A new method called RefGRPO enhances the performance of LLM agents by improving their self-assessment capabilities. This approach addresses the reflection gap where agents misjudge their outputs despite receiving feedback. By implementing a calibration bonus and dynamic scheduling, RefGRPO boosts task accuracy and reduces underconfidence. This advancement could lead to more effective self-improvement and selective prediction in AI applications.