World Action Models (WAMs) jointly predict robot actions and future observations.
However, their visual prediction objectives do not explicitly distinguish task-relevant transitions from incidental scene changes.
For whole-body policies, this distinction matters because task progress depends on coordinated changes in body pose, contact, and object state.
We introduce LANGUAGE-FORCING, a training strategy that uses language to emphasize task-relevant changes in the future visual representations of a whole-body WAM.
Given paired current and ground- truth short-horizon future frames, a frozen vision-language model assesses task progress and predicts plausible subsequent state changes.
We encode these descriptions with a frozen text encoder and align their embeddings with intermediate, action-conditioned future-visual features, complementing the original visual and action prediction objectives.
All auxiliary components are used only during training, introducing no additional inference overhead.
On HumanoidArena, LANGUAGE-FORCING achieves an average success rate of 79.17%, exceeding the strongest competing policy by 9.46 percentage points and the same WAM without language alignment by 10.77 percentage points.
Across five real-world tasks, language alignment raises average success from 53.33% to 71.33% over the same WAM baseline.
