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This paper introduces ForgeWM, a progressive framework that enhances action-conditioned video world models by integrating domain adaptation, teacher-forced causal training, and causal consistency distillation. The approach enables efficient few-step video synthesis while maintaining alignment between discrete and continuous control inputs, achieving superior performance in terms of imaging quality and action accuracy on Minecraft trajectories. Notably, the method allows for a dual-path deployment that combines low-latency interaction with optional refinement, significantly improving user experience in interactive environments.
ForgeWM achieves unmatched action-sign accuracy and motion-profile alignment in few-step video generation, outperforming existing models while reducing latency.
Action-conditioned video world models require low-latency causal generation and reliable responses to game-native controls. Although causal distillation enables one- or few-step video synthesis, extending it to interactive world models remains challenging, as discrete keyboard states and continuous mouse motion must remain aligned with temporally compressed latent chunks during causal training and autoregressive rollout. We introduce ForgeWM, a progressive framework that transforms a bidirectional action-conditioned video generator into efficient few-step world models through domain adaptation, teacher-forced causal training, causal consistency distillation, and on-policy distribution matching with a bidirectional teacher. The resulting budget-specialized students operate at steady-state denoising budgets of 1, 2, and 4 steps. ForgeWM further supports a dual-path deployment protocol combining latency-critical interaction with optional replay-time refinement, where the one-step student re-noises and refines its saved draft. On paired Minecraft trajectories, ForgeWM leads the evaluated systems in Imaging Quality, reference-aligned motion-profile agreement, action-sign accuracy, and mouse-control accuracy, while achieving the lowest reference LPIPS; the same four-stage recipe transfers to gamepad-controlled FPS gameplay. Replay-time refinement matches four-step reference quality while remaining roughly three times closer to the experienced trajectory than regeneration from noise. These results demonstrate ForgeWM's effectiveness for controllable few-step video generation.
