From Reasoning to ActionConsequence-Grounded Alignment
for Vision-Language-Action Models

Reasoning informs decision-making by connecting intended actions with their expected consequences. Building on this principle, recent vision-language-action models incorporate embodied chains of thought (ECoTs) as an intermediate stage in action generation. However, such guidance remains implicit, as actions are conditioned on the generated ECoTs rather than explicitly optimized to align with them. To bridge this gap, we introduce the Consequence-grounded Reasoning-to-Action alignment method called CoRAct, an offline training framework that explicitly aligns reasoning and action through reward-guided optimization in action noise space.

Specifically, CoRAct formulates two physically grounded ECoT components as verifiable signals: movement specifies the intended motion, whereas waypoints encode the expected spatial consequence of execution. By jointly modeling ECoT reasoning, action generation, and action-conditioned future prediction, a unified Mixture-of-Transformers explicitly couples each action with its articulated intent and predicted consequence. For action candidates generated from sampled noise, CoRAct computes alignment rewards based on motion consistency with the described movement and agreement between predicted outcomes and the specified waypoints. These rewards drive group-relative optimization of the initial noise distribution, steering action generation toward consistency with the articulated intent and expected consequence.

Extensive experiments across RoboCasa, VLABench, and real-world manipulation tasks demonstrate consistent gains in task success and generalization over strong ECoT-based VLA baselines. Further analyses reveal improved reasoning–execution alignment and show that our method can ground externally provided ECoTs in executable behavior, supporting zero-shot adaptation to novel scenarios by leveraging knowledge from powerful VLMs.