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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.06877 |
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| _version_ | 1866908418330066944 |
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| author | Guo, Jiaxing Yang, Wenjie Zhang, Shengzhong Xu, Tongshan Du, Lun Zheng, Da Huang, Zengfeng |
| author_facet | Guo, Jiaxing Yang, Wenjie Zhang, Shengzhong Xu, Tongshan Du, Lun Zheng, Da Huang, Zengfeng |
| contents | Outcome-rewarded Large Language Models (LLMs) have demonstrated remarkable success in mathematical problem-solving. However, this success often masks a critical issue: models frequently achieve correct answers through fundamentally unsound reasoning processes, a phenomenon indicative of reward hacking. We introduce MathOlympiadEval, a new dataset with fine-grained annotations, which reveals a significant gap between LLMs' answer correctness and their low process correctness. Existing automated methods like LLM-as-a-judge struggle to reliably detect these reasoning flaws. To address this, we propose ParaStepVerifier, a novel methodology for meticulous, step-by-step verification of mathematical solutions. ParaStepVerifier identifies incorrect reasoning steps. Empirical results demonstrate that ParaStepVerifier substantially improves the accuracy of identifying flawed solutions compared to baselines, especially for complex, multi-step problems. This offers a more robust path towards evaluating and training LLMs with genuine mathematical reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06877 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning Guo, Jiaxing Yang, Wenjie Zhang, Shengzhong Xu, Tongshan Du, Lun Zheng, Da Huang, Zengfeng Computation and Language Outcome-rewarded Large Language Models (LLMs) have demonstrated remarkable success in mathematical problem-solving. However, this success often masks a critical issue: models frequently achieve correct answers through fundamentally unsound reasoning processes, a phenomenon indicative of reward hacking. We introduce MathOlympiadEval, a new dataset with fine-grained annotations, which reveals a significant gap between LLMs' answer correctness and their low process correctness. Existing automated methods like LLM-as-a-judge struggle to reliably detect these reasoning flaws. To address this, we propose ParaStepVerifier, a novel methodology for meticulous, step-by-step verification of mathematical solutions. ParaStepVerifier identifies incorrect reasoning steps. Empirical results demonstrate that ParaStepVerifier substantially improves the accuracy of identifying flawed solutions compared to baselines, especially for complex, multi-step problems. This offers a more robust path towards evaluating and training LLMs with genuine mathematical reasoning. |
| title | Right Is Not Enough: The Pitfalls of Outcome Supervision in Training LLMs for Math Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.06877 |