9 papers
Multi-level Code Optimization via Mixture of Prompts
Yun Peng, Jun Wan, Jiakun Liu +3
Runtime efficiency is a critical factor that impacts both software quality and user satisfaction. There are many approaches proposed for code optimization to improve runtime effici…
An Iterative Test-and-Repair Framework for Competitive Code Generation
Lingxiao Tang, Muyang Ye, Zhaoyang Chu +4
Large language models (LLMs) have made remarkable progress in code generation, but competitive programming remains a challenge. Recent training-based methods have improved code gen…
The Bidirectional Process Reward Model
Lingyin Zhang, Jun Gao, Xiaoxue Ren +1
Process Reward Models (PRMs), which assign fine-grained scores to intermediate reasoning steps within a solution trajectory, have emerged as a promising approach to enhance the rea…
PracRepair: LLM-Empowered Automated Program Repair Inspired by Human-Like Debugging Practices
Yu Cheng, Zhongxin Liu, Zhenchang Xing +3
As software systems grow in scale and complexity, debugging and repair remain costly and time-consuming. Large language models (LLMs) have advanced automated program repair (APR),…
CoRE: A Fine-Grained Code Reasoning Benchmark Beyond Output Prediction
Jun Gao, Yun Peng, Qian Qiao +6
Despite strong performance on code generation tasks, it remains unclear whether large language models (LLMs) genuinely reason about code execution. Existing code reasoning benchmar…
ExecVerify: White-Box RL with Verifiable Stepwise Rewards for Code Execution Reasoning
Lingxiao Tang, He Ye, Zhaoyang Chu +4
Code LLMs still struggle with code execution reasoning, especially in smaller models. Existing methods rely on supervised fine-tuning (SFT) with teacher-generated explanations, pri…