Saurabh Srivastava @_saurabh
Building the next stage of AI @ Essential AI Previously: 2x YC (W15, S18); PhD + Postdoc in Program Synthesis San Francisco, CA Joined November 2008-
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Saurabh Srivastava retweeted
[1/5] We have a quick update to share, which contradicts our hypothesis regarding the abilities of Muon and Adam vis-a-vis Grokking.
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Saurabh Srivastava retweeted
Saurabh Srivastava retweeted
Check out our latest research on data. We're releasing 24T tokens of richly labelled web data. We found it very useful for our internal data curation efforts. Excited to see what you build using Essential-Web v1.0!
Check out our latest research on data. We're releasing 24T tokens of richly labelled web data. We found it very useful for our internal data curation efforts. Excited to see what you build using Essential-Web v1.0!
Saurabh Srivastava retweeted
[1/5] 🚀 Meet Essential-Web v1.0, a 24-trillion-token pre-training dataset with rich metadata built to effortlessly curate high-performing datasets across domains and use cases!
Saurabh Srivastava retweeted
Please check out our thorough study on the advantages of Muon. Second-order optimization is a promising path to more efficient LLM pretraining.
Please check out our thorough study on the advantages of Muon. Second-order optimization is a promising path to more efficient LLM pretraining.
If you are not 2nd order optimizing life, what are you even doing? 🙂 Jokes aside, important work from our team on 2nd order optimizers that has significant practical implications for training models: 🕐 Faster training 📉 Fewer tokens ⚙️ Minimal tuning x.com/essential_ai/s…
If you are not 2nd order optimizing life, what are you even doing? 🙂 Jokes aside, important work from our team on 2nd order optimizers that has significant practical implications for training models: 🕐 Faster training 📉 Fewer tokens ⚙️ Minimal tuning x.com/essential_ai/s…
Saurabh Srivastava retweeted
We have been studying how pre-training and reasoning interact. Check out our latest results!!
We have been studying how pre-training and reasoning interact. Check out our latest results!!
We have an exciting result: reflection can be seen in pre-training. Understanding where capabilities emerge from is critical to building performant models. More to come! x.com/ashVaswani/sta…
We have an exciting result: reflection can be seen in pre-training. Understanding where capabilities emerge from is critical to building performant models. More to come! x.com/ashVaswani/sta…
2025 is going to be exciting: “If you can measure it, you can AI it”
Saurabh Srivastava retweeted
One thing I was very wrong about ~4yrs ago is how fundamental “synthetic data” in ML would be. “Obviously” due to data-proc-inequality, synth data should not help learning. The key flaw in this argument is: we do not use info-theoretically optimal learning methods in practice 1/
Saurabh Srivastava retweeted
I'm partnering with @mikeknoop to launch ARC Prize: a $1,000,000 competition to create an AI that can adapt to novelty and solve simple reasoning problems. Let's get back on track towards AGI. Website: arcprize.org ARC Prize on @kaggle: kaggle.com/competitions/a…
Exciting to see progress on reasoning and math. Gemini Pro and math-specialized seems to do really well. Pleasant surprise seeing functional benchmarks used to identify the reasoning gaps for Pro/Ultra/Flash. Best is a 20.3% gap. Sec 6.1.1. Will we get to Gap-0 in 2025? 26? Or…
Exciting to see progress on reasoning and math. Gemini Pro and math-specialized seems to do really well. Pleasant surprise seeing functional benchmarks used to identify the reasoning gaps for Pro/Ultra/Flash. Best is a 20.3% gap. Sec 6.1.1. Will we get to Gap-0 in 2025? 26? Or…
Good thread. haven’t double checked the numbers.
Good thread. haven’t double checked the numbers.
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