Self-ensemble　adversarial　training　methods　improve　model　robustness　by　ensembling　models　at　different　training　epochs,　such　as　model　weight　averaging　(WA).　However,　previous　research　has　shown　that　self-ensemble　defense　methods　in　adversarial　training　(AT)　still　suffer　from　robust　overfitting,　which　severely　affects　the　generalization　performance.　Empirically,　in　the　late　phases　of　training,　the　AT　becomes　more　overfitting　to　the　extent　that　the　individuals　for　weight　averaging　also　suffer　from　overfitting　and　produce　anomalous　weight　values,　which　causes　the　self-ensemble　model　to　continue　to　undergo　robust　overfitting　due　to　the　failure　in　removing　the　weight　anomalies.　To　solve　this　problem,　we　aim　to　tackle　the　influence　of　outliers　in　the　weight　space　in　this　work　and　propose　an　easy-to-operate　and　effective　Median-Ensemble　Adversarial　Training　(MEAT)　method　to　solve　the　robust　overfitting　phenomenon　existing　in　self-ensemble　defense　from　the　source　by　searching　for　the　median　of　the　historical　model　weights.　Experimental　results　show　that　MEAT　achieves　the　best　robustness　against　the　powerful　AutoAttack　and　can　effectively　allievate　the　robust　overfitting.　We　further　demonstrate　that　most　defense　methods　can　improve　robust　generalization　and　robustness　by　combining　with　MEAT.