arXiv:2510.00915v4 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) replaces costly human labeling with automated verifiers. To reduce verifier hacking, many RLVR systems binarize rewards to $\{0,1\}$, but imperfect verifiers inevitably introduce \emph{false negatives} (rejecting correct answers) and \emph{false positives} (accepting incorrect ones). We formalize verifier unreliability as a stochastic reward channel with asymmetric noise rates $\rho_0$ and $\rho_1$ -- the FP rate and the FN rate, respectively. From this abstraction we derive two lightweigh
The increasing complexity of AI tasks and the scaling of model training drive the need for more efficient and robust reward mechanisms beyond costly human labeling.
This research addresses a fundamental limitation in current Reinforcement Learning with Verifiable Rewards (RLVR) systems, offering a path to more reliable and scalable AI training, especially for agentic systems.
By formalizing and mitigating the impact of imperfect verifiers, this work enables RLVR systems to be deployed more effectively in environments where perfect reward signals are impractical or impossible.
- · AI developers
- · AI-driven automation companies
- · Companies adopting AI agents
- · Robotics
- · Human labelers for reward signals
- · Systems highly reliant on perfect reward signals
Improved reliability and efficiency in training reinforcement learning models with automated verification.
Faster development and deployment of robust AI agents and autonomous systems across various industries.
Reduced operational costs for AI training and expanded applications of AI in safety-critical domains where verifiable rewards are crucial.
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Read at arXiv cs.LG