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This paper presents VLM-Safe-RL, a framework that integrates frozen vision-language models into constrained MDP Lagrangian updates to provide anticipatory cost signals for safe reinforcement learning in high-speed visual control tasks. The method outperforms standard constraint-aware baselines on Safety-Gymnasium FormulaOne L2 and generalizes to held-out environments.