model card

README.md

@@ -0,0 +1,34 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-9B
+tags: [debugging, code, reinforcement-learning, gdpo, agent, precisecoder]
+---
+# PreciseCoder-9B-Agent
+
+📄 [Paper (arXiv:2610.18755)](https://arxiv.org/abs/2610.18755) · 🌐 [Project page](https://precise-coder-rl.github.io/) · 💻 [Code](https://github.com/precise-coder-rl/PreciseCoder)
+
+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
+
+```bibtex
+@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}
+}
+```