<p>Google Research has introduced Retrieve-for-Train (R4T), a framework for search that returns coherent, diverse result sets. It trains a fan-out language model with RL once, using groundedness, diversity, and alignment rewards. That model then synthesizes training data for a 53.9M-parameter diffusion retriever. The retriever generates all retrieval directions in a single pass, running 12× to 20× faster than autoregressive fan-out. No code or model weights have been released yet.</p>
<p>The post <a href="https://www.marktechpost.com/2026/09/16/google-research-introduces-retrieve-for-train-r4t-an-rl-compiled-diffusion-retriever-for-12x-to-20x-faster-query-fan-out/">Google Research Introduces Retrieve-for-Train (R4T): An RL-Compiled Diffusion Retriever for 12× to 20× Faster Query Fan-Out</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>