omar khaled @therealokai
AI enthusiast passionate about research and bridging the Arabic gap in artificial intelligence. و إن شاء الله هعملها linkedin.com/in/dsomarkhale… The Universe Joined September 2023-
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This one paper might kill the AI scaling hype. While Big Tech burns billions on massive datasets, researchers just achieved state-of-the-art agent performance using 78 samples. And it makes a scary amount of sense. Here's the full breakdown:
أكتوبر شهر الوقوع في الحب أنا في أكتوبر من ١٩٩٨:
the closer I get to my MVP being done, the more I don’t want to ship it lol
بفتكر ايام ثانوية عامة ( و اول سنتين فالكلية) لما كان جزء من المدرسين و معيدين الكلية يعاملوني معاملة غريبة فشخ و لما اسألهم يقولولي "هو كدا شكلك مش عاجبني" لحد ما قررت استلسم و احلق
بفتكر ايام ثانوية عامة ( و اول سنتين فالكلية) لما كان جزء من المدرسين و معيدين الكلية يعاملوني معاملة غريبة فشخ و لما اسألهم يقولولي "هو كدا شكلك مش عاجبني" لحد ما قررت استلسم و احلق
حاجة كدا زي "عشان تعرف حسابك قوي ولا لا اكتب الباسورد في كومنت لو ظهر **** يبقي حسابك قوي"
حاجة كدا زي "عشان تعرف حسابك قوي ولا لا اكتب الباسورد في كومنت لو ظهر **** يبقي حسابك قوي"
This Tencent paper shows a way to improve reasoning by training only on raw text using reinforcement learning. It is called Reinforcement Learning on Pre-Training data (RLPT) and it removes the need for human labels. Simple “predict the next segment” rewards are enough to…
"Rule number one, DON'T FKN SAY 'you are absolutely right!' and write code!"
"Rule number one, DON'T FKN SAY 'you are absolutely right!' and write code!"
DSPy and ColBERT are interesting academic experiments imo. Each is a multi-paper repo that has one coherent artifact, combining our latest research together. We typically release the features as open source—hence get users/feedback—well before writing a paper on the new ideas.
DSPy and ColBERT are interesting academic experiments imo. Each is a multi-paper repo that has one coherent artifact, combining our latest research together. We typically release the features as open source—hence get users/feedback—well before writing a paper on the new ideas.
Talking to grad students, too many think that long-term projects (not scattered papers), proper code releases, thoughtful benchmarks are "not incentivized". Most often they're mistaken. If we're talking incentives, *nothing* matches demonstrating impact! Will blog on this soon.
Science of RL optimization is likely humanity’s last open scientific problem
Prediction: In ~3 years academia will be the most desirable place to do fundamental AI research Contributing factors: - small models improve/become significantly more impactful - open weights community broadens its reach - gpus continue to get faster & cheaper - meaningful…
You're in a ML Engineer interview at Meta, and the interviewer asks: "Why does RL work better than supervised learning for LLMs?" Here's how you answer:
كنت بدوّر على كورسات عن إزاي تبني Start-up، بحيث تكون فاهم إيه اللي هيتم وتوسع وجهة نظرك بشكل أكبر. وصلت للكورس ده فحبيت أشاركه معاكم، يمكن يساعد شخص يبدأ مسيرة مهنية جديدة بإذن الله ❤️
Wow, a new post-training method. SFT = efficient but capped 🚦 RL = powerful but slow 🐢 Now enter: Guess-Think-Answer (GTA) GTA fuses guess (SFT), think (reflection), and answer (RL-shaped). Result: ⚡ Faster convergence than RL 📈 Higher ceiling than SFT 🛠️ Gradient…
انت عارف المشكلة فين؟ انك كل مرة بتوصل ل ٨٠٪ … بعد كدة بتوقف و ترجع تعيد تاني. ال ٢٠٪ الاخيرة دي هي اللي فيها كل حاجة
"It's autocomplete" is not a helpful analogy to understand LLMs. A LLM is more like a database that lets query information in natural language. You can query both knowledge, and "patterns" (associative programs seen in the training data, that can be applied to new inputs).
“The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The bitter lesson is that building in human knowledge is a losing game in the long run.” – Sutton
“The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The bitter lesson is that building in human knowledge is a losing game in the long run.” – Sutton
"We should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries." Richard Sutton
Most RL for LLMs involves only 1 step of RL. It’s a contextual bandit problem and there’s no covariate shift because the state (question, instruction) is given. This has many implications, eg DAgger becomes SFT, and it is trivial to design Expectation Maximisation (EM) maximum…
How does backprop work with RL? The virtue of backprop is that it updates EACH individual parameter in proportion to how much wiggling it affects the loss. This is only possible if you know how changing each parameter affects the loss function. But of course with RL this is…
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