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This research introduces AutoVerifier, a novel framework that uses residual-guided non-parametric optimization to correct implicit biases in reference-based answer verification for reasoning models. By converting recurring verifier errors into auditable rule cards and code modules, the method significantly outperforms state-of-the-art verifiers across multiple benchmarks while maintaining safety through replay validation. This advancement directly addresses critical challenges in evaluating large language model reasoning and optimizing reinforcement learning with verifiable rewards.
AutoVerifier: Residual-Guided Non-Parametric Optimization for Reference-Based Answer Verification
arXiv:2608.25637v1 Announce Type: new Abstract: Reference-based verifiers are important for evaluating reasoning models and providing accurate outcome rewards in reinforcement learning with verifiable rewards. To improve verification accuracy, prior work has explored rule-based, model-based, and tool-augmented verifiers for checking answer equivalence across diverse answer forms. However, the equivalence of answer forms such as $1+3.14$ and $1+\pi$ may depend on the question and scoring criterion. We frame such implicit assumptions as verifier inductive biases. To address this challenge, we propose AutoVerifier, a residual-guided non-parametric optimization method that learns these biases from recurring verifier errors. Specifically, AutoVerifier records these biases in rule cards and promotes them to code modules or prompt guidance only after replay validation detects no direct regressions, keeping accepted updates auditable, editable, and reusable. Experiments on four verifier benchmarks demonstrate that AutoVerifier outperforms state-of-the-art verifiers by a large margin.
Abstract:Reference-based verifiers are important for evaluating reasoning models and providing accurate outcome rewards in reinforcement learning with verifiable rewards. To improve verification accuracy, prior work has explored rule-based, model-based, and tool-augmented verifiers for checking answer equivalence across diverse answer forms. However, the equivalence of answer forms such as $1+3.14$ and $1+\pi$ may depend on the question and scoring criterion. We frame such implicit assumptions as verifier inductive biases. To address this challenge, we propose AutoVerifier, a residual-guided non-parametric optimization method that learns these biases from recurring verifier errors. Specifically, AutoVerifier records these biases in rule cards and promotes them to code modules or prompt guidance only after replay validation detects no direct regressions, keeping accepted updates auditable, editable, and reusable. Experiments on four verifier benchmarks demonstrate that AutoVerifier outperforms state-of-the-art verifiers by a large margin.
Computation and Language (cs.CL)
arXiv:2608.25637 [cs.CL]
(or arXiv:2608.25637v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.25637 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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