Test-time reward-guided alignment of language models by importance sampling on pre-logit space

Sekitoshi Kanai, Tsukasa Yoshida, Hiroshi Takahashi +2

Test-time alignment of large language models (LLMs) attracts attention because fine-tuning of LLMs requires high computational costs. In this paper, we propose a new test-time rewa…