Biography

I am a researcher at Alibaba Tongyi Lab, where I lead Z-Image's reinforcement learning alignment. My work mainly focuses on foundation model post-training, spanning SFT, RL, and training infrastructure, while also contributing to pre-training for advanced model capabilities. My research centers on multimodal generation and understanding. Earlier, I worked on multimodal large language models at Tencent Hunyuan, with a focus on improving their advanced reasoning capabilities.

Featured projects

Z-Image Image generation Z-Reward Reward modeling

News

  • 2026 New paper on pixel-space text-to-image diffusion models released on arXiv.
  • 2026 Released Z-Reward, a reasoning-internalized teacher-student reward modeling framework for text-to-image post-training.
  • 2026 Released Z-Image (Base), the high-quality foundation model specializing in rich aesthetics and controllability. The series is now the #2 most popular Chinese model on Hugging Face and ranks 2nd in global Image Generation API usage on fal.ai.
  • 2025 Released Z-Image-Turbo, an efficient 6B foundation model specializing in photorealistic image generation. It ranked #1 among open-weight text-to-image models on the Artificial Analysis Image Arena in December 2025, while achieving sub-second inference latency.
  • 2024 Released MM-IQ, a new benchmark for assessing the core reasoning capabilities of large multimodal models.
  • 2024 New paper on Self-Correction in LLMs released on arXiv.
  • 2024 Paper System-2 Mathematical Reasoning accepted to TMLR.
  • 2023 The extended paper Tri-token Equipped Transformer Model for Image Matting released on arXiv.
  • 2022 TransMatting accepted to ECCV.
  • 2021 Won the 2nd Place Award in NTIRE 2021 Challenge on Multi-modal Aerial View Object Classification at CVPR 2021.

Publications

* Equal contribution. † Project Lead.

Selected work

Tech Report, 2025

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer GitHub Stars HF downloads9.82M/30d

Huanqia Cai, Sihan Cao, Ruoyi Du, Peng Gao, Steven Hoi, et al.

Z-Image Teaser

Z-Image is a state-of-the-art foundation model designed for high efficiency. It excels at generating photorealistic images with superior visual quality while maintaining low computational cost. Z-Image-Turbo ranked #1 among open-weight text-to-image models on Artificial Analysis (Dec 2025).

Paper Code HuggingFace
Tech Report, 2026 Project Lead

Z-Reward: Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions

Xin Jin*, Huanqia Cai*†, Zhen Li, Zechao Zhan, Dengyang Jiang, Aiming Hao, Yuming Jiang, Chunle Guo, Peng Gao, Ming-Ming Cheng, Steven C.H. Hoi

Z-Reward Teaser

Z-Reward is a reasoning-internalized reward modeling framework for visual generation. It decouples reasoning-heavy judgment from efficient reward deployment: a 27B GDSO teacher reasons over rubric-aligned score distributions, while a compact 9B RISD student internalizes the teacher's distributional judgment without generating explicit reasoning chains at inference time.

Project Paper PDF HF Daily Paper

More publications

5 papers
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