Systematic Verification Errors in RLVR Training
A recent study published on arXiv (2605.02909) explores the influence of systematic verification errors on Reinforcement Learning with Verifiable Rewards (RLVR) in large language models. Although RLVR targets tasks with verifiable answers, real-world verifiers, such as static code checkers, can introduce inaccuracies. Earlier research considered these errors as random and independent, concluding they had a negligible impact on overall performance. In contrast, this study reveals that practical verifiers often produce systematic errors, leading models to adopt undesirable behaviors from incorrect reward signals. Experiments focused on arithmetic tasks indicate that while systematic false negatives mimic random noise, systematic false positives can significantly impair model performance, raising concerns about the implications of systematic verification errors in RLVR training.
Key facts
- Study examines systematic verification errors in RLVR for LLMs
- Real-world verifiers like static code checkers can introduce systematic errors
- Prior analyses treated errors as random and independent
- Systematic false negatives have similar effects to random noise
- Systematic false positives can causally degrade performance
- Controlled experiments conducted on arithmetic tasks
- Risk of models learning unwanted consistent behaviors from incorrect reward signals
- Paper published on arXiv with ID 2605.02909
Entities
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