Han Fang is an AI Research Scientist at Meta’s SuperIntelligence Labs, with research interest in RL and agents. In 2023-2024, he led post-training of Llama 2 & 3, debut Meta AI and grew it to 1 billion MAU. He drove the integrated training runs, core capabilities, tool use and orchestration, and data flywheel. In 2025, he focuses on teaching models to think, reason, and use tools. He is currently pushing the research frontier of Agents and RL, and has a deep interest in self-improvement.

Han holds a PhD in Applied Mathematics and has published in top-tier venues with 10K+ citations. He is a recipient of the President’s Award to Distinguished Doctoral Students, the Woo-Jong Kim Dissertation Award, and the Excellence in Research Award.

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featured papers

Generalized Parallel Scaling with Interdependent Generations

Harry Dong, David Brandfonbrener, Eryk Helenowski, Yun He, Mrinal Kumar, Han Fang, Yuejie Chi, Karthik Abinav Sankararaman · ICML 2026

Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization

Zishun Yu, Tengyu Xu, Di Jin, Karthik Abinav Sankararaman, Yun He, Wenxuan Zhou, Zhouhao Zeng, Eryk Helenowski, Chen Zhu, Sinong Wang, Hao Ma, Han Fang · ICML 2025

The Perfect Blend: Redefining RLHF with Mixture of Judges

Tengyu Xu, Eryk Helenowski, Karthik Abinav Sankararaman, Di Jin, Kaiyan Peng, Eric Han, Shaoliang Nie, Chen Zhu, Hejia Zhang, Wenxuan Zhou, Zhouhao Zeng, Yun He, Karishma Mandyam, Arya Talabzadeh, Madian Khabsa, Gabriel Cohen, Yuandong Tian, Hao Ma, Sinong Wang, Han Fang · arXiv 2024

Linformer: Self-Attention with Linear Complexity

Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, Hao Ma · arXiv 2020

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