Sparse-WAM: Accelerating World Action Models via Action-Guided Sparse Imagination
World-action models (WAMs) leverage pretrained video models to improve generalization in robot control by jointly predicting future visual states and actions. This capability comes at a substantial inference cost, as dense future-frame tokens are repeatedly processed during denoising. Prior methods address this by token pruning that prioritizes visual fidelity to reduce denoising costs in video diffusion models. However, these methods do not use action relevance to determine which future-frame tokens to retain during joint denoising in WAMs. In this paper, we propose Sparse-WAM, a training-free framework for action-guided sparse imagination that selectively processes future-frame tokens t...
| Authors | Xinling Xie, Haodong Wang, Jiazhi Mi et al. |
|---|---|
| Primary category | cs.RO |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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