FastWAM

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This section introduces the general functions of LeRobot. Please configure the example according to the target robot, data set and operating environment; before using it with AlohaMini 2 / 2 Pro, you need to confirm the hardware and interface compatibility.

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FastWAM is a World Action Model policy for robot control. The LeRobot integration exposes FastWAM through the standard policy API so it can be configured with policy.type=fastwam, trained with lerobot-train, and loaded through the LeRobot pretrained policy interface.

Model Overview

FastWAM keeps video modeling during training, but uses direct action prediction at inference time instead of iteratively generating future observations. This LeRobot policy wraps the FastWAM action model, adapts LeRobot batches to FastWAM training samples, and provides the standard processor pipeline for normalization and action postprocessing.

The implementation initializes the visual world-model components from Wan-AI/Wan2.2-TI2V-5B by default and predicts action chunks with shape [batch, action_horizon, action_dim].

What the LeRobot Integration Covers

• Standard policy.type=fastwam configuration through LeRobot

• Image, state, action, and language-task batch adaptation

• Action chunk inference through select_action and predict_action_chunk

• Checkpoint save/load through the LeRobot policy APIs

• Configurable LIBERO gripper action postprocessing

Installation Requirements

Install LeRobot from source, then install FastWAM dependencies:

pip install -e ".[fastwam]"

This installs the FastWAM policy extra from pyproject.toml: transformers, diffusers, ftfy, and regex, plus LeRobot’s base dependencies.

For LIBERO evaluation, install the benchmark dependencies too:

pip install -e ".[fastwam,libero]"

This installs both extras. In addition to the FastWAM dependencies above, the libero extra installs LeRobot dataset dependencies, hf-libero on Linux, and scipy.

FastWAM uses the Wan2.2 TI2V backbone. The default model id is:

policy.model_id=Wan-AI/Wan2.2-TI2V-5B

Data Requirements

FastWAM expects a LeRobot dataset with:

• one or more visual observations whose widths concatenate to policy.image_size[1]

• observation.state when policy.proprio_dim is not None

• action

• a language task instruction through the dataset task field, or precomputed context and context_mask tensors

The default visual setup is one image feature named observation.images.image with shape (3, 224, 448). If the dataset uses two cameras, configure policy.input_features so their heights match 224 and their widths sum to 448.

Usage

Create a new FastWAM policy with:

lerobot-train \
  --dataset.repo_id=your-org/your-dataset \
  --policy.type=fastwam \
  --policy.action_dim=7 \
  --policy.proprio_dim=8 \
  --policy.action_horizon=32 \
  --policy.n_action_steps=10 \
  --policy.image_size='[224,448]' \
  --output_dir=./outputs/fastwam_training \
  --job_name=fastwam_training \
  --steps=300000 \
  --batch_size=8 \
  --policy.device=cuda

Evaluate an existing LeRobot-format checkpoint on LIBERO-10 with:

lerobot-eval \
  --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 \
  --policy.device=cuda \
  --policy.torch_dtype=float32 \
  --policy.n_action_steps=10 \
  --env.type=libero \
  --env.task=libero_10 \
  --env.observation_height=224 \
  --env.observation_width=224 \
  --eval.batch_size=1 \
  --eval.n_episodes=50 \
  --seed=0 \
  --env.episode_length=600

For libero_goal, libero_spatial, and libero_object, use --env.episode_length=300.

For real-robot rollout, use the same checkpoint path:

lerobot-rollout \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM0 \
  --policy.path=your-org/fastwam-real-robot

Configuration Notes

Image Features

policy.image_size is the size of the concatenated FastWAM image tensor as (height, width). Each configured image feature must have shape (3, height, camera_width), and all camera widths must sum to the configured width.

Action Chunking

policy.action_horizon controls the number of future actions supervised during training and predicted during inference. policy.n_action_steps controls how many actions are consumed before the policy predicts a fresh chunk. policy.n_action_steps must be less than or equal to policy.action_horizon.

Wan Components

FastWAM loads the Wan VAE, video DiT, text encoder, and tokenizer from the configured Wan model directory or Hugging Face Hub model id. LeRobot-format FastWAM checkpoints saved by save_pretrained also copy the local Wan component files needed by from_pretrained.

Attention Backend

FastWAM’s DiT uses PyTorch’s scaled_dot_product_attention (SDPA) for all attention. It does not use FlashAttention: its Mixture-of-Transformers (MoT) routing needs arbitrary boolean [query, key] attention masks, which the FlashAttention varlen API cannot express. Installing the flash-attn package therefore has no effect on the FastWAM path. (Note that SDPA itself may still select PyTorch’s own flash / memory-efficient / math kernel internally — this is unrelated to the flash-attn package.)

LIBERO Action Toggle

FastWAM LIBERO checkpoints use policy.toggle_action_dimensions=[-1] by default to match the gripper action convention used by the original FastWAM evaluation pipeline:

--policy.toggle_action_dimensions='[-1]'

Results

Evaluated on LIBERO with ZibinDong/fastwam_libero_uncond_2cam224:

Suite	Success rate	n_episodes
libero_spatial	97.6%	500
libero_object	99.0%	500
libero_goal	95.0%	500
libero_10	94.0%	500
average	96.4%	2000

Reproduce: lerobot-eval --policy.path=ZibinDong/fastwam_libero_uncond_2cam224 --policy.device=cuda --policy.torch_dtype=float32 --policy.n_action_steps=10 --env.type=libero --env.task=libero_spatial --env.observation_height=256 --env.observation_width=256 --eval.batch_size=1 --eval.n_episodes=50 --seed=0 --env.episode_length=300 (1x H20 140 GB).

References

• Fast-WAM paper

• Fast-WAM project page

• Fast-WAM code

• Released upstream checkpoints

• Wan2.2 TI2V 5B

Citation

@article{yuan2026fastwam,
  title = {Fast-WAM: Do World Action Models Need Test-time Future Imagination?},
  author = {Tianyuan Yuan and Zibin Dong and Yicheng Liu and Hang Zhao},
  journal = {arXiv preprint arXiv:2603.16666},
  year = {2026},
  url = {https://arxiv.org/abs/2603.16666}
}