Deep　generative　models　have　been　widely　used　in　molecular　generation　tasks　because　they　can　save　time　and　cost　in　drug　development　compared　with　traditional　methods.　Previous　studies　based　on　generative　adversarial　network　(GAN)　models　typically　employ　reinforcement　learning　(RL)　to　constrain　chemical　properties,　resulting　in　efficient　and　novel　molecules.　However,　such　models　have　poor　performance　in　generating　molecules　due　to　instability　in　training.　Therefore,　quantitative　evaluation　of　existing　molecular　generation　models,　especially　GAN　models,　is　necessary.　This　study　aims　to　evaluate　the　performance　of　discrete　GAN　models　using　RL　in　molecular　generation　tasks　and　explore　the　impact　of　different　factors　on　model　performance.　Through　evaluation　experiments　on　QM9　and　ZINC　datasets,　the　results　show　that　noise　sampling　distributions,　training　epochs,　and　training　data　volumes　can　affect　the　performance　of　molecular　generation.　Finally,　we　provide　strategies　for　stable　training　and　improved　performance　for　GAN　models.