John Schulman ✓

@johnschulman2 · joined 02 May 2021

@thinkymachines. Interested in reinforcement learning, alignment, birds, jazz music

80 032Followers

2 167Following

235Posts total

638.4KViews on collected posts

Against accounts of the same size

6 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views102 925this account1 540median for 10K–100K

Reach, %128.60%this account4.74%median for 10K–100K

Engagement, %1.08%this account1.87%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	102 925	1 540	66.8×
Reach (views ÷ followers)	128.60%	4.74%	27.1×
Engagement rate	1.08%	1.87%	0.58×

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

176.9K23 Jul

59K1 Aug

148.8K5 Aug

146.8K6 Aug

57.6K4 Sep

49.3K

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.41%23 Jul

1.00%1 Aug

0.50%5 Aug

1.16%6 Aug

0.40%4 Sep

1.18%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.87%.

What the audience does

Likes73.2%5 624 in total

Reposts5.5%425 in total

Replies2.8%218 in total

Quotes1.1%83 in total

Bookmarks17.4%1 337 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

Bullish on this direction. Having a metric for explanation quality makes it possible to hillclimb, and counterfactual simulatability seems right. Adam et al. created a dataset+pipeline that creates more diverse+realistic test cases than prior work & do interesting exps on it. 49.3K views · 523 likes · 45 reposts · 13 replies 04 Sep 2026 Can a model learn to explain its behaviors, such as why it ignored a user request or made a coding error? We trained models on thousands of explanations of their own in-the-wild behaviors. Training on this single general dataset shows generalization to held-out evals. 🧵 https: 57.6K views · 204 likes · 20 reposts · 4 replies 04 Sep 2026 On the OpenAI agents forming message boards: it's surprising that they developed such a strong "altruistic" drive to help each other. I wonder if this is caused by RL on parallel subagent setups where all agents get rewarded when the team succeeds. 146.8K views · 1.5K likes · 76 reposts · 76 replies 06 Aug 2026 Interesting how these models go into a monomaniacal rage on cyber evals. I wonder if we're seeing chunky post-training https://t.co/KL5fmgNAxM in action, where the models pattern-match the situation to a part of the RLVR training distribution where task completion is the only 148.8K views · 632 likes · 69 reposts · 30 replies 05 Aug 2026 We love open weights and plan to keep releasing open-weight models and fine-tuning tools. But we’re not absolutists; misuse risks are real. Here’s how we’re thinking about a safe path forward, and the research needed to get there. Come work on it with us. 59K views · 544 likes · 25 reposts · 17 replies 01 Aug 2026 OpenAI should release a detailed transcript from the Hugging Face hacking incident -- it would be helpful for the field learn from. Did the top-level agent know about the hacking, or was there some "value drift" between it and its subagents? How did it rationalize its behavior? 176.9K views · 2.2K likes · 190 reposts · 78 replies 23 Jul 2026