AI RESEARCH

Cast a Wider Net: Coordinated Pass@K Policy Optimization for Code Reasoning

arXiv CS.AI

ArXi:2605.27000v1 Announce Type: cross Repeated sampling with a verifier is the standard way to allocate test-time compute for code generation, with pass@$K$ as the canonical metric. Yet the standard policy class draws $K$ independent samples from a single answer distribution, so attempts often collapse onto near-duplicate reasoning paths and waste the budget on redundant rollouts. This failure is costly in competitive programming, where many problems admit multiple distinct algorithmic strategies and pass@$K$ requires only one correct attempt.