4 sources
One of the better people to track for the sequence from Gopher to retrieval-augmented language models and then into Gemini, especially if you care about how DeepMind actually iterated on the frontier-model recipe.
Researcher Profile
Editor reviewedJordan Hoffmann
Compute-optimal scaling for LLM training
Researcher behind DeepMind’s retrieval and compute-optimal language-model work
One of the clearest people to follow for the sequence from retrieval-augmented language models to compute-optimal scaling and then into Gemini.
Organizations
Google DeepMind
Labs
About This Page
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Known For
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01
Chinchilla and compute-optimal scaling
02
RETRO and retrieval-augmented language models
03
Gemini
04
Compute-optimal scaling for LLM training
05
Training Compute-Optimal Large Language Models
06
DeepMind
Start Here
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Related Researchers
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3 sources
A useful page for the DeepMind work that connected large-language-model scaling to the multimodal Gemini push, with a clearer safety-and-evaluation flavor than many purely scaling-focused pages.
3 sources
Worth tracking for the DeepMind thread that links large-model scaling research to the multimodal Gemini stack, rather than treating those as separate eras.
3 sources
A useful profile for the core DeepMind contributor layer behind Chinchilla, Gopher, and Gemini rather than only the more public faces of those systems.
3 sources
A useful profile for the DeepMind researchers who helped carry the lab’s language-model program from scaling-law work into Gemini rather than appearing only on the final product layer.
3 sources
A useful profile for the DeepMind researchers who sat inside the core language-model program as it moved from scaling-law analysis into the Gemini family.