AI RESEARCH
BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models
arXiv CS.LG
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ArXi:2601.22925v2 Announce Type: replace-cross Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LLMs to recommendation scenarios, and utilize beam search during inference to efficiently retrieve $B$ top-ranked recommended items. However, we identify a critical