Post-training for agents and reasoning,
especially reinforcement learning.
I am a Ph.D. candidate at LEAP Lab, Tsinghua University, currently with Moonshot AI. Previously, I was a research intern on Alibaba Qwen’s Post-training Team.
My research spans foundation-model post-training and reinforcement learning, with across my publications. The Flexibility Trap received the ICML 2026 Outstanding Paper Award (2 / 24,661 submissions). Beyond the 80/20 Rule and Absolute Zero also ranked among the top 5 and top 25 most-cited NeurIPS 2025 papers, respectively.
Alongside this research, my recent team projects include Kimi K3.1 Pro and Kimi K3. In HopChain, I explore scalable data synthesis for vision-language reinforcement learning; it is one of the training tasks used in Qwen3.5.
I also build open models: Llama3-Chinese-Chat has 1M+ downloads and reached Hugging Face Trending #7; Xwen-Chat includes a 72B model that surpassed the then state-of-the-art DeepSeek-V3 in chat performance.
Identifies high-entropy tokens as critical decision points in reasoning, accounting for nearly all performance gains from reinforcement learning with verifiable rewards.
Reveals the limits of arbitrary-order reasoning in diffusion language models and introduces JustGRPO, preserving parallel decoding while improving reasoning.
Synthesizes multi-hop visual reasoning data for RLVR, improving generalization on 20 of 24 benchmarks across two Qwen3.5 models.
Scales post-training and joint reinforcement learning across domains to strengthen coding, reasoning, and long-horizon agents, with a one-million-token context window.
A frontier model project advancing scalable agentic post-training and open intelligence.
A native multimodal model family; HopChain is one of its vision-language RLVR training tasks.
Open 7B and 72B chat models post-trained from Qwen2.5, with a focus on stronger conversational capabilities.
Bilingual Llama 3 chat models for Chinese and English, with improved role-play, tool use, and math capabilities. Over one million downloads.
Learns a family of policies and selects state-adaptive balances between policy improvement and constraints for offline-to-online reinforcement learning.
2021–2027 · expected
Department of Automation
Tsinghua University
2017–2021
SHENYUAN Honors College
Beihang University
Reviewer for NeurIPS, ICML, ICLR, ACL, CoRL, ICRA, and related venues.
I share research updates and technical notes on X, Zhihu, and Xiaohongshu.
Z Potentials interview · in Chinese
I’m always happy to connect, exchange ideas, and explore collaborations. Feel free to say hello!
wangshenzhi99 [at] gmail [dot] com