Rohit Sinha

Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR) show improved accuracy on visual reasoning benchmarks. However, we observe that accuracy gains often come at the cost of reasoning quality: generated Chain-of-Thought (CoT) traces are frequently inconsistent with the final answer and poorly grounded in the visual evidence. We systematically study this phenomenon across seven challenging real-world spatial reasoning benchmarks and find that it affects contemporary MRMs such as ViGoRL-Spatial, TreeVGR as well as our own models trained with standard Group Relative Policy Optimization (GRPO). We characterize CoT reasoning quality along two complementary axes: \emph{logical consistency} (does the CoT entail the final answer?) and \emph{visual grounding} (does each reasoning step accurately describe objects, attributes, and spatial relationships in the image?). To address these failure modes, we propose Faithful GRPO (FGRPO), a variant of GRPO that treats consistency and grounding as constraints enforced via Lagrangian dual ascent. FGRPO incorporates batch-level consistency and grounding constraints into the advantage computation within a group, adaptively adjusting the relative importance of constraints during optimization. We evaluate FGRPO on Qwen2.5-VL-7B and 3B backbones across seven spatial reasoning datasets. FGRPO substantially improves reasoning quality, reducing the inconsistency rate from $26.1\%$ to $1.7\%$ and improving visual grounding scores by $+13\%$. It also improves final answer accuracy over standard GRPO, demonstrating that faithful reasoning enables better answers.