ResearchResearch paperReinforcement Learning · Large Language Models1 source · Oct 13, 2026

Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR

Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the reasoning of large language models (LLMs).

In practice, rule-based checkers reject correct answers in unexpected formats, unit tests accept incorrect programs, and LLM judges can be led to approve empty or adversarial responses.
We ask what errors verifiers make, how they affect training, which mitigations work against which errors, and how the findings extend to self-generated, rubric- and judge-based, and verifier-free rewards.
Random errors mostly slow learning, though under a fixed budget or KL regularization they also lower final accuracy.
False positives that the policy can learn to trigger can cause plateaus or collapse, sometimes even with a low initial error rate.

[1]Checking the Checkers: A Synthesis of Verifier Noise and Robustness in RLVR
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 06:44 AM
Reinforcement learning with verifiable rewards (RLVR) has become a principal recipe for improving the reasoning of large language models (LLMs).
In practice, rule-based checkers reject correct answers in unexpected formats, unit tests accept incorrect programs, and LLM judges can be led to approve empty or adversarial responses.

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