Move AI search’s thinking budget from inference into training
Complex search breaks when an LLM must repeatedly reason through database-aware query fan-out at request time. Google’s Retrieve-for-Train instead uses offline reinforcement learning to compile reward-aligned exploration into a lightweight diffusion retriever, producing a coherent slate of complementary queries in one pass. The trade is deliberate: spend compute once during training, not on every user’s search.