Title: Offline Equilibrium Finding. Abstract: Offline reinforcement learning (Offline RL) is an emerging field that has recently begun gaining attention across various application domains due to its ability to learn behavior from earlier collected datasets. Offline RL proved very successful, paving a path to solving previously intractable real-world problems, and we aim to generalize this paradigm to a multi-agent or multiplayer-game setting. To this end, we formally introduce a problem of offline equilibrium finding (OEF) and construct multiple datasets across a wide range of games using several established methods. To solve the OEF problem, we design a model-based method that can directly apply any online equilibrium finding algorithm to the OEF setting while making minimal changes. We focus on three most prominent contemporary online equilibrium finding algorithms and adapt them to the OEF setting, creating three model-based variants: OEF-PSRO and OEF-CFR, which generalize the widely-used algorithms PSRO and Deep CFR to compute Nash equilibria (NEs), and OEF-JPSRO, which generalizes the JPSRO to calculate (Coarse) Correlated equilibria ((C)CEs). We further improve their performance by combining the behavior cloning policy with the model-based policy. Extensive experimental results demonstrate the superiority of our approach over multiple model-based and model-free offline RL algorithms and the necessity of the model-based method for solving OEF problems. We hope that our efforts may help to accelerate research in large-scale equilibrium finding. 