Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards

Reinforcement learning with verifiable rewards (RL VR) is a practical and scalable approach to enhancing large language models in areas such as math, code, and other structured tasks. Two questions motivate this paper: how much of the reported gains survive under strictly parity-controlled evaluation, and whether RL VR is cost-free or exacts a measurable tax. We argue that progress is real, but gains are often overstated due to three forces--an RL VR tax, evaluation pitfalls, and data contamination. Using a partial-prompt contamination audit and matched-budget reproductions across base and RL models, we show that several headline gaps shrink or vanish under clean, parity-controlled evaluation. We then propose a tax-aware training and evaluation protocol that co-optimizes accuracy, grounding, and calibrated abstention and standardizes budgeting and provenance checks. Our position is constructive: RL VR is valuable and industry-ready; we advocate keeping its practical benefits while prioritizing reliability, safety, and measurement. Reinforcement learning with verifiable rewards (RL VR) has become a leading post-training route for improving large language models on math, code, and other structured tasks (Luong et al., 2024; Wen et al., 2025a). By optimizing against automatically computable signals--unit tests for programs, exact numeric or string matches for math, or retrieval-grounded checks for citations--RL VR promises a scalable, label-efficient path to better reasoning. Recent results are striking: across multiple domains, RL VR systems often post large gains on standard benchmarks. Moreover, Figure 2 shows a rise in RL VR-tagged papers alongside improvements on AIME-24/25 through 2024-H1 2025, underscoring both the field's momentum and the need to separate genuine reasoning gains from measurement and budgeting artifacts. Parity-controlled studies show that base models can narrow or erase RL VR gaps when given matched sampling budgets--consistent with smarter search rather than capability expansion (Y ue et al., 2025; Wu et al., 2025a).