Giga-World-Policy-0.5

GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

• Paper: GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch
• Project page: https://open-gigaai.github.io/giga-world-policy/
• Repository: https://github.com/open-gigaai/giga-world-policy

Model summary

| Field | Value |

| --- | --- |

| Class | CasualWorldActionTransformer_MoT |

| Layers | 30 |

| Attention heads | 24 × 128 |

| Visual hidden dim | 3072 |

| Action expert dim | 1024 |

| Action FFN dim | 4096 |

| Latent channels | 48 (in/out) |

| Action channels | 16 (in/out) |

| Embodiments | 2 |

| Text dim (T5) | 4096 |

| Patch size | [1, 2, 2] |

The MoT design keeps a visual expert stream (reference + future latents) and an action expert stream (state + action), with multi-modal self-attention across both.

Files

config.json
diffusion_pytorch_model.safetensors.index.json
diffusion_pytorch_model-00001-of-00003.safetensors
diffusion_pytorch_model-00002-of-00003.safetensors
diffusion_pytorch_model-00003-of-00003.safetensors

Weights are sharded at ~10GB per file. This repo contains the transformer only; runtime also needs the Wan2.2 VAE / scheduler from the base Diffusers checkpoint.

Download

# Hugging Face CLI
huggingface-cli download open-gigaai/Giga-World-Policy-0.5 --local-dir ./Giga-World-Policy-0.5

# or Git LFS
git lfs install
git clone https://huggingface.co/open-gigaai/Giga-World-Policy-0.5

Python:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="open-gigaai/Giga-World-Policy-0.5",
    local_dir="./Giga-World-Policy-0.5",
)

For usage, training, and inference details, see our open source code page.

Citation

@article{gigaworld-policy-0.5,
  title={GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch},
  author={Team, GigaWorld and Ye, Angen and Ma, Angyuan and Wang, Boyuan and Ni, Chaojun and Ye, Fangzheng and Huang, Guan and Li, Guo and Zhao, Guosheng and Yan, Haodong and others},
  journal={arXiv preprint arXiv:2607.13960},
  year={2026}
}