CoRe-Code: Collaborative Reinforcement Learning for Code Generation
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). While recent approaches such as Chain-of-Thought (CoT) and multi-agent systems (MAS) introduce planning, their limited role specialization and coordination hinder performance on complex tasks. To address the challenges of coordination and specialization in multi-agent code generation, we propose Collaborative Reinforcement Code (CoRe-Code), a framework for role specialized LLM agents that enhances inter-agent coordi...
| Authors | Zhihao Dou, Qinjian Zhao, Zhongwei Wan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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