The paper introduces Test Cases Scaling (TCS), a two‑stage reinforcement learning framework that automatically generates sound and discriminative test cases for code large language models (LLMs). Stage 1 trains a test generator to produce tests aligned with a reference solution, while Stage 2 refines the generator to produce counterexample tests that target the solver’s current failure modes. Experiments on TACO and LiveCodeBench show that TCS improves pass@1 and inference‑time answer selection, and the learned test generator can also effectively rank outputs from other LLMs.
By Jiacheng Xu, Wentao Zhang, Zhiyi Lyu, Fuxiang Zhang, Chaojie Wang, Yang Liu, Bo An
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
arXiv:2602. 20213v2 Announce Type: replace-cross Abstract: The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases.
By Jingwei Shi, Xinxiang Yin, Jing Huang, Jinman Zhao, Shengyu Tao
arXiv:2508.14313v4 Announce Type: replace-cross
Abstract: Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or sea...
By Can Jin, Yang Zhou, Qixin Zhang, Hongwu Peng, Di Zhang, Zihan Dong, Marco Pavone, Ligong Han, Zhang-Wei Hong, Tong Che, Dimitris N. Metaxas
The paper introduces X-Coder, a competitive programming model trained entirely on synthetic tasks, verified solutions, and reliable test cases, eliminating the need for real-world data in post‑training. A dual‑verification strategy is used to reduce noise in solutions and test outputs, providing high‑quality reward signals for reinforcement learning. X‑Coder‑14B achieves significant performance gains, scoring 67.5% on LiveCodeBench v5 and 63.4% on v6, surpassing its base model by over 40 points.
By Jie Wu, Haoling Li, Xin Zhang, Jiani Guo, Jane Luo, Xuewei Yang, Steven Liu, Yangyu Huang, Ruihang Chu, Scarlett Li, Yujiu Yang
arXiv:2607. 01181v1 Announce Type: cross Abstract: RL with verifiable rewards (RLVR) has emerged as a powerful paradigm for training LMs on tasks with well-defined success metrics, such as code generation and mathematical reasoning.
By Mehul Damani, Isha Puri, Idan Shenfeld, Jacob Andreas