2 citations · 2 across the 4 of their papers we have counts for
6 papers
Improving LLM Code Generation via Requirement-Aware Curriculum Reinforcement Learning
Shouyu Yin, Zhao Tian, Junjie Chen +1
Code generation, which aims to automatically generate source code from given programming requirements, has the potential to substantially improve software development efficiency. W…
REAgent: Requirement-Driven LLM Agents for Software Issue Resolution
Shiqi Kuang, Zhao Tian, Kaiwei Lin +5
Issue resolution aims to automatically generate patches from given issue descriptions and has attracted significant attention with the rapid advancement of large language models (L…
Advancing Language Models for Code-related Tasks
Zhao Tian
Recent advances in language models (LMs) have driven significant progress in various software engineering tasks. However, existing LMs still struggle with complex programming scena…
On the Effectiveness of Training Data Optimization for LLM-based Code Generation: An Empirical Study
Shiqi Kuang, Zhao Tian, Tao Xiao +2
Large language models (LLMs) have achieved remarkable progress in code generation, largely driven by the availability of high-quality code datasets for effective training. To furth…
Aligning Requirement for Large Language Model's Code Generation
Zhao Tian, Junjie Chen
Code generation refers to the automatic generation of source code based on a given programming specification, which has garnered significant attention particularly with the advance…
Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling
Trae Research Team, Pengfei Gao, Zhao Tian +12
Software issue resolution is a critical challenge in software engineering and has garnered increasing attention in recent years. With the rapid advancement of large language models…