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Root-Cause Analysis (RCA) is crucial for discovering security vulnerabilities from fuzzing outcomes. Automating this process through triaging the crashes observed during the fuzzing process, however, is considered to be challenging. Particularly, today's statistical RCA approaches are known to be exceedingly slow, often taking tens of hours or even a week to analyze a crash. This problem comes from the biased sampling such approaches perform. More specifically, given an input inducing a crash in a program, these approaches sample around the input by mutating it to generate new test cases; these cases are used to fuzz the program, in a hope that a set of program elements (blocks, instructions or predicates) on the execution path of the original input can be adequately sampled so their correlations with the crash can be determined. This process, however, tends to generate the input samples more likely causing the crash, with their execution paths involving a similar set of elements, which become less distinguishable until a large number of samples have been made. We found that this problem can be effectively addressed by sampling around "counterexamples'', the inputs causing a significant change to the current estimates of correlations. These inputs though still involving the elements often do not lead to the crash. They are found to be effective in differentiating program elements, thereby accelerating the RCA process. Based upon the understanding, we designed and implemented a reinforcement learning (RL) technique that rewards the operations involving counterexamples. By balancing random sampling with the exploitation on the counterexamples, our new approach, called RACING, is shown to substantially elevate the scalability and the accuracy of today's statistical RCA, outperforming the state-of-the-art by more than an order of magnitude.