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Sergey Levine

Sergey Levine✓ Add us as a preferred source on Google

X @svlevine
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🔒 Paid proposals held safely in escrow — released only when the work's approved.

📣Sponsored postOne tweet with your link and creative. $1,071→ 🧵Sponsored threadA multi-tweet thread — X rewards long-form, threads out-reac… $2,678→ 🎬Sponsored video postA dedicated video tweet — X pushes video hard, so it gets pr… $1,499→ 📌Pinned postA sponsored post pinned to the top of my profile for the mon… $3,213/mo→ 📍Post + pin comboA sponsored post that also stays pinned for a set number of … $1,714→ 🔁Repost / quote-tweet packageI repost or quote-tweet your posts through the month — distr… $2,142/mo→ 🔗Bio link placementYour link in my bio for the month — X allows only one, so it… $1,607/mo→ 🎙️X Spaces appearance / hostI host or feature you in an X Space (live audio) — a branded… $2,142→ 🤝Cross-promo / mutual shoutoutWe shout each other out — a value swap between accounts. Swap→ 🎬UGC HireBranded content for your own channels & ads from $469→ 🎙️Podcast guest spotInvite Sergey Levine onto your podcast (~45 min, remote) $468→ 📞1:1 ConsultPaid video call — advice, coaching or Q&A $75/hr→

Every booking is a normal escrow-protected deal.

Followers
133.8K
Posts
2.6K
Following
144
Account age
8 yrs
🎁 Free analysisfor Sergey Levine
🕵️Fake Followers CheckReal-audience % & bot risk→ 📊Engagement rateBenchmarked vs. peers→ 💰What they chargeFair sponsored-post price→

Known for

These robots are coordinated by an LLM that decides what tasks to try, where to try it, how to obey a "constitution" with some "Asimov-inspired" laws. Check out more about AutoRT here: https://t.co/PitohaTbdt And in the blog post: https://t.co/OaINc4DGln Here is a thread 👇 157.4K views These robots are coordinated by an LLM that decides what tasks to try, where to try it, how to obey a "constitution" with some "Asimov-inspired" laws. Check out more about AutoRT here: https://t.co/PitohaTbdt And in the blog post: https://t.co/OaINc4DGln Here is a thread 👇 If you have a policy that uses diffusion/flow (e.g. diffusion VLA), you can run RL where the actor chooses the noise, which is then denoised by the policy to produce an action. This method, which we call diffusion steering (DSRL), leads to a remarkably efficient RL method! 🧵👇 153K views If you have a policy that uses diffusion/flow (e.g. diffusion VLA), you can run RL where the actor chooses the noise, which is then denoised by the policy to produce an action. This method, which we call diffusion steering (DSRL), leads to a remarkably efficient RL method! 🧵👇 We figured out how to train diffusion models with RL to generate images aligned with user goals! Our RL method gets ants to play chess and dolphins to ride bikes. Reward from powerful vision-language models (i.e., RL from AI feedback): https://t.co/5Mui7Wb8pB A 🧵👇 129.6K views We figured out how to train diffusion models with RL to generate images aligned with user goals! Our RL method gets ants to play chess and dolphins to ride bikes. Reward from powerful vision-language models (i.e., RL from AI feedback): https://t.co/5Mui7Wb8pB A 🧵👇 Really excited to share what I've been working on with my colleagues at Physical Intelligence! We've developed a prototype robotic foundation model that can fold laundry, assemble a box, bus a table, and many other things. We've written a paper and blog post about it. 🧵👇 115K views Really excited to share what I've been working on with my colleagues at Physical Intelligence! We've developed a prototype robotic foundation model that can fold laundry, assemble a box, bus a table, and many other things. We've written a paper and blog post about it. 🧵👇

📊 Post performance

Recent posts · views
Jan 11latest
115K
Avg views / post
108.6K–155K
Typical post (views)
86%
Reach vs followers
79/100
Very consistent reach
Apr 2018
On X since
Half of recent posts land between 108.6K and 155K views (median 129.6K) — a dependable floor a sponsored post can count on.

🔥 Top post: I always found it puzzling how language models learn so much fro · 315K views

📊 Activity & format

Posting cadence
0.05 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly text
Recent: 5 video · 2 image · 11 text.
Follower / following
929×
Follows 144 back. A strong ratio — an audience that follows them, not a follow-for-follow network.
Verification
✓ X Premium
A paid X Premium (Blue) verified account.

Recent posts

View on X ↗
🔥 Top post I always found it puzzling how language models learn so much from next-token prediction, while video models learn so little from next frame prediction. Maybe it's because LLMs are actually brain scanners in disguise. Idle musings in my new blog post: 315K❤ 1.3K🔁 220
The pi-05 model is now in openpi: https://t.co/NLfraCc7zf Now with pytorch (πtorch?) support too! 81.1K❤ 786🔁 102 I wrote a fun little article about all the ways to dodge the need for real-world robot data. I think it has a cute title. 155K❤ 822🔁 149 ▶ If you have a policy that uses diffusion/flow (e.g. diffusion VLA), you can run RL where the actor chooses the noise, which is then denoised by the policy to produce an action. This method, which we call diffusion steering (DSRL), leads to… 153K❤ 1.2K🔁 177 ▶ Very happy to announce that we are open-sourcing the π₀ model, weights, and some fine-tuned checkpoints! Hoping this leads to lots of great follow-up research: https://t.co/NLfraCc7zf Here is a fun test from our friends at UPenn. 72.5K❤ 809🔁 138 ▶ Really excited to share what I've been working on with my colleagues at Physical Intelligence! We've developed a prototype robotic foundation model that can fold laundry, assemble a box, bus a table, and many other things. We've written a … 115K❤ 943🔁 167 Since cat is out of the bag, it’s time I share: I’ll be starting a new adventure with an incredible team of friends and long-time collaborators to take on the big challenge of robot learning at scale! It's called Physical Intelligence (Pi…… 108.6K❤ 1.2K🔁 123 ▶ These robots are coordinated by an LLM that decides what tasks to try, where to try it, how to obey a "constitution" with some "Asimov-inspired" laws. Check out more about AutoRT here: https://t.co/PitohaTbdt And in the blog post: https://… 157.4K❤ 732🔁 181 We figured out how to train diffusion models with RL to generate images aligned with user goals! Our RL method gets ants to play chess and dolphins to ride bikes. Reward from powerful vision-language models (i.e., RL from AI feedback): htt… 129.6K❤ 820🔁 185 ▶ We're releasing our code for "driving any robot", so you can also try driving your robot using the general navigation model (GNM): https://t.co/ohs8A3qnAw Code goes with the GNM paper: https://t.co/DWtvOWWd0b Should work for locobot, hop… 113.3K❤ 814🔁 176 Final set of CS182 Deep Learning lectures now added to the course playlist: https://t.co/21GvfFRcHo GANs (Lec 19), adv. examples (20), and meta-learning (21)! More materials on the course website: ❤ 1.3K🔁 272 I've updated the CS182: Deep Learning with new lectures on RL (policy gradients, actor critic, Q-learning), autoencoders, latent variable models, and VAEs! Website: https://t.co/0PaBOJElo9 Playlist (lectures 15-18): https://t.co/21GvfFRcH… ❤ 1.1K🔁 219

💡 Facts

🗓️Joined X in 2018 — 8 years ago.
👁️Averages 115K views per post.
📤Posts about 0.1× per week.
💬1.19% engagement rate.
🏅Earned the 100K Followers for passing 100K followers.

🕵️ Fake follower check

Estimated
69/100
Good Credibility score
85%
Real Real audience
Low Fake-follower risk
High Data confidence
  • Est. 85% real, active audience · Low fake-follower risk.
  • Engagement (~0.9× of followers engage each post) is around typical for X.
  • Organic base — far more followers than accounts it follows.
  • Verified account.
  • Established account (8+ years old).
  • 1.19% engagement — below the ~14.4% typical for this size.

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

Associate Professor at UC Berkeley Co-founder, Physical Intelligence

📸 Gallery

If you have a policy that uses diffusion/flow (e.g. diffusion VLA), you can run RL where the actor chooses the noise, which is then denoised by the policy to produce an action. This method, which we call diffusion steering (DSRL), leads to a remarkably efficient RL method! 🧵👇 Very happy to announce that we are open-sourcing the π₀ model, weights, and some fine-tuned checkpoints! Hoping this leads to lots of great follow-up research: https://t.co/NLfraCc7zf Here is a fun test from our friends at UPenn. Really excited to share what I've been working on with my colleagues at Physical Intelligence! We've developed a prototype robotic foundation model that can fold laundry, assemble a box, bus a table, and many other things. We've written a paper and blog post about it. 🧵👇 These robots are coordinated by an LLM that decides what tasks to try, where to try it, how to obey a "constitution" with some "Asimov-inspired" laws. Check out more about AutoRT here: https://t.co/PitohaTbdt And in the blog post: https://t.co/OaINc4DGln Here is a thread 👇 We figured out how to train diffusion models with RL to generate images aligned with user goals! Our RL method gets ants to play chess and dolphins to ride bikes. Reward from powerful vision-language models (i.e., RL from AI feedback): https://t.co/5Mui7Wb8pB A 🧵👇 We're releasing our code for "driving any robot", so you can also try driving your robot using the general navigation model (GNM): https://t.co/ohs8A3qnAw Code goes with the GNM paper: https://t.co/DWtvOWWd0b Should work for locobot, hopefully convenient to hook up to any robot What did we learn from 5 years of robotic deep RL? My colleagues at Google and I tried to distill our experience into a review-style journal paper, covering some of the practical aspects of real-world robotic deep RL: https://t.co/fYGQfFYlKu 🧵->

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

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Sergey Levine has 133,844 followers on X.

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