In this work, we present Link-INVENT as an extension to the existing de novo design platform REINVENT. The suggested algorithm shares some similarities with the SAMOA algorithm as proposed by Langevin et al. in that the code builds upon REINVENT's existing codebase and uses policy-based RL for MPO. However, our algorithm has three crucial differences compared to earlier work. Firstly, the prior trained by Langevin et al. is based on ChEMBL compounds and follows the protocol as reported for REINVENT, which was purposed to sample small molecules as SMILES. Consequently, in their linker generation solution, linkers are sampled when the "*" token (the model's internal representation of characters in a SMILES string), denoting the attachment point, is reached, and based on the conditional probabilities of the SMILES sequence so far. The limitation is that linkers should be generated in the context of both molecular subunits. In the extreme case, the SAMOA algorithm may struggle to generate plausible linkers if the SMILES sequence was "CC*C…" where the length of the SMILES on the right side of the "*" token is greater than the left side, as the conditional probabilities for linker generation would only be based on the sequence so far, i.e., "CC". In contrast, Link-INVENT is trained based on the conditional probabilities of observing a linker given both molecular subunits, similar to the SyntaLinker model reported by Yang et al. Secondly, the data preparation to train the Link-INVENT prior was based on reaction-splicing of the ChEMBL compounds similar to the Lib-INVENT library design model we reported previously. Our training set contains linkers that join molecular subunits ranging from a few atoms in size to larger moieties with rings. As a result, a single Link-INVENT prior is suited for diverse linker generation tasks. Lastly, Link-INVENT was built on the latest version of REINVENT and supports an extensive selection of physico-chemical properties that can be optimized through RL. Moreover, we have implemented additional linker specific properties that can be optimized (in the form of additional scoring function components), ranging from physico-chemical properties to flexibility and rigidity, allowing one to explicitly optimize linker properties. We demonstrate the use of Link-INVENT on fragment linking, scaffold hopping, and PROTACs design case studies. Through RL, the Link-INVENT agent learns to generate favourable linkers connecting molecular subunits that satisfy diverse MPO objectives, facilitating practical application of the model for real-world drug discovery projects. The code is freely available at . Function where diverse multi-parameter optimization (MPO) objectives can be specified. Through reinforcement learning (RL), the agent's policy is iteratively updated to satisfy the target MPO objective and thus generating desirable connected molecules.