Fixing GRPO's credit assignment problem without evaluating every step

Researchers have developed ProVer, a framework that improves credit assignment in large language model agent training by identifying and verifying pivotal decisions rather than uniformly weighting all trajectory steps. The method uses an "agentic judge" to pinpoint segments likely responsible for success or failure, then verifies these segments by comparing outcomes of policy continuations, avoiding the need to evaluate every intermediate state. ProVer achieves 7-10% performance improvements over the standard GRPO approach across multiple benchmarks while requiring only modest additional computational overhead.