Abstract: Offline reinforcement learning (RL) is widely used to find an optimal policy using a pre-collected dataset, without further interaction with the environment. Recent RL theory has made significant progress in developing sample-efficient offline RL algorithms with various relaxed assumptions on data coverage, with specific focuses on either infinite-horizon discounted or finite-horizon episodic Markov decision processes (MDPs). In this work, we revisit the LP framework and the induced duality principle for offline RL, specifically for *infinite-horizon average-reward* MDPs. By virtue of this LP formulation and the duality principle, our result achieves the $\tilde O(1/\sqrt{n})$ near-optimal rate under partial data coverage assumptions. Our key enabler is to *relax* the equality *constraint* and introduce proper new *inequality constraints* in the dual formulation of the LP. We hope our insights can shed new lights on the use of LP formulations and the induced duality principle, in offline RL.