Prediction Serves Action: Learning Action Effects in World Action Models

World Action Models (WAMs) improve action learning by introducing future prediction as auxiliary supervision. Existing WAMs, however, largely retain the complete future observation as the predictive target, even when future prediction is conditioned on robot actions. Such targets contain both scene changes and substantial persistent content, distributing supervision across the complete future observation even though different components might contribute differently to action learning. We therefore introduce Action Effect, a predictive target that focuses on the changes and consequences associated with an action and provides more targeted future supervision. Based on this idea, we propose Effect-WAM, a lightweight WAM that models Action Effects directly within a single DiT action-generation pathway, without a separate future branch. Effect-WAM employs a tightly coupled Propose-Anticipate-Act (PAA) mechanism that forms an intermediate action hypothesis, anticipates the corresponding effects, and uses the resulting Effect representations to refine subsequent action generation. We further construct complementary 2D semantic and robot-centric 3D geometric supervision to learn structured Action Effect representations. Experiments on LIBERO and RoboTwin 2.0, together with real-world evaluations, show strong manipulation performance with a lightweight architecture, supporting Action Effect modeling as an effective alternative to complete future generation. Code will be released.