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

Researchers identify systematic measurement flaws in reinforcement learning with verifiable rewards (RLVR) studies, revealing that widely reported performance gains are often inflated by budget mismatches, data contamination, and calibration drift rather than genuine capability improvements. The paper proposes rigorous evaluation standards to properly assess RLVR effectiveness in AI development.

The research challenges the reliability of recent RLVR benchmarks used to train large language models on structured tasks like mathematics and code generation. Rather than dismissing RLVR as ineffective, the authors demonstrate that headline improvements frequently stem from experimental design flaws rather than algorithmic breakthroughs. Three primary confounds distort results: unequal computational budgets between RLVR and baseline systems, "attempt inflation" where models convert abstentions into confident answers through calibration drift, and benchmark contamination that conflates memorization with reasoning capability.

This work addresses a critical gap in AI development methodology. As LLMs become integral to production systems, the accuracy of capability measurements directly impacts deployment decisions and research prioritization. Contaminated benchmarks and budget mismatches create false confidence in system reliability, particularly dangerous for applications requiring verified correctness. The authors' controlled reproductions show several celebrated performance gaps shrink substantially or vanish entirely under proper experimental controls.

The proposed evaluation framework—budget-matched saturation curves with variance tracking, calibration monitoring, judge robustness testing, and explicit contamination screening—establishes a higher bar for RLVR claims. This rigor benefits both the AI research community and practitioners, preventing overinvestment in techniques with overstated benefits. The findings suggest current RLVR deployments should undergo re-evaluation under these standards. For organizations relying on recent RLVR results for product decisions, this analysis necessitates reassessing actual capability gains versus reported improvements. The research doesn't eliminate RLVR as a viable technique but repositions it within realistic performance boundaries.

• →Many widely cited RLVR performance improvements are inflated by unequal budgets, calibration drift, and benchmark contamination rather than genuine capability gains
• →Researchers identify three specific confounds that distort RLVR evaluation: budget mismatch, attempt inflation, and data contamination in benchmarks
• →Controlled reproductions show celebrated performance gaps shrink or disappear when experiments use matched budgets and uncontaminated datasets
• →The paper proposes minimum standards including budget-matched saturation curves, calibration tracking, and explicit contamination screening for valid RLVR claims
• →RLVR remains potentially effective for verifiable domains but current measurements often obscure reliability costs and overstate reasoning improvements