让路径规划模型灵活适应不同偏好，同时提升寻路效率与成功率。

Recent learning-based path planners use neural networks to process occupancy representations and approximate heuristics for classical search algorithms, yielding near-optimal paths with reduced search effort. However, these methods are tied to a fixed objective, usually the shortest-path objective, implicit in their supervision. This limits their flexibility to accommodate alternative criteria. We introduce $\textbf{FlexPath}$, a two-stage learned search-guidance framework that first learns a recall-oriented connectivity prior initialized from shortest-path planner demonstrations and then refines this prior using differentiable path-shape objectives, thereby separating demonstration-based learning of $\textbf{connectivity-biased guidance}$ from subsequent $\textbf{objective specific refinement}$. Beyond enabling adaptation to new routing preferences, the two-stage procedure improves standard shortest-path planning itself: on TMP, FlexPath improves optimal-path recovery from 75.0\% to 88.6\% over TransPath while reducing search expansions by 13.8\%. Ablations show that neither prior learning nor objective fine-tuning alone matches the full pipeline; their combination yields the strongest path cost and search efficiency. We further demonstrate the preference adaptation by adapting guidance to non-shortest-path objectives such as obstacle clearance, class-conditioned obstacle clearance and waypoint following. For clearance with $d_{\min}=2$, FlexPath achieves 96.2\% full clearance satisfaction on feasible instances while maintaining low search effort, and it reaches 98.4\% waypoint-following success.