Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR
Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs). While RLVR is designed for tasks with verifiable ground-truth answers, real-world verifiers (e.g., static code checkers) can introduce errors into the reward signal. Prior analyses have largely treated such errors as random and independent across samples, concluding that errors merely slow training with limited effect on final performance. However, practical verifiers tend to exhibit systematic errors. This introduces a risk of models learning unwanted consistent behavior from a structurally incorrect reward signal. In this work, we...
| Authors | Kazuki Egashira, Mark Vero, Jasper Dekoninck et al. |
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| Primary category | cs.LG |
| Published | 6 April 2026 |
| Last updated at source | 17 August 2026 |
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