Accelerating retrogressive thaw slump (RTS) activity is reshaping permafrost landscapes on the Qinghai-Tibet Plateau (QTP) and threatening infrastructure. Forward-looking assessment requires identifying slopes approaching failure rather than mapping past RTS occurrence. Conventional susceptibility models treat RTS initiation as a binary outcome, making pre-initiation locations effectively equivalent regardless of when they subsequently fail. Yet under progressive environmental forcing, initiation timing can provide an implicit ordering of susceptibility: earlier initiation may indicate greater susceptibility than later initiation. We therefore defined Years Before Initiation (YBI) from annual inventories and developed a weakly supervised ranking framework that uses initiation-time ordering to learn susceptibility gradients. The Annual-YBI model was compared with two conventional models (Static Binary and Annual Binary) sharing the same neural network architecture. Although all models performed similarly in five-fold cross-validation (ROC-AUC ≈ 0.988), the Annual-YBI model captured 89.45 % of 199 temporally held-out RTS initiations in 2021–2022 within the top 10 % susceptibility area, compared with 79.90 % and 63.82 % for Annual Binary and Static Binary models, respectively. SHAP analysis identified NDVI, slope, and ground ice content as primary predisposing factors, with modulation by annual hydroclimatic variables. The 2021–2022 maximum susceptibility composite classified 1.70 × 10⁵ km² (15.6 % of QTP permafrost) as high or very high susceptibility, including 42.8 % of permafrost within the 20-km Qinghai–Tibet Engineering Corridor buffer. These results show that initiation timing provides weak supervision for susceptibility gradients that binary labels cannot resolve, improving identification of locations prone to future RTS initiation and offering potential for transfer to other permafrost regions.