The field of Retrieval-Augmented Generation (RAG) is moving towards more effective and efficient methods for leveraging external knowledge sources to improve the accuracy and coherence of generated text. Recent developments focus on bridging the gap between the retriever and generator components, with a emphasis on process-supervised approaches that optimize the retrieval process to maximize the probability of generating correct answers. Another key direction is the development of more transparent and scalable evaluation frameworks, enabling the assessment of RAG applications across multiple quality metrics. Noteworthy papers include: SIRAG, which proposes a process-supervised multi-agent framework to improve the coordination between retriever and generator. A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent, which introduces a novel evaluation framework to assess RAG applications. Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation, which proposes a method to embed critical and contextually coherent domain knowledge into large language models.