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README.md
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# PreciseCoder-9B
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Qwen3.5-9B post-trained with reinforcement learning for **precise debugging**: given buggy Python
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code, produce a minimal, correct fix (PreciseCoder project).
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`no-op` = share of clean (bug-free) inputs returned unchanged; `damage` = share broken by an edit.
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Load with `transformers` / vLLM (>=0.17) as a standard Qwen3.5 model (thinking enabled by default).
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---
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# PreciseCoder-9B
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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)
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Qwen3.5-9B post-trained with reinforcement learning for **precise debugging**: given buggy Python
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code, produce a minimal, correct fix (PreciseCoder project).
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`no-op` = share of clean (bug-free) inputs returned unchanged; `damage` = share broken by an edit.
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Load with `transformers` / vLLM (>=0.17) as a standard Qwen3.5 model (thinking enabled by default).
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## Citation
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```bibtex
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@article{precisecoder,
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title = {{PreciseCoder}: Faithful and Generalizable Debugging Beyond Unit-Test Rewards},
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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},
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journal = {arXiv preprint arXiv:2610.18755},
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year = {2026}
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}
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```
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