Preference-Guided Reflective Sampling for Aligning Language Models
- URL: http://arxiv.org/abs/2408.12163v2
- Date: Fri, 4 Oct 2024 11:40:58 GMT
- Title: Preference-Guided Reflective Sampling for Aligning Language Models
- Authors: Hai Ye, Hwee Tou Ng,
- Abstract summary: Iterative data generation and model re-training can effectively align large language models(LLMs) to human preferences.
In this work, we propose Preference-Guided Reflective Sampling (PRS)
Unlike random sampling, PRS employs a tree-based generation framework to enable more efficient sampling.
PRS shows strong performance when applied in iterative offline RL training.
- Score: 27.69410513313001
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Iterative data generation and model re-training can effectively align large language models(LLMs) to human preferences. The process of data sampling is crucial, as it significantly influences the success of policy improvement. Repeated random sampling is a widely used method that independently queries the model multiple times to generate outputs. In this work, we propose a more effective sampling method, named Preference-Guided Reflective Sampling (PRS). Unlike random sampling, PRS employs a tree-based generation framework to enable more efficient sampling. It leverages adaptive self-refinement techniques to better explore the sampling space. By specifying user preferences in natural language, PRS can further optimize response generation according to these preferences. As a result, PRS can align models to diverse user preferences. Our experiments demonstrate that PRS generates higher-quality responses with significantly higher rewards. On AlpacaEval and Arena-Hard, PRS substantially outperforms repeated random sampling in best-of-$N$ sampling. Moreover, PRS shows strong performance when applied in iterative offline RL training.
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