Language Model Merging in Iterative Preference Learning
Keywords: preference learning, model merging
Abstract: Learning from preferences has become a scalable paradigm for training high-capacity language models, as it is not limited to human-produced data, allowing models to surpass human performance.
Advanced feedback learning algorithms are typically online or iterative for high sample efficiency.
Among these, iterative preference optimization is popular due to its simplicity, efficiency, and robustness.
However, in iterative preference optimization, models do not necessarily achieve optimal performance since they sequentially learn data from different distributions.
A simple way to bridge the gap is model ensemble, which incurs excessive inference costs.
Inspired by the theoretical analysis for preference learning, we propose a simple model merging strategy that approximates model ensemble without additional training and inference costs, leading to Pareto-superior models.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 10955
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