EvolveCoder: Evolving Test Cases via Adversarial Verification for Code Reinforcement Learning
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving code generation in large language models, but its effectiveness is limited by weak and static verification signals in existing coding RL datasets. In this paper, we propose a solution-conditioned and adversarial verification framework that iteratively refines test cases based on the execution behaviors of candidate solutions, with the goal of increasing difficulty, improving discriminative power, and reducing redundancy. Based on this framework, we introduce EvolveCoder-22k, a large-scale coding reinforcement learning dataset constructed through multiple rounds of adversarial test case evolution. E...
| Authors | Chi Ruan, Dongfu Jiang, Huaye Zeng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
Models and datasets referencing this paper
- TIGER-Lab/EvolveCoder-22KDataset
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