Jason Wei ✓
@_jasonwei · sf · joined 22 Oct 2020
ai researcher @meta, past: openai, google 🧠
113 406Followers
737Following
1 265Posts total
3.9MViews on collected posts
Последние посты
Ищем новые посты…
Cognitive reward shapes in sports and career
Sports are amazing environments to learn. When you play a sport for thousands of hours, you start to see the world through that sport. It is a simple fact—your biological neural network is being conditioned to respond to the behavior
64K views · 211 likes
· 21 reposts · 20 replies
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@_jasonwei but it also seems like larger models have a stronger cognitive core, not just more world knowledge - in the sense of being able to integrate new information as well as grok fundamentals better, and this seems to have continued to improve even going from opus to fable.
6.6K views · 44 likes
· 2 reposts · 2 replies
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@_jasonwei great arguments, but i partially disagree, knowledge scales with parameters, but tool use doesn't, because it is a behavior, not entirely cognition.
i have built RL environments to eval this: single tool use, composing/multi tool use, and tool creation across four do
1.6K views · 12 likes
· 2 reposts · 1 replies
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@_jasonwei one way to get past the larger models thing i've been using is to periodically use larger models to make deterministic "compound tools" (made out of more primitive tools) that the smaller models cannot screw up.
pretty handy form of @erikdunteman "muscle memory" - se
7.9K views · 61 likes
· 5 reposts · 4 replies
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When language models first started using tools well, I was sympathetic to the narrative that instead of scaling up language models, all we needed was a strong enough "cognitive core", say 1B parameters, and anything else could be done with tool use, like browsing the internet or
297.8K views · 1.2K likes
· 112 reposts · 74 replies
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What's left for humans in a world where machine intelligence has so many advantages?
I recently got a Tesla, and using full self-driving has been a wake up call to just how many advantages AI has over humans. The few times I disengaged it because I thought it was going into the
66.5K views · 394 likes
· 36 reposts · 31 replies
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On HealthBench Pro, Muse Spark 1.1 achieves similar performance with GPT-5.6 Sol (maybe slightly better) at a fraction of the cost. Affordable health superintelligence is our north star!
26.9K views · 133 likes
· 16 reposts · 15 replies
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We benchmarked Muse Spark 1.1 and GPT-5.6 Sol on HealthBench Professional, OpenAI's benchmark of 525 real clinician tasks 🏥🩺
Muse Spark 1.1 tops our board: better overall score than GPT-5.6 Sol, statistically on par on the length-adjusted score at a fraction of the cost https://
208.1K views · 114 likes
· 13 reposts · 17 replies
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@_jasonwei We actually have a justification for this very intuition "I would never surpass the full ability of someone else because they were playing to their strengths which I didn’t have" -- we show that RL can adapt/calibrate CoTs to the model's own capability, while SFT often
14.2K views · 64 likes
· 8 reposts · 1 replies
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@_jasonwei this is how bill ackman would post he's joining meta
4.2K views · 127 likes
· 0 reposts · 1 replies
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@_jasonwei absolutely iconic to post this right now
9.4K views · 60 likes
· 0 reposts · 0 replies
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Becoming an RL diehard in the past year and thinking about RL for most of my waking hours inadvertently taught me an important lesson about how to live my own life.
One of the big concepts in RL is that you always want to be “on-policy”: instead of mimicking other people’s
380.2K views · 3.5K likes
· 358 reposts · 133 replies
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@_jasonwei @gm8xx8 isn't data kind of easy to generate? Just like. Stick cameras everywhere
45.2K views · 339 likes
· 3 reposts · 49 replies
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@_jasonwei Synthetic Data is renewable energy of AI?
6.8K views · 47 likes
· 3 reposts · 0 replies
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@_jasonwei Jesus, he looks rough. https://t.co/jhZmxJCcBW
20.1K views · 63 likes
· 0 reposts · 12 replies
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Yall heard it from the man himself https://t.co/Pie7QLCjA7
2.8M views · 3.9K likes
· 427 reposts · 153 replies
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На фоне аккаунтов своего размера
6 постов за последние 90 дней рядом с диапазоном 100K–1M подписчиков. показывается большему числу людей, чем аккаунты того же размера.
Медианные просмотры35 942этот аккаунт5 983медиана для 100K–1M
Охват, %31.69%этот аккаунт1.49%медиана для 100K–1M
Вовлечённость, %0.72%этот аккаунт0.93%медиана для 100K–1M
| Показатель | Этот аккаунт | Медиана для 100K–1M | Отношение |
|---|---|---|---|
| Медианные просмотры на пост | 35 942 | 5 983 | 6.01× |
| Охват (просмотры ÷ подписчики) | 31.69% | 1.49% | 21.3× |
| Вовлечённость | 0.72% | 0.93% | 0.78× |
Другие в этом диапазоне → Сравнить с другим аккаунтом → Как считаются эти ориентиры →
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
6.8K13 Dec
45.2K
380.2K16 Jul
9.4K
4.2K
14.2K
208.1K13 Jul
26.9K14 Jul
66.5K12 Aug
297.8K17 Aug
7.9K
1.6K18 Aug
6.6K19 Aug
64K
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.73%13 Dec
0.87%
1.07%16 Jul
0.64%
3.08%
0.52%
0.08%13 Jul
0.62%14 Jul
0.71%12 Aug
0.47%17 Aug
0.90%
0.92%18 Aug
0.74%19 Aug
0.40%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.
What the audience does
Likes61.5%10 247 in total
Reposts6.0%1 006 in total
Replies3.1%513 in total
Quotes2.7%457 in total
Bookmarks26.7%4 449 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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