@inproceedings{zhang-etal-2026-robust-code,
title = "Robust Code {RL} via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping",
author = "Zhang, Yiwen and
Yan, Xiaodong and
Huang, Zhenyu and
Zhao, Deng and
Jiang, Liang and
Cui, Qing and
Wen, Zujie and
Zhang, Zhiqiang and
Zhou, Jun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1114/",
pages = "23568--23588",
ISBN = "979-8-89176-462-0",
abstract = "Reinforcement Learning from Verifiable Rewards (RLVR) is pivotal for enhancing LLM code generation, yet its efficacy is often hindered by insufficient test case coverage, leading to reward hacking and policy degradation. To address this, we propose RobustTests, a framework featuring a faulty-code-driven test case synthesis strategy. By leveraging ``near-correct'' faulty codes, RobustTests captures latent logical discrepancies and employs validator agents with behavioral feature clustering to filter invalid or redundant test cases. Additionally, a stepwise dense reward function based on pass rates is introduced to mitigate false negatives and enhance training robustness. Using this pipeline, we construct an augmented version of the CodeContests+ dataset with superior diagnostic utility. Experimental results show that RL fine-tuning of Qwen3-32B via RobustTests achieves a 3{\%} absolute gain on LiveCodeBench, demonstrating its effectiveness in advancing LLM code generation proficiency. Codes and data are available at https://huggingface.co/datasets/sid6/RobustTests."
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<abstract>Reinforcement Learning from Verifiable Rewards (RLVR) is pivotal for enhancing LLM code generation, yet its efficacy is often hindered by insufficient test case coverage, leading to reward hacking and policy degradation. To address this, we propose RobustTests, a framework featuring a faulty-code-driven test case synthesis strategy. By leveraging “near-correct” faulty codes, RobustTests captures latent logical discrepancies and employs validator agents with behavioral feature clustering to filter invalid or redundant test cases. Additionally, a stepwise dense reward function based on pass rates is introduced to mitigate false negatives and enhance training robustness. Using this pipeline, we construct an augmented version of the CodeContests+ dataset with superior diagnostic utility. Experimental results show that RL fine-tuning of Qwen3-32B via RobustTests achieves a 3% absolute gain on LiveCodeBench, demonstrating its effectiveness in advancing LLM code generation proficiency. Codes and data are available at https://huggingface.co/datasets/sid6/RobustTests.</abstract>
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%0 Conference Proceedings
%T Robust Code RL via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping
%A Zhang, Yiwen
%A Yan, Xiaodong
%A Huang, Zhenyu
%A Zhao, Deng
%A Jiang, Liang
%A Cui, Qing
%A Wen, Zujie
%A Zhang, Zhiqiang
%A Zhou, Jun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-robust-code
%X Reinforcement Learning from Verifiable Rewards (RLVR) is pivotal for enhancing LLM code generation, yet its efficacy is often hindered by insufficient test case coverage, leading to reward hacking and policy degradation. To address this, we propose RobustTests, a framework featuring a faulty-code-driven test case synthesis strategy. By leveraging “near-correct” faulty codes, RobustTests captures latent logical discrepancies and employs validator agents with behavioral feature clustering to filter invalid or redundant test cases. Additionally, a stepwise dense reward function based on pass rates is introduced to mitigate false negatives and enhance training robustness. Using this pipeline, we construct an augmented version of the CodeContests+ dataset with superior diagnostic utility. Experimental results show that RL fine-tuning of Qwen3-32B via RobustTests achieves a 3% absolute gain on LiveCodeBench, demonstrating its effectiveness in advancing LLM code generation proficiency. Codes and data are available at https://huggingface.co/datasets/sid6/RobustTests.
%U https://aclanthology.org/2026.findings-emnlp.1114/
%P 23568-23588
Markdown (Informal)
[Robust Code RL via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping](https://aclanthology.org/2026.findings-emnlp.1114/) (Zhang et al., Findings 2026)
ACL
- Yiwen Zhang, Xiaodong Yan, Zhenyu Huang, Deng Zhao, Liang Jiang, Qing Cui, Zujie Wen, Zhiqiang Zhang, and Jun Zhou. 2026. Robust Code RL via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 23568–23588, Budapest, Hungary. Association for Computational Linguistics.