FRED: World action model learns future video representations via self-distillation

The study proposes the FRED framework, which predicts future video representations from noise in a single step via self-distillation, using representations extracted from real future observations as targets, while historical observations provide temporal context for the video model. Across diverse real-world manipulation scenarios, FRED outperforms compared world action models, with notable gains on long-horizon and out-of-domain tasks under zero-shot environment generalization; using only human video demonstrations, it achieves a 50.0% success rate on tasks absent from robot training data, versus 19.4% for the best comparable model.