5 papers · 1 filter
ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision?
Lishui Fan, Mouxiang Chen, Tingwei Zhu +4
Code generation is important in software engineering, and Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm to improve it through execution-based feedbac…
Balancing Latency and Accuracy of Code Completion via Local-Cloud Model Cascading
Hanzhen Lu, Lishui Fan, Jiachi Chen +3
Line-level code completion requires a critical balance between high accuracy and low latency. Existing methods suffer from a trade-off: large language models (LLMs) provide high-qu…
ReCode: Reinforcing Code Generation with Reasoning-Process Rewards
Lishui Fan, Yu Zhang, Mouxiang Chen +1
In practice, rigorous reasoning is often a key driver of correct code, while Reinforcement Learning (RL) for code generation often neglects optimizing reasoning quality. Bringing p…
FGIT: Fault-Guided Fine-Tuning for Code Generation
Lishui Fan, Zhongxin Liu, Haoye Wang +3
Modern instruction-tuned large language models (LLMs) have made remarkable progress in code generation. However, these LLMs fine-tuned with standard supervised fine-tuning (SFT) so…
FlexFL: Flexible and Effective Fault Localization with Open-Source Large Language Models
Chuyang Xu, Zhongxin Liu, Xiaoxue Ren +3
Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated…