Google Research has published a new framework called Retrieve-for-Train that addresses critical performance bottlenecks in AI-powered search systems. The challenge: existing large language models generate redundant or semantically similar search queries when asked to decompose complex user searches into complementary sub-queries, a problem researchers term 'paraphrastic collapse.' Additionally, performing this decomposition at inference time requires substantial computational overhead—hundreds of intermediate reasoning tokens—that creates latency delays incompatible with modern search expectations. The Retrieve-for-Train framework solves these issues by shifting the computational burden from inference time to training time. Researchers use reinforcement learning to train a fan-out language model offline, teaching it to generate property-aligned sub-queries that optimize for diversity and relevance as a coherent set rather than individual items. This supervision is then distilled into a compact 53.9-million-parameter diffusion model that can generate an entire complementary slate of search results in a single non-autoregressive pass, eliminating the sequential token-generation delays inherent in standard LLMs. The approach enables sub-second response times for complex searches while maintaining expert-level result quality without the computational overhead of extended chain-of-thought reasoning. For production search systems handling millions of queries, eliminating inference-time thinking tokens represents a significant efficiency gain. The research, presented at ICML 2026, suggests a broader shift in how AI systems can be optimized—by moving expensive reasoning into offline training rather than deferring it to user-facing inference.