Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918527823249408 |
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| author | Wu, Mingze Anand, Abhinav Verma, Shweta Mezini, Mira |
| author_facet | Wu, Mingze Anand, Abhinav Verma, Shweta Mezini, Mira |
| contents | Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for code generation involves LLM inference and verification of the generated output, which can take considerable time and resources. In this paper, we explore the application of offline RL to code-generating models by leveraging existing code datasets. Our experiments demonstrate that offline RL is an effective training strategy for improving LLM performance. We show that offline RL can be especially beneficial for small LLMs and challenging coding problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28409 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning Wu, Mingze Anand, Abhinav Verma, Shweta Mezini, Mira Artificial Intelligence Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for code generation involves LLM inference and verification of the generated output, which can take considerable time and resources. In this paper, we explore the application of offline RL to code-generating models by leveraging existing code datasets. Our experiments demonstrate that offline RL is an effective training strategy for improving LLM performance. We show that offline RL can be especially beneficial for small LLMs and challenging coding problems. |
| title | Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.28409 |