Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915163414724608 |
|---|---|
| author | Xie, Tian Gao, Zitian Ren, Qingnan Luo, Haoming Hong, Yuqian Dai, Bryan Zhou, Joey Qiu, Kai Wu, Zhirong Luo, Chong |
| author_facet | Xie, Tian Gao, Zitian Ren, Qingnan Luo, Haoming Hong, Yuqian Dai, Bryan Zhou, Joey Qiu, Kai Wu, Zhirong Luo, Chong |
| contents | Inspired by the success of DeepSeek-R1, we explore the potential of rule-based reinforcement learning (RL) in large reasoning models. To analyze reasoning dynamics, we use synthetic logic puzzles as training data due to their controllable complexity and straightforward answer verification. We make some key technical contributions that lead to effective and stable RL training: a system prompt that emphasizes the thinking and answering process, a stringent format reward function that penalizes outputs for taking shortcuts, and a straightforward training recipe that achieves stable convergence. Our 7B model develops advanced reasoning skills-such as reflection, verification, and summarization-that are absent from the logic corpus. Remarkably, after training on just 5K logic problems, it demonstrates generalization abilities to the challenging math benchmarks AIME and AMC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14768 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning Xie, Tian Gao, Zitian Ren, Qingnan Luo, Haoming Hong, Yuqian Dai, Bryan Zhou, Joey Qiu, Kai Wu, Zhirong Luo, Chong Computation and Language Artificial Intelligence Inspired by the success of DeepSeek-R1, we explore the potential of rule-based reinforcement learning (RL) in large reasoning models. To analyze reasoning dynamics, we use synthetic logic puzzles as training data due to their controllable complexity and straightforward answer verification. We make some key technical contributions that lead to effective and stable RL training: a system prompt that emphasizes the thinking and answering process, a stringent format reward function that penalizes outputs for taking shortcuts, and a straightforward training recipe that achieves stable convergence. Our 7B model develops advanced reasoning skills-such as reflection, verification, and summarization-that are absent from the logic corpus. Remarkably, after training on just 5K logic problems, it demonstrates generalization abilities to the challenging math benchmarks AIME and AMC. |
| title | Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.14768 |