Text-based person retrieval (TPR) has become a crucial task in cross-modal retrieval due to its broad application in fields such as public safety and criminal investigation. Existing TPR methods typically rely on fully fine-tuning large-scale pretrained vision-language models like CLIP, which incurs high computational costs and tends to exhibit poor generalization in unseen domains due to overfitting. Fortunately, Parameter-Efficient Transfer Learning (PETL) has emerged as a lightweight alternative. However, applying PETL to TPR remains challenging, as its limited adaptation capacity struggles to capture intricate identity cues and becomes highly susceptible to gradient interference from unreliable image-text pairs. To address these challenges, we present a PETL-based framework named UniGR that unifies granularity and reliability for robust and efficient TPR. Specifically, we design a multi-granularity relational adapter (MRA) to capture both coarse-grained global and fine-grained local relational features among tokens, equipping the generic backbone with the task-specific, precise understanding needed for TPR. To combat the noise sensitivity of PETL, a reliability-aware reweighting strategy (RRS) is introduced to adaptively down-weight unreliable samples during training. Furthermore, we propose a parameter-free cross-modal cyclic verification (CMCV) module to mitigate ambiguities in cross-modal matching computations and refine retrieval ranking further. Experiments on benchmarks corroborate the superiority of UniGR among parameter-efficient methods. Remarkably, with only 4.5% of trainable parameters, UniGR outperforms most fully fine-tuned methods while maintaining strong generalization.