AI RESEARCH
F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking
arXiv CS.LG
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ArXi:2605.12995v1 Announce Type: new Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models (LLMs) broaden this into a generative process: given a candidate pool, an LLM can generate a subset and order it within a single autoregressive pass. However, this flexibility