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Diverse Preference Learning for Capabilities and Alignment

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study on Diverse Preference Learning for Capabilities and Alignment reveals significant issues with current alignment algorithms such as RLHF and DPO, which are shown to limit the diversity of outputs from large language models (LLMs). By employing a KL divergence regularizer, these algorithms tend to favor majority opinions, resulting in repetitive text structures and a narrower range of societal perspectives. To address these shortcomings, the authors introduce Soft Preference Learning, a method that decouples entropy and cross-entropy terms in the KL penalty. This innovative approach not only improves the accuracy of LLMs on challenging tasks but also enhances the diversity of their outputs. LLMs trained with Soft Preference Learning demonstrate better logit calibration and are capable of representing a wider array of societal viewpoints, thereby contributing to a more inclusive and varied discourse in AI-generated content.
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