<ul><li>s3 is an open-source framework introduced by researchers at the University of Illinois Urbana-Champaign to efficiently build retrieval-augmented generation (RAG) systems, benefiting developers creating real-world large language model (LLM) applications.</li><li>The s3 framework separates the retriever (searcher) from the generator, training a search agent independently to improve retrieval quality without impacting the final answer generated by the LLM.</li><li>s3 introduces the Gain Beyond RAG (GBR) reward signal to incentivize the searcher to find documents that enhance the generator's output quality, leading to better performance compared to static and end-to-end tuned baselines.</li><li>The model-agnostic approach of s3 allows for strong gains in data efficiency, achieving notable results with minimal training examples, making it practical and cost-effective for enterprises with limited resources.</li></ul>

s3: The new RAG framework that trains search agents with minimal data

Discover more