I am Weihao Zeng, a PhD student supervised by Prof. Junxian He at the Hong Kong University of Science and Technology starting in the fall of 2025.

My main focus is on the post-training aspect of LLMs, specifically including:

• Benchmarking and improving models for long-horizon, realistic agentic tasks (Toolathlon)
• Improving model reasoning capabilities using reinforcement learning (RL) / self-evolution techniques (SimpleRL, B-STaR)
• Exploring efficient data engineering methods for post-training (Deita, Auto Evol-Instruct)

Feel free to email me for any form of academic cooperation: [email protected]

• 2026-01: Two papers have been accepted by ICLR 2026!
• 2025-10: We introduce Toolathlon, a benchmark for language agents offering diverse applications and tools, realistic environment setup, and reliable execution-based evaluation! Toolathlon Leaderboard
• 2025-03: We introduce SimpleRL-Zoo, a deep investigation of zero RL training across diverse model families and sizes! SimpleRL-Zoo Twitter
• 2025-01: Announce our latest effort on O/R-1 Style Model and Scalable Reinforcement Learning for LLM Reasoning! SimpleRL Twitter
• 2025-01: 🎉🎉 Our B-STaR have been accepted by ICLR 2025!
• 2024-09: 🎉🎉 Our Auto Evol-Instruct have been accepted by EMNLP 2024!
• 2024-01: 🎉🎉 Our Deita have been accepted by ICLR 2024!

1. SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

   Weihao Zeng*, Yuzhen Huang*, Qian Liu, Wei Liu, Keqing He, Zejun Ma, Junxian He

   COLM 2025 SimpleRL-Zoo Github

2. The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution

   Junlong Li*, Wenshuo Zhao*, Jian Zhao*, Weihao Zeng*, Haoze Wu*, Xiaochen Wang, Rui Ge, Yuxuan Cao, Yuzhen Huang, Wei Liu, Junteng Liu, Zhaochen Su, Yiyang Guo, Fan Zhou, Lueyang Zhang, Juan Michelini, Xingyao Wang, Xiang Yue, Shuyan Zhou, Graham Neubig, Junxian He

   ICLR 2026 Paper GitHub Leaderboard

3. Pushing Test-Time Scaling Limits of Deep Search with Asymmetric Verification

   Weihao Zeng, Keqing He, Chuqiao Kuang, Xiaoguang Li, Junxian He

   ICLR 2026 Paper GitHub

4. 7B Model and 8K Examples: Emerging Reasoning with Reinforcement Learning is Both Effective and Efficient

   Weihao Zeng*, Yuzhen Huang*, Wei Liu, Keqing He, Qian Liu, Zejun Ma, Junxian He

   HKUST NLP Blog SimpleRL Twitter Github

5. B-STAR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

   Weihao Zeng*, Yuzhen Huang*, Lulu Zhao, Yijun Wang, Zifei Shan, Junxian He

   ICLR 2025 paper

6. What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

   Wei Liu*, Weihao Zeng*, Keqing He, Yong Jiang, Junxian He

   ICLR 2024 paper

7. Automatic Instruction Evolving for Large Language Models

   Weihao Zeng, Can Xu, Yingxiu Zhao, Jian-Guang Lou, Weizhu Chen

   EMNLP 2024 paper

Full Publications on Google Scholar

• April 2025, Qingke Talk, SimpleRL-Zoo and B-STaR: Improving reasoning performance and efficiency through reinforcement learning
• Mar 2025, Westlake University, SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild.
• Feb 2025, Northwestern University, SimpleRL: Emerging Reasoning with Reinforcement Learning is Both Effective and Efficient.
• Feb 2025 Tiktok, SimpleRL: Emerging Reasoning with Reinforcement Learning is Both Effective and Efficient.
• Feb 2025, Huawei Noah's Ark Lab, SimpleRL: Emerging Reasoning with Reinforcement Learning is Both Effective and Efficient.

• National Scholarship in China (2019/2023)
• 2022-09: 🏆🏆 Achieved the 1st Award on SereTOD Challenge 2022 track 2, EMNLP 2022!
• 2021-08: 🏆🏆 Achieved the 4th Award on SMP 2021 Conversational AI Challenge!
• 2021-09: 🏆🏆 Achieved the 8th Place on CCIR 2021 Intelligent NLU Challenge!