$\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$
<|reference_start|>$\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$: Direct Preference Optimization (DPO) has emerged as a compelling approach for training Large Language Models (LLMs) to adhere to human preferences. However, the performance of DPO is sensitive to the fine-tuning of its trade-off parameter $\beta$, as well as to the quality of the preference data. We analyze the impact of $\beta$ and data quality on DPO, uncovering that optimal $\beta$ values vary with the informativeness of pairwise data. Addressing the limitations of static $\beta$ values, we introduce a novel framework that dynamically calibrates $\beta$ at the batch level, informed by data quality considerations. Additionally, our method incorporates $\beta$-guided data filtering to safeguard against the influence of outliers. Through empirical evaluation, we demonstrate that our dynamic $\beta$ adjustment technique significantly improves DPO's performance across a range of models and datasets, offering a more robust and adaptable training paradigm for aligning LLMs with human feedback. The code is available at \url{https://github.com/junkangwu/beta-DPO}.<|reference_end|>
@article{wu2024$\beta$-dpo:,
    title={$\beta$-DPO: Direct Preference Optimization with Dynamic $\beta$},
    author={Junkang Wu, Yuexiang Xie, Zhengyi Yang, Jiancan Wu, Jinyang Gao, Bolin
  Ding, Xiang Wang, Xiangnan He},
    journal={arXiv preprint arXiv:2407.08639},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08639},
    primaryClass={cs.AI cs.LG}
}