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Enhancing Bug-Exposing Test Generation via Step-Level Credit Assignment and Self-Distillation from Valuable Failures

Wenjie Gan, Zhaoxi Zhang, Jiahui Liang, Weikang Li, Wanyuan Wang, Li Zhang, Jian Dong · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27694 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

大型言語モデルは強力なコード生成能力を示したが、その出力にはしばしば微妙なバグが含まれている。そのようなコードを自動的に修正するには、誤った行動を露出するインプット、正しい期待される出力と並行して、正しいコード生成能力を示すインプットが必要である。最近のRLベースのテスト生成方法は、すべての推論ステップに同じ結果の報酬を適用し、有用な推論を抑制し、欠陥のステップを強化する可能性がある。彼らはまた、バグを引き起こす入力を見つけるが間違った出力を予測する軌道から効果的に学ぶことができず、検証されたバグを引き起こす進歩を不十分に利用する。これらの制

Machine translation (Meta M2M100); the English original follows.

Abstract

Large Language Models have shown strong code generation capabilities, yet their outputs often contain subtle bugs. Automatically repairing such code requires informative tests: inputs that expose faulty behavior, paired with correct expected outputs. Recent RL-based test generation methods apply the same outcome reward to all reasoning steps, which can discourage useful reasoning and reinforce flawed steps. They also fail to effectively learn from trajectories that find bug-triggering inputs but predict incorrect outputs, leaving their verified bug-triggering progress insufficiently exploited. To address these limitations, we propose SCALF, a process-supervised RL approach that combines Step-level Credit Assignment with targeted Learning from valuable Failed trajectories. SCALF derives step-specific advantages from process rewards, allowing each reasoning stage to receive its own learning signal. It further routes trajectories that expose bugs but predict incorrect outputs to on-policy self-distillation, using execution feedback to learn from their valuable bug-triggering progress. Across three benchmarks, SCALF outperforms strong prompting and post-training baselines by up to 2.97, 5.26, and 3.53 percentage points in joint success rate, output correctness rate, and bug-triggering rate, respectively. Our code and datasets are available at https://anonymous.4open.science/r/SCALF-F4D1.

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Topics: Correctness, Computer science, Process (computing), Code (set theory), Machine learning, Source code

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