(Short Notes) RARE: Retrieval-Augmented Reasoning Modeling5 min readJust now-- What Problem Does It Solve?Large Language Models (LLMs) struggle with:Hallucinating facts They often make up or misremember domain-specific knowledge (like in medicine or law).Weak reasoning Even when they have facts, they may not reason through them correctly, especially in complex or niche fields.Huge size and cost Current methods require massive models to store all knowledge and reasoning capacity. Core Problem: Can we create smaller, smarter models that reason well using external knowledge, instead of memorizing everything? How Does It Solve the Problem?The paper proposes RARE (Retrieval-Augmented Reasoning Modeling), a new training paradigm that:Decouples knowledge storage and reasoning:External knowledge is stored outside the model (in a retrievable format).Reasoning skills are trained inside the model.Retrieves knowledge during training and inference using standard tools (like BM25), and injects that into the model as context.Focuses training on:Understanding and applying the retrieved info (not memorizing it).Contextual reasoning the model learns how to think using provided knowledge.Think of it like giving a student open-book exams and training them to reason, not memorize the book. What Are the Key Findings?Small models can beat large models:RARE-trained 8B models (like Llama-3.18B) outperformed GPT-4 (a trillion-parameter model) on several domain tasks like medical Q&A.Example: On PubMedQA, RARE-Qwen-2.57B scored 78.63% vs GPT-4s 75.20%, and even better than GPT-4 + retrieval!Reasoning improves significantly:Compared to standard Retrieval-Augmented Generation (RAG), RARE models reason better, not just retrieve better.