Google Research's Retrieve-for-Train Trains Diffusion Model to Bypass Inference-Time Search Bottlenecks
Google Research published an ICML 2026 paper, "Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion." The Retrieve-for-Train framework uses offline reinforcement learning to compile reward-aligned query fan-outs into supervision, then distills them into a lightweight diffusion retriever, enabling single-pass fan-out at inference without test-time thinking tokens.
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“Instead of relying on expensive inference-time reasoning, the Retrieve-for-Train framework uses reinforcement learning once to train a lightweight diffusion model.”
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