BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models 事件

BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models arXiv:2601.22925v3 Announce Type: replace-cross Abstract: 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 trainin