Towards Better Correctness and Efficiency in Code Generation

• URL: http://arxiv.org/abs/2508.20124v1
• Date: Sun, 24 Aug 2025 16:47:19 GMT
• Title: Towards Better Correctness and Efficiency in Code Generation
• Authors: Yunlong Feng, Yang Xu, Xiao Xu, Binyuan Hui, Junyang Lin,
• Abstract summary: We propose an efficiency-oriented reinforcement learning framework guided by a novel performance reward.<n>Online exploration is most effective when starting from a high-correctness baseline.<n>Experiments show the effectiveness of the method, which improves code correctness by 10.18% and runtime efficiency by 7.75% on a 7B model.
• Score: 47.06216040246783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While code large language models have demonstrated remarkable progress in code generation, the generated code often exhibits poor runtime efficiency, limiting its practical application in performance-sensitive scenarios. To address this limitation, we propose an efficiency-oriented reinforcement learning framework guided by a novel performance reward. Based on this framework, we take a deeper dive into the code efficiency problem, identifying then proposing methods to overcome key bottlenecks: (1) Dynamic exploration overcomes the static data constraints of offline fine-tuning, enabling the discovery of more efficient code implementations. (2) The error-insensitive reinforcement learning method and high-contrast efficiency signals are crucial for mitigating systematic errors and achieving effective optimization. (3) Online exploration is most effective when starting from a high-correctness baseline, as this allows for efficiency improvements without sacrificing accuracy. With these discoveries, we finally propose a two-stage tuning method, which achieves high and balanced performance across correctness and efficiency. The results of experiments show the effectiveness of the method, which improves code correctness by 10.18\% and runtime efficiency by 7.75\% on a 7B model, achieving performance comparable to much larger model.

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