Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination

arXiv:2606.10040 · cs.RO · Submitted 2026-06-08 · Read on arXiv

cs.RO

Submitted: 2026-06-08

Updated: 2026-09-26

Project page: https://efficientwam.github.io

Terminology

• World-Action Model — 11× in this paper
• Distillation — 9× in this paper · explained in 2 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Pruning — 6× in this paper · explained in 4 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Flow Matching — 4× in this paper · explained in 8 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• KV cache — 2× in this paper · explained in 3 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency Distillation — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Joint Attention — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• World Action Models: The Next Frontier in Embodied AI
• World Model for Robot Learning: A Comprehensive Survey
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• World Action Models are Zero-shot Policies
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• Motus: A Unified Latent Action World Model
• Causal World Modeling for Robot Control
• Wan: Open and Advanced Large-Scale Video Generative Models
• DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation
• Consistency Policy: Accelerated Visuomotor Policies via Consistency Distillation
• Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets
• Being-H0.7: A Latent World-Action Model from Egocentric Videos
• GigaWorld-Policy: An Efficient Action-Centered World--Action Model
• Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising
• Token Expand-Merge: Training-Free Token Compression for Vision-Language-Action Models
• Efficient Vision-Language-Action Models for Embodied Manipulation: A Systematic Survey
• Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
• Distilling the Knowledge in a Neural Network
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• StarVLA- alpha: Reducing Complexity in Vision-Language-Action Systems

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