CoRe-Code: Collaborative Reinforcement Learning for Code Generation
The paper introduces CoRe-Code, a framework for collaborative reinforcement learning aimed at improving code generation. It addresses the limitations of existing methods by enhancing coordination and specialization among language model agents. Experimental results demonstrate that CoRe-Code outperforms current approaches in accuracy and efficiency across various benchmarks.
- ▪CoRe-Code enhances inter-agent coordination for more accurate and efficient code generation.
- ▪The framework uses a Planner-Coder paradigm to produce high-level plans and execute them.
- ▪Experiments show consistent improvements in accuracy and execution efficiency compared to existing methods.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24812 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 40E9sSSPHS8D |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
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| Publisher visit | Yes — open original |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.24812 (cs) [Submitted on 24 May 2026] Title:CoRe-Code: Collaborative Reinforcement Learning for Code Generation Authors:Zhihao Dou, Qinjian Zhao, Zhongwei Wan, Xiaoyu Xia, Sumon Biswas View a PDF of the paper titled CoRe-Code: Collaborative Reinforcement Learning for Code Generation, by Zhihao Dou and 4 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have achieved strong performance in code generation, but most methods rely on autoregressive decoding without global planning, often leading to locally coherent yet globally suboptimal solutions (e.g., failing test cases or inefficient complexity).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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