Basic Information
s3 is a research and development framework for training search agents used in retrieval-augmented generation (RAG). It focuses on teaching language models how to perform better document search and retrieval via reinforcement learning without modifying the generator model. The project provides a modular pipeline for preparing corpora and indices, precomputing a naïve RAG cache, deploying retrieval and generator services, running RL-based training, performing inference, and evaluating results. The README highlights efficiency, claiming strong QA performance using far less data than prior methods. The codebase is intended for engineers and researchers who want to train and benchmark search components that work with black-box LLMs and to reproduce experiments presented in the accompanying arXiv paper.
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