Efficient LLM Alignment via Hierarchical Coarse-to-Fine Refinement

Zhiyi Lyu, Yewen Li, Xinrun Wang, Jiangshan Zhang, Yunkun Xu, shengjun cheng, Bo An

Keywords: Alignment, Large language models

TL;DR: We introduce a novel decoding-time alignment method that modifies the decoding distribution at both the prompt and response levels, enhancing decoding efficiency and reducing ineffective exploration.

Abstract:

Alignment of Large Language Models (LLMs) intends to make LLMs behave to satisfy human preferences and values. Widely used methods, $\textbf{e.g.}$, Reinforcement Learning from Human Feedback (RLHF), usually involve the additional training of LLMs with a reward model or the dataset reflecting human preferences. However, these training-based methods cannot quickly adapt to different preferences. Recent methods leverage search during the decoding process to align LLMs with preferences. However, these methods ignore the influence of prompts on the decoding distribution, thus hindering the performance. In this work, we propose $ \textbf{\textbf{HCFR}}$, a $\textbf{H}$ierarchical $\textbf{C}$oarse-to-$\textbf{F}$ine $\text{R}$efinement for efficient LLM alignment. Specifically, $\textbf{\textbf{HCFR}}$ includes a two-stage refinement: i) $\textbf{coarse refinement}$ which rephrases the prompts from users through self-refinement, and ii) $\textbf{fine refinement}$ which leverages the search methods, $\textit{e.g.}$, Monte Carlo Tree Search (MCTS), for the responses with the guidance of a pre-trained reward model.
Experimental results on HH-RLHF and UltraChat demonstrate that $\textbf{\textbf{HCFR}}$ can significantly outperform existing methods, $\textit{e.g.}$, ARGS, CARDS, and Rejection sampling, in terms of performance and efficiency, $\textit{i.e.}$, achieving a 71.3\% win-tie rate in GPT-4 evaluations while reducing time consumption by 42\%.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 10124