自动修复验证器的隐含偏见，提升答案一致性判断准确率
AutoVerifier: Residual-Guided Non-Parametric Optimization for Reference-Based Answer Verification
- 通过记录错误模式生成可复用的规则卡片
- 在不引入新参数情况下，准确率超越现有方法
- 适合需要高可信答案验证的推理模型评估场景
基于参考答案的验证器对评估推理模型和强化学习中的可验证奖励至关重要。现有方法采用规则、模型或工具增强来检查不同表达形式下的答案等价性，但如 $1+3.14$ 与 $1+π$ 的等价性可能依赖于问题背景和评分标准。本文将此类隐含假设视为验证器归纳偏见。为此提出 AutoVerifier，一种残差引导的非参数优化方法，从重复出现的验证错误中学习这些偏见。具体地，AutoVerifier 将这些偏见记录为规则卡片，并仅在回放验证未发现直接退化后，将其升级为代码模块或提示引导，确保更新可审计、可编辑、可复用。在四个验证器基准上的实验表明，AutoVerifier 显著优于当前最优验证器。
原文摘要 · Abstract (English)
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 $1+π$ 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。