SwiftWA: An Efficient Action-Centered World-Action Model

World–Action Models (WAMs) initialized from pre-trainedvideo generation backbones have demonstrated remarkable potential forrobot policy learning. However, existing approaches face two critical bot-tlenecks: joint reasoning over future visual dynamics and actions incurssubstantial inference overhead, and joint modeling entangles visual andmotion representations, making motion prediction dependent on videoforecasts. To address these issues, we introduce GigaWorld-Policy, anaction-centered WAM that learns 2D pixel–action dynamics while en-abling efficient action decoding with optional video generation. Specif-ically, the model predicts future action sequences conditioned on thecurrent observation and, during training, generates future videos condi-tioned on the predicted actions and the same observation. Supervisionfrom both action prediction and video generation provides richer learn-ing signals and encourages physically plausible actions through visual-dynamics constraints. With a causal design that prevents future-videotokens from influencing action tokens, explicit future-video generationis optional at inference time, enabling faster deployment. To supportthis paradigm, we curate a diverse, large-scale robot dataset to pre-train the model. Experiments on real-world robotic platforms show thatGigaWorld-Policy runs 9× faster than the WAM baseline Motus whileimproving task success rates by 7%; compared with π0.5 , it improvesperformance by 95% on RoboTwin 2.0.