Yang Song

@DrYangSong

Research Principal of MSL

yang-song.net

Joined July 2014

Yang Song
@DrYangSong
Aug 10
I’m personally very excited to see us take this step. I’ve long believed that the power of AGI should be broadly distributed, and this is the first of many moves at MSL toward making superintelligence accessible to everyone.
Mark Zuckerberg
@finkd
Aug 10
Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats
Yang Song
@DrYangSong
Apr 8
Today we’re excited to release Muse Spark. It’s our first end-to-end test of the new stacks we’ve built at MSL, and a true testament to this incredible team. We’re eager to learn from your feedback!
ai.meta.com
Introducing Muse Spark: Scaling Towards Personal Superintelligence
Yang Song
@DrYangSong
Oct 29, 2025
Applications change, but the principles are enduring. After a year's hard work led by @JCJesseLai, we are really excited to share this deep, systematic dive into the mathematical principles of diffusion models. This is a monograph we always wished we had.
Chieh-Hsin (Jesse) Lai
@JCJesseLai
Oct 29, 2025
Tired to go back to the original papers again and again? Our monograph: a systematic and fundamental recipe you can rely on! 📘 We’re excited to release 《The Principles of Diffusion Models》— with @DrYangSong, @gimdong58085414, @mittu1204, and @StefanoErmon. It traces the core
Yang Song
@DrYangSong
Aug 5, 2025
Very excited to see this model released to the open-source community. It's still hard to believe that our latest techniques allow it to be so incredibly powerful yet so remarkably small.
Volodymyr Kyrylov
@darkproger
Aug 5, 2025
super excited to have contributed to gpt-oss. We have put a lot of love into both training the model and making the developer examples, check them out: github.com/openai/gpt-oss
Yang Song
@DrYangSong
Jun 13, 2025
Thrilled to see DAPS, our new technique for diffusion-based inverse problem solving, selected as an oral at #CVPR2025! Huge kudos to brilliant student collaborators @WendaChu32619 and @BingliangZhang. Follow them for more great research, and don’t miss their talk on June 15!
Wenda Chu
@WendaChu32619
Jun 13, 2025
I'm excited to share our new work, DAPS, an inference-time diffusion sampler for solving general inverse problems without fine-tuning. DAPS significantly improves sample quality across multiple image restoration tasks, particularly in complicated nonlinear inverse problems. DAPS
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