PreciseCoder-9B
Qwen3.5-9B post-trained with reinforcement learning for precise debugging: given buggy Python code, produce a minimal, correct fix (PreciseCoder project).
Recipe. GDPO with two rewards, unit-test pass and unit-gated F1 (edit-block F1 against the ground-truth diff), weighted 2:1.
Eval:
| eval | result |
|---|---|
| PDB test (1205 BigCodeBench + LiveCodeBench debugging tasks) | unit 0.613 · precision 0.691 · recall 0.762 · F1 0.700 · edit lines 2.9 |
| PDB-Wild (474 tasks: BCB 37 + LCB 209 + SWE-smith 228) | unit 0.323 · precision 0.429 · recall 0.474 · F1 0.432 · edit lines 12.0 |
| GT Debug (1512 clean inputs) | no-op 50.4% · damage 12.9% · unit pass 87.1% |
no-op = share of clean (bug-free) inputs returned unchanged; damage = share broken by an edit.
Load with transformers / vLLM (>=0.17) as a standard Qwen3.5 model (thinking enabled by default).
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