3 sources
A strong systems page because his work repeatedly shows up where inference efficiency meets usable long context, especially in attention sinks, StreamingLLM, post-training quantization, and later long-context head designs.
Researcher Profile
Editor reviewedYuandong Tian
Streaming + long-context stability (attention sinks)
Co-founder of a stealth startup; formerly Research Scientist Director at Meta AI
A high-signal researcher for the systems side of modern AI, especially where reinforcement learning, memory-efficient large-model training, and long-context inference meet.
Organizations
Meta
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
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
OpenGo and reinforcement-learning systems
02
Memory-efficient large-model training
03
Streaming and long-context LLM methods
04
Streaming + long-context stability (attention sinks)
05
Efficient Streaming Language Models with Attention Sinks
06
Long context
Start Here
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Signature Works
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Supporting Sources
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