In this work, we introduced Link-INVENT as an extension to the de novo design platform, REINVENT. Link-INVENT is a recurrent neural network (RNN)-based generative model trained to propose linker ideas given two input molecular subunits. In contrast to previous methods for linker design involving database searching which are inherently limited to a pre-defined collection of chemical ideas , Link-INVENT builds linkers at the token level, proposing linkers as SMILES and can generalize in chemical space. Moreover, Link-INVENT's Scoring Function provides users with the ability to optimize bespoke multiparameter optimization (MPO) objectives via reinforcement learning (RL), offering control that is not present in existing deep learning (DL)-based approaches to linker generation. A vast number of molecular properties can be optimized, ranging from previously implemented components available in REINVENT that operate on the entire molecule to newly implemented linker specific components which provide control over linker properties.