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license: apache-2.0
base_model: Qwen/Qwen3.5-9B
tags: [debugging, code, reinforcement-learning, gdpo, agent, precisecoder]
---
# PreciseCoder-9B-Agent
馃搫 [Paper (arXiv:2610.18755)](https://arxiv.org/abs/2610.18755) 路 馃寪 [Project page](https://precise-coder-rl.github.io/) 路 馃捇 [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 路 precision 0.721 路 recall 0.796 路 F1 0.731 路 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}
}
```
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