Semi-supervised　domain　generalization　(SSDG)　focuses　on　training　a　model　on　a　single　labeled　source　domain　and　several　unlabeled　source　domains　simultaneously　for　the　purpose　of　generalizing　to　out-of-distribution　domain.　To　prevent　model　overfitting　on　the　unlabeled　source　domains　and　enhance　model　generalization,　we　propose　an　effective　two-stage　framework　that　disentangle　the　SSDG　task　into　a　burn-in　stage　and　a　mutual-training　stage.　In　the　burn-in　stage,　we　train　a　domain　adaptation　model　to　reduce　domain　gap　and　produce　high-accuracy　pseudo　labels　for　the　unlabeled　data.　In　the　second　stage,　we　devise　two　peer　networks　equipped　with　style　randomization　modules　to　take　advantage　of　both　the　style　and　class　information　from　the　pseudo-labeled　data.　The　peer　networks　are　mutually　teaching　each　other,　which　allows　them　to　avoid　overfitting　to　noisy　data　and　ultimately　improves　generalization　ability.　Extensive　experiments　show　that　our　method　achieves　state-of-the-art　performance　on　various　benchmarks,　demonstrating　the　effectiveness　of　our　method　on　SSDG.