Dense retrieval models have become crucial for information retrieval tasks, but their success is frequently reliant on large, computationally expensive architectures. While existing approaches like REFINE [1] achieve strong performance through model fusion requiring dual model serving and weighted interpolation at inference time, this work proposes a novel fusion-free teacher-student knowledge distillation framework that achieves competitive performance with significantly simpler deployment. The key innovation lies in transferring fine-grained cross-encoder relevance judgments directly into a single deployable bi-encoder through listwise knowledge distillation with temperature-scaled soft labels, eliminating the need for runtime model fusion while preserving retrieval quality. Unlike standard contrastive fine-tuning that relies solely on hard binary labels, this approach combines InfoNCE contrastive loss with KL divergence-based distillation from cross-encoder probability distributions, enabling the student to learn nuanced ranking signals beyond simple positive-negative distinctions. The distillation framework uses cross-encoder/ms-marco-MiniLM-L12-v2 as the teacher model and trains a student encoder, bge-large-en-v1.5, on synthetically generated query-document pairs from SQuAD and RAG datasets with hybrid hard negative mining (BM25 + dense retrieval) and dynamic negative refresh. The student model is trained with a combined objective function balancing contrastive learning and knowledge distillation, with performance evaluated using standard retrieval metrics including Recall@k, MAP, NDCG, and MRR. Results demonstrate that the distilled student model significantly outperforms the vanilla BGE baseline across both datasets, achieving 19.66% improvement in Recall@3 on SQuAD and 3.63% on RAG, while maintaining simpler deployment architecture compared to fusion-based methods. Notably, the approach outperforms REFINE by 3.3% on RAG despite eliminating runtime fusion overhead. These findings demonstrate that teacher-student distillation with listwise soft labels provides an effective and practical approach for enhancing dense retrieval models in data-scarce scenarios without requiring extensive architectural modifications, dual-model serving, or large-scale training data.