Valid online inference is an important problem in contemporary multiple testing research, to which various solutions have been proposed recently. It is well-known that these methods can suffer from a significant loss of power if the null $p$-values are conservative. This occurs frequently, for instance whenever discrete tests are performed. To reduce conservatism, we introduce the method of super-uniformity reward (SURE). This approach works by incorporating information about the individual null cumulative distribution functions (or upper bounds of them), which we assume to be available. Our approach yields several new "rewarded" procedures that theoretically control online error criteria based either on the family-wise error rate (FWER) or the marginal false discovery rate (mFDR). We prove that the rewarded procedures uniformly improve upon the non-rewarded ones, and illustrate their performance for simulated and real data.