The paper surveys 58 studies on verifier errors in reinforcement learning with verifiable rewards (RLVR), examining how such errors affect training and which mitigations are effective. It finds that the type of error—rather than its frequency—has a larger impact on learning dynamics, with random errors mainly slowing progress and false positives potentially causing learning plateaus or collapse. The authors compile findings into comparison tables, reconcile conflicting reports, and propose a reporting protocol to standardize future research.
By Savyasachi Gupta, Aniruddha Srinivas Joshi
arXiv:2605. 02909v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs).
By Kazuki Egashira, Mark Vero, Jasper Dekoninck, Florian E. Dorner, Robin Staab, Martin Vechev
The paper introduces Behavioral Self-Rewarding (BSR), a method that lets reinforcement learning agents with verifiable rewards judge and compare their own debugging trajectories to generate additional bounded auxiliary rewards. BSR aggregates pairwise preferences among trajectories that share the same verifier outcome, adding reward contrast while maintaining the original pass/fail ordering. Experiments on SWE-bench Verified and SWE-bench-Live with Qwen3-14B and Qwen3-32B show consistent performance gains, demonstrating that a policy’s own comparative judgments can serve as a useful learning signal.
By Yiran Wu, Andrew Zhao, Yurui Chang, Qingyun Wu, Xuan Feng
The paper investigates the reliability of rule- and model-based verifiers used in reinforcement learning with verifiable reward (RLVR) for mathematical reasoning. It finds that rule-based verifiers often miss equivalent answers in different formats, causing false negatives that degrade RL performance as models improve. Model-based verifiers achieve higher static accuracy but become vulnerable to reward hacking during RL, misclassifying certain response patterns as correct after fine-tuning.
By Yuzhen Huang, Weihao Zeng, Xingshan Zeng, Qi Zhu, Junxian He
arXiv:2610.19298v1 Announce Type: new
Abstract: Reinforcement learning for code usually rewards passing tests, which a model can do while remaining wrong on the inputs the tests do not cover. A progr...
By George Ma, Yuhao Zhang, Bing He, Zeren Shui, Ferhat Erata, Murali Krishna Ramanathan, Somayeh Sojoudi, Hao Zhou
arXiv:2607. 26656v1 Announce Type: cross Abstract: Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real CVEs, we find that 71.
By Yikun Li, Ting Zhang, Jiakun Liu, Jinfeng Jiang, Yuheng Yieh, Yixin Yang, Wen Bin Leow, Yide Yin, Yintong Huo, Eng Lieh Ouh, Lwin Khin Shar, David Lo