# Retrieve-for-Train

The work proposes Retrieve-for-Train, which first trains a fan-out language model with offline reinforcement learning (built on Gemma3-4B and Qwen3-4B, emitting 10 sub-queries per prompt) under a composite reward of groundedness, Vendi-Score diversity, and alignment that scores the whole result set, then distills that behavior into a 53.9M-parameter diffusion retriever that generates the complete target set in one non-autoregressive parallel pass in continuous embedding space, outperforming single-query search, zero-shot expansion, and a Best-of-N baseline on open-ended abstract retrieval and weakly supervised compositional retrieval while achieving a 12 to 20 speedup over autoregressive approaches.

The work proposes Retrieve-for-Train, which first trains a fan-out language model with offline reinforcement learning (built on Gemma3-4B and Qwen3-4B, emitting 10 sub-queries per prompt) under a composite reward of groundedness, Vendi-Score diversity, and alignment that scores the whole result set, then distills that behavior into a 53.9M-parameter diffusion retriever that generates the complete target set in one non-autoregressive parallel pass in continuous embedding space, outperforming single-query search, zero-shot expansion, and a Best-of-N baseline on open-ended abstract retrieval and weakly supervised compositional retrieval while achieving a 12 to 20 speedup over autoregressive approaches.