DriftWorld: Fast World Modeling through Drifting
arXiv:2607.15065v1 [cs.RO] 16 Jul 2026
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Susie Lu1 Haonan Chen2 Weirui Ye1 Yilun Du2 Massachusetts Institute of Technology, 2 Harvard University
Abstract: Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly. This creates a bottleneck for diffusion-based world models: multistep sampling makes each rollout expensive, limiting large-scale action search at inference time. We introduce DriftWorld, an action-conditioned world model based on drifting generative models. Rather than denoising iteratively at inference, DriftWorld learns an action-conditioned drift during training, allowing it to generate future frames from the current observation and a candidate action sequence in a single forward pass at 30+ fps, which is 17× faster on average than diffusionbased baselines. We evaluate DriftWorld on standard vision-based robotic manipulation benchmarks, including Bridge-V2, RT-1, Language Table, Push-T, and Robomimic. By producing rollouts that are both accurate and fast, DriftWorld achieves state-of-the-art decision-making performance with far less inference time than diffusion-based world model baselines. Beyond online control, DriftWorld can also serve as an offline simulator for ranking real-world robot policies, with rollout-based scores correlating with ground truth at up to 0.99. These results show that drifting models are a strong fit for robot world modeling, where fast, high-quality imagination directly supports planning and policy evaluation. Videos and code are available at this website. Keywords: World Models, Action-Conditioned Video Generation
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Introduction
Predictive world models have emerged as powerful tools for robot learning, letting robots predict the outcomes of their actions without executing in the real world. With recent advances in video generation, action-conditioned world models can now simulate fine-grained robot–object interactions [1, 2], render controllable multi-view videos [3], and produce long-horizon, temporally consistent rollouts [4]. Such models lead to two key applications. First, robots can improve their policies by rolling out candidate action proposals in the world model and selecting the best one to execute in the real world. Second, the model can serve as an offline simulator for policy evaluation. However, the practical value of these world models is bottlenecked by inference speed. State-of-theart models are predominantly diffusion-based [3, 5], so they rely on iterative, multi-step denoising to generate future frames. This multi-step sampling is too slow for real-time planning. For instance, recent work on generative predictive control [5] reports that diffusion world model rollouts consume 90–95% of runtime, resulting in 3 or more seconds per decision cycle. As a result, it is impractical to simulate the hundreds of rollouts needed to identify the best candidate actions. To overcome this bottleneck, we introduce DriftWorld, a fast action-conditioned world model based on drifting generative models [6]. Unlike diffusion-based models, DriftWorld generates high-quality future frames in a single forward pass. DriftWorld achieves 1-step generation by learning a drifting field at training time that maps the prior noise distribution onto the data distribution. Consequently, inference does not require the iterative, multi-step sampling used in diffusion models. The original drifting model was designed for class-conditional image generation. Adapting it to action-conditioned video generation requires rethinking three components: (1) a conditional drifting