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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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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