# Sparse-WAM: Accelerating World Action Models via Action-Guided Sparse Imagination

arXiv: https://arxiv.org/abs/2609.38984 · HTML: https://mlsanity.com/paper/2609.38984/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 24 of 24)

- [World Action Models are Zero-shot Policies](https://mlsanity.com/paper/2602.15922/) (2026)
- [Fast-WAM: Do World Action Models Need Test-time Future Imagination?](https://mlsanity.com/paper/2603.16666/) (2026)
- [LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models](https://mlsanity.com/paper/2510.13626/) (2025)
- [Motus: A Unified Latent Action World Model](https://mlsanity.com/paper/2512.13030/) (2025)
- [Cosmos 3: Omnimodal World Models for Physical AI](https://mlsanity.com/paper/2606.02800/) (2026)
- [GigaWorld-Policy: An Efficient Action-Centered World--Action Model](https://mlsanity.com/paper/2603.17240/) (2026)
- [VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching](https://mlsanity.com/paper/2502.02175/) (2025)
- [LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies](https://mlsanity.com/paper/2606.15768/) (2026)
- [SpecPrune-VLA: Accelerating Vision-Language-Action Models via Action-Aware Self-Speculative Pruning](https://mlsanity.com/paper/2509.05614/) (2025)
- [Do World Action Models Generalize Better than VLAs? A Robustness Study](https://mlsanity.com/paper/2603.22078/) (2026)
- [Action-aware Dynamic Pruning for Efficient Vision-Language-Action Manipulation](https://mlsanity.com/paper/2509.22093/) (2025)
- [RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies](https://mlsanity.com/paper/2604.09860/) (2026)
- [Light-WAM: Efficient World Action Models with State-Fusion Action Decoding](https://mlsanity.com/paper/2606.08242/) (2026)
- [Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination](https://mlsanity.com/paper/2606.10040/) (2026)
- [Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference](https://mlsanity.com/paper/2511.16449/) (2025)
- [Faster-WAM: Efficient Inference-Time Future Conditioning for Robust World Action Models](https://mlsanity.com/paper/2608.04404/) (2026)
- [HALO: A Unified Vision-Language-Action Model for Embodied Multimodal Chain-of-Thought Reasoning](https://mlsanity.com/paper/2602.21157/) (2026)
- [Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation](https://mlsanity.com/paper/2606.15869/) (2026)
- [C3ache: Accelerating World Action Models with Cross Inference Chunk Cache](https://mlsanity.com/paper/2606.08962/) (2026)
- [WorldCache: Accelerating World Models for Free via Heterogeneous Token Caching](https://mlsanity.com/paper/2603.06331/) (2026)
- [SAFE-Pruner: Semantic Attention-Guided Future-Aware Token Pruning for Efficient Vision-Language-Action Manipulation](https://mlsanity.com/paper/2605.29662/) (2026)
- [TwinQuant: Learnable Subspace Decomposition for 4-Bit LLM Quantization](https://mlsanity.com/paper/2606.01556/) (2026)
- [MosaicQuant: Inlier-Outlier Disaggregation for Unified 4-Bit LLM Quantization](https://mlsanity.com/paper/2606.15652/) (2026)
- [OnlineWM: Causality-Aware Active Online Learning for Effective World Modeling](https://mlsanity.com/paper/2609.23753/) (2026)

## Abstract (arXiv)

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 to accelerate WAM inference. We observe substantial overlap in the spatial distribution of attention from action tokens to future-frame tokens (action-to-future attention) between consecutive denoising steps, despite continued updates to the future representations. Motivated by this, we develop Action-Guided Token Selection to retain frame-specific action-relevant regions together with cross-frame context. However, a naive implementation can incur attention-scoring and token-packing overhead that offsets the computational savings from pruning. We therefore introduce Pilot, an efficient engine that reduces sparse inference overhead through lightweight scoring and cross-step reuse of token selections. On LIBERO with FastWAM-Joint and RoboLab-120 with Cosmos 3 Edge, Sparse-WAM achieves inference speedups of approximately 2.0× and 1.8×, respectively, over dense eager inference on an NVIDIA RTX 4090, while largely preserving task performance.