AI 服务暂时不可用，以下为来源正文，待恢复后补全翻译。
Search and recommendation systems increasingly need to return a set of results, not one best match. A query like ‘camping gear’ should return a tent, a sleeping bag, a stove, and a headlamp. It should not return 10 near-identical tents. Google Research has introduced Retrieve-for-Train (R4T) for this problem. The framework uses reinforcement learning (RL) once, offline, to learn good query fan-out. It then distills that behavior into a small diffusion model that produces all retrieval directions in 1 pass. Why Standard Fan-Out Falls Short Query fan-out splits one broad prompt into several sub-queries. The research team identifies 2 problems when a generic LLM does this at inference time. The first is paraphrastic collapse. For ‘Bohemian festival style,’ zero-shot Qwen3-4B wrote ‘bohemian festival fashion’ and ‘festival bohemian clothes.’ These near-synonyms retrieve a homogeneous slate. The second is latency. Autoregressive generation plus repeated retrieval calls is slow. Best-of-N sampling improves quality but multiplies inference cost. The 3-Step R4T Pipeline Fan-out LM training: A fan-out language model (FOLM) generates k sub-queries. A frozen dense retriever executes them. A set-level reward scores the whole retrieved set, not each item alone. Supervision synthesis: The trained FOLM samples 128 fan-outs per query at temperature 0.9. These become (query, target set) training pairs with no human labels. For open-ended tasks, targets are retrieved-content embeddings. For compositional tasks, targets are sub-query embeddings. Diffusive retriever training: A 53.9M-parameter diffusion transformer learns to map a query embedding to a full set of target embeddings. It uses a variance-exploding formulation within the EDM framework. At inference, it generates all embeddings in a single non-autoregressive pass. Nearest-neighbor search then maps each embedding to database items. Reward Design and Reward Hacking For open-ended abstract retrieval (OAR), the reward combines 3 weighted terms: Groundedness (λg = 0.6): penalizes distance between each sub-query embedding and its nearest database item. Diversity (λd = 0.2): the Vendi Score over representative retrieved items, such as the top-1 item per sub-query. Alignment (λa = 0.2): mean cosine similarity between each sub-query and the original query. For weakly supervised compositional retrieval (WSCR), the reward is the fraction of reference-set items the fan-out retrieves. The ablation explains why all 3 OAR terms are important. With groundedness alone, Gemma3-4B converged to strings like ‘line ending line ending line ending.’ Adding alignment made collapse even faster, as the policy repeated paraphrases of the query. Adding diversity closed both shortcuts. Training uses GRPO with soft PPO regularization, which adds forward and reverse KL penalties. Key settings include group size 8, learning rate 1×10⁻⁷, and global batch size 512. Interactive Explainer Results Experiments used the Polyvore fashion outfit dataset with a CLIP-based matryoshka encoder at 128 dimensions. They also used a proprietary expert-playlist music dataset with MuLan embeddings. Every fan-out method produced k = 10 sub-queries, and Best-of-N used N = 5. OAR quality was scored by an LLM judge on 5-point Likert scales. On Polyvore, Gemma3-4B R4T-FOLM averaged 49.1, versus 40.9 for Best-of-N and 38.5 zero-shot. Diversity rose from 56.0 zero-shot to 76.8. R4T-Diffusion retained most of it at 74.3. On Music, Gemma3-4B R4T-FOLM averaged 58.1 versus 49.2 for Best-of-N. Groundedness is not reported for R4T-Diffusion, since it produces no text sub-queries. WSCR results on Polyvore show a coverage and diversity trade-off. R4T-FOLM (Qwen) reached 20.9 Recall@5K and 64.6 Hit@5K, versus 15.7 and 52.1 for Gemini-2.5-Flash. Its Vendi Score, however, dropped to 27.5. The authors link this to reduced output entropy under strong RL optimization. R4T-Diffusion (Qwen) kept a higher Vendi Score of 34.7 with 16.5 Recall@5K. Efficiency At batch size 8, autoregressive fan-out took about 1.46 seconds. The diffusion model took 0.07 seconds. At batch size 1024, autoregressive fan-out reached nearly 50 seconds, versus 4.21 seconds for diffusion. The authors report a consistent 12× to 20× speedup. Key Takeaways R4T runs RL once offline, then deploys a 53.9M-parameter diffusion retriever. 3 rewards (groundedness, diversity, alignment) block reward hacking and paraphrastic collapse. Gemma3-4B R4T-FOLM lifted Polyvore OAR average from 40.9 (Best-of-N) to 49.1. Diffusion fan-out runs 12× to 20× faster than autoregressive fan-out. Check out the Paper and Technical details. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Google Research Introduces Retrieve-for-Train (R4T): An RL-Compiled Diffusion Retriever for 12× to 20× Faster Query Fan-Out appeared first on MarkTechPost.