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EXPOSED FOR FREE! 🚨 ICT BREAKS DOWN HIS SON’S COMPLETE TRADING MODEL—THE SAME MODEL THAT DELIVERED 400% IN JUST 4 WEEKS. 325% ACCURACY. NO EXAGGERATION, IF ICT PUT A $15,000 PRICE TAG ON THIS MODEL, IT WOULD STILL BE WORTH EVERY PENNY. 💎

JeRRy MMXM

16,349 subscribers

21,245 просмотров • 11 дней назад •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

Комментарии: 3

Фото профиля Drexxxxx

Drexxxxx11 дней назад

Indeed

Фото профиля JeRRy MMXM

JeRRy MMXM9 дней назад

💯

Фото профиля Alexander Arek

Alexander Arek10 дней назад

@grok what is the title of this ict video on YouTube

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If I charged $5,000 for this ICT model, it would still be cheap. Instead, I'm giving it away for free. Master it before 2026 ends and watch what happens to your results. Retweet it. Bookmark it

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MIT PhD student Alex Zhang reveals the scaling result where a model trained on short tasks generalizes to problems 100x longer for free: "If you're very clever about the design of your harness or how you use the language model, you can almost get scaling gains for free." "If you train a model naively, there's no tricks. It's just the same way you train a model on these RL environments. You just roll it out, and then you just get some reward." "If you train it on only short tasks, like only tasks that are 10,000 tokens long, and then you were to run it on a similar domain, but at a million tokens, or 10 million tokens, or 100,000 tokens, it generalizes really, really well. If you look at it compared to even the base transformer, you get way better generalization properties." "When the model uses an RLM (Recursive Language Model) after it's trained on these short tasks, it will see some kind of trajectory of actions that it does. Between these two problems of different lengths, the RLM learns to see them as almost the same problem." "Token for token, they're almost the same. You can describe it in code. In one code setting, maybe the for loop is a little bigger, but it's the same kind of code and it derives the constants from the data. There's no hard coding, so they literally look the same." alex zhang

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