4 sources
A high-signal researcher for understanding how DeepMind approaches generality, especially in areas where reinforcement learning, multimodality, and large-scale systems meet.
Researcher Profile
Editor reviewedYuhuai (Tony) Wu
Reasoning, planning, math
Research scientist at Google
A strong researcher to follow if you care about reasoning-heavy language models, especially the line connecting chain-of-thought style methods, evaluation frameworks, and more agentic prompting patterns.
Also Known As
Yuhuai WuTony WuTony
Organizations
Google
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last reviewed
March 18, 2026
Official And External Links
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
Reasoning and acting in language models
02
Evaluation infrastructure around language models
03
Reasoning bootstrapping methods such as STaR
04
Reasoning, planning, math
05
ReAct: Synergizing Reasoning and Acting in Language Models
06
xAI
Start Here
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Signature Works
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People worth exploring next because they share topics, labs, or source material with this profile.
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A long-running builder of ML intuition whose influence spans Bayesian methods, reinforcement learning, and recent work on generalist and generative environments.
Start HereNando de Freitas
1 source
Co-authored ReAct: a simple, high-leverage template for tool-using LLM agents.
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Co-authored ReAct: a simple, high-leverage template for tool-using LLM agents.
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Co-authored ReAct: a simple, high-leverage template for tool-using LLM agents.
1 source
Co-authored ReAct: a simple, high-leverage template for tool-using LLM agents.