Pengcheng Jiang

Google compiles a set-level search reward into a diffusion retriever that fans out in one pass

Retrieve-for-Train runs reinforcement learning once against a fixed corpus and distils the winning sub-query sets into a lightweight diffusion model. Adopting it puts a retrain schedule on whoever owns the catalogue.