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Denny Zhou ✓

@denny_zhou · joined 06 Aug 2013

@Meta Superintelligence. Prev: Founded & led the Reasoning Team at @Google Brain / Google DeepMind (2021-2026), co-founded COLM

39 455Followers
594Following
927Posts total
745.2KViews on collected posts

Latest posts

with math no longer the bottleneck, the pace of scientific discovery is exploding 6.9K views · 44 likes · 0 reposts · 7 replies Open on X →
@denny_zhou it's pretty clear who (or what) will be writing and reading those papers https://t.co/h0InR8ANTA
4.9K views · 40 likes · 0 reposts · 0 replies Open on X →
@denny_zhou This is kind of a disaster right? 5.3K views · 21 likes · 0 reposts · 2 replies Open on X →
@denny_zhou https://t.co/pOlMT89DO6
8.2K views · 90 likes · 5 reposts · 0 replies Open on X →
ICLR 2027 has received more submissions than all previous years (2013–2026) combined https://t.co/loUOKvf6si
189.4K views · 1.1K likes · 161 reposts · 34 replies Open on X →
AI is giving everyone superpowers. What are you doing with yours today? 5.3K views · 18 likes · 2 reposts · 4 replies Open on X →
It’s time for arXiv to charge submission fees. Now NeurIPS, ICML, and ICLR are moving in that direction. Why not arXiv? I proposed submission fees back in 2024 27.9K views · 74 likes · 2 reposts · 10 replies Open on X →
The technique of RL finetuning for reasoning was independently discovered by several labs. At Google DeepMind, credit goes to Jonathan Lai (@_JLai) and James An (@jamesjyan117153) on my team. 20.5K views · 81 likes · 3 reposts · 1 replies Open on X →
@denny_zhou The same as what I suggested in my paper “critique of world models” — simulative reasoning, or thought experiment, as opposed to formal logic. LLM is a world model in the lingual space. 8.6K views · 36 likes · 2 reposts · 1 replies Open on X →
Slides for my lecture “LLM Reasoning” at Stanford CS 25: https://t.co/WDI6w0HN8A Key points: 1. Reasoning in LLMs simply means generating a sequence of intermediate tokens before producing the final answer. Whether this resembles human reasoning is irrelevant. The crucial 468.1K views · 3.1K likes · 485 reposts · 53 replies Open on X →

Against accounts of the same size

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

Median views6 908this account924median for 10K–100K
Reach, %17.51%this account3.62%median for 10K–100K
Engagement, %0.73%this account1.52%median for 10K–100K
MetricThis accountMedian for 10K–100KRatio
Median views per post6 9089247.48×
Reach (views ÷ followers)17.51%3.62%4.84×
Engagement rate0.73%1.52%0.48×

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

468.1K24 Jul
8.6K25 Jul
20.5K15 Aug
27.9K14 Sep
5.3K18 Sep
189.4K20 Sep
8.2K
5.3K
4.9K
6.9K21 Sep

Last 10 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.79%24 Jul
0.45%25 Jul
0.42%15 Aug
0.31%14 Sep
0.46%18 Sep
0.73%20 Sep
1.15%
0.43%
0.81%
0.74%21 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes45.7%4 603 in total
Reposts6.6%660 in total
Replies1.1%112 in total
Quotes1.3%126 in total
Bookmarks45.3%4 561 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.

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