We present RSFuzz, a new technique to enhance grammar-based fuzzing by reducing the generation of coverage-equivalent inputs during testing. Grammar-based fuzzers apply production rules from a given grammar to generate well-structured inputs for the target program. However, a key limitation is that many existing fuzzers still produce a large number of “coverage-equivalent” inputs—those that revisit already explored program paths—thereby restricting their ability to uncover new bugs and improve coverage. To address this issue, RSFuzz automatically identifies recurrent sequences of production rules that cause coverage-equivalent inputs and prevents their reuse during fuzzing. A key challenge in practice lies in the large number of coverage-equivalent input groups, each with many inputs, making it difficult to identify the underlying recurrent sequences. RSFuzz tackles this challenge with a customized algorithm that iteratively groups coverage-equivalent inputs, selects promising groups, and extracts recurrent sequences for each group based on accumulated data while running any grammar-based fuzzer. We integrated RSFuzz with existing random and probabilistic fuzzers and evaluated it on eight real-world programs using JavaScript and JSON input formats. Experimental results show that incorporating RSFuzz with both fuzzers exclusively detects 108 and 31 crashes with distinct stack traces, increases line coverage by 6.5% and 5.1%, and reduces duplicate-coverage input generation by 29.7% and 37.7%, respectively, compared to their performance without RSFuzz.