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Deep learning paper reviews, blogs about machine learning techniques, LLM discussions

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Latest Issues

Don’t Buy a GPU for Every Robot

Eight GPUs can return tons of answers to a fleet of robots, yet yield almost zero useful work. ROSA, developed by NVIDIA Research and Stanford, argues that the fleet chokes not because the models are weak, but because everyone sends request...

9 days ago
1

Stop Training for the Highest Reward - Start Training for Sampling at Scale

A common problem in training LLMs with RL, using methods such as PPO, GRPO, and others, is that the optimization performed during training does not always match the way we actually use the models at inference time - that is, when we generat...

21 days ago
1

To Copy or Not to Copy? The Secret to Faster LLMs Isn’t Just Copy-Pasting

We thought accelerating LLMs using text “copy-paste” (surprise, this is a thing, and we’ll describe it shortly) was a magic bullet. Or so we thought.

Well, I’ll spill the secret so as not to keep you in suspense, it involves copying combin...

a month ago
1

Self-Play Pretraining with Zero Data

We’ve gotten used to the idea that training a massive, powerful language model requires insane amounts of data (hundreds of terabytes), preferably generated by humans.

I absolutely love papers that make me revisit concepts I learned decade...

a month ago
2

From Homework Solvers to Autonomous Researchers: The AI That Learned to Invent Math

Poincaré said: “Science is built up with facts, as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house.”

And indeed, LLMs are now producing results that eluded human mathematicians for de...

a month ago
2

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Authors

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  • Mike Erlihson, Mathy AI

    Seasoned Data Scientist and Math PhD with >20 years of experience in a wide range of algorithm development/data domains

  • Shmulik Cohen | AI Superhero

    AI Engineer at Apiiro. I write AI Superhero - practical posts on what actually happens when you put models, agents, and security tooling into production. Less hype, more of what I learned the hard way.

  • Shimon Moyal

    AI & Data Engineer

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