World Action Models (WAMs) have emerged as a promising alternative to Vision-Language-Action (VLA) models for embodied control because they explicitly model how visual observations may evolve under action. Most existing WAMs follow an imagine-then-execute paradigm, incurring substantial test-time latency from iterative video denoising, yet it remains unclear whether explicit future imagination is actually necessary for strong action performance. In this paper, we ask whether WAMs need explicit future imagination at test time, or whether their benefit comes primarily from video modeling during training. We disentangle the role of video modeling during training from explicit future generation during inference by proposing Fast-WAM, a WAM architecture that retains video co-training during training but skips future prediction at test time. We further instantiate several Fast-WAM variants to enable a controlled comparison of these two factors. Across these variants, we find that Fast-WAM remains competitive with imagine-then-execute variants, while removing video co-training causes a much larger performance drop. Empirically, Fast-WAM achieves competitive results with state-of-the-art methods both on simulation benchmarks (LIBERO and RoboTwin) and real-world tasks, without embodied pretraining. It runs in real time with 190ms latency, over 4× faster than existing imagine-then-execute WAMs. These results suggest that the main value of video prediction in WAMs may lie in improving world representations during training rather than generating future observations at test time. Project page: https://yuantianyuan01.github.io/FastWAM/
Figures & tables
Figure 1: Three representative WAM paradigms. (A) Joint-modeling WAMs denoise future video and action tokens together. (B) Causal WAMs first generate future observations and then condition action prediction on the generated future representation. (C) Fast-WAM retains video co-training during training but removes explicit future generation at inference time, directly predicting actions from latent world representations in a single forward pass.
Figure 2: Fast-WAM architecture and the structured attention mask used to disentangle video co-training from action generation.
Figure 3: Real-world towel-folding task on the Galaxea R1 Lite platform. Folding a deformable object requires long-horizon planning and precise closed-loop manipulation, making it a challenging benchmark for evaluating both task success and execution efficiency.
Method
Embodied PT.
Clean
Rand.
Average
π0 [ 10 ]
✓
65.92
58.40
62.2
π0.5 [ 11 ]
✓
82.74
76.76
79.8
Motus [ 5 ]
✓
88.66
87.02
87.8
Motus from WAN2.2
✗
77.56
77.00
77.3
LingBot-VA [ 3 ]
✓
92.90
91.50
92.2
LingBot-VA from WAN2.2
✗
80.60
–
80.6
Table 1: Results on RoboTwin. Fast-WAM matches strong pretrained WAM baselines without using embodied pretraining, while the two imagine-then-execute variants remain highly comparable and removing video co-training causes a substantial drop.
Method
Embodied PT.
Spatial
Object
Goal
Long
Average
OpenVLA [ 9 ]
✓
84.7
88.4
79.2
53.7
76.5
π0 [ 10 ]
✓
96.8
98.8
95.8
85.2
94.1
π0.5 [ 11 ]
✓
98.8
98.2
98.0
92.4
96.9
LingBot-VA [ 3 ]
✓
98.5
99.6
97.2
98.5
98.5
Motus [ 5 ]
✓
96.8
99.8
96.6
97.6
97.7
Fast-WAM (Ours)
✗
98.2
100.0
97.0
95.2
97.6
Table 2: Results on LIBERO. Fast-WAM achieves competitive overall performance without embodied pretraining, remains close to both imagine-then-execute variants, and outperforms the no-video-co-training ablation by a clear margin.
Figure 4: Real-world results on the long-horizon towel-folding task. The left panel plots success rate against average completion time, where upper-left is better. The right panel compares inference latency. Fast-WAM achieves strong real-world performance with substantially lower latency than imagine-then-execute variants, while removing video co-training degrades both success rate and completion time.
Appendix figures & tables1 asset
Supplementary material from the paper’s appendix.
Appendix
Task
Fast-WAM (Ours)
Fast-WAM-Joint
Fast-WAM-IDM
Fast-WAM w.o. co-train
LingBot-VA
π0.5
Motus
Clean
Rand.
Clean
Rand.
Clean
Rand.
Clean
Rand.
Clean
Rand.
Clean
Rand.
Clean
Rand.
Adjust Bottle
100
100
98
99
94
99
98
100
90
94
100
99
89
93
Beat Block Hammer
99
97
100
98
98
98
80
92
96
98
96
93
95
88
Blocks Ranking RGB
100
100
100
100
100
99
88
86
99
98
92
85
99
97
Blocks Ranking Size
94
98
83
91
79
90
56
62
94
96
49
26
75
63
Click Alarmclock
100
100
100
100
98
100
100
98
99
100
98
89
100
100
Appendix
Table 3: Per-task success rates on RoboTwin under clean and randomized evaluation settings.
World-Action Models (WAMs) have emerged as a promising paradigm for embodied control by coupling future visual prediction with action generation. However, most existing WAMs rely on photorealistic future prediction, which incurs high inference latency and makes real-time robot deployment difficult. This motivates a more efficient WAM design that preserves the control benefits of future visual prediction while reducing its inference cost. We introduce Efficient-WAM, a World-Action Model that reduces the cost of future imagination while preserving its control benefit. Efficient-WAM improves inference efficiency via a compact video expert transferred from WAN-2.2-5B, token-sparse video latents, and asymmetric video-action denoising that allocates fewer sampling steps to video than to actions. Instead of optimizing the future branch for visual fidelity, Efficient-WAM treats future video prediction as a compact guidance signal for action generation. Comprehensive experiments on RoboTwin 2.0 and real-world manipulation tasks show that Efficient-WAM maintains strong action performance despite visibly coarse future predictions. While maintaining competitive control capabilities, our 1B-parameter model can reduce per-chunk latency to around 100 ms during physical deployment, achieving a 30x speedup over existing WAMs.
Jiajun Li, Tiecheng Guo, Yifan Ye +9
1The University of Hong Kong · 2Peking University · 3Muka Robotics +2
World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes this process for efficiency; however, our matched implementations show lower generalization for Fast-WAM than for future-aware alternatives, especially with scarce robot demonstrations and in out-of-distribution scenarios. To bridge this gap, we introduce LAWA, a WAM architecture that uses compact latent actions as an operational representation of future intentions, enabling efficient test-time future imagination without generating future observations. Specifically, a discrete tokenizer enhanced by action-free pre-training produces manipulation-centric codebook targets. LAWA jointly denoises a continuous latent state anchored to these targets with executable action chunks while omitting the future-video branch at inference. On RoboCasa, LAWA achieves state-of-the-art average success rates of 65.6% and 80.8% in the few-shot and full data settings, improving over the matched Fast-WAM baseline by 9.6 and 4.5 points, respectively. It also preserves the performance level of the matched Joint-WAM variant while requiring 42.9% lower inference latency. LAWA also demonstrates competitive zero-shot robustness on LIBERO-Plus and superior performance on real-world tasks. These results show that future imagination need not be discarded: retaining it with compact latent actions yields an effective trade-off among performance, generalization, and latency. Code and models will be released.
World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense multi-frame future tokens make inference costly, full video prediction spends capacity on action-irrelevant temporal and appearance details, and long-horizon future imagination may introduce errors that mislead action prediction. These issues raise a simple question: Does world action model really need video generation? We propose ImageWAM, a simple WAM framework that repurposes pretrained image editing models for robot action prediction. In contrast to video generation, image editing provides a better-matched prior: it only needs to model a target-frame transformation, focuses on action-relevant current-to-target visual differences, and grounds task instructions to localized visual changes through edit pretraining. In practice, ImageWAM does not decode the target frame at inference time; instead, it conditions a flow-matching action expert on the KV caches produced by image-editing denoising, using them as a compact world-action context. ImageWAM outperforms standard VLA baselines and matching competitive WAMs without additional policy pretraining across different simulator and real-world experiments. It also reduces FLOPs to 1/6 and latency to 1/4 of video-based WAMs. Attention analysis further shows that editing caches focus on task-relevant change regions, supporting image editing as an effective alternative to video-based world-action modeling.
Yuyang Zhang, Wenyao Zhang, Zekun Qi +7
Shanghai Jiao Tong University · Tsinghua University · Tencent Robotics X +1