PreciseCoder-9B-Agent

📄 Paper (arXiv:2610.18755) · 🌐 Project page · 💻 Code

Qwen3.5-9B post-trained with multi-turn agentic reinforcement learning for precise debugging: the model inspects buggy Python code with tools and repairs it with minimal edits (PreciseCoder project).

Recipe. Agentic GDPO from the base model, with two rewards weighted 2:1: unit-test pass and unit-gated F1 (edit-block F1 against the ground-truth diff). The agent loop has up to 5 turns with view, str_replace_editor and submit tools. 328 steps, final checkpoint.

Eval:

eval	result
PDB test, agentic (1205 BigCodeBench + LiveCodeBench debugging tasks, 5 turns)	unit 0.685 · precision 0.721 · recall 0.796 · F1 0.731 · edit lines 2.9

Load with transformers / vLLM (>=0.17) as a standard Qwen3.5 model (thinking enabled by default).

Citation

@article{precisecoder,
  title   = {{PreciseCoder}: Faithful and Generalizable Debugging Beyond Unit-Test Rewards},
  author  = {Zhan, Yunkai and Chai, Miaosen and Wang, Shangshang and Zhong, Jike and Yang, Yuqing and Fu, Deqing and Chang, Serina and Zhu, Wang Bill},
  journal = {arXiv preprint arXiv:2610.18755},
  year    = {2026}
}