An Open, Modular Exploration Towards Systematic World–Action Model Pretraining
What is OpenWAM
OpenWAM is an open research stack for systematically developing World-Action Models (WAMs). It turns tightly coupled design choices into modular components and controlled experiments. It consists of:
OpenWAM-Infra: A modular infrastructure for composing and comparing model, representation, training, inference, deployment, and evaluation choices.
OpenWAM-Study: Controlled studies that derive practical principles for inheriting world knowledge, coupling world and action learning, and scaling across domains.
OpenWAM-α: An open pretrained WAM that applies these principles at scale, trained on 518.5M frames (about 6,400 hours) of egocentric human and robot data.
News
[2026/09/09] 📄 OpenWAM Paper is released on arXiv.
[2026/09/06] 🤖 OpenWAM is integrated into XPolicyLab.
[2026/09/06] 🤗 We release all pretrained and finetuned models on huggingface.
The script installs the upstream cosmos packages into the active environment and compiles transformer-engine (CUDA toolkit with nvcc required), then automatically restores the package versions OpenWAM pins.
Assets Preparation
The downloaders are interactive by default; every menu step also has a flag (--name, --source, --root, --yes, see --help) so they can run unattended, and the default storage location is resolved relative to the repository regardless of the working directory. Component downloaders store assets under
assets/ and update the matching YAML path; the released-checkpoint downloader
keeps each checkpoint's self-contained config unchanged.
Data is saved under assets/benchmark_data/<benchmark>/. Normalization
statistics are prepared when needed, and the selected dataloader configuration
is updated.
Checkpoints are saved under assets/openwam_ckpt/openwam_alpha/ or
assets/openwam_ckpt/openwam_study/<type>/. Each checkpoint directory contains
its own config and can be deployed directly with:
bash scripts/deploy.sh <ckpt_dir_path>
For fine-tuning, set training.finetune_ckpt_path in configs/train.yaml to the
downloaded checkpoint directory. Benchmark data is still required.
Quick Start
Quick Start provides a minimal end-to-end example: prepare the assets, train
or fine-tune a policy, deploy its checkpoint, and run a first inference check.
The example uses the DualSystem JointSelfAttention architecture, the
Wan2.2-TI2V-5B video backbone, and the Mutual attention mask.
Resource recommendation: We recommend 8 GPUs with 80 GB VRAM each for
training. This configuration supports normal training for all architectures
using Wan2.2-5B or smaller video backbones. More GPUs are better when
available and can further improve training throughput.
From Scratch Training
Download LIBERO and let the downloader update its dataloader configuration:
training.debug=true runs 20 steps, saves at steps 10 and 20, and uses a
constant learning rate. Check the run output, then set
training.debug=false for normal training. Training defaults and CLI
overrides are defined in configs/train.yaml.
Debug outputs use training.output_path, whose default is
outputs/openwam_checkpoints.
Deploy the debug checkpoint and inspect one input-output cycle:
bash scripts/deploy.sh <debug_ckpt_dir_path>
Keep the server running, then open another terminal and run the two
inference helpers. Deployment enables compile by default, so the first
inference may take longer while compilation warms up; later requests are
typically faster:
The single-request helper checks ping, one prediction, and reset. The
continuous helper sends repeated predictions over one connection and
reports the returned action dimension and latency. Stop the deployment
process after the checks.
The default model configuration already matches the required setup above.
You can set the same field in configs/train.yaml
instead of passing it on the command line. num_frames=33 and
video_stride=4 in configs/dataloader/libero.yaml
produce the 32-step action horizon expected by the sampler; no separate
action_chunk override is needed.
OpenWAM is configured through composable Hydra YAML files. Select an architecture,
backbone, dataloader, and runtime behavior by changing configuration values or
overriding them on the command line. The task guides below are the maintained
entry points for using and extending the repository:
fine-tune the released foundation checkpoint to execute downstream task
💡 Agent tip: Pick the guide that matches your task and feed it directly
to your agent — less explaining, more building.
Installation and Assets Preparation above cover environment setup and model or
dataset downloads. Benchmark-specific environment and evaluation details remain
in the benchmarks directory.
@article{wang2026openwam,
title = {OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining},
author = {Yuran Wang and Siqiao Huang and Mingleyang Li and Chenhao Zhang and Jiaqi Liang and Weiyang Jin and Yue Chen and Xuemin Chi and Donghao Zhou and Qize Yu and Yu-Kai Wang and Yuhan Rui and Shenzhe Yao and Zhen Yuan and Zhenhao Shen and Kefei Zhu and Zijie Zhu and Ning Gao and Xiaowei Chi and Guanqi He and Shanghang Zhang and Hao Dong and Lin Shao and Hang Zhao},
year = {2026},
journal = {arXiv preprint arXiv: 2609.07398}
}
Contact
Have a question, want to share results, or just follow development? Join us:
OpenWAM 是一个开放的研究技术栈，用于系统性地为机器人构建世界-动作模型（World-Action Models，WAM），它把通常紧密耦合的设计选择拆解为模块化、可替换的组件，方便你在受控实验中进行比较。