Title: Empirical Sufficiency Featuring Reward Delay Calibration. Abstract: Appropriate credit assignment for delay rewards is a fundamental challenge in various deep reinforcement learning tasks. To tackle this problem, we introduce a delay reward calibration paradigm inspired from a classification perspective. We hypothesize that when an agent's behavior satisfies an equivalent sufficient condition to be awarded, well-represented state vectors should share similarities. To this end, we define an empirical sufficient distribution, where the state vectors within the distribution will lead agents to environmental reward signals in consequent steps. Therefore, an overfitting classifier is established to handle the distribution and generate calibrated rewards. We examine the correctness of sufficient state extraction by tracking the real-time extraction and building hybrid different reward functions in environments with different levels of awarding latency. The results demonstrate that the classifier could generate timely and accurate calibrated rewards, and the rewards could make the training more efficient. Finally, we find that the sufficient states extracted by our model resonate with observations of human cognition.