FocalPO: Enhancing Preference Optimizing by Focusing on Correct Preference Rankings

• URL: http://arxiv.org/abs/2501.06645v1
• Date: Sat, 11 Jan 2025 21:41:27 GMT
• Title: FocalPO: Enhancing Preference Optimizing by Focusing on Correct Preference Rankings
• Authors: Tong Liu, Xiao Yu, Wenxuan Zhou, Jindong Gu, Volker Tresp,
• Abstract summary: We introduce FocalPO, a DPO variant that prioritizes enhancing the model's understanding of pairs that it can already rank correctly. Inspired by Focal Loss used in vision tasks, FocalPO achieves this by adding a modulating factor to dynamically scale DPO loss.
• Score: 40.605411087380226
• License:
• Abstract: Efficient preference optimization algorithms such as Direct Preference Optimization (DPO) have become a popular approach in aligning large language models (LLMs) with human preferences. These algorithms implicitly treat the LLM as a reward model, and focus on training it to correct misranked preference pairs. However, recent work~\citep{chen2024preference} empirically finds that DPO training \textit{rarely improves these misranked preference pairs}, despite its gradient emphasizing on these cases. We introduce FocalPO, a DPO variant that instead \textit{down-weighs} misranked preference pairs and prioritizes enhancing the model's understanding of pairs that it can already rank correctly. Inspired by Focal Loss used in vision tasks, FocalPO achieves this by adding a modulating factor to dynamically scale DPO loss. Our experiment demonstrates that FocalPO surpasses DPO and its variants on popular benchmarks like Alpaca Eval 2.0 using Mistral-Base-7B and Llama-3-Instruct-8B. Additionally, we empirically reveals how FocalPO affects training on correct and incorrect sample groups, further underscoring its effectiveness.

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