In　semi-supervised　semantic　segmentation　(SSS),　weak-to-strong　consistency　regularization　techniques　are　widely　utilized　in　recent　works,　typically　combined　with　input-level　and　feature-level　perturbations.　However,　the　integration　between　weak-to-strong　consistency　regularization　and　network　perturbation　has　been　relatively　rare.　We　note　several　problems　with　existing　network　perturbations　in　SSS　that　may　contribute　to　this　phenomenon.　By　revisiting　network　perturbations,　we　introduce　a　new　approach　for　network　perturbation　to　expand　the　existing　weak-to-strong　consistency　regularization　for　unlabeled　data.　Additionally,　we　present　a　volatile　learning　process　for　labeled　data,　which　is　uncommon　in　existing　research.　Building　upon　previous　work　that　includes　input-level　and　feature-level　perturbations,　we　present　MLPMatch　(Multi-Level-Perturbation　Match),　an　easy-to-implement　and　efficient　framework　for　semi-supervised　semantic　segmentation.　MLPMatch　has　been　validated　on　the　Pascal　VOC　and　Cityscapes　datasets,　achieving　state-of-the-art　performance.　Code　is　available　from　https://github.com/LlistenL/MLPMatch.