WARM : Enhances the efficiency and reliability of large language models (LLMs) through weighted average reward modeling.

WARM

AI Model #Artificial Intelligence #Large Language Models #Reward Modeling #Weighted Averaging Standard Picks Open Source

Overview :

WARM is a solution that aligns large language models (LLMs) with human preferences using weighted average reward models (WARM). It first fine-tunes multiple reward models and then averages them in the weight space. Through weighted averaging, WARM improves efficiency compared to traditional prediction ensemble methods while enhancing reliability under distributional shift and preference inconsistency. Our experiments demonstrate that WARM outperforms traditional methods on summarization tasks, and using the best N and RL methods, WARM improves the overall quality and alignment of LLM predictions.

Target Users :

Used for aligning large language models with human preferences, improving prediction quality and alignment.

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Use Cases

Reward model optimization for large language models

Experiments on improving language model prediction quality

Research on aligning language models with human preferences

Features

Weighted Average Reward Modeling

Aligning Large Language Models with Human Preferences