Reward hacking is a common phenomenon and an unavoidable issue as long as designing a perfect reward before optimization is difficult . As has been pointed out in the past , reward hacking, in which multi-objective optimization seems to succeed but the molecule is actually unpromising, occurred in this study, as shown in Figs. . DyRAMO was designed with the intention of generating molecules within an overlapping reliable region by introducing the idea of adjustable AD for each property to avoid such reward hacking. As shown in Fig. , by appropriately adjusting the reliability levels, promising molecules, including known inhibitors, were designed by appropriately adjusting their reliability levels. In addition, because the exploration of an appropriate combination of reliability levels can be daunting, we formulated the reliability level exploration as a black-box optimization of a function that computes the DSS score that balances both the reliability and multi-objective optimization of the predicted properties. This allowed us to introduce an efficient exploration method using BO, and the optimal reliability levels were found quickly, as shown in Fig. . Furthermore, as described in Section 2.3, properties can be prioritized by assigning appropriate weights to the DSS score. This study introduces a new solution to the problem of multi-objective optimization using generative models, which is likely to become even more important in the future.