SparseRM: A Lightweight Preference Modeling with Sparse Autoencoder

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM

The introduction of SparseRM marks a significant advancement in the field of AI, particularly in the post-training phase of large language models (LLMs). Traditional reward models (RMs) often require extensive preference annotations and substantial computational resources, making them challenging to implement effectively. SparseRM overcomes these limitations by leveraging Sparse Autoencoder (SAE) to extract and interpret preference-relevant features from LLM representations. This method not only reduces the number of trainable parameters to less than 1% compared to mainstream RMs but also enhances the model's performance across various preference modeling tasks. The ability of SparseRM to integrate seamlessly into existing alignment pipelines further underscores its potential for efficient model alignment, which is essential for developing AI systems that align closely with human preferences.

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