Abstract: Offline preference-based reinforcement learning (PbRL) offers an effective solution to overcome the challenges associated with designing rewards and the high costs of online interactions. In offline PbRL, agents are provided with a fixed dataset containing human preferences between pairs of trajectories. Previous studies mainly focus on recovering the rewards from the preferences, followed by policy optimization with an off-the-shelf offline RL algorithm. However, given that preference label in PbRL is inherently trajectory-based, accurately learning transition-wise rewards from such label can be challenging, potentially leading to misguidance during subsequent offline RL training. To address this issue, we introduce our method named $\textit{Flow-to-Better (FTB)}$, which leverages the pairwise preference relationship to guide a generative model in producing preferred trajectories, avoiding Temporal Difference (TD) learning with inaccurate rewards. Conditioning on a low-preference trajectory, $\textit{FTB}$ uses a diffusion model to generate a better one with a higher preference, achieving high-fidelity full-horizon trajectory improvement. During diffusion training, we propose a technique called $\textit{Preference Augmentation}$ to alleviate the problem of insufficient preference data. As a result, we surprisingly find that the model-generated trajectories not only exhibit increased preference and consistency with the real transition but also introduce elements of $\textit{novelty}$ and $\textit{diversity}$, from which we can derive a desirable policy through imitation learning. Experimental results on D4RL benchmarks demonstrate that FTB achieves a remarkable improvement compared to state-of-the-art offline PbRL methods. Furthermore, we show that FTB can also serve as an effective data augmentation method for offline RL.