Iterative Length-regularized Direct Preference Optimization: A Case Study On Improving 7B Language Models To GPT-4 Level

Liu Jie, Zhou Zhanhui, Liu Jiaheng, Bu Xingyuan, Yang Chao, Zhong Han-sen, Ouyang Wanli. Arxiv 2024

[Paper]    
Efficiency And Optimization GPT Model Architecture Reinforcement Learning Tools Training Techniques

Direct Preference Optimization (DPO), a standard method for aligning language models with human preferences, is traditionally applied to offline preferences. Recent studies show that DPO benefits from iterative training with online preferences labeled by a trained reward model. In this work, we identify a pitfall of vanilla iterative DPO - improved response quality can lead to increased verbosity. To address this, we introduce iterative length-regularized DPO (iLR-DPO) to penalize response length. Our empirical results show that iLR-DPO can enhance a 7B model to perform on par with GPT-4 without increasing verbosity. Specifically, our 7B model achieves a \(50.5%\) length-controlled win rate against \(\texttt{GPT-4 Preview}\) on AlpacaEval 2.0, and excels across standard benchmarks including MT-Bench, Arena-Hard and OpenLLM Leaderboard. These results demonstrate the effectiveness of iterative DPO in aligning language models with human feedback.

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