Do Vision-Language-Action models mean what they say? Embodied chain-of-thought offers a promising way to leverage additional inference-time computation for robot policies, with empirical evidence that it can improve downstream task performance. However, in practice the extent to which this chain-of-thought actually influences a robot policy’s chosen actions is not fully understood yet. In our new paper on #embodied# #reasoning#, we formally define the notion of faithfulness i.e., whether the chain-of-thought truthfully reflects the policy's decision for #Physical# #AI# and argue that the faithfulness of robot policies should play an explicit role in algorithm design. We train a critic of embodied faithfulness, identifying the signatures of inconsistent and incoherent reasoning, and use it as a reward signal during reinforcement learning (RL) post-training of a robot policy. We call our critic Pinocchio 🤥: a model that learns to tell when a robot policy is 'lying' about the justifications for a decision. Pinocchio scores two complementary failure modes: whether the reasoning is grounded in what the robot actually sees, and whether it is coherent step-to-step through the chain-of-thought. We find that post-training for faithfulness yields a +18% improvement in reasoning consistency over standard baselines, and critically, makes the policy 1.6× more responsive to rare, out-of-distribution scenarios that confuse SoTA models. These findings suggest that faithful embodied reasoning is not merely a post-hoc interpretability property, but an important ingredient for building more robust and trustworthy autonomous systems. 📄 Paper: 🌐 Project page: 💻 Code: Coming soon! Great work led by Matthew Foutter, in collaboration with Matteo Cercola Lena Wild Yunshan Wang Michelle Li and @DanieleGammelli @StanfordEng @StanfordAILab @StanfordHAI