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Résumé : Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by grounding responses in external evidence, mitigating hallucinations, and enabling access to up-to-date, domain-specific knowledge. However, existing RAG frameworks often suffer from unstable self-supervised optimization signals and inconsistent factual grounding. We introduce CREAM-RAG (Consistency-Regularized Enhanced Augmented Model for RAG), a unified framework that integrates retrieval, Direct Preference Optimization (DPO)-based self-reward reinforcement learning, and a consistency regularization objective to stabilize reward dynamics during fine-tuning. By enforcing alignment between multiple retrieved contexts and generated responses, CREAM-RAG improves factual faithfulness and semantic coherence without external supervision. Empirical evaluations on the LLaMA-2-7B model demonstrate that CREAM-RAG achieves a 35.04% average improvement over the base model across reasoning and factuality benchmarks, highlighting its effectiveness in reducing hallucinations and enhancing retrieval-grounded reasoning.