Chelsea Finn ✓

@chelseabfinn · Palo Alto, CA · joined 19 Jun 2014

Asst Prof of CS & EE @Stanford Co-founder of Physical Intelligence @physical_int PhD from @Berkeley_EECS, EECS BS from @MIT

101 309Followers

397Following

704Posts total

508KViews on collected posts

Against accounts of the same size

7 posts from the last 90 days, next to the 100K–1M follower range. shown widely, but few of those viewers react.

Median views69 350this account21 891median for 100K–1M

Reach, %68.45%this account6.81%median for 100K–1M

Engagement, %0.57%this account1.14%median for 100K–1M

Metric	This account	Median for 100K–1M	Ratio
Median views per post	69 350	21 891	3.17×
Reach (views ÷ followers)	68.45%	6.81%	10.1×
Engagement rate	0.57%	1.14%	0.50×

Others in this range → Compare with another account → How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

72.3K16 Apr

69.3K9 Jul

53.2K

27.8K

83.3K30 Jul

77.9K9 Aug

72.6K17 Aug

51.6K23 Aug

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.00%16 Apr

0.12%9 Jul

0.58%

1.44%

0.41%30 Jul

0.92%9 Aug

0.36%17 Aug

0.58%23 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 1.14%.

What the audience does

Likes63.2%2 749 in total

Reposts5.9%257 in total

Replies1.8%80 in total

Quotes0.8%34 in total

Bookmarks28.3%1 232 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Latest posts

One of the most important aspects of scientific discovery is deciding where to draw insights from. While LLMs are promising tools for science, we lack datasets & evaluations for this step. Help contribute to a public dataset for exactly this: https://t.co/X8NPVnVIwm 51.6K views · 267 likes · 22 reposts · 7 replies 23 Aug 2026 We’re asking the research community to help us build a benchmark for research taste. Scientific discovery starts with a fundamental step: which prior work is worth building on? We want to capture this undocumented layer through our collective knowledge. Please sign up: https:// 72.6K views · 208 likes · 36 reposts · 5 replies 17 Aug 2026 Pretraining a Q-function often doesn’t actually help RL finetuning, compared to initializing Q from scratch. We find that pretraining Q-functions on data from diverse policies is critical to see improvements from pretraining. Paper: https://t.co/dlj5RXVFED 77.9K views · 641 likes · 54 reposts · 13 replies 09 Aug 2026 Pretraining has worked remarkably well across domains We show this doesn’t hold for Q-functions in online RL from a pretrained policy — and propose IPE, a more effective way to learn Q-functions for online RL fine-tuning (1/6) https://t.co/G5SYHmgz83 83.3K views · 305 likes · 29 reposts · 5 replies 30 Jul 2026 I'm giving a talk tomorrow at ICML on emergent physical generalization, including π0.7 🤖 3:15 pm @ SCALE workshop in Ballroom 201 https://t.co/xppCOkktOJ 27.8K views · 368 likes · 20 reposts · 12 replies 09 Jul 2026 I'm giving a talk on how we can move beyond the scalar reward bottleneck for both robotics & LLMs. ICML RLxF workshop tomorrow at 1:30 pm. 53.2K views · 276 likes · 21 reposts · 9 replies 09 Jul 2026 RL is hitting a ceiling with human feedback. What if the world itself becomes the signal? Join us at the RLxF: RL from World Feedback 🌍 workshop at ICML 2026 @icmlconf tomorrow (July 10th)! Web page: https://t.co/cN0itnL1yI https://t.co/vpMTQe4VCm 69.3K views · 68 likes · 11 reposts · 1 replies 09 Jul 2026 LLM post-training used to mean fine-tuning to a downstream task Robotics has been stuck in this setting, needing task-specific fine-tuning for best performance π07 changes this: It works out of the box & outperforms fine-tuned specialists Details: https://t.co/QbO3E4D3QN h 72.3K views · 616 likes · 64 reposts · 28 replies 16 Apr 2026