SGPO: Self-Generated Preference Optimization based on Self-Improver

• URL: http://arxiv.org/abs/2507.20181v1
• Date: Sun, 27 Jul 2025 08:55:40 GMT
• Title: SGPO: Self-Generated Preference Optimization based on Self-Improver
• Authors: Hyeonji Lee, Daejin Jo, Seohwan Yun, Sungwoong Kim,
• Abstract summary: Large language models (LLMs) require alignment to human preferences for practical and reliable deployment.<n>We propose Self-Generated Preference Optimization based on Self-Improver (SGPO)<n>The improver refines responses from a policy model to self-generate preference data for direct preference optimization (DPO) of the policy model.<n> Experimental results on AlpacaEval 2.0 and Arena-Hard show that the proposed SGPO significantly improves performance over DPO and baseline self-improving methods.
• Score: 6.528083376369728
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs), despite their extensive pretraining on diverse datasets, require effective alignment to human preferences for practical and reliable deployment. Conventional alignment methods typically employ off-policy learning and depend on human-annotated datasets, which limits their broad applicability and introduces distribution shift issues during training. To address these challenges, we propose Self-Generated Preference Optimization based on Self-Improver (SGPO), an innovative alignment framework that leverages an on-policy self-improving mechanism. Specifically, the improver refines responses from a policy model to self-generate preference data for direct preference optimization (DPO) of the policy model. Here, the improver and policy are unified into a single model, and in order to generate higher-quality preference data, this self-improver learns to make incremental yet discernible improvements to the current responses by referencing supervised fine-tuning outputs. Experimental results on AlpacaEval 2.0 and Arena-Hard show that the proposed SGPO significantly improves performance over DPO and baseline self-improving methods without using external preference data.

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