Iterative Length-Regularized Direct Preference Optimization: A Case
  Study on Improving 7B Language Models to GPT-4 Level
Authors: name: Jie Liu, name: Zhanhui Zhou, name: Jiaheng Liu, name: Xingyuan Bu, name: Chao Yang, name: Han-Sen Zhong, name: Wanli Ouyang

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 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.