Test-Time Preference Optimization: On-the-Fly Alignment via Iterative
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Authors: name: Yafu Li, name: Xuyang Hu, name: Xiaoye Qu, name: Linjie Li, name: Yu Cheng

Large language models (LLMs) demonstrate impressive performance but lack the
flexibility to adapt to human preferences quickly without retraining. In this
work, we introduce Test-time Preference Optimization (TPO), a framework that
aligns LLM outputs with human preferences during inference, removing the need
to update model parameters. Rather than relying on purely numerical rewards,
TPO translates reward signals into textual critiques and uses them as textual
rewards to iteratively refine its response. Evaluations on benchmarks covering
instruction following, preference alignment, safety, and mathematics reveal
that TPO progressively improves alignment with human preferences. Notably,
after only a few TPO steps, the initially unaligned Llama-3.1-70B-SFT model can
surpass the aligned counterpart, Llama-3.1-70B-Instruct. Furthermore, TPO
scales efficiently with both the search width and depth during inference.
Through case studies, we illustrate how TPO exploits the innate capacity of LLM
to interpret and act upon reward signals. Our findings establish TPO as a
practical, lightweight alternative for test-time preference optimization,
achieving alignment on the fly. Our code is publicly available at
https://github.com/yafuly/TPO.