@inproceedings{yuan-etal-2026-curing,
    title = "Curing Miracle Steps in {LLM} Mathematical Reasoning with Rubric Rewards",
    author = "Yuan, Youliang  and
      Mang, Qiuyang  and
      Chen, Jingbang  and
      Wan, Hong  and
      Liu, Xiaoyuan  and
      Xu, Junjielong  and
      Huang, Jen-tse  and
      Wang, Wenxuan  and
      Jiao, Wenxiang  and
      He, Pinjia",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.844/",
    pages = "18556--18577",
    ISBN = "979-8-89176-390-6",
    abstract = "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."
}