Beyond pass@k: Redundancy-Aware RLVR for Multi-Sample Code Generation 事件

Beyond pass@k: Redundancy-Aware RLVR for Multi-Sample Code Generation arXiv:2605.28022v1 Announce Type: new Abstract: LLMs for code generation are commonly evaluated in repeated-sampling settings using Pass@k, where multiple candidate programs are executed against unit tests under a finite sampling budget. While recent verifier-based reinforcement learning (RLVR) methods improve executable correctness, how these objectives affect redundancy among sampled programs remains poorly understood. In t