用过程评分机制抑制大模型数学推理中的‘奇迹步骤’，提升真实推理能力。
Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards
- 设计过程导向的评分模型，基于问题特异性规则评估推理链条
- 在AIME2024上将验证通过率从26.7%提升至62.6%，奇迹步骤减少71%
- 适合关注模型可解释性与可靠性的数学推理研究者
本文发现当前模型易受奖励劫持影响，导致推理能力被严重高估。人类验证表明存在大量假阳性——即通过无效过程得出正确答案。我们系统分析并构建了失败模式分类体系，识别出‘奇迹步骤’：无有效推导直接跳到正确结果。探针实验显示，此类现象与答案回忆捷径相关，包括预训练阶段的记忆化获取。为此，我们提出过程导向的评分模型（Rubric Reward Model, RRM），对推理轨迹进行严格逻辑审查，明确惩罚逻辑漏洞。将其融入强化学习训练后，四个数学基准测试表现均优于仅依据结果的监督方式。尤其在AIME2024上，验证通过率从26.7%提升至62.6%，奇迹步骤下降71%。结果表明，奖励过程而非仅结果，是构建可信模型的关键。
原文摘要 · Abstract (English)
In this paper, we observe that current models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. This is evidenced by a high incidence of false positives-solutions that reach the correct answer through an unsound process. Through a systematic analysis with human verification, we establish a taxonomy of these failure modes, identifying patterns like Miracle Steps-abrupt jumps to a correct output without a valid preceding derivation. Probing experiments suggest that these Miracle Steps are linked to answer-recall shortcuts, including memorization from pretraining, where the model accesses the correct answer independently of its reasoning chain. To mitigate this systemic issue, we introduce the Rubric Reward Model (RRM), a process-oriented reward function that evaluates the entire reasoning trajectory against problem-specific rubrics. The RRM explicitly penalizes logical flaws and encourages rigorous deduction. When integrated into an RL pipeline, RRM-based training consistently outperforms outcome-only supervision across four math benchmarks. Notably, it boosts Verified Pass@1024 on AIME2024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%. Our work demonstrates that rewarding the solution process is crucial for building accurate and reliable models.
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