5 papers
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…
Parallel Scaling Law for Language Models
Mouxiang Chen, Binyuan Hui, Zeyu Cui +5
It is commonly believed that scaling language models should commit a significant space or time cost, by increasing the parameters (parameter scaling) or output tokens (inference-ti…
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…