3 papers
cs.AI2025
SCoder: Iterative Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs
Xinyu Zhang, Changzhi Zhou, Linmei Hu +5
Existing code large language models (LLMs) often rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs. In this pa…
cs.CL2025
RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation
Changzhi Zhou, Xinyu Zhang, Dandan Song +6
Code generation has attracted increasing attention with the rise of Large Language Models (LLMs). Many studies have developed powerful code LLMs by synthesizing code-related instru…
cs.SE2025
Revisit Self-Debugging with Self-Generated Tests for Code Generation
Xiancai Chen, Zhengwei Tao, Kechi Zhang +7
Large language models (LLMs) have shown significant advancements in code generation, but still face challenges on tasks beyond their basic capabilities. Recently, the notion of sel…