Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908280731729920 |
|---|---|
| author | Li, Junsong Zhou, Jie Yang, Yutao Zhan, Bihao Pan, Qianjun Ding, Yuyang Chen, Qin Bo, Jiang Lin, Xin He, Liang |
| author_facet | Li, Junsong Zhou, Jie Yang, Yutao Zhan, Bihao Pan, Qianjun Ding, Yuyang Chen, Qin Bo, Jiang Lin, Xin He, Liang |
| contents | Automatic math correction aims to check students' solutions to mathematical problems via artificial intelligence technologies. Most existing studies focus on judging the final answer at the problem level, while they ignore detailed feedback on each step in a math problem-solving process, which requires abilities of semantic understanding and reasoning. In this paper, we propose a reinforcement learning (RL)-based method to boost large language model (LLM) for step-level automatic math correction, named StepAMC. Particularly, we convert the step-level automatic math correction within the text classification task into an RL problem to enhance the reasoning capabilities of LLMs. Then, we design a space-constrained policy network to improve the stability of RL. Then, we introduce a fine-grained reward network to convert the binary human feedback into a continuous value. We conduct extensive experiments over two benchmark datasets and the results show that our model outperforms the eleven strong baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning Li, Junsong Zhou, Jie Yang, Yutao Zhan, Bihao Pan, Qianjun Ding, Yuyang Chen, Qin Bo, Jiang Lin, Xin He, Liang Computation and Language Artificial Intelligence Machine Learning Automatic math correction aims to check students' solutions to mathematical problems via artificial intelligence technologies. Most existing studies focus on judging the final answer at the problem level, while they ignore detailed feedback on each step in a math problem-solving process, which requires abilities of semantic understanding and reasoning. In this paper, we propose a reinforcement learning (RL)-based method to boost large language model (LLM) for step-level automatic math correction, named StepAMC. Particularly, we convert the step-level automatic math correction within the text classification task into an RL problem to enhance the reasoning capabilities of LLMs. Then, we design a space-constrained policy network to improve the stability of RL. Then, we introduce a fine-grained reward network to convert the binary human feedback into a continuous value. We conduct extensive experiments over two benchmark datasets and the results show that our model outperforms the eleven strong baselines. |
| title | Teaching LLMs for Step-Level Automatic Math Correction via Reinforcement Learning |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.18432 |