Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.20367 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915432918679552 |
|---|---|
| author | Wang, Junqiao Zhang, Zeng He, Yangfan Zhang, Zihao Song, Xinyuan Song, Yuyang Shi, Tianyu Li, Yuchen Xu, Hengyuan Wu, Kunyu Yi, Xin Wan, Zhongwei Yuan, Xinhang Wang, Zijun Lu, Kuan Huo, Menghao Jingqun, Tang Qian, Guangwu Li, Keqin Chen, Qiuwu He, Lewei |
| author_facet | Wang, Junqiao Zhang, Zeng He, Yangfan Zhang, Zihao Song, Xinyuan Song, Yuyang Shi, Tianyu Li, Yuchen Xu, Hengyuan Wu, Kunyu Yi, Xin Wan, Zhongwei Yuan, Xinhang Wang, Zijun Lu, Kuan Huo, Menghao Jingqun, Tang Qian, Guangwu Li, Keqin Chen, Qiuwu He, Lewei |
| contents | With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of RL in code optimization and generation, highlighting its role in enhancing compiler optimization, resource allocation, and the development of frameworks and tools. Subsequent sections first delve into the intricate processes of compiler optimization, where RL algorithms are leveraged to improve efficiency and resource utilization. The discussion then progresses to the function of RL in resource allocation, emphasizing register allocation and system optimization. We also explore the burgeoning role of frameworks and tools in code generation, examining how RL can be integrated to bolster their capabilities. This survey aims to serve as a comprehensive resource for researchers and practitioners interested in harnessing the power of RL to advance code generation and optimization techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20367 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey Wang, Junqiao Zhang, Zeng He, Yangfan Zhang, Zihao Song, Xinyuan Song, Yuyang Shi, Tianyu Li, Yuchen Xu, Hengyuan Wu, Kunyu Yi, Xin Wan, Zhongwei Yuan, Xinhang Wang, Zijun Lu, Kuan Huo, Menghao Jingqun, Tang Qian, Guangwu Li, Keqin Chen, Qiuwu He, Lewei Software Engineering Computation and Language With the rapid evolution of large language models (LLM), reinforcement learning (RL) has emerged as a pivotal technique for code generation and optimization in various domains. This paper presents a systematic survey of the application of RL in code optimization and generation, highlighting its role in enhancing compiler optimization, resource allocation, and the development of frameworks and tools. Subsequent sections first delve into the intricate processes of compiler optimization, where RL algorithms are leveraged to improve efficiency and resource utilization. The discussion then progresses to the function of RL in resource allocation, emphasizing register allocation and system optimization. We also explore the burgeoning role of frameworks and tools in code generation, examining how RL can be integrated to bolster their capabilities. This survey aims to serve as a comprehensive resource for researchers and practitioners interested in harnessing the power of RL to advance code generation and optimization techniques. |
| title | Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey |
| topic | Software Engineering Computation and Language |
| url | https://arxiv.org/abs/2412.20367 |