone can give ScalingOpt a star ⭐️. Thank you very much 🙏
Website: https://tianshijing.github.io/ScalingOpt
Github: https://github.com/tianshijing/ScalingOpt
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reacted to Juanxi's post with 👍🤗❤️🔥🤗 3 months ago
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ScalingOpt is continuously evolving! We are steadily expanding the Community section with new content. For our Blog, we've launched by featuring work from Jianlin Su and are actively translating insightful posts from scientific communities into English to share on ScalingOpt (we'll keep curating excellent community blogs and providing English versions alongside the originals).
We operate under the Creative Commons Attribution-NonCommercial principle, sharing knowledge freely and openly. We welcome your ideas, suggestions, and feedback to help shape ScalingOpt's future.
If you find this initiative valuable, please consider following and starring the project to show your support. Thank you!
We operate under the Creative Commons Attribution-NonCommercial principle, sharing knowledge freely and openly. We welcome your ideas, suggestions, and feedback to help shape ScalingOpt's future.
If you find this initiative valuable, please consider following and starring the project to show your support. Thank you!
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Backbone-Optimizer Coupling Bias: The Hidden Co-Design Principle
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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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