Official repository for the paper "Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation".

[📖 Paper] [🤗 one-step Model]

• [2025.12.15] AR3D-R1 #3 paper of the day in HuggingFace Daily Papers ! 🔥
• [2025.12.11] We release the checkpoint of one-step AR3D-R1 and the inference code! 🔥
• [2025.12.11] We release the arxiv paper. 🔥

Please set up the Python environment by:

conda env create -f environment.yml
conda activate environment_name

pip install -r requirements.txt

My environment setup is mainly based on ShapeLLM-Omni. If you only need inference, installing this repository is sufficient.

You can download the checkpoint from here

python demo.py

• Release complete two-step training & evaluation code

• Release one-step training code

• [Image Generation CoT] Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step?
• [T2I-R1] T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
• [ShapeLLM-Omni] ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding
• [Trellis] Structured 3D Latents for Scalable and Versatile 3D Generation

If you find AR3D-R1 useful for your research or projects, we would greatly appreciate it if you could cite the following paper:

@article{tang2025we,
  title={Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation},
  author={Tang, Yiwen and Guo, Zoey and Zhu, Kaixin and Zhang, Ray and Chen, Qizhi and Jiang, Dongzhi and Liu, Junli and Zeng, Bohan and Song, Haoming and Qu, Delin and others},
  journal={arXiv preprint arXiv:2512.10949},
  year={2025}
}