Large language models (LLMs) can generate executable solver code from natural-language descriptions of optimization problems. However, existing verification signals focus on whether the generated formulation produces the correct objective value. Such signals can overlook constraint-level errors: incorrect formulations may still produce the same optimum when extra or missing constraints do not affect the tested instance. We propose constraint injection, a verification method that directly tests whether the generated code implements the intended constraints. We use feasible solutions to detect incorrectly added constraints and one-constraint-violating solutions to detect omissions. Together with the optimal value check, these tests verify both the objective and the constraint set. We evaluate this approach on vehicle routing problems (VRPs), which provide a challenging testbed with diverse, tightly coupled operational constraints. We develop VRPCoder, an 8B model for generating Gurobi code from natural-language VRP descriptions, and VRPBench, an expert-verified benchmark spanning 21 VRP variants across four subsets. The verifier serves as a rejection-sampling criterion for synthetic data construction and as a rollout-level reward for reinforcement learning (RL). On VRPBench, VRPCoder-RL reaches 93% overall Pass@1, outperforms Gemini-3.1-Pro Preview on three subsets, exceeds Claude-Sonnet-4.5 by 28 percentage points, and exceeds the strongest prior OR-LLM by 78 percentage points.

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