jameszhan33 commited on
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Replace with GDPO checkpoint (qwen36-27b-GDPO_u2f1_f1ur_h200, step 328)

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README.md CHANGED
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  ---
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  license: apache-2.0
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  base_model: Qwen/Qwen3.6-27B
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- tags: [debugging, code, reinforcement-learning, grpo, precisecoder]
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  ---
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  # PreciseCoder-27B
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  Qwen3.6-27B post-trained with reinforcement learning for **precise debugging**: given buggy Python
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  code, produce a minimal, correct fix (PreciseCoder project). Best-validation checkpoint, global step 328 (2 epochs).
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- **Recipe.** GRPO from the base model (no SFT warm-start) with reward = (unit + 0.5路F1) / 1.5, where F1 is the
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- edit-block precision/recall F1 against the ground-truth diff (PDB evaluator), on the PDB train split (buggy inputs only).
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- Trained with verl on 2 nodes (n=6 rollouts, temperature 1.0, lr 1e-6, batch 32, max response 6144 tokens,
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- KL loss 0.005, rollout TP=2). Minimal-edit prompt template.
 
 
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  **Eval** (PDB benchmark, BigCodeBench + LiveCodeBench debugging tasks):
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  | eval | result |
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  |---|---|
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- | pdb_test (buggy inputs, 4k think + 2k answer) | unit 0.777 路 precision 0.784 路 recall 0.850 路 edit lines 2.7 |
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- | gt_holdout (1512 clean inputs, 16k think + 4k answer) | no-op 47.2% 路 damage 10.2% 路 unit pass 89.7% |
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- | PDB-Wild (228 SWE-smith repo-level bugs) | unit 0.531 路 precision 0.544 路 recall 0.622 路 F1 0.559 路 edit lines 9.4 |
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  `no-op` = share of clean (bug-free) inputs returned unchanged; `damage` = share broken by an edit.
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- Computed over answers with an extractable code block.
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  Load with `transformers` / vLLM (>=0.17) as a standard Qwen3.6 model (thinking enabled by default).
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- Source: verl experiment `qwen36-27b-GRPO_0.5F1` (Delta, Sep 2026).
 
 
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  ---
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  license: apache-2.0
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  base_model: Qwen/Qwen3.6-27B
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+ tags: [debugging, code, reinforcement-learning, gdpo, precisecoder]
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  ---
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  # PreciseCoder-27B
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  Qwen3.6-27B post-trained with reinforcement learning for **precise debugging**: given buggy Python
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  code, produce a minimal, correct fix (PreciseCoder project). Best-validation checkpoint, global step 328 (2 epochs).
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+ **Recipe.** GDPO from the base model (no SFT warm-start): two reward signals, unit-test pass (weight 2.0) and
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+ unit-gated F1 (weight 1.0), each normalized within the rollout group before being combined into the advantage.
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+ F1 is the edit-block precision/recall F1 against the ground-truth diff (PDB evaluator), counted only when the
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+ unit tests pass. Trained on the PDB train split (buggy inputs only) with verl on 1 node of 8脳H200 (n=6 rollouts,
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+ temperature 1.0, lr 1e-6, batch 32, max response 6144 tokens, KL loss 0.005, rollout TP=2, token-level rollout
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+ importance sampling). Minimal-edit prompt template.
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  **Eval** (PDB benchmark, BigCodeBench + LiveCodeBench debugging tasks):
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  | eval | result |
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  |---|---|
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+ | pdb_test (buggy inputs, 4k think + 2k answer) | unit 0.793 路 precision 0.792 路 recall 0.867 路 F1 0.810 路 edit lines 2.6 |
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+ | gt_holdout (1512 clean inputs, 16k think + 4k answer) | no-op 45.8% 路 damage 11.1% 路 unit pass 89.0% |
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+ | PDB-Wild (228 SWE-smith repo-level bugs) | unit 0.522 路 precision 0.551 路 recall 0.629 路 F1 0.566 路 edit lines 9.2 |
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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 Qwen3.6 model (thinking enabled by default).
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+ Source: verl experiment `qwen36-27b-GDPO_u2f1_f1ur_h200` (Delta, Oct 2026). The previous release
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+ (GRPO, reward (unit + 0.5路F1)/1.5) is at revision `82cf7e4`.
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