Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards
Hyeonbin Hwang, Doyoung Kim, Seungone Kim, Seonghyeon Ye, Minjoon Seo
Abstract
Training on large amounts of rationales (i.e., CoT Fine-tuning) has been found effective for improving mathematical reasoning of large language models (LLMs). However, acquiring human-authored solutions or augmenting rationales from proprietary models is costly and not scalable. In this paper, we study the problem of whether LLMs could self-improve mathematical reasoning capabilities. To this end, we propose Self-Explore, where the LLM is tasked to explore the first wrong step (i.e., the first pit) within the rationale and use such signals as fine-grained rewards for further improvement. On the GSM8K and MATH test set, Self-Explore achieves 11.57% and 2.89% improvement on average across three LLMs compared to supervised fine-tuning (SFT). Our code is available here]9.- Anthology ID:
- 2024.findings-emnlp.78
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1444–1466
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.78/
- DOI:
- 10.18653/v1/2024.findings-emnlp.78
- Cite (ACL):
- Hyeonbin Hwang, Doyoung Kim, Seungone Kim, Seonghyeon Ye, and Minjoon Seo. 2024. Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 1444–1466, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards (Hwang et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.78.pdf