Offline reinforcement learning (RL) aims to optimize the return given a fixed dataset of agent trajectories without additional interactions with the environment. While algorithm development has progressed rapidly, significant theoretical advances have also been made in understanding the fundamental challenges of offline RL. However, bridging these theoretical insights with practical algorithm design remains an ongoing challenge. In this survey, we explore key intuitions derived from theoretical work and their implications for offline RL algorithms. We begin by listing the conditions needed for the proofs, including function representation and data coverage assumptions. Function representation conditions tell us what to expect for generalization, and data coverage assumptions describe the quality requirement of the data. We then examine counterexamples, where offline RL is not solvable without an impractically large amount of data. These cases highlight what cannot be achieved for all algorithms and the inherent hardness of offline RL. Building on techniques to mitigate these challenges, we discuss the conditions that are sufficient for offline RL. These conditions are not merely assumptions for theoretical proofs, but they also reveal the limitations of these algorithms and remind us to search for novel solutions when the conditions cannot be satisfied.