Keep the Future, Drop the Rollout: RIFT for World Action Models

Abstract:World action models (WAMs) condition robot actions on predicted futures, but iterative video rollout increases deployment latency. We ask whether action generation requires the evolving rollout trajectory or only its future representation. Across four WAMs on 40 simulated robotic manipulation tasks, paired closed-loop interventions show that blocking access to the future cache or reassigning its values changes execution and reduces success. Yet in the evaluated co-denoising settings, reusing one fixed final-clean key/value (K/V) cache throughout action denoising nearly preserves unmodified execution, with $1.7$--$1.9$ cm end-effector average displacement error. Obtaining this cache still requires iterative video generation. We therefore propose RIFT (Rollout-free Imagination via Future Tokens), which uses learned anticipation tokens to construct a complete future K/V cache in one backbone pass. On LIBERO, RIFT achieves $98.8\%$ overall success, outperforming all evaluated rollout-based methods while yielding a $3.1$--$9.2\times$ inference speedup. Without further training, it achieves $81.1\%$ overall success on the out-of-distribution LIBERO-Plus benchmark, a $+9.7$ percentage-point improvement over the strongest evaluated baseline. On RoboTwin, it achieves $92.9\%$ and $92.6\%$ success on clean and randomized scenes, respectively, the highest among the evaluated methods. On real-world manipulation tasks, RIFT achieves $45.3\%$ average success, a $+6.0$ percentage-point improvement over Fast-WAM-Joint. These results support rollout-free future conditioning without iterative video generation at deployment.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2608.11521 [cs.RO]

(or arXiv:2608.11521v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.11521 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.