Direct Preference Optimization (DPO) has been widely adopted for large language model alignment due to its simple training procedure and lack of an explicit reward model. However, in iterative DPO, when the policy model from the previous iteration is repeatedly used as the reference model for subsequent rounds, noise in preference data and errors in the reference model accumulate over time. This accumulation can lead to late-stage over-optimization, performance fluctuations, and degraded generalization. To address these issues, we propose TPMM-DPO, a trajectory-aware preference-guided model merging method. The method treats the sequence of policy models generated during iterative DPO as an optimization trajectory and adaptively integrates them using learned fusion weights, thereby constructing a smoother and more robust reference model. In contrast to conventional iterative DPO, which relies solely on a single previous model, TPMM-DPO effectively mitigates error accumulation induced by noisy preferences and improves training stability. Experimental results show that standard iterative DPO often suffers from performance degradation in the middle and later stages of training, whereas TPMM-DPO consistently improves generation quality and achieves higher win rates and reward scores on both in-domain and out-of-domain evaluations. Further ablation studies and robustness analyses demonstrate that, compared with simple averaging, learnable-weight fusion more effectively alleviates late-stage performance degradation caused by noisy preferences.