Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards
arXiv:2509.21882v3 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other structured tasks. However, we argue that many headline RLVR gains are not yet well validated because reports often conflate policy improvement with three confounds: (i) budget mismatch between RLVR and baseline evaluations, (ii) attempt inflation and calibration drift that convert abstentions into confident answers, and (iii) benchmark data contamination. Using budget-matched reproductions and partial-pr
The proliferation of Reinforcement Learning with Verifiable Rewards (RLVR) in large language models necessitates rigorous examination of its claimed performance given the increasing impact of these models.
This research highlights critical measurement gaps and hidden costs in RLVR, which could lead to overstatements of AI capabilities and misallocation of resources in AI development.
The findings suggest that the perceived progress in LLM performance via RLVR might be less robust than reported, requiring a re-evaluation of current validation methodologies.
- · AI ethics researchers
- · Independent AI evaluators
- · Developers of robust validation methods
- · LLM developers relying on inflated RLVR metrics
- · Investors making decisions based on unverified AI performance claims
- · Benchmarks susceptible to contamination
Immediate scrutiny and potentially lowered expectations for AI models that use RLVR for performance claims.
Increased demand for transparent and budget-matched evaluation methodologies across the AI industry.
A shift in research focus towards more intrinsically verifiable and robust AI training paradigms, rather than relying on potentially misleading reward systems.
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Read at arXiv cs.LG