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Researchers from Microsoft have introduced a novel approach to Retrieval Augmented Generation (RAG) known as Chain-of-Retrieval Augmented Generation (CoRAG). This method enhances the ability of AI systems to retrieve and reason over relevant information step by step before generating final answers. CoRAG addresses the limitations of traditional RAG methods, which typically perform a single retrieval step before generating responses, by allowing dynamic query reformulation based on evolving states. The approach has shown over 10 points improvement in multi-hop question answering tasks, setting a new state-of-the-art performance on the KILT benchmark for knowledge-intensive tasks. The training of CoRAG involves rejection sampling to generate intermediate retrieval chains, augmenting existing RAG datasets.
🚀 Introducing RAGEN—the world’s first reproduction of DeepSeek-R1(-Zero) methods for training agentic AI models! We’re betting big on the future of RL + LLM + Agents 🤖✨. This release is a minimally viable leap toward that vision. Code and more intro 🔗:… https://t.co/AG6lUYjA23
why did R1's RL suddenly start working, when previous attempts to do similar things failed? theory: we've basically spent the last few years running a massive acausally distributed chain of thought data annotation program on the pretraining dataset. deepseek's approach with R1…
What we 🏗️ built, 🚢 shipped, and 🚀 shared last week: Agent Evaluation with @ragas_io. We learned: 🪡 It’s all about LLM traces; test-set generation is not ready yet. 📊 Agent metrics must be combined with LLM and RAG metrics! 🎥 Recording: https://t.co/RHudkxJLto…
