Yuandong Tian is a research-driven engineer and entrepreneur with 13 years of experience building large-scale AI systems, currently co-founding Recursive in San Francisco. He spent a decade at Meta FAIR leading RL and LLM research—shipping projects like OpenGo (distributed RL across thousands of GPUs) and pioneering latent-space reasoning and long-context LLM efficiency. Trained as a roboticist with a PhD from Carnegie Mellon, he blends deep academic rigor with production-grade engineering from prior work at Google X on self-driving systems. A hands-on open-source contributor, he improved core engines in high-profile projects such as Facebook’s DarkForest Go and the ELF research platform, focusing on model performance and dependency-safe refactors. He also holds advanced Japanese proficiency (JLPT N2), an uncommon asset for coordinating international research collaborations. Practical, curious, and results-oriented, he connects cutting-edge research to deployable systems and startups.
An End-To-End, Lightweight and Flexible Platform for Game Research
Role in this project:
Back-end Developer
Contributions:176 commits, 14 PRs, 328 pushes in 11 months
Contributions summary:Yuandong primarily refactored and updated core classes, specifically altering class inheritance and dependencies within the game research platform. The changes involved refactoring code related to AI, including renaming classes and updating header files. They also removed a dependency on rlmethod_forward. These contributions indicate a focus on improving the platform's structure and dependencies.
Contributions:24 commits, 2 PRs, 36 pushes in 5 months
Contributions summary:Yuandong primarily focused on improving the engine's core functionality within the DarkForest Go project. They modified various Lua scripts related to CNN player implementation (cnnPlayerV2/cnnPlayerMCTSV2.lua), fixing issues and adjusting parameters. Further contributions included updates to the training script and configuration files, along with the removal of legacy files and updates to documentation (README). This indicates an effort to enhance the model's performance and integration within the Go engine.
go
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