| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3.5-9B |
| tags: [debugging, code, reinforcement-learning, grpo, precisecoder] |
| --- |
| # PreciseCoder-9B |
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| Qwen3.5-9B post-trained with reinforcement learning for **precise debugging**: given buggy Python |
| code, produce a minimal, correct fix (PreciseCoder project). |
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| **Recipe.** GDPO with two rewards, unit-test pass and unit-gated F1 (edit-block F1 against the ground-truth diff), weighted 2:1. |
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| **Eval:** |
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| | eval | result | |
| |---|---| |
| | PDB test (1205 BigCodeBench + LiveCodeBench debugging tasks) | unit 0.613 路 precision 0.691 路 recall 0.762 路 F1 0.700 路 edit lines 2.9 | |
| | PDB-Wild (474 tasks: BCB 37 + LCB 209 + SWE-smith 228) | unit 0.323 路 precision 0.429 路 recall 0.474 路 F1 0.432 路 edit lines 12.0 | |
| | GT Debug (1512 clean inputs) | no-op 50.4% 路 damage 12.9% 路 unit pass 87.1% | |
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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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