The emergence of ChatGPT in late 2022 has rendered generative discourse models essential to daily living. The increasing user expectations have elevated the necessity to improve these model's capacities to address complex issues in current research. This study examines the influence of Recursive Embedding Fine-Tuning (REFT) on enhancing retrieval accuracy and overall efficacy in biomedical question answering systems. Integrating REFT with the Reason and Action framework (ReAct) and retrieval-augmented generation (RAG) resulted in significant enhancements in the model's capacities to extract information and execute logical reasoning. We evaluated the REFT technique using the PubMed BIOASQ dataset across many reasoning tasks, encompassing both long-form and shortform question answering in English, along with supportive and comparison reasoning contexts. This study highlights the importance of the Reasoning and Action (ReAct) technique in REFT-based models, addressing previous shortcomings in handling long-form question-answering tasks in biological disciplines. Our experimental findings demonstrate significant improvements in performance across key metrics, with the model achieving 97.88% accuracy@10 after minimal training cycles, indicating a 1.1% enhancement in retrieval efficacy. The model exhibited notable learning efficiency, reducing training loss by 62.7% in the early epochs and achieving rapid stability with a final loss of 0.074. Additional metrics, including NDCG@10 (91.59%) and MRR@10 (89.52%), further validate the effectiveness of our approach in enhancing retrieval accuracy and ranking precision in biological question answering systems.