arXiv Computation and Language By Sungjae Lee, Youngsik Yoon, Seockbean Song, Siwei Wang, Wei Chen, Jungseul Ok

Confidence-Gated Transductive Test Generation for Code Reranking

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The paper introduces Confidence-Gated Transductive Test Generation (CoTT), a method that combines an efficient inductive approach with selective transductive generation triggered only when inductive confidence is low. This adaptive strategy enhances the reliability of test case outputs while limiting additional computational effort. Experiments on code reranking benchmarks demonstrate that CoTT surpasses previous baselines in performance metrics and reduces overall cost compared to applying transductive generation to every input.

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arXiv AI
Aug 26

Robust Code RL via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping

The paper introduces RobustTests, a framework that improves reinforcement learning for code generation by synthesizing test cases from faulty code and refining rewards with a dense, stepwise function. It uses validator agents and behavioral clustering to filter out invalid or redundant tests, and incorporates pass‑rate‑based rewards to counter hallucination noise. Experiments on CodeContests and LiveCodeBench show that fine‑tuning Qwen3‑32B with RobustTests yields a 3% absolute performance gain over baseline methods.

By Yiwen Zhang, Xiaodong Yan, Zhenyu Huang, Deng Zhao, Liang Jiang, Qing Cui, Zujie Wen, Zhiqiang Zhang, Jun Zhou